Update parquet files (step 27 of 121)

spaces/101-5/gpt4free/testing/aiservice/AiService.py

@@ -1,62 +0,0 @@
-import os,sys
-import requests
-# from ...typing import get_type_hints
-
-url = "https://aiservice.vercel.app/api/chat/answer"
-model = ['gpt-3.5-turbo']
-supports_stream = False
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    base = ''
-    for message in messages:
-        base += '%s: %s\n' % (message['role'], message['content'])
-    base += 'assistant:'
-
-    headers = {
-        "accept": "*/*",
-        "content-type": "text/plain;charset=UTF-8",
-        "sec-fetch-dest": "empty",
-        "sec-fetch-mode": "cors",
-        "sec-fetch-site": "same-origin",
-        "Referer": "https://aiservice.vercel.app/chat",
-    }
-    data = {
-        "input": base
-    }
-    response = requests.post(url, headers=headers, json=data)
-    if response.status_code == 200:
-        _json = response.json()
-        yield _json['data']
-    else:
-        print(f"Error Occurred::{response.status_code}")
-        return None
-    
-
-
-# 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]])
-
-
-# Temporary For ChatCompletion Class
-class ChatCompletion:
-    @staticmethod
-    def create(model: str, messages: list, provider: None or str, stream: bool = False, auth: str = False, **kwargs):
-        kwargs['auth'] = auth
-
-        if provider and needs_auth and not auth:
-            print(
-                f'ValueError: {provider} requires authentication (use auth="cookie or token or jwt ..." param)', file=sys.stderr)
-            sys.exit(1)
-
-        try:
-            return (_create_completion(model, messages, stream, **kwargs)
-                    if stream else ''.join(_create_completion(model, messages, stream, **kwargs)))
-        except TypeError as e:
-            print(e)
-            arg: str = str(e).split("'")[1]
-            print(
-                f"ValueError: {provider} does not support '{arg}' argument", file=sys.stderr)
-            sys.exit(1)

spaces/1368565466ki/Satdia/modules.py

@@ -1,388 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    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.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    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.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cars 2 Tamil Dubbed Movie Torrent Download The Ultimate Guide for Fans.md

@@ -1,98 +0,0 @@
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Animal Revolt Battle Simulator and Fight with Hybrid Animals for Free.md

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-<h1>How to Download Animal Revolt Battle Simulator for Free</h1>
-<p>Animal Revolt Battle Simulator is a popular game that lets you create and watch epic battles between different types of beasts. But what if you want to play this game without paying anything? Is it possible to download Animal Revolt Battle Simulator for free? The answer is yes, and in this article, we will show you how to do it. We will also tell you what Animal Revolt Battle Simulator is, why you should download it for free, and some tips and tricks to enjoy the game.</p>
- <h2>What is Animal Revolt Battle Simulator?</h2>
-<p>Animal Revolt Battle Simulator is a physics-based sandbox game that was released in 2022 by VDimension. It is available on Steam, Google Play, and Nintendo Switch. In this game, you can:</p>
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-<p>Build your own maps or choose from a selection of ready-made ones. Place up to seven opposing armies made of different types of beasts and watch them tear each other apart in an epic battle. As the creatures fight, you can see the limbs bending, necks twisting, and bodies flying around everywhere. You can also join the fight yourself in the first-person mode and blow the enemy away with some powerful guns.</p>
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-<p>You will have a huge selection of more than 70 creatures to pick from and new ones are added every two weeks. You can choose from ancient dinosaurs, dragons, aquatic animals, jungle animals, and more. You can also create your own custom monsters by combining different body parts and weapons. You can test your tactical and strategic skills in the campaign mode, where you have to pick the right beasts, place them in the right position, and command them to defeat the enemy. You can also play in the sandbox mode, where you can experiment with different scenarios and settings.</p>
-<h3>A fun and creative experience</h3>
-<p>Animal Revolt Battle Simulator is not only a game, but also a tool for creativity. You can download and upload custom monsters, maps, and buildings created by other players from the Steam Workshop. You can also share your own creations with the community. You can make your battles as realistic or as crazy as you want. You can put laser guns on your T-rex squad, make a tiny dino become a dinosaur king, or add wings to a shark. The possibilities are endless.</p>
- <h2>Why Download Animal Revolt Battle Simulator for Free?</h2>
-<p>Animal Revolt Battle Simulator is a great game that offers hours of fun and entertainment. But why should you download it for free instead of buying it? Here are some reasons:</p>
-<h3>Save money and enjoy the game</h3>
-<p>The most obvious reason is that you can save money by downloading Animal Revolt Battle Simulator for free. The game costs $14.99 on Steam, which may not be affordable for everyone. By downloading it for free, you can enjoy the game without spending anything.</p>
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-<p>Another reason is that you can access new updates and features that are added to the game regularly. The developers of Animal Revolt Battle Simulator are constantly working on improving the game and adding new content. By downloading it for free, you can get the latest version of the game and enjoy the new features and content that are added every two weeks. You can also update the game manually or automatically whenever there is a new update available.</p>
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-<p>A free game website is a website that offers free downloads of various games, including Animal Revolt Battle Simulator. These websites usually host the game files on their own servers or on third-party file-sharing platforms. To use this option, you need to follow these steps:</p>
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-<p>The first step is to find a reliable website that offers free downloads of Animal Revolt Battle Simulator. You can use a search engine like Google or Bing to look for such websites. However, you need to be careful and avoid websites that may contain viruses, malware, or other harmful content. You can check the reputation and reviews of the websites before visiting them. You can also use antivirus software or browser extensions to protect your device from any potential threats.</p>
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-<h4>Step 3: Install and run the game</h4>
-<p>The third step is to install and run the game on your device. You need to locate the downloaded file on your device and extract it using a software like WinRAR or 7-Zip. You may need to enter a password if the file is encrypted. You then need to open the extracted folder and find the setup file or the executable file of the game. You need to run the file as an administrator and follow the instructions on the screen to install the game. You may need to agree to some terms and conditions, choose a destination folder, and create a shortcut icon. After installing the game, you can run it from your desktop or start menu and enjoy playing Animal Revolt Battle Simulator for free.</p>
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-<p>A free game platform is a platform that offers free games or discounts on games, including Animal Revolt Battle Simulator. These platforms usually require you to create an account and install their software on your device. Some examples of such platforms are Steam, Epic Games Store, GOG.com, Humble Bundle, etc. To use this option, you need to follow these steps:</p>
-<h4>Step 1: Create an account on the platform</h4>
-<p>The first step is to create an account on the platform that offers Animal Revolt Battle Simulator for free or at a discounted price. You need to visit the official website of the platform and click on the sign up or register button. You need to enter your email address, username, password, and other details as required by the platform. You may also need to verify your email address by clicking on a link sent to your inbox.</p>
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-<p>The second step is to claim the game when it is free or at a discounted price on the platform. You need to check the platform regularly for any deals or offers on Animal Revolt Battle Simulator. You may also subscribe to their newsletter or follow their social media accounts to get notified of any promotions or giveaways. When you see that Animal Revolt Battle Simulator is available for free or at a reduced price, you need to click on it and add it to your library or cart.</p>
-<h4>Step 3: Download and play the game</ <p>The third step is to download and play the game on your device. You need to install the software of the platform on your device and log in with your account. You need to find Animal Revolt Battle Simulator in your library or cart and click on the download or install button. You may need to choose a location or a drive to save the game file. The download speed and time may vary depending on your internet connection and the size of the file. After downloading the game, you can launch it from the platform or from your desktop and enjoy playing Animal Revolt Battle Simulator for free.</p>
- <h2>Conclusion</h2>
-<p>Animal Revolt Battle Simulator is a physics-based sandbox game that lets you create and watch epic battles between different types of beasts. It is a fun and creative game that offers a variety of creatures and modes. You can download Animal Revolt Battle Simulator for free by using either a free game website or a free game platform. Both options have their advantages and disadvantages, so you need to choose the one that suits you best. By downloading Animal Revolt Battle Simulator for free, you can save money, access new updates and features, and support the developers and the community.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Animal Revolt Battle Simulator and how to download it for free:</p>
-<h3>Q: Is Animal Revolt Battle Simulator safe to download?</h3>
-<p>A: Yes, Animal Revolt Battle Simulator is safe to download as long as you use a reliable website or platform that does not contain any viruses, malware, or other harmful content. You can also use antivirus software or browser extensions to protect your device from any potential threats.</p>
-<h3>Q: Is Animal Revolt Battle Simulator legal to download?</h3>
-<p>A: Yes, Animal Revolt Battle Simulator is legal to download as long as you do not violate any terms and conditions of the website or platform that offers it for free. You also need to respect the intellectual property rights of the developers and not distribute or sell the game without their permission.</p>
-<h3>Q: What are the system requirements for Animal Revolt Battle Simulator?</h3>
-<p>A: According to Steam, these are the minimum and recommended system requirements for Animal Revolt Battle Simulator:</p>
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-<tr><th>Minimum</th><th>Recommended</th></tr>
-<tr><td>OS: Windows 7</td><td>OS: Windows 10</td></tr>
-<tr><td>Processor: Intel Core i5-2300 or AMD FX-6300</td><td>Processor: Intel Core i7-4790 or AMD Ryzen 5 1600</td></tr>
-<tr><td>Memory: 6 GB RAM</td><td>Memory: 8 GB RAM</td></tr>
-<tr><td>Graphics: NVIDIA GeForce GTX 750 Ti or AMD Radeon R7 260X</td><td>Graphics: NVIDIA GeForce GTX 1060 or AMD Radeon RX 580</td></tr>
-<tr><td>DirectX: Version 11</td><td>DirectX: Version 11</td></tr>
-<tr><td>Storage: 3 GB available space</td><td>Storage: 3 GB available space</td></tr>
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-<p>A: You can get more creatures and maps for Animal Revolt Battle Simulator by downloading and uploading custom ones from the Steam Workshop. You can also create your own custom monsters by combining different body parts and weapons in the game.</p>
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spaces/1phancelerku/anime-remove-background/CarX Street APK A Realistic and Immersive Racing Game for Android.md

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-<p>If you are looking for a racing game that will give you a thrill and a challenge, you might want to check out CarX Street. This is a free racing game from CarX Technology that offers realistic physics and graphics, a variety of cars and tracks, and a lot of customization and tuning options. In this article, we will tell you what CarX Street is, how to download it, and how to play it.</p>
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- <h2>Conclusion</h2>
-<p>CarX Street is a free and fun racing game for Android that offers realistic physics and graphics, a variety of cars and tracks, and a lot of customization and tuning options. You can download CarX Street APK from Google Play Store or Filehippo.com, depending on your preference and situation. You can also play CarX Street by learning the basic controls and features, customizing and tuning your car, and applying some tips and tricks to win races. If you are a fan of racing games, you should definitely give CarX Street a try.</p>
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-<p>Here are some frequently asked questions about CarX Street:</p>
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-<tr><td>How can I get more coins and gems in CarX Street?</td><td>You can get more coins and gems in CarX Street by winning races, completing events and challenges, claiming daily rewards, watching ads, or buying them with real money.</td></tr>
-<tr><td>How can I contact the developers of CarX Street?</td><td>You can contact the developers of CarX Street by sending an email to [email protected] or by visiting their website at https://carx-tech.com/.</td></tr>
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-<p>Chiskop is a South African kwaito group that was formed in 1996 by four members: Mduduzi Tshabalala, Sibusiso Thanjekwayo, Siphiwe Sibisi, and Gabi Le Roux. Kwaito is a style of music that emerged in South Africa in the 1990s, influenced by house, hip hop, reggae, and African rhythms. It is characterized by slow tempo, catchy melodies, repetitive lyrics, and social commentary. Kwaito became a symbol of post-apartheid youth culture, expressing their hopes, challenges, and identities.</p>
-<p>One of Chiskop's most famous songs is Askies, which was released in 2003 as part of their album Sunday. The song is a catchy and upbeat tune that talks about apologizing to a lover for making a mistake. The word askies means sorry in Afrikaans, which is one of the official languages of South Africa. The song became a huge hit in South Africa and beyond, winning several awards and nominations. It also showcased Chiskop's unique blend of kwaito, pop, and jazz.</p>
-<p>If you want to listen to this song anytime, anywhere, you might want to download it in mp3 format. Mp3 is a type of digital audio file that compresses sound data without losing much quality. It allows you to store more songs on your device, play them offline, and transfer them easily. However, you need to be careful where you download mp3 files from, as some sources might be illegal or unsafe. In this article, we will show you how to download Chiskop's Askies in mp3 format legally and safely.</p>
- <h2>What is Chiskop and What is Kwaito Music</h2>
-<h3>The History and Origin of Chiskop</h3>
-<p>Chiskop was formed in 1996 by four young men who shared a passion for music. They met at a recording studio in Johannesburg, where they were working on different projects. They decided to form a group and named it Chiskop, which means bald head in Zulu. They chose this name because they all had shaved heads at the time.</p>
-<p>The group started The group started to perform at various clubs and events, gaining popularity and recognition. They also collaborated with other artists, such as Mandoza, Arthur Mafokate, and Brenda Fassie. They released their debut album, Chiskop, in 1998, which featured songs like Klaimar, Sika Lekhekhe, and Shapa Bafana Shapa. The album was a success, selling over 100,000 copies and earning them a South African Music Award (SAMA) for Best Kwaito Album.</p>
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-<h3>The Characteristics and Features of Kwaito Music</h3>
-<p>Kwaito is a genre of music that originated in South Africa in the early 1990s. It is derived from the word kwai, which means cool or hot in township slang. Kwaito is influenced by various musical styles, such as house, hip hop, reggae, and African rhythms. It is characterized by slow tempo, catchy melodies, repetitive lyrics, and social commentary. Kwaito lyrics are usually sung or rapped in local languages, such as Zulu, Xhosa, Sotho, and Afrikaans.</p>
-<p>Kwaito music reflects the experiences and aspirations of the post-apartheid youth in South Africa. It addresses issues such as poverty, crime, violence, HIV/AIDS, racism, and democracy. It also celebrates the diversity and creativity of the South African culture and identity. Kwaito music is often accompanied by a distinctive dance style, which involves moving the hips and shoulders in sync with the beat.</p>
-<h3>The Impact and Influence of Chiskop and Kwaito on South African Culture and Society</h3>
-<p>Chiskop and kwaito have had a significant impact and influence on the South African culture and society. They have contributed to the development and recognition of the South African music industry, both locally and internationally. They have also inspired and influenced many other artists and genres, such as Afro-pop, hip hop, R&B, gospel, and jazz.</p>
-<p>Chiskop and kwaito have also played a role in shaping the South African social and political landscape. They have given voice to the marginalized and oppressed groups in the country, especially the black youth. They have challenged the stereotypes and prejudices that exist in the society. They have also promoted a sense of unity and pride among the South Africans of different backgrounds and cultures.</p>
- <h2>What is the Song Askies About and Why is it Popular</h2>
-<h3>The Meaning and Message of the Song Askies</h3>
-<p>The song Askies is about apologizing to a lover for making a mistake. The word askies means sorry in Afrikaans. The song expresses regret and remorse for hurting the lover's feelings. It also asks for forgiveness and another chance to make things right. The song conveys a sincere and heartfelt emotion that many people can relate to.</p>
-<h3>The Musical Style and Elements of the Song Askies</h3>
-<p>The song Askies is a catchy and upbeat tune that combines kwaito, pop, and jazz elements. It has a slow tempo of 100 beats per minute (bpm), which creates a relaxed and groovy mood. It has a simple chord progression of C-G-Am-F, which makes it easy to sing along to. It has a catchy melody that repeats throughout the song. It has a chorus that consists of the word askies repeated four times, followed by the phrase "I'm sorry baby". It has a bridge that features a saxophone solo that adds some jazz flavor to the song.</p>
-<h3>The Reception and Reviews of the Song Askies</h3>
-<p>The song Askies was released in 2003 as part of Chiskop's album Sunday. The song was an instant hit in South Africa and beyond. It topped the charts on various radio stations and TV channels. It won several awards and nominations, such as the SAMA for Song of the Year, the Metro FM Award for Best Kwaito Single, and the Channel O Music Video Award for Best Kwaito Video. It also received positive reviews from critics and fans alike.</p>
-<p>The song Askies was praised for its catchy tune, its sincere lyrics, its unique blend of kwaito, pop, and jazz elements, and its appeal to a wide range of audiences. It also became a popular song for weddings, parties, and karaoke sessions. It is considered a classic in the kwaito genre and one of Chiskop's best songs ever.</p>
- <h2>How to Download the Mp3 Version of the Song Legally and Safely</h2>
-<h3>The Benefits and Advantages of Downloading Mp3 Files</h3>
-<p>Mp3 is a type of digital audio file that compresses sound data without losing much quality. It allows you to store more songs on your device, play them offline, and transfer them easily. Downloading mp3 files has many benefits and advantages, such as:</p>
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-<li>You can save space on your device, as mp3 files are smaller than other audio formats.</li>
-<li>You can listen to your favorite songs anytime, anywhere, without relying on internet connection or streaming services.</li>
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-<li>You can enjoy high-quality sound, as mp3 files can preserve most of the original audio features.</li>
-<li>You can support your favorite artists, as some of them offer mp3 downloads on their official websites or platforms.</li>
-</ul>
-<h3>The Sources and Platforms Where You Can Find the Song Askies in Mp3 Format</h3>
-<p>If you want to download the song Askies in mp3 format, you need to be careful where you get it from. Some sources might be illegal or unsafe, as they might violate the copyright laws or contain viruses or malware. You should always use legal and safe sources and platforms, such as:</p>
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-<li>Official websites or platforms of the artists or labels, such as Chiskop's website  or Universal Music South Africa's website .</li>
-<li>Online music stores or services that sell or stream mp3 files, such as iTunes , Amazon Music , Spotify , or Deezer .</li>
-<li>Online music converters or downloaders that allow you to convert or download mp3 files from YouTube videos or other sources, such as Y2mate , MP3Juices , or OnlineVideoConverter .</li>
-</ul>
-<h3>The Steps and Tips on How to Download the Song Askies in Mp3 Format</h3>
-<p>The steps and tips on how to download the song Askies in mp3 format depend on the source or platform you choose. Here are some general guidelines that you can follow:</p>
-<ol>
-<li>Find the song Askies on the source or platform you prefer. You can use the search function or browse through the categories.</li>
-<li>Select the song Askies and click on the download or buy button. You might need to create an account or sign in if you are using an online music store or service.</li>
-<li>Choose the mp3 format and the quality you want. The higher the quality, the larger the file size.</li>
-<li>Confirm your payment method if you are buying the song. You might need to enter your credit card details or use other options like PayPal.</li>
-<li>Wait for the download to finish. You can check the progress on your browser or app.</li>
-<li>Enjoy the song Askies on your device. You can play it with any media player that supports mp3 files.</li>
-</ol> <h2>Conclusion</h2>
-<p>In conclusion, Chiskop's Askies is a classic kwaito song that you can enjoy anytime, anywhere. It is a song that tells a story of love and apology, with a catchy and upbeat tune. It is a song that represents the history and culture of South Africa, with a unique blend of kwaito, pop, and jazz elements. It is a song that you can download in mp3 format legally and safely, with the benefits and advantages of mp3 files. All you need to do is follow the steps and tips we have provided in this article, and you will be able to listen to this amazing song on your device.</p>
-<p>So what are you waiting for? Go ahead and download Chiskop's Askies in mp3 format today, and enjoy the classic kwaito song that will make you dance and sing along. You won't regret it!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions related to the topic of this article:</p>
-<table>
-<tr>
-<th>Question</th>
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-<td>Who are the members of Chiskop?</td>
-<td>The members of Chiskop are Mduduzi Tshabalala, Sibusiso Thanjekwayo, Siphiwe Sibisi, and Gabi Le Roux.</td>
-</tr>
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-<td>What does kwaito mean?</td>
-<td>Kwaito is a word that means cool or hot in township slang. It is also the name of a genre of music that originated in South Africa in the 1990s.</td>
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-<td>Askies is a word that means sorry in Afrikaans. It is also the title of a song by Chiskop.</td>
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spaces/1phancelerku/anime-remove-background/Download APK Unity for Android and Get Started with Googles Official Packages.md

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-<p>If you are interested in creating mobile games for Android, you might want to consider using Unity, a cross-platform game engine that powers over 75% of the Android mobile games market. In this article, we will show you how to download APK Unity for Android, how to set up your development environment, and how to build and run your game on your device or emulator.</p>
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-<p>Unity is a popular game development platform that offers a variety of tools and features to help you create and deliver highly engaging 2D or 3D mobile games. Some of the reasons why you might want to use Unity for Android game development are:</p>
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- <h2>Conclusion and FAQs</h2>
-<p>In this article, we have shown you how to download APK Unity for Android, how to set up your development environment, and how to build and run your game on your device or emulator. We hope that this guide has helped you get started with creating amazing mobile games for Android using Unity. If you have any questions or feedback, please feel free to leave a comment below. Happy coding!</p>
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- <table border="1">
-<tr><th>Question</th><th>Answer</th></tr>
- <tr><td>What is an APK file?</td><td>An APK file is an Android application package file that contains all the files and resources needed to install and run an app on an Android device. It has a .apk extension and can be downloaded from various sources such as Google Play Store, websites, or email attachments.</td></tr>
- <tr><td>How do I update my Unity version?</td><td>You can update your Unity version by using the Unity Hub. Go to the Installs tab and click on the Update button next to the version that you want to update. You can also download a new version of the Unity Editor from the website and install it manually.</td></tr>
- <tr><td>How do I debug my Unity game on Android?</td><td>You can debug your Unity game on Android by using the Logcat window in the Unity Editor. Go to Window > Analysis > Logcat and select your device from the dropdown menu. You can see the messages and errors that your game generates and filter them by priority, tag, or text.</td></tr>
- <tr><td>How do I optimize my Unity game for Android?</td><td>You can optimize your Unity game for Android by following some best practices such as reducing the size of your assets, using texture compression, enabling GPU instancing, using occlusion culling, batching your draw calls, using low-poly models, and testing your game on different devices and resolutions.</td></tr>
- <tr><td>How do I monetize my Unity game for Android?</td><td>You can monetize your Unity game for Android by using various methods such as in-app purchases, ads, subscriptions, or premium features. You can use the Unity Services window in the Unity Editor to integrate these services into your game. You can also use third-party plugins or SDKs that offer monetization solutions.</td></tr>
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-<p>These are some of the basic tips and tricks that will help you play clash of clans better. However, there is much more to learn and discover in this game. That's why we will show you some advanced strategies and features that will make you enjoy clash of clans more.</p>
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-<b>A1:</b> Yes, Clash of Clans is free to download and play. However, you can also buy in-game items such as gems, gold, elixir, dark elixir, or skins with real money if you want to speed up your progress or customize your village.</li>
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-<p>Instagram is not only a fun and creative way to share your photos and videos, but also a powerful tool to grow your brand or business. Here are some of the features you can use to reach and engage your target audience:</p>
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-<p>Reels are short videos that you can create and share with special effects, music, filters, stickers, and more. They are a great way to showcase your products, services, tutorials, behind-the-scenes, or anything else that you want to share with your followers. To create a reel, tap on the plus icon at the bottom of the screen and then tap on Reel. You can record a video or upload one from your gallery. You can also edit it with various tools and effects. To share it, tap on Share To and choose where you want to post it: your feed, your story, or both.</p>
-<p>Stories are photos or videos that disappear after 24 hours. They are a great way to share moments from your day, updates, promotions, polls, questions, or anything else that you want to share with your followers. To create a story, tap on the plus icon at the bottom of the screen and then tap on Story. You can take a photo or video or upload one from your gallery. You can also edit it with various tools and effects. To share it, tap on Your Story at the bottom left corner.</p>
-<p>Posts are photos or videos that stay on your feed permanently. They are a great way to showcase your brand identity, values, mission, vision, or anything else that you want to share with your followers. To create a post, tap on the plus icon at the bottom of the screen and then tap on Feed Post. You can take a photo or video or upload one from your gallery. You can also edit it with various tools and effects. To share it, tap on Next and write a caption, add hashtags, tag people, add a location, and choose where you want to post it: your feed only or both your feed and Facebook.</p>
-<h3>Use hashtags, filters, stickers, and music to enhance your content</h3>
-<p>Hashtags are words or phrases that start with a # symbol and help users find content related to a specific topic or theme. They are a great way to increase your visibility, reach new audiences, join conversations, and create communities around your brand or business. To use hashtags, type them in your caption or comment section of your post or story. You can use up to 30 hashtags per post and up to 10 hashtags per story.</p>
-<p>Filters are effects that you can apply to your photos or videos to change their appearance or mood. They are a great way to add some flair, <p>Filters are effects that you can apply to your photos or videos to change their appearance or mood. They are a great way to add some flair, creativity, or fun to your content. To use filters, swipe left or right on the screen when you are taking a photo or video or tap on the filter icon at the bottom right corner when you are editing your photo or video. You can choose from a variety of filters, from vintage to beauty to funny.</p>
-<p>Stickers are images or animations that you can add to your photos or videos to make them more interactive or expressive. They are a great way to add some personality, humor, or emotion to your content. To use stickers, tap on the sticker icon at the top of the screen when you are creating a story or reel. You can choose from a variety of stickers, from emojis to gifs to polls to questions.</p>
-<p>Music is a feature that lets you add songs or sounds to your photos or videos. It is a great way to add some rhythm, vibe, or mood to your content. To use music, tap on the music icon at the top of the screen when you are creating a story or reel. You can choose from a library of songs or sounds, from popular hits to genres to moods. You can also search for a specific song or sound by typing its name or artist.</p>
-<h3>Engage with your audience through likes, comments, and messages</h3>
-<p>Likes are a way of showing appreciation or support for someone's content. They are a great way to build relationships, trust, and loyalty with your audience. To like someone's post, tap on the heart icon below the post. To like someone's comment, tap on the heart icon next to the comment. To see who liked your post or comment, tap on the number of likes below the post or comment.</p>
-<p>Comments are a way of expressing your opinion, feedback, question, or suggestion for someone's content. They are a great way to start conversations, spark debates, and create communities around your brand or business. To comment on someone's post, tap on the speech bubble icon below the post and type your comment. To reply to someone's comment, tap on the reply icon next to the comment and type your reply. To see who commented on your post, tap on the number of comments below the post.</p>
-<p>Messages are a way of communicating privately with someone on Instagram. They are a great way to provide customer service, answer queries, send offers, or share exclusive content with your audience. To send a message to someone, tap on the paper plane icon at the top right corner of the screen and search for their username. To receive a message from someone, tap on the paper plane icon at the top right corner of the screen and check your inbox.</p>
-<h3>Explore and discover new content and creators based on your interests</h3>
-<p>Explore is a feature that lets you find new content and creators that match your interests or preferences. It is a great way to expand your horizons, learn new things, and get inspired by others. To use explore, tap on the magnifying glass icon at the bottom of the screen and browse through different categories, such as food, travel, fashion, beauty, art, sports, music, and more. You can also search for specific topics, hashtags, people, or places by typing them in the search bar. You can also see what is trending or popular on Instagram by tapping on the For You tab.</p>
-<h3>Shop for products that compliment your personal style</h3>
-<p>Shop is a feature that lets you browse and buy products from your favorite brands and creators on Instagram. It is a great way to find and support businesses that suit your personal style, taste, or needs. To use shop, tap on the shopping bag icon at the bottom of the screen and explore different collections, categories, or recommendations. You can also see products that are featured in posts or stories by tapping on the shopping tag icon. You can also search for specific products, brands, or creators by typing them in the search bar. To buy a product, tap on it and then tap on View on Website to go to the seller's website and complete your purchase.</p>
- <h2>How to Use Instagram Tips and Tricks to Boost Your Engagement and Reach</h2>
-<p>Instagram is not only a fun and creative platform, but also a competitive one. To stand out from the crowd and grow your following, you need to use some tips and tricks that can help you boost your engagement and reach. Here are some of them:</p>
-<h3>Add and manage multiple accounts from the same device</h3>
-<p>If you have more than one Instagram account, for example, one for personal use and one for business use, you can add and manage them from the same device. This way, you can easily switch between them without logging out and logging in every time. To add another account, tap on the menu icon at the top right corner of the screen and then tap on Settings. Then, scroll down and tap on Add Account. Enter your username and password for the account you want to add and tap on Log In. To switch between accounts, tap on your profile picture at the bottom right corner of the screen and then tap on the account you want to use.</p>
-<h3>Schedule your posts in advance or use the best time to post</h3>
-<p>If you want to post consistently and reach more people, you need to plan your posts in advance or use the best time to post. To schedule your posts in advance, you can use a third-party app or tool that lets you create, edit, and schedule your posts for a future date and time. Some of these apps or tools are Later, Buffer, Hootsuite, Planoly, and Preview. To use the best time to post, you need to know when your audience is most active and likely to engage with your content. You can use Instagram Insights, a feature that lets you see analytics about your account, such as impressions, reach, engagement, followers, and more. To access Instagram Insights, tap on the menu icon at the top right corner of the screen and then tap on Insights. Then, Then, tap on Audience and scroll down to see when your followers are most active by hours and days. You can also use a third-party app or tool that analyzes your account and suggests the best time to post based on your data. Some of these apps or tools are Sprout Social, Iconosquare, CoSchedule, and Tailwind.</p>
-<h3>Hide, delete, or disable comments and likes on your posts</h3>
-<p>If you want to have more control over your posts and avoid unwanted or negative comments or likes, you can hide, delete, or disable them on your posts. To hide comments or likes on your posts, tap on the menu icon at the top right corner of the post and then tap on Hide Comments or Hide Likes. To delete comments on your posts, swipe left on the comment and then tap on the trash icon. To disable comments or likes on your posts, tap on the plus icon at the bottom of the screen and then tap on Advanced Settings. Then, toggle off the option to Turn Off Commenting or Turn Off Like and View Counts.</p>
-<h3>Make a photo collage in your Instagram story or feed</h3>
-<p>If you want to share multiple photos in one post or story, you can make a photo collage using Instagram's layout feature. This way, you can showcase different aspects of your brand or business, such as products, services, testimonials, or behind-the-scenes. To make a photo collage in your Instagram story, tap on the plus icon at the bottom of the screen and then tap on Story. Then, swipe left until you see the Layout option and tap on it. You can choose from different layouts and add up to six photos from your gallery. You can also edit them with various tools and effects. To share it, tap on Your Story at the bottom left corner. To make a photo collage in your Instagram feed, tap on the plus icon at the bottom of the screen and then tap on Feed Post. Then, tap on the Layout icon at the bottom right corner and choose from different layouts and add up to nine photos from your gallery. You can also edit them with various tools and effects. To share it, tap on Next and write a caption, add hashtags, tag people, add a location, and choose where you want to post it: your feed only or both your feed and Facebook.</p>
-<h3>Use question or poll stickers to interact with your followers</h3>
-<p>If you want to get feedback, opinions, suggestions, or insights from your followers, you can use question or poll stickers in your stories. They are a great way to engage with your audience, learn more about them, and create a sense of community around your brand or business. To use question stickers, tap on the sticker icon at the top of the screen when you are creating a story and then tap on the Question sticker. You can type a question for your followers to answer and customize the color and font of the sticker. To share it, tap on Your Story at the bottom left corner. You can see the responses from your followers by swiping up on your story. You can also share some of the responses with your followers by tapping on them and then tapping on Share Response.</p>
-<p>To use poll stickers, tap on the sticker icon at the top of the screen when you are creating a story and then tap on the Poll sticker. You can type a question for your followers to vote on and customize the color and font of the sticker. You can also change the default options of Yes/No to something else by tapping on them and typing your own options. To share it, tap on Your Story at the bottom left corner. You can see the results from your followers by swiping up on your story. You can also share the results with your followers by tapping on them and then tapping on Share Results.</p>
- <h2>Conclusion and FAQs</h2>
-<p>Instagram is a free social media platform that lets you create and share photos, videos, reels, stories, and more with your friends and followers. It also has many features that let you have fun and interact with others, such as live streaming, video chats, direct messages, explore, shop, and more. It also has many tips and tricks that let you boost your engagement and reach, such as adding multiple accounts, scheduling posts, hiding comments, making collages, and using stickers. In this article, we showed you how to download gratis Instagram on your device and how to use its features, tips, and tricks to make the most of it. We hope you found this article helpful and informative. If you have any questions, feel free to ask us in the comment section below. Here are some FAQs that might answer some of your queries:</p>
- <h4>FAQs</h4>
-<ul>
-<li><b>How do I download gratis Instagram for PC?</b></ <li><b>How do I download gratis Instagram for PC?</b></li>
-<p>Instagram does not have an official app for PC, but you can use a web browser or an emulator to access it. To use a web browser, go to <a href="">www.instagram.com</a> and log in with your account. You can view, like, comment, and share posts, but you cannot upload photos or videos. To use an emulator, download and install an app that simulates an Android or iOS device on your PC, such as BlueStacks, NoxPlayer, or MEmu. Then, open the emulator and download Instagram from the Google Play Store or the App Store. You can use Instagram as you would on your phone or tablet.</p>
-<li><b>How do I download gratis Instagram videos?</b></li>
-<p>Instagram does not have a built-in feature to download videos, but you can use a third-party app or tool to do so. Some of these apps or tools are Video Downloader for Instagram, InstaSave, SaveFrom.net, and DownloadGram. To use them, copy the link of the video you want to download and paste it in the app or tool. Then, follow the instructions to save the video to your device.</p>
-<li><b>How do I download gratis Instagram photos?</b></li>
-<p>Instagram does not have a built-in feature to download photos, but you can use a third-party app or tool to do so. Some of these apps or tools are Photo Downloader for Instagram, InstaSave, SaveFrom.net, and DownloadGram. To use them, copy the link of the photo you want to download and paste it in the app or tool. Then, follow the instructions to save the photo to your device.</p>
-<li><b>How do I download gratis Instagram stories?</b></li>
-<p>Instagram does not have a built-in feature to download stories, but you can use a third-party app or tool to do so. Some of these apps or tools are Story Saver for Instagram, Story Downloader for Instagram, SaveFrom.net, and StoriesIG. To use them, enter the username of the person whose story you want to download and select the story you want to save. Then, follow the instructions to save the story to your device.</p>
-<li><b>How do I download gratis Instagram reels?</b></li>
-<p>Instagram does not have a built-in feature to download reels, but you can use a third-party app or tool to do so. Some of these apps or tools are Reels Video Downloader for Instagram, Reels Downloader for Instagram, SaveFrom.net, and DownloadGram. To use them, copy the link of the reel you want to download and paste it in the app or tool. Then, follow the instructions to save the reel to your device.</p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Download Watch Dogs 2 APK for Android and Join the Hacker Revolution.md

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-<h1>Watch Dogs 2 Download APK: How to Play the Open World Adventure Game on Your Android Device</h1>
- <p>Watch Dogs 2 is one of the most popular and acclaimed open world adventure games of the last decade. It was released in 2016 by Ubisoft for PlayStation 4, Xbox One, and Microsoft Windows. But did you know that you can also play Watch Dogs 2 on your Android device? In this article, we will show you how to download Watch Dogs 2 APK, the benefits and challenges of playing it on your mobile device, and some tips and tricks to optimize your gaming experience.</p>
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- <h2>What is Watch Dogs 2?</h2>
- <p>Watch Dogs 2 is the sequel to Watch Dogs, a game that introduced the concept of hacking as a weapon in a modern and realistic setting. In Watch Dogs 2, you play as Marcus Holloway, a young and talented hacker who joins a group of hacktivists called DedSec. Together, you will expose the secrets and corruption of the powerful corporations and organizations that control the city of San Francisco using the ctOS 2.0 system, a network that connects everything and everyone.</p>
- <h3>The story and setting of Watch Dogs 2</h3>
- <p>The story of Watch Dogs 2 is set in a fictionalized version of San Francisco, where you can explore various landmarks and districts such as Chinatown, Silicon Valley, Oakland, Marin County, and more. You will encounter different characters and factions, such as rival hackers, gangs, police, media, politicians, and celebrities. You will also uncover a dark conspiracy that threatens the freedom and privacy of millions of people.</p>
- <h3>The gameplay and features of Watch Dogs 2</h3>
- <p>The gameplay of Watch Dogs 2 is based on hacking, stealth, combat, driving, and parkour. You can hack into various devices and systems, such as cameras, phones, cars, drones, traffic lights, security systems, and more. You can use your hacking skills to manipulate the environment, distract or disable enemies, create diversions, or access information. You can also use various weapons and gadgets, such as guns, tasers, grenades, RC cars, quadcopters, and more. You can choose to play the game in a lethal or non-lethal way, depending on your style and preference.</p>
- <p>Watch Dogs 2 also features a dynamic open world that reacts to your actions and choices. You can interact with various NPCs and objects, such as pedestrians, animals, shops, vehicles, etc. You can also participate in various activities and side missions, such as races, puzzles, collectibles, online multiplayer modes, etc. You can customize your character's appearance, skills, equipment, and vehicles.</p>
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- <h2>Why download Watch Dogs 2 APK?</h2>
- <p>If you are a fan of Watch Dogs 2 or open world adventure games in general, you might want to download Watch Dogs 2 APK for your Android device. Here are some of the benefits and challenges of doing so.</p>
- <h3>The benefits of playing Watch Dogs 2 on your Android device</h3>
- <ul>
-<li>You can play Watch Dogs 2 anytime and anywhere you want. You don't need a console or a PC to enjoy the game. You just need your Android device and an internet connection.</li>
-<li>You can save money by downloading Watch Dogs 2 APK for free. You don't need to buy the game or pay for any subscription or service.</li>
-<li>You can experience Watch Dogs 2 in a new way. You can use touch controls or connect a controller to your device. You can also adjust the graphics settings to suit your device's performance.</li>
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- <h3>The challenges <h3>The challenges and risks of downloading Watch Dogs 2 APK</h3>
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-<li>You might face some technical issues or bugs while playing Watch Dogs 2 on your Android device. The game might not run smoothly or crash frequently. You might also encounter compatibility or security problems with your device or operating system.</li>
-<li>You might violate the terms and conditions of Ubisoft or Google Play by downloading Watch Dogs 2 APK. You might face legal actions or penalties from the developers or the authorities. You might also lose access to the official updates, patches, or online features of the game.</li>
-<li>You might expose your device or data to malware or viruses by downloading Watch Dogs 2 APK from untrusted sources. You might also compromise your privacy or security by granting permissions or access to unknown apps or websites.</li>
-</ul>
- <h2>How to download Watch Dogs 2 APK?</h2>
- <p>If you still want to download Watch Dogs 2 APK for your Android device, you need to follow some steps and precautions. Here are some of them.</p>
- <h3>The steps to download and install Watch Dogs 2 APK</h3>
- <ol>
-<li>First, you need to enable the installation of apps from unknown sources on your device. You can do this by going to Settings > Security > Unknown Sources and toggling it on.</li>
-<li>Next, you need to find a reliable and safe source to download Watch Dogs 2 APK. You can search online for various websites or blogs that offer the APK file. Make sure to check the reviews, ratings, and comments of other users before downloading anything.</li>
-<li>Then, you need to download the Watch Dogs 2 APK file and the OBB data file to your device. The APK file is the application file that contains the game's code and resources. The OBB file is the expansion file that contains the game's data and graphics. You can usually find both files in a zip or rar format.</li>
-<li>After that, you need to extract the zip or rar file using a file manager app or a zip extractor app. You will get two folders: one with the APK file and one with the OBB file.</li>
-<li>Next, you need to install the Watch Dogs 2 APK file by tapping on it and following the instructions on the screen. You might need to grant some permissions or access to the app during the installation process.</li>
-<li>Then, you need to move the OBB file to the right location on your device. You can do this by using a file manager app or a zip extractor app. You need to move the OBB file to the Android > OBB > com.ubisoft.watchdogs2 folder on your device's internal storage. If you don't have this folder, you can create it manually.</li>
-<li>Finally, you can launch the Watch Dogs 2 app from your device's app drawer or home screen. You might need to verify your identity or complete some additional steps before playing the game.</li>
-</ol>
- <h3>The tips and tricks to optimize your gaming experience</h3>
- <ul>
-<li>Make sure that your device meets the minimum requirements to run Watch Dogs 2 APK. You need at least 4 GB of RAM, 64 GB of storage space, and Android 7.0 or higher.</li>
-<li>Make sure that your device has enough battery life and a stable internet connection while playing Watch Dogs 2 APK. You can also use a power bank or a Wi-Fi booster to avoid any interruptions or lags.</li>
-<li>Make sure that you adjust the graphics settings and controls of Watch Dogs 2 APK according to your device's performance and preference. You can do this by going to Settings > Graphics and Settings > Controls in the game's menu.</li>
-<li>Make sure that you backup your game data and progress regularly while playing Watch Dogs 2 APK. You can do this by using a cloud service or a local storage option in the game's menu.</li>
-<li>Make sure that you enjoy Watch Dogs 2 APK responsibly and ethically while playing it on your Android device. You can do this by respecting other players, avoiding piracy, and supporting the developers.</li>
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- <h2>Conclusion</h2>
- <p>Watch Dogs 2 is an amazing open world adventure game that lets you hack into anything and everything in San Francisco. You can play it on your Android device by downloading Watch Dogs 2 APK, but you need to be aware of the benefits and challenges of doing so. You also need to follow some steps and precautions to download and install Watch Dogs 2 APK safely and smoothly. And you need to follow some tips and tricks to optimize your gaming experience and have fun.</p>
- <p>If you are ready to join Marcus Holloway and DedSec in their quest to expose the truth and fight for freedom, then download Watch Dogs 2 APK now and start hacking!</p>
- <h3>Summary <h3>Summary of the main points</h3>
- <p>Here are the main points of this article:</p>
- <ul>
-<li>Watch Dogs 2 is a popular and acclaimed open world adventure game that lets you hack into anything and everything in San Francisco.</li>
-<li>You can play Watch Dogs 2 on your Android device by downloading Watch Dogs 2 APK, which is the application file that contains the game's code and resources.</li>
-<li>Playing Watch Dogs 2 on your Android device has some benefits, such as convenience, cost-effectiveness, and novelty, but also some challenges, such as technical issues, legal risks, and security threats.</li>
-<li>To download and install Watch Dogs 2 APK safely and smoothly, you need to enable the installation of apps from unknown sources, find a reliable and safe source to download the APK file and the OBB data file, extract the zip or rar file, install the APK file, move the OBB file to the right location, and launch the app.</li>
-<li>To optimize your gaming experience and have fun, you need to make sure that your device meets the minimum requirements, has enough battery life and a stable internet connection, adjust the graphics settings and controls, backup your game data and progress, and enjoy Watch Dogs 2 responsibly and ethically.</li>
-</ul>
- <h3>Call to action</h3>
- <p>If you liked this article, please share it with your friends and family who are also interested in Watch Dogs 2 or open world adventure games. You can also leave a comment below and let us know what you think about Watch Dogs 2 APK. And if you want to read more articles like this one, please subscribe to our newsletter and follow us on social media. Thank you for reading!</p>
- <h2>FAQs</h2>
- <p>Here are some of the frequently asked questions about Watch Dogs 2 APK:</p>
- <ol>
-<li><b>Is Watch Dogs 2 APK safe to download?</b></li>
-<p>Watch Dogs 2 APK is safe to download if you follow the steps and precautions mentioned in this article. However, there is always a risk of downloading apps from unknown sources, so you should do it at your own discretion and responsibility.</p>
-<li><b>Is Watch Dogs 2 APK free to download?</b></li>
-<p>Watch Dogs 2 APK is free to download from various websites or blogs that offer it. However, you should be aware that downloading Watch Dogs 2 APK might violate the terms and conditions of Ubisoft or Google Play, and you might face legal actions or penalties from them.</p>
-<li><b>Is Watch Dogs 2 APK compatible with my device?</b></li>
-<p>Watch Dogs 2 APK is compatible with most Android devices that have at least 4 GB of RAM, 64 GB of storage space, and Android 7.0 or higher. However, some devices might not be able to run Watch Dogs 2 APK smoothly or at all due to different specifications or performance issues.</p>
-<li><b>How can I update Watch Dogs 2 APK?</b></li>
-<p>You can update Watch Dogs 2 APK by downloading the latest version of the APK file and the OBB data file from the same source that you downloaded them before. You can then follow the same steps to install and move them to your device. However, you should be aware that updating Watch Dogs 2 APK might not be possible or compatible with your device or game data.</p>
-<li><b>How can I contact Ubisoft or Google Play if I have any problems or questions about Watch Dogs 2 APK?</b></li>
-<p>You can contact Ubisoft or Google Play by visiting their official websites or social media accounts. You can also use their customer service or support channels to get help or feedback. However, you should be aware that Ubisoft or Google Play might not be able to help you or answer your questions about Watch Dogs 2 APK since it is not an official or authorized app.</p>
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spaces/1phancelerku/anime-remove-background/Download YouTube Playlist with These Reddit-Approved Tools and Tips.md

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-<h1>How to Download YouTube Playlists from Reddit</h1>
-<p>If you are looking for some interesting and useful YouTube playlists to watch or listen to, you might want to check out reddit. Reddit is a popular online community where people share and discuss all kinds of topics, including YouTube playlists. You can find playlists for music, movies, documentaries, tutorials, podcasts, and more on reddit.</p>
-<p>But how do you download these playlists from reddit? One of the best tools for downloading YouTube playlists is youtube-dl. Youtube-dl is a powerful and versatile command-line program that allows you to download videos from YouTube and hundreds of other sites. You can download videos in any quality and format, with subtitles, metadata, thumbnails, and more.</p>
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-<p>In this article, we will show you how to install youtube-dl and ffmpeg on your computer, how to use youtube-dl commands to download YouTube playlists, and how to find YouTube playlists on reddit. By the end of this article, you will be able to download any YouTube playlist from reddit with ease.</p>
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-<h3>What is reddit and how it can help you find YouTube playlists <h3>What is reddit and how it can help you find YouTube playlists</h3>
-<p>Reddit is a website where users can post, vote, and comment on various types of content, such as links, images, videos, text, and more. Reddit is divided into thousands of subreddits, which are communities dedicated to specific topics or interests. For example, there are subreddits for music, movies, gaming, science, politics, and more.</p>
-<p>Reddit can help you find YouTube playlists because many users share and recommend playlists on different subreddits. You can find playlists for any genre, mood, theme, or occasion on reddit. For example, you can find playlists for relaxing, studying, working out, partying, traveling, and more. You can also find playlists for specific artists, albums, songs, or genres.</p>
-<p>Reddit can also help you discover new and interesting playlists that you might not find elsewhere. You can browse through the posts and comments of other users and see what they like and dislike. You can also ask for suggestions or feedback on your own playlists. You can also participate in challenges, contests, polls, and discussions related to YouTube playlists on reddit.</p>
- <h3>What is youtube-dl and why it is the best tool for downloading YouTube playlists</h3>
-<p>Youtube-dl is a free and open-source command-line program that allows you to download videos from YouTube and hundreds of other sites. Youtube-dl is written in Python and works on Windows, Mac OS X, Linux, and other platforms. You can download youtube-dl from [4](https://youtube-dl.org/).</p>
-<p>Youtube-dl is the best tool for downloading YouTube playlists because it offers many features and options that make it easy and convenient to download any playlist you want. Some of the features and options of youtube-dl are:</p>
-<ul>
-<li>You can download a single video, a playlist, or a channel with a single command.</li>
-<li>You can choose the best video and audio quality and format for your downloads.</li>
-<li>You can customize the output file name and location for your downloads.</li>
-<li>You can download videos with subtitles, metadata, thumbnails, and more.</li>
-<li>You can resume interrupted downloads and skip already downloaded files.</li>
-<li>You can download videos from other sites besides YouTube with youtube-dl.</li>
-<li>You can update youtube-dl to the latest version with a simple command.</li>
-<li>You can use youtube-dl with a GUI wrapper or a web interface if you prefer a graphical user interface.</li>
-</ul>
-<p>Youtube-dl is also fast, reliable, secure, and easy to use. You just need to copy and paste the URL of the video or playlist you want to download and run the appropriate command in your terminal or command prompt. Youtube-dl will do the rest for you.</p>
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- <h3>How to install youtube-dl and ffmpeg on your computer</h3>
-<p>To use youtube-dl to download YouTube playlists from reddit, you need to install youtube-dl and ffmpeg on your computer. Ffmpeg is a software that youtube-dl uses to convert video and audio files to different formats. You can download ffmpeg from [3](https://ffmpeg.org/download.html).</p>
-<p>The installation process of youtube-dl and ffmpeg varies depending on your operating system. Here are the general steps for installing youtube-dl and ffmpeg on Windows, Mac OS X, and Linux:</p>
- <h4>Windows</h4>
-<ol>
-<li>Download the latest youtube-dl.exe file from [2](https://youtube-dl.org/downloads/latest/youtube-dl.exe) and save it in a folder of your choice.</li>
-<li>Download the latest ffmpeg.zip file from [1](https://www.gyan.dev/ffmpeg/builds/) (choose the static build) and extract it in the same folder as youtube-dl.exe.</li>
-<li>Add the folder where you saved youtube-dl.exe and ffmpeg.exe to your system's PATH environment variable. You can follow this guide on [0](https://www.architectryan.com/2018/03/17/add-to-the-path-on-windows-10/) how to do that.</li>
-<li>Open a terminal or command prompt window and type youtube-dl -h to check if youtube-dl is working properly.</li>
-</ol>
- <h4>Mac OS X</h4>
-<ol>
-<li>Install Homebrew if you don't have it already. Homebrew is a package manager that makes it easy to install software on Mac OS X. You can follow this guide on [11](https://brew.sh/) how to install Homebrew.</li>
-<li>Open a terminal window and type brew install youtube-dl ffmpeg to install youtube-dl and ffmpeg with Homebrew.</li>
-<li>Type youtube-dl -h to check if youtube-dl is working properly.</li>
-</ <h4>Linux</h4>
-<ol>
-<li>Open a terminal window and type sudo apt-get update to update your system's package list.</li>
-<li>Type sudo apt-get install youtube-dl ffmpeg to install youtube-dl and ffmpeg with your system's package manager.</li>
-<li>Type youtube-dl -h to check if youtube-dl is working properly.</li>
-</ol>
- <h2>How to Download YouTube Playlists with youtube-dl</h2>
-<p>Now that you have installed youtube-dl and ffmpeg on your computer, you are ready to download YouTube playlists with youtube-dl. Here are the basic steps for downloading YouTube playlists with youtube-dl:</p>
- <h3>How to use youtube-dl commands to download a single video, a playlist, or a channel</h3>
-<p>The basic syntax for downloading a single video, a playlist, or a channel with youtube-dl is:</p>
-<pre><code>youtube-dl [OPTIONS] URL </code></pre>
-<p>Where URL is the URL of the video, playlist, or channel you want to download, and OPTIONS are the optional parameters you can use to customize your download. For example, you can use the following command to download the first 10 videos of a playlist:</p>
-<pre><code>youtube-dl --playlist-end 10 https://www.youtube.com/playlist?list=PL4o29bINVT4EG_y-k5jGoOu3-Am8Nvi10 </code></pre>
-<p>You can find the URL of the video, playlist, or channel you want to download by copying it from your browser's address bar or by right-clicking on the video or playlist and selecting Copy link address. You can also use the URL of a reddit post or comment that contains a link to a YouTube video or playlist.</p>
- <h3>How to choose the best video and audio quality and format for your downloads</h3>
-<p>By default, youtube-dl will download the best available quality and format for your videos. However, you can also specify the quality and format you want by using the -f or --format option. The syntax for using the -f option is:</p>
-<pre><code>youtube-dl -f FORMAT [OPTIONS] URL </code></pre>
-<p>Where FORMAT is a code that represents the video and audio quality and format you want. You can find the list of available formats for a video by using the -F or --list-formats option. For example, you can use the following command to see the available formats for a video:</p>
-<pre><code>youtube-dl -F https://www.youtube.com/watch?v=dQw4w9WgXcQ </code></pre>
-<p>This will output something like this:</p>
-<pre><code>[youtube] dQw4w9WgXcQ: Downloading webpage [info] Available formats for dQw4w9WgXcQ: format code  extension  resolution note 249          webm       audio only tiny   57k , opus @ 50k (48000Hz), 1.95MiB 250          webm       audio only tiny   76k , opus @ 70k (48000Hz), 2.59MiB 140          m4a        audio only tiny  130k , m4a_dash container, mp4a.40.2@128k (44100Hz), 5.28MiB 251          webm       audio only tiny  149k , opus @160k (48000Hz), 5.07MiB 394          mp4        256x144    144p   83k , av01.0.05M.08, 25fps, video only, 2.77MiB 278          webm       256x144    144p   95k , webm container, vp9, 25fps, video only, 3.19MiB 160          mp4        256x144    144p   99k , avc1.4d400c, 25fps, video only, 2.11MiB 395          mp4        426x240    240p  184k , av01.0.05M.08, 25fps, video only, 5.02MiB 242          webm       426x240    240p  220k , vp9, 25fps, video only, 5.88MiB 133          mp4        426x240    240p  242k , avc1.4d4015, 25fps, video only, 4.11MiB 396          mp4        640x360    360p  338k , av01.0.05M.08, 25fps, video only, 9.12MiB 243          webm       640x360    360p  404k , vp9, 25fps, video only, 10.67MiB 134          mp4        640x360    360p  465k , avc1.4d401e, 25fps, video only, 7.88MiB 397          mp4        854x480    480p  617k , av01.0.05M.08, 25fps, video only, 16.57MiB 244          webm       854x480    480p  752k , vp9, 25fps, video only, 19.72MiB 135          mp4        854x480    480p 1016k , avc1.4d401f, 25fps, video only, 17.13MiB 398          mp4        1280x720   720p60 1138k , av01.0.08M.08, 60fps, video only, 30.57MiB 247          webm       1280x720   720p60 1165k , vp9, 60fps, video only, 30.79MiB 136          mp4        1280x720   720p60 2326k , avc1.4d4020, fps=60, video only, 39.27MiB 399          mp4        1920x1080   (best) </code></pre>
-<p>The format code consists of the extension (such as mp4 or webm), the resolution (such as 640x360 or 1280x720), and the note (such as audio only or video only). You can choose the format code that suits your needs and preferences.</p>
-<p>For example, if you want to download the video in the highest quality and resolution available, you can use the following command:</p>
-<pre><code>youtube-dl -f best https://www.youtube.com/watch?v=dQw4w9WgXcQ </code></pre>
-<p>If you want to download the video in a specific resolution and format, such as mp4 and 720p60, you can use the following command:</p>
-<pre><code>youtube-dl -f mp4-720p60 https://www.youtube.com/watch?v=dQw4w9WgXcQ </code></pre>
-<p>If you want to download the video and audio separately and then merge them into a single file, you can use the following command:</p>
-<pre><code>youtube-dl -f bestvideo+bestaudio https://www.youtube.com/watch?v=dQw4w9WgXcQ </code></pre>
-<p>This will download the best video and audio formats available and then combine them with ffmpeg.</p>
- <h3>How to customize the output file name and location for your downloads</h3>
-<p>By default, youtube-dl will save your downloads in the current working directory with the original file name of the video or playlist. However, you can also customize the output file name and location for your downloads by using the -o or --output option. The syntax for using the -o option is:</p>
-<pre><code>youtube-dl -o TEMPLATE [OPTIONS] URL </code></pre>
-<p>Where TEMPLATE is a string that defines how you want to name and organize your downloads. You can use various variables and placeholders in your template to include information such as title, id, uploader, date, resolution, format, etc. You can find the list of available variables and placeholders on [12](https://github.com/ytdl-org/youtube-dl/blob/master/README.md#output-template).</p>
-<p>For example, if you want to save your downloads in a folder named YouTube with the format of title-id.extension, you can use the following command:</p>
-<pre><code>youtube-dl -o YouTube/%(title)s-%(id)s.%(ext)s [OPTIONS] URL </code></pre>
-<p>This will create a folder named YouTube in your current working directory and save your downloads with names like Rick Astley - Never Gonna Give You Up-dQw4w9WgXcQ.mp4.</p>
- <h3>How to download videos with subtitles, metadata, and thumbnails</h3>
-<p>Youtube-dl can also download videos with subtitles, metadata, and thumbnails if they are available on YouTube. Subtitles are text files that contain the dialogue or narration of the video. Metadata are information files that contain details such as title, description, tags, rating, etc. Thumbnails are image files that show a preview of the video.</p>
-<p>To download videos with subtitles, you can use the --write-sub or --write-auto-sub options. The --write-sub option will download the subtitles that are manually created by the uploader or the community. The --write-auto-sub option will download the subtitles that are automatically generated by YouTube. You can also use the --sub-lang option to specify the language of the subtitles you want to download. For example, you can use the following command to download videos with English subtitles:</p>
-<pre><code>youtube-dl --write-sub --sub-lang en [OPTIONS] URL </code></pre>
-<p>To download videos with metadata, you can use the --write-info-json or --write-description options. The --write-info-json option will download a JSON file that contains all the metadata of the video. The --write-description option will download a text file that contains the description of the video. You can also use the --add-metadata option to embed the metadata into the video file itself. For example, you can use the following command to download videos with metadata:</p>
-<pre><code>youtube-dl --write-info-json --write-description --add-metadata [OPTIONS] URL </code></pre>
-<p>To download videos with thumbnails, you can use the --write-thumbnail option. This will download a JPEG or PNG file that shows a preview of the video. You can also use the --embed-thumbnail option to embed the thumbnail into the video file itself. For example, you can use the following command to download videos with thumbnails:</p>
-<pre><code>youtube-dl --write-thumbnail --embed-thumbnail [OPTIONS] URL </code></pre>
- <h2>How to Find YouTube Playlists on Reddit</h2>
-<p>Now that you know how to download YouTube playlists with youtube-dl, you might wonder how to find YouTube playlists on reddit. There are many ways to find YouTube playlists on reddit, but here are some of the most common and effective ones:</p>
- <h3>How to use reddit search and filters to find relevant posts and subreddits</h3>
-<p>One of the easiest ways to find YouTube playlists on reddit is to use reddit search and filters. Reddit search allows you to search for keywords or phrases across all subreddits or within a specific subreddit. You can also use filters to narrow down your search results by relevance, date, popularity, etc.</p>
-<p>For example, if you want to find YouTube playlists for relaxing music, you can type relaxing music playlist in the reddit search bar and hit enter. This will show you all the posts that contain these words in any subreddit. You can then use the filters on the top or side of the page to sort or filter your results by relevance, new, hot, top, etc.</p>
-<p>If you want to find YouTube playlists for relaxing music in a specific subreddit, such as r/Music, you can type relaxing music playlist subreddit:Music in the reddit search bar and hit enter. This will show you all the posts that contain these words in r/Music only.</p>
-<p>You can also use other filters and operators in your reddit search queries to refine your results further. For example, you can use quotation marks to search for an exact phrase, such as "relaxing music playlist". You can use a minus sign to exclude a word or phrase from your search results, such as relaxing music playlist -spotify. You can use OR to search for multiple words or phrases at once, such as relaxing music playlist OR ambient music playlist. You can find more information on how to use reddit search and filters on [13](https://www.reddit.com/wiki/search).</p>
- <h3>How to use reddit comments and upvotes to find the best playlists</h3>
-<p>Another way to find YouTube playlists on reddit is to use reddit comments and upvotes. Reddit comments are where users share their opinions, feedback, suggestions, questions, and answers on various posts. Reddit upvotes are where users express their approval or appreciation of a post or comment by clicking on an arrow icon.</p>
-<p>You can use reddit comments and upvotes to find the best playlists by reading what other users have to say about them and seeing how popular they are. For example, if you find a post that links to a YouTube playlist for relaxing music, you can read the comments section and see what other users think about it. You can also see how many upvotes or downvotes it has received and how it ranks among other posts.</p>
-<p>You can also use reddit comments and upvotes to ask for recommendations or feedback on your own playlists or share your own playlists with others. For example, if you have created a YouTube playlist for relaxing music and want some suggestions on how to improve it or what other songs to add, you can post it on a relevant subreddit and ask for feedback from other users. You can also browse through other users' playlists and comment on them or upvote them if you like them.</p>
- <h3>How to use reddit bots and tools to enhance your reddit experience</h3>
-<p>A third way to find YouTube playlists on reddit is to use reddit bots and tools. Reddit bots are automated programs that perform specific tasks or functions on reddit, such as replying to comments, posting links, providing information, etc. Reddit tools are websites or applications that provide additional features or services for reddit users, such as searching, filtering, analyzing, etc.</p>
-<p>You can use reddit bots and tools to enhance your reddit experience by making it easier, faster, or more fun to find YouTube playlists on reddit. For example, you can use the following bots and tools:</p>
-<ul>
-<li>[14](https://reddit.musicplayer.io/) is a website that allows you to play YouTube playlists from reddit posts or comments in a simple and elegant interface. You can also create your own playlists from reddit links and share them with others.</li>
-<li>[15](https://www.reddit.com/user/Reddit-Playlister) is a bot that creates Spotify playlists from YouTube links posted on reddit. You can summon the bot by commenting !playlist on a post or comment that contains YouTube links. The bot will reply with a link to a Spotify playlist that contains the same songs as the YouTube links.</li>
-<li>[16](https://www.reddit.com/user/playlistbot) is a bot that creates YouTube playlists from reddit posts or comments that contain multiple YouTube links. You can summon the bot by commenting !playlist on a post or comment that contains YouTube links. The bot will reply with a link to a YouTube playlist that contains all the YouTube links.</li>
-<li>[17](https://www.reddit.com/r/RedditPlaylists/) is a subreddit where users share and request YouTube playlists from reddit. You can browse through the posts and comments and find playlists for various topics and genres. You can also post your own playlists or requests for playlists.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In this article, we have shown you how to download YouTube playlists from reddit with youtube-dl. We have also shown you how to install youtube-dl and ffmpeg on your computer, how to use youtube-dl commands to download YouTube playlists, and how to find YouTube playlists on reddit. By following these steps, you will be able to download any YouTube playlist from reddit with ease.</p>
-<p>Downloading YouTube playlists from reddit can be a great way to enjoy and discover new and interesting content on YouTube. You can find playlists for any topic, genre, mood, or occasion on reddit. You can also share your own playlists or ask for recommendations or feedback from other users. You can also use youtube-dl to download videos from other sites besides YouTube with youtube-dl.</p>
-<p>If you are looking for a simple and convenient way to download YouTube playlists from reddit without using commands, you might want to check out [18](https://www.youtubedownloader.com/). This is a website that allows you to download videos and playlists from YouTube and other sites with just a few clicks. You can also convert videos to different formats, edit videos, burn DVDs, and more.</p>
-<p>We hope you found this article helpful and informative. If you have any questions or comments, please feel free to leave them below. Thank you for reading!</p>
- <h2>FAQs</h2>
-<ul>
-<li><b>Q: Is youtube-dl legal and safe to use?</b></li>
-<li>A: youtube-dl is legal and safe to use as long as you respect the terms of service of YouTube and the content creators. You should only download videos for personal use and not distribute them without permission.</li>
-<li><b>Q: How can I update youtube-dl to the latest version?</b></li>
-<li>A: You can update youtube-dl by running the command youtube-dl -U in your terminal or command prompt. You can also download the latest version from [19](https://github.com/ytdl-org/youtube-dl/releases/latest).</li>
-<li><b>Q: How can I download videos from other sites besides YouTube with youtube-dl?</b></li>
-<li>A: youtube-dl supports hundreds of sites besides YouTube. You can find the list of supported sites by running the command youtube-dl --list-extractors in your terminal or command prompt. You can also check the documentation on [20](https://github.com/ytdl-org/youtube-dl/blob/master/README.md#readme).</li>
-<li><b>Q: How can I download videos faster with youtube-dl?</b></li>
-<li>A: You can download videos faster with youtube-dl by using the --external-downloader option and specifying a faster downloader such as aria2c or axel. For example, you can run the command youtube-dl --external-downloader aria2c [8](https://www.youtube.com/watch?v=dQw4w9WgXcQ) to download a video with aria2c.</li>
-<li><b>Q: How can I download videos with a GUI instead of using commands with youtube-dl?</b></li>
-<li>A: You can download videos with a GUI instead of using commands with youtube-dl by using one of the many GUI wrappers available for youtube-dl. Some examples are [21](https://github.com/MrS0m30n3/youtube-dl-gui), [22](https://github.com/oleksis/youtube-dl-gui), and [23](https://github.com/jely2002/youtube-dl-gui).</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />

spaces/AIConsultant/MusicGen/audiocraft/data/music_dataset.py

@@ -1,270 +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.
-"""Dataset of music tracks with rich metadata.
-"""
-from dataclasses import dataclass, field, fields, replace
-import gzip
-import json
-import logging
-from pathlib import Path
-import random
-import typing as tp
-
-import torch
-
-from .info_audio_dataset import (
-    InfoAudioDataset,
-    AudioInfo,
-    get_keyword_list,
-    get_keyword,
-    get_string
-)
-from ..modules.conditioners import (
-    ConditioningAttributes,
-    JointEmbedCondition,
-    WavCondition,
-)
-from ..utils.utils import warn_once
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class MusicInfo(AudioInfo):
-    """Segment info augmented with music metadata.
-    """
-    # music-specific metadata
-    title: tp.Optional[str] = None
-    artist: tp.Optional[str] = None  # anonymized artist id, used to ensure no overlap between splits
-    key: tp.Optional[str] = None
-    bpm: tp.Optional[float] = None
-    genre: tp.Optional[str] = None
-    moods: tp.Optional[list] = None
-    keywords: tp.Optional[list] = None
-    description: tp.Optional[str] = None
-    name: tp.Optional[str] = None
-    instrument: tp.Optional[str] = None
-    # original wav accompanying the metadata
-    self_wav: tp.Optional[WavCondition] = None
-    # dict mapping attributes names to tuple of wav, text and metadata
-    joint_embed: tp.Dict[str, JointEmbedCondition] = field(default_factory=dict)
-
-    @property
-    def has_music_meta(self) -> bool:
-        return self.name is not None
-
-    def to_condition_attributes(self) -> ConditioningAttributes:
-        out = ConditioningAttributes()
-        for _field in fields(self):
-            key, value = _field.name, getattr(self, _field.name)
-            if key == 'self_wav':
-                out.wav[key] = value
-            elif key == 'joint_embed':
-                for embed_attribute, embed_cond in value.items():
-                    out.joint_embed[embed_attribute] = embed_cond
-            else:
-                if isinstance(value, list):
-                    value = ' '.join(value)
-                out.text[key] = value
-        return out
-
-    @staticmethod
-    def attribute_getter(attribute):
-        if attribute == 'bpm':
-            preprocess_func = get_bpm
-        elif attribute == 'key':
-            preprocess_func = get_musical_key
-        elif attribute in ['moods', 'keywords']:
-            preprocess_func = get_keyword_list
-        elif attribute in ['genre', 'name', 'instrument']:
-            preprocess_func = get_keyword
-        elif attribute in ['title', 'artist', 'description']:
-            preprocess_func = get_string
-        else:
-            preprocess_func = None
-        return preprocess_func
-
-    @classmethod
-    def from_dict(cls, dictionary: dict, fields_required: bool = False):
-        _dictionary: tp.Dict[str, tp.Any] = {}
-
-        # allow a subset of attributes to not be loaded from the dictionary
-        # these attributes may be populated later
-        post_init_attributes = ['self_wav', 'joint_embed']
-        optional_fields = ['keywords']
-
-        for _field in fields(cls):
-            if _field.name in post_init_attributes:
-                continue
-            elif _field.name not in dictionary:
-                if fields_required and _field.name not in optional_fields:
-                    raise KeyError(f"Unexpected missing key: {_field.name}")
-            else:
-                preprocess_func: tp.Optional[tp.Callable] = cls.attribute_getter(_field.name)
-                value = dictionary[_field.name]
-                if preprocess_func:
-                    value = preprocess_func(value)
-                _dictionary[_field.name] = value
-        return cls(**_dictionary)
-
-
-def augment_music_info_description(music_info: MusicInfo, merge_text_p: float = 0.,
-                                   drop_desc_p: float = 0., drop_other_p: float = 0.) -> MusicInfo:
-    """Augment MusicInfo description with additional metadata fields and potential dropout.
-    Additional textual attributes are added given probability 'merge_text_conditions_p' and
-    the original textual description is dropped from the augmented description given probability drop_desc_p.
-
-    Args:
-        music_info (MusicInfo): The music metadata to augment.
-        merge_text_p (float): Probability of merging additional metadata to the description.
-            If provided value is 0, then no merging is performed.
-        drop_desc_p (float): Probability of dropping the original description on text merge.
-            if provided value is 0, then no drop out is performed.
-        drop_other_p (float): Probability of dropping the other fields used for text augmentation.
-    Returns:
-        MusicInfo: The MusicInfo with augmented textual description.
-    """
-    def is_valid_field(field_name: str, field_value: tp.Any) -> bool:
-        valid_field_name = field_name in ['key', 'bpm', 'genre', 'moods', 'instrument', 'keywords']
-        valid_field_value = field_value is not None and isinstance(field_value, (int, float, str, list))
-        keep_field = random.uniform(0, 1) < drop_other_p
-        return valid_field_name and valid_field_value and keep_field
-
-    def process_value(v: tp.Any) -> str:
-        if isinstance(v, (int, float, str)):
-            return str(v)
-        if isinstance(v, list):
-            return ", ".join(v)
-        else:
-            raise ValueError(f"Unknown type for text value! ({type(v), v})")
-
-    description = music_info.description
-
-    metadata_text = ""
-    if random.uniform(0, 1) < merge_text_p:
-        meta_pairs = [f'{_field.name}: {process_value(getattr(music_info, _field.name))}'
-                      for _field in fields(music_info) if is_valid_field(_field.name, getattr(music_info, _field.name))]
-        random.shuffle(meta_pairs)
-        metadata_text = ". ".join(meta_pairs)
-        description = description if not random.uniform(0, 1) < drop_desc_p else None
-        logger.debug(f"Applying text augmentation on MMI info. description: {description}, metadata: {metadata_text}")
-
-    if description is None:
-        description = metadata_text if len(metadata_text) > 1 else None
-    else:
-        description = ". ".join([description.rstrip('.'), metadata_text])
-    description = description.strip() if description else None
-
-    music_info = replace(music_info)
-    music_info.description = description
-    return music_info
-
-
-class Paraphraser:
-    def __init__(self, paraphrase_source: tp.Union[str, Path], paraphrase_p: float = 0.):
-        self.paraphrase_p = paraphrase_p
-        open_fn = gzip.open if str(paraphrase_source).lower().endswith('.gz') else open
-        with open_fn(paraphrase_source, 'rb') as f:  # type: ignore
-            self.paraphrase_source = json.loads(f.read())
-        logger.info(f"loaded paraphrasing source from: {paraphrase_source}")
-
-    def sample_paraphrase(self, audio_path: str, description: str):
-        if random.random() >= self.paraphrase_p:
-            return description
-        info_path = Path(audio_path).with_suffix('.json')
-        if info_path not in self.paraphrase_source:
-            warn_once(logger, f"{info_path} not in paraphrase source!")
-            return description
-        new_desc = random.choice(self.paraphrase_source[info_path])
-        logger.debug(f"{description} -> {new_desc}")
-        return new_desc
-
-
-class MusicDataset(InfoAudioDataset):
-    """Music dataset is an AudioDataset with music-related metadata.
-
-    Args:
-        info_fields_required (bool): Whether to enforce having required fields.
-        merge_text_p (float): Probability of merging additional metadata to the description.
-        drop_desc_p (float): Probability of dropping the original description on text merge.
-        drop_other_p (float): Probability of dropping the other fields used for text augmentation.
-        joint_embed_attributes (list[str]): A list of attributes for which joint embedding metadata is returned.
-        paraphrase_source (str, optional): Path to the .json or .json.gz file containing the
-            paraphrases for the description. The json should be a dict with keys are the
-            original info path (e.g. track_path.json) and each value is a list of possible
-            paraphrased.
-        paraphrase_p (float): probability of taking a paraphrase.
-
-    See `audiocraft.data.info_audio_dataset.InfoAudioDataset` for full initialization arguments.
-    """
-    def __init__(self, *args, info_fields_required: bool = True,
-                 merge_text_p: float = 0., drop_desc_p: float = 0., drop_other_p: float = 0.,
-                 joint_embed_attributes: tp.List[str] = [],
-                 paraphrase_source: tp.Optional[str] = None, paraphrase_p: float = 0,
-                 **kwargs):
-        kwargs['return_info'] = True  # We require the info for each song of the dataset.
-        super().__init__(*args, **kwargs)
-        self.info_fields_required = info_fields_required
-        self.merge_text_p = merge_text_p
-        self.drop_desc_p = drop_desc_p
-        self.drop_other_p = drop_other_p
-        self.joint_embed_attributes = joint_embed_attributes
-        self.paraphraser = None
-        if paraphrase_source is not None:
-            self.paraphraser = Paraphraser(paraphrase_source, paraphrase_p)
-
-    def __getitem__(self, index):
-        wav, info = super().__getitem__(index)
-        info_data = info.to_dict()
-        music_info_path = Path(info.meta.path).with_suffix('.json')
-
-        if Path(music_info_path).exists():
-            with open(music_info_path, 'r') as json_file:
-                music_data = json.load(json_file)
-                music_data.update(info_data)
-                music_info = MusicInfo.from_dict(music_data, fields_required=self.info_fields_required)
-            if self.paraphraser is not None:
-                music_info.description = self.paraphraser.sample(music_info.meta.path, music_info.description)
-            if self.merge_text_p:
-                music_info = augment_music_info_description(
-                    music_info, self.merge_text_p, self.drop_desc_p, self.drop_other_p)
-        else:
-            music_info = MusicInfo.from_dict(info_data, fields_required=False)
-
-        music_info.self_wav = WavCondition(
-            wav=wav[None], length=torch.tensor([info.n_frames]),
-            sample_rate=[info.sample_rate], path=[info.meta.path], seek_time=[info.seek_time])
-
-        for att in self.joint_embed_attributes:
-            att_value = getattr(music_info, att)
-            joint_embed_cond = JointEmbedCondition(
-                wav[None], [att_value], torch.tensor([info.n_frames]),
-                sample_rate=[info.sample_rate], path=[info.meta.path], seek_time=[info.seek_time])
-            music_info.joint_embed[att] = joint_embed_cond
-
-        return wav, music_info
-
-
-def get_musical_key(value: tp.Optional[str]) -> tp.Optional[str]:
-    """Preprocess key keywords, discarding them if there are multiple key defined."""
-    if value is None or (not isinstance(value, str)) or len(value) == 0 or value == 'None':
-        return None
-    elif ',' in value:
-        # For now, we discard when multiple keys are defined separated with comas
-        return None
-    else:
-        return value.strip().lower()
-
-
-def get_bpm(value: tp.Optional[str]) -> tp.Optional[float]:
-    """Preprocess to a float."""
-    if value is None:
-        return None
-    try:
-        return float(value)
-    except ValueError:
-        return None

spaces/ANLPRL/NER_On_Oral_Medicine/app.py

@@ -1,96 +0,0 @@
-from transformers import AutoTokenizer, TFAutoModel
-import tensorflow as tf
-#from keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-import pickle
-import numpy as np
-from keras.models import load_model
-import streamlit as st
-import io
-import PyPDF2
-import re
-from PIL import Image
-
-image = Image.open('header-image.png')
-st.image(image)
-
-   
-def preprocess(text):
-    # Define a regular expression pattern for URLs, non-alphabetic characters, and user names
-    pattern = re.compile(r'https?://\S+|[^0-9A-Za-z\' t]|@\w+')
-    # Use the regular expression to find all URLs, non-alphabetic characters, and user names in the text
-    matches = pattern.findall(text)
-    #Replace the URLs, non-alphabetic characters, and user names with an empty string
-    for match in matches:
-        text = text.replace(match, ' ')
-    return text
-
-
-
-def predict(new_data):
-    #Load the trained model
-    # Create a LabelEncoder object
-    with open("labelencoder1.pkl", 'rb') as f:
-        le = pickle.load(f)
-    model= tf.keras.models.load_model("biobert-rnn1.h5")
-    tokenizer = AutoTokenizer.from_pretrained("dmis-lab/biobert-base-cased-v1.1")
-    biobert_model = TFAutoModel.from_pretrained("dmis-lab/biobert-base-cased-v1.1", from_pt=True)
-    X_tokens = [tokenizer.encode(text, add_special_tokens=True) for text in new_data.split()]
-    X_padded = pad_sequences(X_tokens, maxlen=22, dtype='long', truncating='post', padding='post')
-    X_tensor = tf.convert_to_tensor(X_padded)
-    X_embeddings = biobert_model(X_tensor)[0]
-    pred=model.predict(X_embeddings)
-    predicted_labels = list(le.inverse_transform(np.argmax(pred, axis=1)))
-    text=new_data.split()
-    prev_label=" "
-    data=[]
-    labels=[]
-    for i,(word,label) in enumerate(zip(text,predicted_labels)):
-        if label!="Other":
-            label=label.split('-')[1]
-        if prev_label==label:
-                data[-1]=data[-1]+" "+word
-        else:
-            data.append(word)
-            labels.append(label)
-        prev_label=label
-    return(data,labels)
-
-def highlight(sentence):
-    highlighted_text = ""
-    entity_colors = {"Symptom":"#87cefa","Medical Condition":"#ffb6c1"}
-    words, labels = predict(sentence)
-    for words, label in zip(words, labels):
-        if label!="Other" and words!="a":
-            if label in ["Medical Condition","Symptom"]:
-                    word_color = entity_colors.get(label, "yellow")
-                    label_color = entity_colors.get(label + '-label', "<b>black</b>")
-                    highlighted_text += f'<mark style="background-color: {word_color}; color: {label_color}; padding: 0 0.25rem; border-radius: 0.25rem; border: 2px solid {word_color}; border-bottom-width: 1px">{words}<sup style="background-color: white; color: black; border: 1px solid black; border-radius: 2px; padding: 0 0.15rem; font-size: 70%; margin-left: 0.15rem; font-weight: bold;">{label}</sup></mark> '
-            else:
-                highlighted_text += f'{words} '
-        else:
-            highlighted_text += f'{words} '
-    st.markdown(highlighted_text, unsafe_allow_html=True)
-
-st.subheader('Named Entity Recognizer for Oral Medicine and Radiology')
-sentence = st.text_area('Enter some text:')
-
-st.write("OR") 
-
-selected_options = st.selectbox(
-'Choose a text from dropdown: ',
-(" ",
-    'Anemia and gingival bleeding are connected in that anemia can be a contributing cause to the occurrence of gingival bleeding . Anemia is a condition characterized by a shortage in the number or quality of red blood cells, which can lead to a reduced ability of the blood to carry oxygen throughout the body.', 
-    'Hemophilia is a genetic illness that mainly affects the blood ability to clot properly. Individuals with significant hemophilia are at an elevated possibility of experiencing unforeseen bleeding episodes, which can occur in various parts of the body, including the mouth. Gingival bleeding can be a sign of hemophilia and can present as  gum bleeding or mouth sores.', 
-    "Von Willebrand disease VWD  is a genetic condition that impairs the blood's ability to  clot properly. One of the symptoms of VWD is spontaneous gingival bleeding , which can occur without any apparent cause or trauma"))  # set default to None
-
-
-# Define the colors for each label
-
-if st.button('Analyze'):
-    if sentence:
-        highlight(sentence)
-    elif selected_options:
-        highlight(selected_options)
-    else:
-        st.write('Please enter a sentence or select an option from the dropdown or upload a file.')

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/work_dirs/resnext101_4xb32_2048e_3c_noF/resnext101_4xb32_2048e_3c_noF.py

@@ -1,131 +0,0 @@
-optim_wrapper = dict(
-    type='OptimWrapper',
-    optimizer=dict(
-        type='Adam',
-        lr=0.0001,
-        betas=(0.9, 0.999),
-        eps=1e-08,
-        weight_decay=0,
-        amsgrad=False),
-    accumulative_counts=8)
-param_scheduler = [
-    dict(
-        type='LinearLR',
-        start_factor=1e-05,
-        by_epoch=True,
-        end=10,
-        convert_to_iter_based=True),
-    dict(
-        type='MultiStepLR',
-        by_epoch=True,
-        milestones=[30, 210, 390, 570, 750, 930, 1110, 1290, 1470, 1650, 1830],
-        gamma=0.9)
-]
-train_cfg = dict(by_epoch=True, max_epochs=2048, val_interval=16)
-val_cfg = dict()
-test_cfg = dict()
-default_scope = 'mmpretrain'
-default_hooks = dict(
-    timer=dict(type='IterTimerHook'),
-    logger=dict(type='LoggerHook', interval=10),
-    param_scheduler=dict(type='ParamSchedulerHook'),
-    checkpoint=dict(type='CheckpointHook', interval=16, save_best='auto'),
-    sampler_seed=dict(type='DistSamplerSeedHook'),
-    visualization=dict(type='VisualizationHook', enable=False))
-env_cfg = dict(
-    cudnn_benchmark=False,
-    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
-    dist_cfg=dict(backend='nccl'))
-vis_backends = [dict(type='LocalVisBackend')]
-visualizer = dict(
-    type='UniversalVisualizer',
-    vis_backends=[dict(type='LocalVisBackend'),
-                  dict(type='WandbVisBackend')])
-log_level = 'INFO'
-load_from = None
-resume = False
-randomness = dict(seed=None, deterministic=False)
-dataset_type = 'CustomDataset'
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='RandomResizedCrop', scale=224),
-    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
-    dict(type='PackInputs')
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='ResizeEdge', scale=256, edge='short'),
-    dict(type='CenterCrop', crop_size=224),
-    dict(type='PackInputs')
-]
-train_dataloader = dict(
-    pin_memory=True,
-    persistent_workers=True,
-    collate_fn=dict(type='default_collate'),
-    batch_size=32,
-    num_workers=5,
-    dataset=dict(
-        type='CustomDataset',
-        data_root='../2_preprocess_data_3000',
-        with_label=True,
-        ann_file='',
-        data_prefix='train',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(type='RandomResizedCrop', scale=224),
-            dict(type='RandomFlip', prob=0.5, direction='horizontal'),
-            dict(type='PackInputs')
-        ]),
-    sampler=dict(type='DefaultSampler', shuffle=True))
-val_dataloader = dict(
-    pin_memory=True,
-    persistent_workers=True,
-    collate_fn=dict(type='default_collate'),
-    batch_size=32,
-    num_workers=5,
-    dataset=dict(
-        type='CustomDataset',
-        data_root='../2_preprocess_data_3000',
-        with_label=True,
-        ann_file='',
-        data_prefix='val',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(type='ResizeEdge', scale=256, edge='short'),
-            dict(type='CenterCrop', crop_size=224),
-            dict(type='PackInputs')
-        ]),
-    sampler=dict(type='DefaultSampler', shuffle=False))
-val_evaluator = dict(type='Accuracy', topk=(1, 3))
-test_dataloader = dict(
-    pin_memory=True,
-    persistent_workers=True,
-    collate_fn=dict(type='default_collate'),
-    batch_size=32,
-    num_workers=5,
-    dataset=dict(
-        type='CustomDataset',
-        data_root='../2_preprocess_data_3000',
-        with_label=True,
-        ann_file='',
-        data_prefix='val',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(type='ResizeEdge', scale=256, edge='short'),
-            dict(type='CenterCrop', crop_size=224),
-            dict(type='PackInputs')
-        ]),
-    sampler=dict(type='DefaultSampler', shuffle=False))
-test_evaluator = dict(type='Accuracy', topk=(1, 3))
-model = dict(
-    type='ImageClassifier',
-    backbone=dict(type='ResNeXt', depth=101, in_channels=3),
-    neck=dict(type='GlobalAveragePooling'),
-    head=dict(
-        type='LinearClsHead',
-        num_classes=7,
-        in_channels=2048,
-        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
-        topk=(1, 3)))
-launcher = 'pytorch'
-work_dir = './work_dirs/resnext101_4xb32_2048e_3c_noF'

spaces/Aaaaaaaabdualh/poetry2023/README.md

@@ -1,13 +0,0 @@
----
-title: Poetry2023
-emoji: 👁
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.16.0
-app_file: app.py
-pinned: false
-duplicated_from: akhooli/poetry2023
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AbandonedMuse/UnlimitedMusicGen/CHANGELOG.md

@@ -1,33 +0,0 @@
-## [0.0.2a2] - 2023-07-20
-
-Music Generation set to a max of 720 seconds (12 minutes) to avoid memory issues.
-
-Video editing options (thanks @Surn and @oncorporation).
-
-Music Conditioning segment options
-
-
-## [0.0.2a] - TBD
-
-Improved demo, fixed top p (thanks @jnordberg).
-
-Compressor tanh on output to avoid clipping with some style (especially piano).
-Now repeating the conditioning periodically if it is too short.
-
-More options when launching Gradio app locally (thanks @ashleykleynhans).
-
-Testing out PyTorch 2.0 memory efficient attention.
-
-Added extended generation (infinite length) by slowly moving the windows.
-Note that other implementations exist: https://github.com/camenduru/MusicGen-colab.
-
-## [0.0.1] - 2023-06-09
-
-Initial release, with model evaluation only.
-
-
-# Changelog
-
-All notable changes to this project will be documented in this file.
-
-The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/holygrail/methods/LayoutMode3.js

@@ -1,68 +0,0 @@
-/*
-Elements:
-    ```
-    HHH
-    LCR
-    LFR
-    ```
-*/
-
-import {
-    GetAddHeaderConfig,
-    GetAddLeftSideConfig, GetAddContentConfig, GetAddRightSideConfig,
-    GetAddFooterConfig,
-    GetAddContainerConfig
-} from './GetAddChildConfig.js';
-import CreatExpandContainer from './CreatExpandContainer.js';
-
-var LayoutMode0 = function (config) {
-    var scene = this.scene;
-
-    // Add Header
-    var header = config.header;
-    if (header) {
-        this.add(header, GetAddHeaderConfig(config));
-    }
-
-    /*
-    L C R
-    L F R
-    */    
-    var bodySizer0 = CreatExpandContainer(scene, 0);
-    this.add(bodySizer0, GetAddContainerConfig(config));
-
-    // Add Left-side
-    var leftSide = config.leftSide;
-    if (leftSide) {
-        bodySizer0.add(leftSide, GetAddLeftSideConfig(config));
-    }
-
-    /*
-    C
-
-    F
-    */    
-    var bodySizer1 = CreatExpandContainer(scene, 1);
-    bodySizer0.add(bodySizer1, GetAddContainerConfig(config));
-
-    // Add content
-    var content = config.content;
-    if (content) {
-        bodySizer1.add(content, GetAddContentConfig(config));
-    }
-
-    // Add Footer
-    var footer = config.footer;
-    if (footer) {
-        bodySizer1.add(footer, GetAddFooterConfig(config));
-    }
-
-    // Add Right-side
-    var rightSide = config.rightSide;
-    if (rightSide) {
-        bodySizer0.add(rightSide, GetAddRightSideConfig(config));
-    }
-
-}
-
-export default LayoutMode0;

spaces/AiMimicry/sovits-models/hubert/hubert_model_onnx.py

@@ -1,217 +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
-
-
-class HubertSoft(Hubert):
-    def __init__(self):
-        super().__init__()
-
-    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)
-
-    def forward(self, x):
-        return self.units(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

spaces/Akmyradov/TurkmenTTSweSTT/uroman/bin/uroman-quick.pl

@@ -1,58 +0,0 @@
-#!/usr/bin/perl -w
-
-# uroman  Nov. 12, 2015 - July 25, 2016
-# version v0.7
-# Author: Ulf Hermjakob
-
-# Usage: uroman-quick.pl {-l [tur|uig|ukr|yid]} < STDIN
-# currently only for Arabic script languages, incl. Uyghur
-
-$|=1;
-
-use FindBin;
-use Cwd "abs_path";
-use File::Basename qw(dirname);
-use File::Spec;
-
-my $bin_dir = abs_path(dirname($0));
-my $root_dir = File::Spec->catfile($bin_dir, File::Spec->updir());
-my $data_dir = File::Spec->catfile($root_dir, "data");
-my $lib_dir = File::Spec->catfile($root_dir, "lib");
-
-use lib "$FindBin::Bin/../lib";
-use NLP::Romanizer;
-use NLP::UTF8;
-$romanizer = NLP::Romanizer;
-%ht = ();
-$lang_code = "";
-
-while (@ARGV) {
-   $arg = shift @ARGV;
-   if ($arg =~ /^-+(l|lc|lang-code)$/) {
-      $lang_code = lc (shift @ARGV || "")
-   } else {
-      print STDERR "Ignoring unrecognized arg $arg\n";
-   }
-}
-
-$romanization_table_arabic_block_filename = File::Spec->catfile($data_dir, "romanization-table-arabic-block.txt");
-$romanization_table_filename = File::Spec->catfile($data_dir, "romanization-table.txt");
-
-$romanizer->load_romanization_table(*ht, $romanization_table_arabic_block_filename);
-$romanizer->load_romanization_table(*ht, $romanization_table_filename);
-
-$line_number = 0;
-while (<>) {
-   $line_number++;
-   my $line = $_;
-   print $romanizer->quick_romanize($line, $lang_code, *ht) . "\n";
-   if ($line_number =~ /0000$/) {
-      print STDERR $line_number;
-   } elsif ($line_number =~ /000$/) {
-      print STDERR ".";
-   }
-}
-print STDERR "\n";
-
-exit 0;
-

spaces/Akshay-More-007/starcoder/apikey.py

@@ -1 +0,0 @@
-apikey_hungingface = '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'

spaces/AlexWang/lama/models/ade20k/segm_lib/nn/parallel/__init__.py

@@ -1 +0,0 @@
-from .data_parallel import UserScatteredDataParallel, user_scattered_collate, async_copy_to

spaces/Alichuan/VITS-Umamusume-voice-synthesizer/ONNXVITS_models.py

@@ -1,509 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import ONNXVITS_modules as modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-    super().__init__()
-    filter_channels = in_channels # it needs to be removed from future version.
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.log_flow = modules.Log()
-    self.flows = nn.ModuleList()
-    self.flows.append(modules.ElementwiseAffine(2))
-    for i in range(n_flows):
-      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.flows.append(modules.Flip())
-
-    self.post_pre = nn.Conv1d(1, filter_channels, 1)
-    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    self.post_flows = nn.ModuleList()
-    self.post_flows.append(modules.ElementwiseAffine(2))
-    for i in range(4):
-      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.post_flows.append(modules.Flip())
-
-    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-    self.w = None
-    self.reverse = None
-    self.noise_scale = None
-  def forward(self, x, x_mask, g=None):
-    w = self.w
-    reverse = self.reverse
-    noise_scale = self.noise_scale
-
-    x = torch.detach(x)
-    x = self.pre(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.convs(x, x_mask)
-    x = self.proj(x) * x_mask
-
-    if not reverse:
-      flows = self.flows
-      assert w is not None
-
-      logdet_tot_q = 0 
-      h_w = self.post_pre(w)
-      h_w = self.post_convs(h_w, x_mask)
-      h_w = self.post_proj(h_w) * x_mask
-      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-      z_q = e_q
-      for flow in self.post_flows:
-        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-        logdet_tot_q += logdet_q
-      z_u, z1 = torch.split(z_q, [1, 1], 1) 
-      u = torch.sigmoid(z_u) * x_mask
-      z0 = (w - u) * x_mask
-      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
-      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
-
-      logdet_tot = 0
-      z0, logdet = self.log_flow(z0, x_mask)
-      logdet_tot += logdet
-      z = torch.cat([z0, z1], 1)
-      for flow in flows:
-        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-        logdet_tot = logdet_tot + logdet
-      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
-      return nll + logq # [b]
-    else:
-      flows = list(reversed(self.flows))
-      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
-      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-      for flow in flows:
-        z = flow(z, x_mask, g=x, reverse=reverse)
-      z0, z1 = torch.split(z, [1, 1], 1)
-      logw = z0
-      return logw
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      n_vocab,
-      out_channels,
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout):
-    super().__init__()
-    self.n_vocab = n_vocab
-    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 = nn.Embedding(n_vocab, hidden_channels)
-    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
-    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, x, x_lengths):
-    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
-    x = torch.transpose(x, 1, -1) # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_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 x, 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())
-
-    self.reverse = None
-  def forward(self, x, x_mask, g=None):
-    reverse = self.reverse 
-    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 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_in : [b, c, t] -> [b, h, t] 
-    x = self.enc(x, x_mask, g=g) # x_in : [b, h, t], g : [b, h, 1],  x = x_in + 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 # z, m, logs : [b, h, t]
-
-
-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):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-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 MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2,3,5,7,11]
-
-        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)
-            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 SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self, 
-    n_vocab,
-    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,
-    n_speakers=0,
-    gin_channels=0,
-    use_sdp=True,
-    **kwargs):
-
-    super().__init__()
-    self.n_vocab = n_vocab
-    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.n_speakers = n_speakers
-    self.gin_channels = gin_channels
-
-    self.use_sdp = use_sdp
-
-    self.enc_p = TextEncoder(n_vocab,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout)
-    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
-    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, 4, gin_channels=gin_channels)
-
-    self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-
-    if n_speakers > 0:
-      self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-  def forward(self, x, x_lengths, sid=None, noise_scale=.667, length_scale=1, noise_scale_w=.8, max_len=None):
-    torch.onnx.export(
-        self.enc_p,
-        (x, x_lengths),
-        "ONNX_net/enc_p.onnx",
-        input_names=["x", "x_lengths"],
-        output_names=["xout", "m_p", "logs_p", "x_mask"],
-        dynamic_axes={
-          "x" : [1],
-          "xout" : [2],
-          "m_p" : [2],
-          "logs_p" : [2],
-          "x_mask" : [2]
-        },
-        verbose=True,
-    )
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    self.dp.reverse = True
-    self.dp.noise_scale = noise_scale_w
-    torch.onnx.export(
-        self.dp,
-        (x, x_mask, g),
-        "ONNX_net/dp.onnx",
-        input_names=["x", "x_mask", "g"],
-        output_names=["logw"],
-        dynamic_axes={
-          "x" : [2],
-          "x_mask" : [2],
-          "logw" : [2]
-        },
-        verbose=True,
-    )
-    logw = self.dp(x, x_mask, g=g)
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-
-    self.flow.reverse = True
-    torch.onnx.export(
-        self.flow,
-        (z_p, y_mask, g),
-        "ONNX_net/flow.onnx",
-        input_names=["z_p", "y_mask", "g"],
-        output_names=["z"],
-        dynamic_axes={
-          "z_p" : [2],
-          "y_mask" : [2],
-          "z" : [2]
-        },
-        verbose=True,
-    )
-    z = self.flow(z_p, y_mask, g=g)
-    z_in = (z * y_mask)[:,:,:max_len]
-
-    torch.onnx.export(
-        self.dec,
-        (z_in, g),
-        "ONNX_net/dec.onnx",
-        input_names=["z_in", "g"],
-        output_names=["o"],
-        dynamic_axes={
-          "z_in" : [2],
-          "o" : [2]
-        },
-        verbose=True,
-    )
-    o = self.dec(z_in, g=g)
-    return o

spaces/Alichuan/VITS-Umamusume-voice-synthesizer/attentions.py

@@ -1,300 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    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.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    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.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x

spaces/Alinadi98/movie_recommendation_system/README.md

@@ -1,12 +0,0 @@
----
-title: Movie Recommendation System
-emoji: 🐢
-colorFrom: yellow
-colorTo: yellow
-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

spaces/Ameaou/academic-chatgpt3.1/docs/self_analysis.md

@@ -1,256 +0,0 @@
-# chatgpt-academic项目自译解报告
-（Author补充：以下分析均由本项目调用ChatGPT一键生成，如果有不准确的地方，全怪GPT😄）
-
-## 对程序的整体功能和构架做出概括。然后用一张markdown表格整理每个文件的功能。
-
-整体概括：
-
-该程序是一个基于自然语言处理和机器学习的科学论文辅助工具，主要功能包括聊天机器人、批量总结PDF文档、批量翻译PDF文档、生成函数注释、解析项目源代码等。程序基于 Gradio 构建 Web 服务，并集成了代理和自动更新功能，提高了用户的使用体验。
-
-文件功能表格：
-
-| 文件名 | 文件功能 |
-| --- | --- |
-| check_proxy.py | 用于检查代理的正确性和可用性 |
-| colorful.py | 包含不同预设置颜色的常量，并用于多种UI元素 |
-| config.py | 用于全局配置的类 |
-| config_private.py | 与config.py文件一起使用的另一个配置文件，用于更改私密信息 |
-| core_functional.py | 包含一些TextFunctional类和基础功能函数 |
-| crazy_functional.py | 包含大量高级功能函数和实验性的功能函数 |
-| main.py | 程序的主入口，包含GUI主窗口和主要的UI管理功能 |
-| theme.py | 包含一些预设置主题的颜色 |
-| toolbox.py | 提供了一些有用的工具函数 |
-| crazy_functions\crazy_utils.py | 包含一些用于实现高级功能的辅助函数 |
-| crazy_functions\Latex全文润色.py | 实现了对LaTeX文件中全文的润色和格式化功能 |
-| crazy_functions\Latex全文翻译.py | 实现了对LaTeX文件中的内容进行翻译的功能 |
-| crazy_functions\_\_init\_\_.py | 用于导入crazy_functional.py中的功能函数 |
-| crazy_functions\下载arxiv论文翻译摘要.py | 从Arxiv上下载论文并提取重要信息 |
-| crazy_functions\代码重写为全英文_多线程.py | 针对中文Python文件，将其翻译为全英文 |
-| crazy_functions\总结word文档.py | 提取Word文件的重要内容来生成摘要 |
-| crazy_functions\批量Markdown翻译.py | 批量翻译Markdown文件 |
-| crazy_functions\批量总结PDF文档.py | 批量从PDF文件中提取摘要 |
-| crazy_functions\批量总结PDF文档pdfminer.py | 批量从PDF文件中提取摘要 |
-| crazy_functions\批量翻译PDF文档_多线程.py | 批量翻译PDF文件 |
-| crazy_functions\理解PDF文档内容.py | 批量分析PDF文件并提取摘要 |
-| crazy_functions\生成函数注释.py | 自动生成Python文件中函数的注释 |
-| crazy_functions\解析项目源代码.py | 解析并分析给定项目的源代码 |
-| crazy_functions\询问多个大语言模型.py | 向多个大语言模型询问输入文本并进行处理 |
-| crazy_functions\读文献写摘要.py | 根据用户输入读取文献内容并生成摘要 |
-| crazy_functions\谷歌检索小助手.py | 利用谷歌学术检索用户提供的论文信息并提取相关信息 |
-| crazy_functions\高级功能函数模板.py | 实现高级功能的模板函数 |
-| request_llm\bridge_all.py | 处理与LLM的交互 |
-| request_llm\bridge_chatglm.py | 使用ChatGLM模型进行聊天 |
-| request_llm\bridge_chatgpt.py | 实现对话生成的各项功能 |
-| request_llm\bridge_tgui.py | 在Websockets中与用户进行交互并生成文本输出 |
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-## [0/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\check_proxy.py
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-该文件主要包括四个函数：check_proxy、backup_and_download、patch_and_restart 和 auto_update。其中，check_proxy 函数用于检查代理是否可用；backup_and_download 用于进行一键更新备份和下载；patch_and_restart 是一键更新协议的重要函数，用于覆盖和重启；auto_update 函数用于查询版本和用户意见，并自动进行一键更新。该文件主要使用了 requests、json、shutil、zipfile、distutils、subprocess 等 Python 标准库和 toolbox 和 colorful 两个第三方库。
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-## [1/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\colorful.py
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-该程序文件实现了一些打印文本的函数，使其具有不同的颜色输出。当系统为Linux时直接跳过，否则使用colorama库来实现颜色输出。程序提供了深色和亮色两种颜色输出方式，同时也提供了对打印函数的别名。对于不是终端输出的情况，对所有的打印函数进行重复定义，以便在重定向时能够避免打印错误日志。
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-## [2/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\config.py
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-该程序文件是一个配置文件，其主要功能是提供使用API密钥等信息，以及对程序的体验进行优化，例如定义对话框高度、布局等。还包含一些其他的设置，例如设置并行使用的线程数、重试次数限制等等。
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-## [3/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\config_private.py
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-这是一个名为config_private.py的Python文件，它用于配置API_KEY和代理信息。API_KEY是一个私密密钥，用于访���某些受保护的API。USE_PROXY变量设置为True以应用代理，proxies变量配置了代理网络的地址和协议。在使用该文件时，需要填写正确的API_KEY和代理信息。
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-## [4/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\core_functional.py
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-该文件是一个Python模块，名为"core_functional.py"。模块中定义了一个字典，包含了各种核心功能的配置信息，如英语学术润色、中文学术润色、查找语法错误等。每个功能都包含一些前言和后语，在前言中描述了该功能的任务和要求，在后语中提供一些附加信息。此外，有些功能还定义了一些特定的处理函数和按钮颜色。
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-## [5/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functional.py
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-这是一个Python程序文件，文件名是crazy_functional.py。它导入了一个名为HotReload的工具箱，并定义了一个名为get_crazy_functions()的函数。这个函数包括三个部分的插件组，分别是已经编写完成的第一组插件、已经测试但距离完美状态还差一点点的第二组插件和尚未充分测试的第三组插件。每个插件都有一个名称、一个按钮颜色、一个函数和一个是否加入下拉菜单中的标志位。这些插件提供了多种功能，包括生成函数注释、解析项目源代码、批量翻译PDF文档、谷歌检索、PDF文档内容理解和Latex文档的全文润色、翻译等功能。其中第三组插件可能还存在一定的bug。
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-## [6/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\main.py
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-该Python脚本代码实现了一个用于交互式对话的Chatbot机器人。它使用了Gradio框架来构建一个Web界面，并在此基础之上嵌入了一个文本输入框和与Chatbot进行交互的其他控件，包括提交、重置、停止和清除按钮、选择框和滑块等。此外，它还包括了一些类和函数和一些用于编程分析的工具和方法。整个程序文件的结构清晰，注释丰富，并提供了很多技术细节，使得开发者可以很容易地在其基础上进行二次开发、修改、扩展和集成。
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-## [7/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\theme.py
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-该程序文件名为theme.py，主要功能为调节Gradio的全局样式。在该文件中，调节了Gradio的主题颜色、字体、阴影、边框、渐变等等样式。同时，该文件还添加了一些高级CSS样式，比如调整表格单元格的背景和边框，设定聊天气泡的圆角、最大宽度和阴影等等。如果CODE_HIGHLIGHT为True，则还进行了代码高亮显示。
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-## [8/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\toolbox.py
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-这是一个名为`toolbox.py`的源代码文件。该文件包含了一系列工具函数和装饰器，用于聊天Bot的开发和调试。其中有一些功能包括将输入参数进行重组、捕捉函数中的异常并记录到历史记录中、生成Markdown格式的聊天记录报告等。该文件中还包含了一些与转换Markdown文本相关的函数。
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-## [9/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\crazy_utils.py
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-这是一个Python程序文件 `crazy_utils.py`，它包含了两个函数：
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-- `input_clipping(inputs, history, max_token_limit)`：这个函数接收三个参数，inputs 是一个字符串，history 是一个列表，max_token_limit 是一个整数。它使用 `tiktoken` 、`numpy` 和 `toolbox` 模块，处理输入文本和历史记录，将其裁剪到指定的最大标记数，避免输入过长导致的性能问题。如果 inputs 长度不超过 max_token_limit 的一半，则只裁剪历史；否则，同时裁剪输入和历史。
-- `request_gpt_model_in_new_thread_with_ui_alive(inputs, inputs_show_user, llm_kwargs, chatbot, history, sys_prompt, refresh_interval=0.2, handle_token_exceed=True, retry_times_at_unknown_error=2)`：这个函数接收八个参数，其中后三个是列表类型，其他为标量或句柄等。它提供对话窗口和刷新控制，执行 `predict_no_ui_long_connection` 方法，将输入数据发送至 GPT 模型并获取结果，如果子任务出错，返回相应的错误信息，否则返回结果。
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-## [10/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\Latex全文润色.py
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-这是一个名为"crazy_functions\Latex全文润色.py"的程序文件，其中包含了两个函数"Latex英文润色"和"Latex中文润色"，以及其他辅助函数。这些函数能够对 Latex 项目进行润色处理，其中 "多文件润色" 函数是一个主要函数，它调用了其他辅助函数用于读取和处理 Latex 项目中的文件。函数使用了多线程和机器学习模型进行自然语言处理，对文件进行简化和排版来满足学术标准。注释已删除并可以在函数内部查找。
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-## [11/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\Latex全文翻译.py
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-这个程序文件包括一个用于对整个Latex项目进行翻译的函数 `Latex英译中` 和一个用于将中文翻译为英文的函数 `Latex中译英`。这两个函数都会尝试导入依赖库 tiktoken， 若无法导入则会提示用户安装。`Latex英译中` 函数会对 Latex 项目中的文件进行分离并去除注释，然后运行多线程翻译。`Latex中译英` 也做同样的事情，只不过是将中文翻译为英文。这个程序文件还包括其他一些帮助函数。
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-## [12/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\__init__.py
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-这是一个 Python 包，包名为 `crazy_functions`，在 `__init__.py` 文件中定义了一些函数，包含以下函数:
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-- `crazy_addition(a, b)`：对两个数进行加法运算，并将结果返回。
-- `crazy_multiplication(a, b)`：对两个数进行乘法运算，并将结果返回。
-- `crazy_subtraction(a, b)`：对两个数进行减法运算，并将结果返回。
-- `crazy_division(a, b)`：对两个数进行除法运算，并将结果返回。
-- `crazy_factorial(n)`：计算 `n` 的阶乘并返回结果。
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-这些函数可能会有一些奇怪或者不符合常规的实现方式（由函数名可以看出来），所以这个包的名称为 `crazy_functions`，可能是暗示这些函数会有一些“疯狂”的实现方式。
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-## [13/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\下载arxiv论文翻译摘要.py
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-该程序实现了一个名为“下载arxiv论文并翻译摘要”的函数插件，作者是“binary-husky”。该函数的功能是，在输入一篇arxiv论文的链接后，提取摘要、下载PDF文档、翻译摘要为中文，并将翻译结果保存到文件中。程序使用了一些Python库，如requests、pdfminer和beautifulsoup4等。程序入口是名为“下载arxiv论文并翻译摘要”的函数，其中使用了自定义的辅助函数download_arxiv_和get_name。程序中还使用了其他非函数的辅助函数和变量，如update_ui、CatchException、report_exception和get_conf等。
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-## [14/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\代码重写为全英文_多线程.py
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-该文件是一个多线程Python脚本，包含多个函数和利用第三方库进行的API请求。主要功能是将给定文件夹内的Python代码文件中所有中文转化为英文，然后输出转化后的英文代码。重要的功能和步骤包括：
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-1. 清空历史，以免输入溢出
-2. 尝试导入依赖，如果缺少依赖，则给出安装建议
-3. 集合文件
-4. 显示随意内容以防卡顿的感觉
-5. Token限制下的截断与处理
-6. 多线程操作请求转换中文变为英文的代码
-7. 所有线程同时开始执行任务函数
-8. 循环轮询各个线程是否执行完毕
-9. 把结果写入文件
-10. 备份一个文件
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-## [15/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\总结word文档.py
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-这是一个名为"总结word文档.py"的程序文件，使用python编写。该文件导入了"toolbox"和"crazy_utils"模块，实现了解析docx格式和doc格式的文件的功能。该文件包含了一个名为"解析docx"的函数，通过对文件内容应用自然语言处理技术，生成文章片段的中英文概述。具体实现过程中，该函数使用了"docx"模块和"win32com.client"模块来实现对docx和doc格式文件的解析，同时使用了"request_gpt_model_in_new_thread_with_ui_alive"函数来向GPT模型发起请求。最后，该文件还实现了一个名为"总结word文档"的函数来批量总结Word文档。
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-## [16/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\批量Markdown翻译.py
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-这个程序文件实现了一个批量Markdown翻译功能，可以将一个源代码项目中的Markdown文本翻译成指定语言（目前支持中<-英和英<-中）。程序主要分为三个函数，`PaperFileGroup`类用于处理长文本的拆分，`多文件翻译`是主要函数调用了`request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency`函数进行多线程翻译并输出结果，`Markdown英译中`和`Markdown中译外`分别是英译中和中译英的入口函数，用于解析项目路径和调用翻译函数。程序依赖于tiktoken等库实现。
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-## [17/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\批量总结PDF文档.py
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-这是一个名为“批量总结PDF文档”的Python脚本，包含了多个函数。其中有一个函数名为“clean_text”，可以对PDF提取出的原始文本进行清洗和格式化处理，将连字转换为其基本形式，并根据heuristic规则判断换行符是否是段落分隔，并相应地进行替换。另一个函数名为“解析PDF”，可以接收一个PDF文件清单，并对清单中的每一个PDF进行解析，提取��文本并调用“clean_text”函数进行清洗和格式化处理，然后向用户发送一个包含文章简介信息的问题并等待用户回答。最后，该脚本也包含一个名为“批量总结PDF文档”的主函数，其中调用了“解析PDF”函数来完成对PDF文件的批量处理。
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-## [18/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\批量总结PDF文档pdfminer.py
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-这个文件是一个Python模块，文件名为pdfminer.py，它定义了一个函数批量总结PDF文档。该函数接受一些参数，然后尝试导入pdfminer和beautifulsoup4库。该函数将读取pdf文件或tex文件中的内容，对其进行分析，并使用GPT模型进行自然语言摘要。文件中还有一个辅助函数readPdf，用于读取pdf文件中的内容。
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-## [19/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\批量翻译PDF文档_多线程.py
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-这是一个Python脚本，文件名是crazy_functions\批量翻译PDF文档_多线程.py。该脚本提供了一个名为“批量翻译PDF文档”的函数，可以批量翻译PDF文件并生成报告文件。该函数使用了多个模块和函数（如toolbox、crazy_utils、update_ui等），使用了Python的异常处理和多线程功能，还使用了一些文本处理函数和第三方库（如fitz和tiktoken）。在函数执行过程中，它会进行一些参数检查、读取和清理PDF文本、递归地切割PDF文件、获取文章meta信息、多线程翻译、整理报告格式等操作，并更新UI界面和生成报告文件。
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-## [20/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\理解PDF文档内容.py
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-这是一个解析PDF文件内容的Python程序，程序文件名为"理解PDF文档内容.py"，程序主要由5个步骤组成：第0步是切割PDF文件；第1步是从摘要中提取高价值信息，放到history中；第2步是迭代地历遍整个文章，提取精炼信息；第3步是整理history；第4步是设置一个token上限，防止回答时Token溢出。程序主要用到了Python中的各种模块和函数库，如：toolbox, tiktoken, pymupdf等。
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-## [21/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\生成函数注释.py
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-这是一个名为"生成函数注释"的函数，带有一个装饰器"@CatchException"，可以捕获异常。该函数接受文件路径、参数和聊天机器人等参数，用于对多个Python或C++文件进行函数注释，使用了"toolbox"和"crazy_utils"模块中的函数。该函数会逐个读取指定文件中的内容，并使用聊天机器人进行交互，向用户请求注释信息，然后将生成的注释与原文件内容一起输出到一个markdown表格中。最后，该函数返回一个字符串，指示任务是否已完成。另外还包含一个名为"批量生成函数注释"的函数，它与"生成函数注释"函数一起用于批量处理多个文件。
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-## [22/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\解析项目源代码.py
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-这个程序文件实现了对一个源代码项目进行分析的功能。其中，函数`解析项目本身`、`解析一个Python项目`、`解析一个C项目的头文件`、`解析一个C项目`、`解析一个Java项目`和`解析一个Rect项目`分别用于解析不同类型的项目。函数`解析源代码新`实现了对每一个源代码文件的分析，并将分析结果汇总，同时还实现了分组和迭代处理，提高了效率。最后，函数`write_results_to_file`将所有分析结果写入文件。中间，还用到了`request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency`和`request_gpt_model_in_new_thread_with_ui_alive`来完成请求和响应，并用`update_ui`实时更新界面。
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-## [23/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\询问多个大语言模型.py
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-这是一个Python程序，文件名为"crazy_functions\询问多个大语言模型.py"。该程序实现了一个同时向多个大语言模型询问的功能，接收用户输入文本以及模型参数，向ChatGPT和ChatGLM模型发出请求，并将对话记录显示在聊天框中，同时刷新界面。
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-## [24/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\读文章写摘要.py
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-该程序文件是一个Python模块，文件名为"读文章写摘要.py"，主要包含两个函数："解析Paper"和"读文章写摘要"。其中，"解析Paper"函数接受文件路径、参数等参数，逐个打印文件内容并使用GPT模型生成对该文件的摘要；"读文章写摘要"函数则接受一段文本内容和参数，将该文本内容及其所有.tex文件逐个传递给"解析Paper"函数进行处理，并使用GPT模型生成文章的中英文摘要。文件还导入了一些工具函数，如异常处理、信息上报和文件写入等。
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-## [25/31] 请对下面��程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\谷歌检索小助手.py
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-该文件代码包含了一个名为`get_meta_information`的函数和一个名为`谷歌检索小助手`的装饰器函数，用于从谷歌学术中抓取文章元信息，并从用户提供的搜索页面中分析所有文章的相关信息。该文件使用了许多第三方库，如requests、arxiv、BeautifulSoup等。其中`get_meta_information`函数中还定义了一个名为`string_similar`的辅助函数，用于比较字符串相似度。
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-## [26/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\高级功能函数模板.py
-
-该程序文件是一个 Python 模块，包含一个名为“高阶功能模板函数”的函数。该函数接受多个参数，其中包括输入文本、GPT 模型参数、插件模型参数、聊天显示框、聊天历史等。 该函数的主要功能是根据输入文本，使用 GPT 模型生成一些问题，并等待用户回答这些问题（使用 Markdown 格式），然后将用户回答加入到聊天历史中，并更新聊天显示框。该函数还包含了一些异常处理和多线程的相关操作。该程序文件还引用了另一个 Python 模块中的两个函数，分别为“CatchException”和“update_ui”，并且还引用了一个名为“request_gpt_model_in_new_thread_with_ui_alive”的自定义函数。
-
-## [27/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\request_llm\bridge_all.py
-
-这个文件是用来处理与LLM的交互的。包含两个函数，一个是 predict_no_ui_long_connection 用来处理长文本的输出，可以多线程调用；另一个是 predict 用来处理基础的对话功能。这个文件会导入其他文件中定义的方法进行调用，具体调用哪个方法取决于传入的参数。函数中还有一些装饰器和管理多线程的逻辑。
-
-## [28/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\request_llm\bridge_chatglm.py
-
-这个程序文件实现了一个使用ChatGLM模型进行聊天的功能。具体实现过程是：首先进行初始化，然后使用GetGLMHandle类进行ChatGLM模型的加载和运行。predict_no_ui_long_connection函数用于多线程聊天，而predict函数用于单线程聊天，它们的不同之处在于前者不会更新UI界面，后者会。这个文件还导入了其他模块和库，例如transformers、time、importlib等，并使用了多进程Pipe。
-
-## [29/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\request_llm\bridge_chatgpt.py
-
-这个程序文件是用于对话生成的，主要包含三个函数：predict、predict_no_ui、predict_no_ui_long_connection。其中，predict是用于普通对话的函数，具备完备的交互功能，但不具备多线程能力；predict_no_ui是高级实验性功能模块调用的函数，参数简单，可以多线程并行，方便实现复杂的功能逻辑；predict_no_ui_long_connection解决了predict_no_ui在处理长文档时容易断开连接的问题，同样支持多线程。程序中还包含一些常量和工具函数，用于整合信息，选择LLM模型，生成http请求，发送请求，接收响应等。它需要配置一个config文件，包含代理网址、API等敏感信息。
-
-## [30/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\request_llm\bridge_tgui.py
-
-该程序文件实现了一个基于Websockets的文本生成服务和对话功能。其中，有三个函数：`run()`、`predict()`和`predict_no_ui_long_connection()`。`run()`函数用于连接到Websocket服务并生成文本结果；`predict()`函数用于将用户输入作为文本生成的输入，同时在UI上显示对话历史记录，并在不断更新UI的过程中不断更新生成的文本输出；`predict_no_ui_long_connection()`函数与`predict()`函数类似，但没有UI，并在一段时间内返回单个生成的文本。整个程序还引入了多个Python模块来完成相关功能，例如`asyncio`、`websockets`、`json`等等。
-
-## 根据以上分析，对程序的整体功能和构架重新做出概括。然后用一张markdown表格整理每个文件的功能（包括check_proxy.py, colorful.py, config.py, config_private.py, core_functional.py, crazy_functional.py, main.py, theme.py, toolbox.py, crazy_functions\crazy_utils.py, crazy_functions\Latex全文润色.py, crazy_functions\Latex全文翻译.py, crazy_functions\__init__.py, crazy_functions\下载arxiv论文翻译摘要.py, crazy_functions\代码重写为全英文_多线程.py, crazy_functions\总结word文档.py）。
-
-程序功能概括：该程序是一个聊天机器人，可以通过 Web 界面与用户进行交互。它包含了丰富的功能，如文本润色、翻译、代码重写、在线查找等，并且支持多线程处理。用户可以通过 Gradio 框架提供的 Web 界面进行交互，程序还提供了一些调试工具，如toolbox 模块，方便程序开发和调试。
-
-���表概述了每个文件的功能：
-
-| 文件名                                                      | 功能                                                         |
-| ----------------------------------------------------------- | ------------------------------------------------------------ |
-| check_proxy.py                                              | 检查代理是否可用                                             |
-| colorful.py                                                 | 用于打印文本的字体颜色输出模块                               |
-| config.py                                                   | 用于程序中的各种设置，如并行线程数量和重试次数的限制等     |
-| config_private.py                                           | 配置API_KEY和代理信息的文件                                   |
-| core_functional.py                                           | 包含具体的文本处理功能的模块                                 |
-| crazy_functional.py                                          | 包括各种插件函数的模块，提供了多种文本处理功能               |
-| main.py                                                     | 包含 Chatbot 机器人主程序的模块                              |
-| theme.py                                                    | 用于调节全局样式的模块                                       |
-| toolbox.py                                                  | 包含工具函数和装饰器，用于聊天Bot的开发和调试                |
-| crazy_functions\crazy_utils.py                              | 包含一些辅助函数，如文本裁剪和消息捕捉等                       |
-| crazy_functions\Latex全文润色.py                           | 对 Latex 项目进行润色处理的功能模块                          |
-| crazy_functions\Latex全文翻译.py                           | 对 Latex 项目进行翻译的功能模块                              |
-| crazy_functions\__init__.py                                 | 定义一些奇特的数学函数等                                      |
-| crazy_functions\下载arxiv论文翻译摘要.py                  | 下载 Arxiv 论文并翻译摘要的功能模块                          |
-| crazy_functions\代码重写为全英文_多线程.py                 | 将Python程序中所有中文转化为英文的功能模块                    |
-| crazy_functions\总结word文档.py                            | 解析 docx 和 doc 格式的文件，生成文章片段的中英文概述的功能模块 |
-
-## 根据以上分析，对程序的整体功能和构架重新做出概括。然后用一张markdown表格整理每个文件的功能（包括check_proxy.py, colorful.py, config.py, config_private.py, core_functional.py, crazy_functional.py, main.py, theme.py, toolbox.py, crazy_functions\crazy_utils.py, crazy_functions\Latex全文润色.py, crazy_functions\Latex全文翻译.py, crazy_functions\__init__.py, crazy_functions\下载arxiv论文翻译摘要.py, crazy_functions\代码重写为全英文_多线程.py, crazy_functions\总结word文档.py, crazy_functions\批量Markdown翻译.py, crazy_functions\批量总结PDF文档.py, crazy_functions\批量总结PDF文档pdfminer.py, crazy_functions\批量翻译PDF文档_多线程.py, crazy_functions\理解PDF文档内容.py, crazy_functions\生成函数注释.py, crazy_functions\解析项目源代码.py, crazy_functions\询问多个大语言模型.py, crazy_functions\读文章写摘要.py, crazy_functions\谷歌检索小助手.py, crazy_functions\高级功能函数模板.py, request_llm\bridge_all.py, request_llm\bridge_chatglm.py, request_llm\bridge_chatgpt.py, request_llm\bridge_tgui.py）。
-
-根据以上分析，整个程序是一个集成了多个有用工具和功能的文本处理和生成工具，提供了多种在不同场景下使用的功能，包括但不限于对话生成、文本摘要、PDF文件批量处理、代码翻译和实用工具等。主要的Python模块包括"toolbox.py"、"config.py"、"core_functional.py"和"crazy_functional.py"等，并且还使用了许多第三方库和模块实现相关功能。以下是每个程序文件的功能：
-
-| 文件名 | 文件功能 |
-| --- | --- |
-| check_proxy.py | 用于检查代理的正确性和可用性 |
-| colorful.py | 包含不同预设置颜色的常量，并用于多种UI元素 |
-| config.py | 用于全局配置的类 |
-| config_private.py | 与config.py文件一起使用的另一个配置文件，用于更改私密信息 |
-| core_functional.py | 包含一些TextFunctional类和基础功能函数 |
-| crazy_functional.py | 包含大量高级功能函数和实验性的功能函数 |
-| main.py | 程序的主入口，包含GUI主窗口和主要的UI管理功能 |
-| theme.py | 包含一些预设置主题的颜色 |
-| toolbox.py | 提供了一些有用的工具函数 |
-| crazy_functions\crazy_utils.py | 包含一些用于实现高级功能的辅助函数 |
-| crazy_functions\Latex全文润色.py | 实现了对LaTeX文件中全文的润色和格式化功能 |
-| crazy_functions\Latex全文翻译.py | 实现了对LaTeX文件中的内容进行翻译的功能 |
-| crazy_functions\_\_init\_\_.py | 用于导入crazy_functional.py中的功能函数 |
-| crazy_functions\下载arxiv论文翻译摘要.py | 从Arxiv上下载论文并提取重要信息 |
-| crazy_functions\代码重写为全英文_多线程.py | 针对中文Python文件，将其翻译为全英文 |
-| crazy_functions\总结word文档.py | 提取Word文件的重要内容来生成摘要 |
-| crazy_functions\批量Markdown翻译.py | 批量翻译Markdown文件 |
-| crazy_functions\批量总结PDF文档.py | 批量从PDF文件中提取摘要 |
-| crazy_functions\批量总结PDF文档pdfminer.py | 批量从PDF文件中提取摘要 |
-| crazy_functions\批量翻译PDF文档_多线程.py | 批量翻译PDF文件 |
-| crazy_functions\理解PDF文档内容.py | 批量分析PDF文件并提取摘要 |
-| crazy_functions\生成函数注释.py | 自动生成Python文件中函数的注释 |
-| crazy_functions\解析项目源代码.py | 解析并分析给定项目的源代码 |
-| crazy_functions\询问多个大语言模型.py | 向多个大语言模型询问输入文本并进行处理 |
-| crazy_functions\读文献写摘要.py | 根据用户输入读取文献内容并生成摘要 |
-| crazy_functions\谷歌检索小助手.py | 利用谷歌学术检索用户提供的论文信息并提取相关信息 |
-| crazy_functions\高级功能函数模板.py | 实现高级功能的模板函数 |
-| request_llm\bridge_all.py | 处理与LLM的交互 |
-| request_llm\bridge_chatglm.py | 使用ChatGLM模型进行聊天 |
-| request_llm\bridge_chatgpt.py | 实现对话生成的各项功能 |
-| request_llm\bridge_tgui.py | 在Websockets中与用户进行交互并生成文本输出 |
-

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py

@@ -1,348 +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.
-
-from typing import Callable, List, Optional, Union
-
-import torch
-
-from ...models import UNet2DConditionModel, VQModel
-from ...schedulers import DDPMScheduler
-from ...utils import (
-    is_accelerate_available,
-    is_accelerate_version,
-    logging,
-    randn_tensor,
-    replace_example_docstring,
-)
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-        >>> import numpy as np
-
-        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
-        >>> from transformers import pipeline
-        >>> from diffusers.utils import load_image
-
-
-        >>> def make_hint(image, depth_estimator):
-        ...     image = depth_estimator(image)["depth"]
-        ...     image = np.array(image)
-        ...     image = image[:, :, None]
-        ...     image = np.concatenate([image, image, image], axis=2)
-        ...     detected_map = torch.from_numpy(image).float() / 255.0
-        ...     hint = detected_map.permute(2, 0, 1)
-        ...     return hint
-
-
-        >>> depth_estimator = pipeline("depth-estimation")
-
-        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
-        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
-        ... )
-        >>> pipe_prior = pipe_prior.to("cuda")
-
-        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
-        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
-        ... )
-        >>> pipe = pipe.to("cuda")
-
-
-        >>> img = load_image(
-        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-        ...     "/kandinsky/cat.png"
-        ... ).resize((768, 768))
-
-        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")
-
-        >>> prompt = "A robot, 4k photo"
-        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"
-
-        >>> generator = torch.Generator(device="cuda").manual_seed(43)
-
-        >>> image_emb, zero_image_emb = pipe_prior(
-        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
-        ... ).to_tuple()
-
-        >>> images = pipe(
-        ...     image_embeds=image_emb,
-        ...     negative_image_embeds=zero_image_emb,
-        ...     hint=hint,
-        ...     num_inference_steps=50,
-        ...     generator=generator,
-        ...     height=768,
-        ...     width=768,
-        ... ).images
-
-        >>> images[0].save("robot_cat.png")
-        ```
-"""
-
-
-# Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2.downscale_height_and_width
-def downscale_height_and_width(height, width, scale_factor=8):
-    new_height = height // scale_factor**2
-    if height % scale_factor**2 != 0:
-        new_height += 1
-    new_width = width // scale_factor**2
-    if width % scale_factor**2 != 0:
-        new_width += 1
-    return new_height * scale_factor, new_width * scale_factor
-
-
-class KandinskyV22ControlnetPipeline(DiffusionPipeline):
-    """
-    Pipeline for text-to-image generation using Kandinsky
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        scheduler ([`DDIMScheduler`]):
-            A scheduler to be used in combination with `unet` to generate image latents.
-        unet ([`UNet2DConditionModel`]):
-            Conditional U-Net architecture to denoise the image embedding.
-        movq ([`VQModel`]):
-            MoVQ Decoder to generate the image from the latents.
-    """
-
-    def __init__(
-        self,
-        unet: UNet2DConditionModel,
-        scheduler: DDPMScheduler,
-        movq: VQModel,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            unet=unet,
-            scheduler=scheduler,
-            movq=movq,
-        )
-        self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
-
-    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
-    def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            if latents.shape != shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
-            latents = latents.to(device)
-
-        latents = latents * scheduler.init_noise_sigma
-        return latents
-
-    # Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2.KandinskyV22Pipeline.enable_model_cpu_offload
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
-        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
-        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
-        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        hook = None
-        for cpu_offloaded_model in [self.unet, self.movq]:
-            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        image_embeds: Union[torch.FloatTensor, List[torch.FloatTensor]],
-        negative_image_embeds: Union[torch.FloatTensor, List[torch.FloatTensor]],
-        hint: torch.FloatTensor,
-        height: int = 512,
-        width: int = 512,
-        num_inference_steps: int = 100,
-        guidance_scale: float = 4.0,
-        num_images_per_prompt: int = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        return_dict: bool = True,
-    ):
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            hint (`torch.FloatTensor`):
-                The controlnet condition.
-            image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
-                The clip image embeddings for text prompt, that will be used to condition the image generation.
-            negative_image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
-                The clip image embeddings for negative text prompt, will be used to condition the image generation.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            height (`int`, *optional*, defaults to 512):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to 512):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 100):
-                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 4.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *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
-                tensor will ge generated by sampling using the supplied random `generator`.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
-                (`np.array`) or `"pt"` (`torch.Tensor`).
-            callback (`Callable`, *optional*):
-                A function that calls every `callback_steps` steps during inference. The function is called with the
-                following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function is called. If not specified, the callback is called at
-                every step.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`
-        """
-        device = self._execution_device
-
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        if isinstance(image_embeds, list):
-            image_embeds = torch.cat(image_embeds, dim=0)
-        if isinstance(negative_image_embeds, list):
-            negative_image_embeds = torch.cat(negative_image_embeds, dim=0)
-        if isinstance(hint, list):
-            hint = torch.cat(hint, dim=0)
-
-        batch_size = image_embeds.shape[0] * num_images_per_prompt
-
-        if do_classifier_free_guidance:
-            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-            negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-            hint = hint.repeat_interleave(num_images_per_prompt, dim=0)
-
-            image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
-                dtype=self.unet.dtype, device=device
-            )
-            hint = torch.cat([hint, hint], dim=0).to(dtype=self.unet.dtype, device=device)
-
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps_tensor = self.scheduler.timesteps
-
-        num_channels_latents = self.movq.config.latent_channels
-
-        height, width = downscale_height_and_width(height, width, self.movq_scale_factor)
-
-        # create initial latent
-        latents = self.prepare_latents(
-            (batch_size, num_channels_latents, height, width),
-            image_embeds.dtype,
-            device,
-            generator,
-            latents,
-            self.scheduler,
-        )
-
-        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-            added_cond_kwargs = {"image_embeds": image_embeds, "hint": hint}
-            noise_pred = self.unet(
-                sample=latent_model_input,
-                timestep=t,
-                encoder_hidden_states=None,
-                added_cond_kwargs=added_cond_kwargs,
-                return_dict=False,
-            )[0]
-
-            if do_classifier_free_guidance:
-                noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                _, variance_pred_text = variance_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-                noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)
-
-            if not (
-                hasattr(self.scheduler.config, "variance_type")
-                and self.scheduler.config.variance_type in ["learned", "learned_range"]
-            ):
-                noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(
-                noise_pred,
-                t,
-                latents,
-                generator=generator,
-            )[0]
-
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-        # post-processing
-        image = self.movq.decode(latents, force_not_quantize=True)["sample"]
-
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
-
-        if output_type not in ["pt", "np", "pil"]:
-            raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")
-
-        if output_type in ["np", "pil"]:
-            image = image * 0.5 + 0.5
-            image = image.clamp(0, 1)
-            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_utils.py

@@ -1,177 +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 importlib
-import os
-from dataclasses import dataclass
-from enum import Enum
-from typing import Any, Dict, Optional, Union
-
-import torch
-
-from ..utils import BaseOutput
-
-
-SCHEDULER_CONFIG_NAME = "scheduler_config.json"
-
-
-# NOTE: We make this type an enum because it simplifies usage in docs and prevents
-# circular imports when used for `_compatibles` within the schedulers module.
-# When it's used as a type in pipelines, it really is a Union because the actual
-# scheduler instance is passed in.
-class KarrasDiffusionSchedulers(Enum):
-    DDIMScheduler = 1
-    DDPMScheduler = 2
-    PNDMScheduler = 3
-    LMSDiscreteScheduler = 4
-    EulerDiscreteScheduler = 5
-    HeunDiscreteScheduler = 6
-    EulerAncestralDiscreteScheduler = 7
-    DPMSolverMultistepScheduler = 8
-    DPMSolverSinglestepScheduler = 9
-    KDPM2DiscreteScheduler = 10
-    KDPM2AncestralDiscreteScheduler = 11
-    DEISMultistepScheduler = 12
-    UniPCMultistepScheduler = 13
-    DPMSolverSDEScheduler = 14
-
-
-@dataclass
-class SchedulerOutput(BaseOutput):
-    """
-    Base class for the scheduler's step function output.
-
-    Args:
-        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-    """
-
-    prev_sample: torch.FloatTensor
-
-
-class SchedulerMixin:
-    """
-    Mixin containing common functions for the schedulers.
-
-    Class attributes:
-        - **_compatibles** (`List[str]`) -- A list of classes that are compatible with the parent class, so that
-          `from_config` can be used from a class different than the one used to save the config (should be overridden
-          by parent class).
-    """
-
-    config_name = SCHEDULER_CONFIG_NAME
-    _compatibles = []
-    has_compatibles = True
-
-    @classmethod
-    def from_pretrained(
-        cls,
-        pretrained_model_name_or_path: Dict[str, Any] = None,
-        subfolder: Optional[str] = None,
-        return_unused_kwargs=False,
-        **kwargs,
-    ):
-        r"""
-        Instantiate a Scheduler class from a pre-defined JSON configuration file inside a directory or Hub repo.
-
-        Parameters:
-            pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
-                Can be either:
-
-                    - A string, the *model id* of a model repo on huggingface.co. Valid model ids should have an
-                      organization name, like `google/ddpm-celebahq-256`.
-                    - A path to a *directory* containing the schedluer configurations saved using
-                      [`~SchedulerMixin.save_pretrained`], e.g., `./my_model_directory/`.
-            subfolder (`str`, *optional*):
-                In case the relevant files are located inside a subfolder of the model repo (either remote in
-                huggingface.co or downloaded locally), you can specify the folder name here.
-            return_unused_kwargs (`bool`, *optional*, defaults to `False`):
-                Whether kwargs that are not consumed by the Python class should be returned or not.
-            cache_dir (`Union[str, os.PathLike]`, *optional*):
-                Path to a directory in which a downloaded pretrained model configuration should be cached if the
-                standard cache should not be used.
-            force_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
-                cached versions if they exist.
-            resume_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
-                file exists.
-            proxies (`Dict[str, str]`, *optional*):
-                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
-                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
-            output_loading_info(`bool`, *optional*, defaults to `False`):
-                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
-            local_files_only(`bool`, *optional*, defaults to `False`):
-                Whether or not to only look at local files (i.e., do not try to download the model).
-            use_auth_token (`str` or *bool*, *optional*):
-                The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
-                when running `transformers-cli login` (stored in `~/.huggingface`).
-            revision (`str`, *optional*, defaults to `"main"`):
-                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
-                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
-                identifier allowed by git.
-
-        <Tip>
-
-         It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
-         models](https://huggingface.co/docs/hub/models-gated#gated-models).
-
-        </Tip>
-
-        <Tip>
-
-        Activate the special ["offline-mode"](https://huggingface.co/transformers/installation.html#offline-mode) to
-        use this method in a firewalled environment.
-
-        </Tip>
-
-        """
-        config, kwargs, commit_hash = cls.load_config(
-            pretrained_model_name_or_path=pretrained_model_name_or_path,
-            subfolder=subfolder,
-            return_unused_kwargs=True,
-            return_commit_hash=True,
-            **kwargs,
-        )
-        return cls.from_config(config, return_unused_kwargs=return_unused_kwargs, **kwargs)
-
-    def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
-        """
-        Save a scheduler configuration object to the directory `save_directory`, so that it can be re-loaded using the
-        [`~SchedulerMixin.from_pretrained`] class method.
-
-        Args:
-            save_directory (`str` or `os.PathLike`):
-                Directory where the configuration JSON file will be saved (will be created if it does not exist).
-        """
-        self.save_config(save_directory=save_directory, push_to_hub=push_to_hub, **kwargs)
-
-    @property
-    def compatibles(self):
-        """
-        Returns all schedulers that are compatible with this scheduler
-
-        Returns:
-            `List[SchedulerMixin]`: List of compatible schedulers
-        """
-        return self._get_compatibles()
-
-    @classmethod
-    def _get_compatibles(cls):
-        compatible_classes_str = list(set([cls.__name__] + cls._compatibles))
-        diffusers_library = importlib.import_module(__name__.split(".")[0])
-        compatible_classes = [
-            getattr(diffusers_library, c) for c in compatible_classes_str if hasattr(diffusers_library, c)
-        ]
-        return compatible_classes

spaces/Andy1621/uniformer_image_detection/configs/_base_/datasets/coco_instance.py

@@ -1,48 +0,0 @@
-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(
-        type=dataset_type,
-        ann_file=data_root + 'annotations/instances_train2017.json',
-        img_prefix=data_root + 'train2017/',
-        pipeline=train_pipeline),
-    val=dict(
-        type=dataset_type,
-        ann_file=data_root + 'annotations/instances_val2017.json',
-        img_prefix=data_root + 'val2017/',
-        pipeline=test_pipeline),
-    test=dict(
-        type=dataset_type,
-        ann_file=data_root + 'annotations/instances_val2017.json',
-        img_prefix=data_root + 'val2017/',
-        pipeline=test_pipeline))
-evaluation = dict(metric=['bbox', 'segm'])

spaces/Andy1621/uniformer_image_detection/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py

@@ -1,39 +0,0 @@
-_base_ = [
-    '../_base_/models/faster_rcnn_r50_fpn.py',
-    '../_base_/datasets/cityscapes_detection.py',
-    '../_base_/default_runtime.py'
-]
-model = dict(
-    pretrained=None,
-    roi_head=dict(
-        bbox_head=dict(
-            type='Shared2FCBBoxHead',
-            in_channels=256,
-            fc_out_channels=1024,
-            roi_feat_size=7,
-            num_classes=8,
-            bbox_coder=dict(
-                type='DeltaXYWHBBoxCoder',
-                target_means=[0., 0., 0., 0.],
-                target_stds=[0.1, 0.1, 0.2, 0.2]),
-            reg_class_agnostic=False,
-            loss_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))))
-# optimizer
-# lr is set for a batch size of 8
-optimizer = dict(type='SGD', lr=0.01, 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,
-    # [7] yields higher performance than [6]
-    step=[7])
-runner = dict(
-    type='EpochBasedRunner', max_epochs=8)  # actual epoch = 8 * 8 = 64
-log_config = dict(interval=100)
-# For better, more stable performance initialize from COCO
-load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'  # noqa

spaces/Andy1621/uniformer_image_detection/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py

@@ -1,13 +0,0 @@
-_base_ = './fsaf_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=64,
-        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'))

spaces/Andy1621/uniformer_image_detection/mmcv_custom/__init__.py

@@ -1,5 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from .checkpoint import load_checkpoint
-
-__all__ = ['load_checkpoint']

spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py

@@ -1,5 +0,0 @@
-_base_ = [
-    '../_base_/models/deeplabv3plus_r50-d8.py',
-    '../_base_/datasets/cityscapes.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_40k.py'
-]

spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes.py

@@ -1,8 +0,0 @@
-_base_ = [
-    '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
-]
-model = dict(
-    backbone=dict(dilations=(1, 1, 1, 2), strides=(1, 2, 2, 1)),
-    decode_head=dict(dilation=6),
-    auxiliary_head=dict(dilation=6))

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/extensions.py

@@ -1,224 +0,0 @@
-import traceback
-from functools import partial
-from inspect import signature
-
-import gradio as gr
-
-import extensions
-import modules.shared as shared
-from modules.logging_colors import logger
-
-state = {}
-available_extensions = []
-setup_called = set()
-
-
-def apply_settings(extension, name):
-    if not hasattr(extension, 'params'):
-        return
-
-    for param in extension.params:
-        _id = f"{name}-{param}"
-        if _id not in shared.settings:
-            continue
-
-        extension.params[param] = shared.settings[_id]
-
-
-def load_extensions():
-    global state, setup_called
-    state = {}
-    for i, name in enumerate(shared.args.extensions):
-        if name in available_extensions:
-            if name != 'api':
-                logger.info(f'Loading the extension "{name}"...')
-            try:
-                exec(f"import extensions.{name}.script")
-                extension = getattr(extensions, name).script
-                apply_settings(extension, name)
-                if extension not in setup_called and hasattr(extension, "setup"):
-                    setup_called.add(extension)
-                    extension.setup()
-
-                state[name] = [True, i]
-            except:
-                logger.error(f'Failed to load the extension "{name}".')
-                traceback.print_exc()
-
-
-# This iterator returns the extensions in the order specified in the command-line
-def iterator():
-    for name in sorted(state, key=lambda x: state[x][1]):
-        if state[name][0]:
-            yield getattr(extensions, name).script, name
-
-
-# Extension functions that map string -> string
-def _apply_string_extensions(function_name, text, state, is_chat=False):
-    for extension, _ in iterator():
-        if hasattr(extension, function_name):
-            func = getattr(extension, function_name)
-
-            # Handle old extensions without the 'state' arg or
-            # the 'is_chat' kwarg
-            count = 0
-            has_chat = False
-            for k in signature(func).parameters:
-                if k == 'is_chat':
-                    has_chat = True
-                else:
-                    count += 1
-
-            if count == 2:
-                args = [text, state]
-            else:
-                args = [text]
-
-            if has_chat:
-                kwargs = {'is_chat': is_chat}
-            else:
-                kwargs = {}
-
-            text = func(*args, **kwargs)
-
-    return text
-
-
-# Extension functions that map string -> string
-def _apply_chat_input_extensions(text, visible_text, state):
-    for extension, _ in iterator():
-        if hasattr(extension, 'chat_input_modifier'):
-            text, visible_text = extension.chat_input_modifier(text, visible_text, state)
-
-    return text, visible_text
-
-
-# custom_generate_chat_prompt handling - currently only the first one will work
-def _apply_custom_generate_chat_prompt(text, state, **kwargs):
-    for extension, _ in iterator():
-        if hasattr(extension, 'custom_generate_chat_prompt'):
-            return extension.custom_generate_chat_prompt(text, state, **kwargs)
-
-    return None
-
-
-# Extension that modifies the input parameters before they are used
-def _apply_state_modifier_extensions(state):
-    for extension, _ in iterator():
-        if hasattr(extension, "state_modifier"):
-            state = getattr(extension, "state_modifier")(state)
-
-    return state
-
-
-# Extension that modifies the chat history before it is used
-def _apply_history_modifier_extensions(history):
-    for extension, _ in iterator():
-        if hasattr(extension, "history_modifier"):
-            history = getattr(extension, "history_modifier")(history)
-
-    return history
-
-
-# Extension functions that override the default tokenizer output - The order of execution is not defined
-def _apply_tokenizer_extensions(function_name, state, prompt, input_ids, input_embeds):
-    for extension, _ in iterator():
-        if hasattr(extension, function_name):
-            prompt, input_ids, input_embeds = getattr(extension, function_name)(state, prompt, input_ids, input_embeds)
-
-    return prompt, input_ids, input_embeds
-
-
-# Allow extensions to add their own logits processors to the stack being run.
-# Each extension would call `processor_list.append({their LogitsProcessor}())`.
-def _apply_logits_processor_extensions(function_name, processor_list, input_ids):
-    for extension, _ in iterator():
-        if hasattr(extension, function_name):
-            result = getattr(extension, function_name)(processor_list, input_ids)
-            if type(result) is list:
-                processor_list = result
-
-    return processor_list
-
-
-# Get prompt length in tokens after applying extension functions which override the default tokenizer output
-# currently only the first one will work
-def _apply_custom_tokenized_length(prompt):
-    for extension, _ in iterator():
-        if hasattr(extension, 'custom_tokenized_length'):
-            return getattr(extension, 'custom_tokenized_length')(prompt)
-
-    return None
-
-
-# Custom generate reply handling - currently only the first one will work
-def _apply_custom_generate_reply():
-    for extension, _ in iterator():
-        if hasattr(extension, 'custom_generate_reply'):
-            return getattr(extension, 'custom_generate_reply')
-
-    return None
-
-
-def _apply_custom_css():
-    all_css = ''
-    for extension, _ in iterator():
-        if hasattr(extension, 'custom_css'):
-            all_css += getattr(extension, 'custom_css')()
-
-    return all_css
-
-
-def _apply_custom_js():
-    all_js = ''
-    for extension, _ in iterator():
-        if hasattr(extension, 'custom_js'):
-            all_js += getattr(extension, 'custom_js')()
-
-    return all_js
-
-
-def create_extensions_block():
-    to_display = []
-    for extension, name in iterator():
-        if hasattr(extension, "ui") and not (hasattr(extension, 'params') and extension.params.get('is_tab', False)):
-            to_display.append((extension, name))
-
-    # Creating the extension ui elements
-    if len(to_display) > 0:
-        with gr.Column(elem_id="extensions"):
-            for row in to_display:
-                extension, _ = row
-                extension.ui()
-
-
-def create_extensions_tabs():
-    for extension, name in iterator():
-        if hasattr(extension, "ui") and (hasattr(extension, 'params') and extension.params.get('is_tab', False)):
-            display_name = getattr(extension, 'params', {}).get('display_name', name)
-            with gr.Tab(display_name, elem_classes="extension-tab"):
-                extension.ui()
-
-
-EXTENSION_MAP = {
-    "input": partial(_apply_string_extensions, "input_modifier"),
-    "output": partial(_apply_string_extensions, "output_modifier"),
-    "chat_input": _apply_chat_input_extensions,
-    "state": _apply_state_modifier_extensions,
-    "history": _apply_history_modifier_extensions,
-    "bot_prefix": partial(_apply_string_extensions, "bot_prefix_modifier"),
-    "tokenizer": partial(_apply_tokenizer_extensions, "tokenizer_modifier"),
-    'logits_processor': partial(_apply_logits_processor_extensions, 'logits_processor_modifier'),
-    "custom_generate_chat_prompt": _apply_custom_generate_chat_prompt,
-    "custom_generate_reply": _apply_custom_generate_reply,
-    "tokenized_length": _apply_custom_tokenized_length,
-    "css": _apply_custom_css,
-    "js": _apply_custom_js
-}
-
-
-def apply_extensions(typ, *args, **kwargs):
-    if typ not in EXTENSION_MAP:
-        raise ValueError(f"Invalid extension type {typ}")
-
-    return EXTENSION_MAP[typ](*args, **kwargs)

spaces/AriaMei/TTSdemo/monotonic_align/__init__.py

@@ -1,19 +0,0 @@
-import numpy as np
-import torch
-from .monotonic_align.core import maximum_path_c
-
-
-def maximum_path(neg_cent, mask):
-  """ Cython optimized version.
-  neg_cent: [b, t_t, t_s]
-  mask: [b, t_t, t_s]
-  """
-  device = neg_cent.device
-  dtype = neg_cent.dtype
-  neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
-  path = np.zeros(neg_cent.shape, dtype=np.int32)
-
-  t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
-  t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
-  maximum_path_c(path, neg_cent, t_t_max, t_s_max)
-  return torch.from_numpy(path).to(device=device, dtype=dtype)

spaces/Ariharasudhan/YoloV5/utils/downloads.py

@@ -1,108 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Download utils
-"""
-
-import logging
-import os
-import subprocess
-import urllib
-from pathlib import Path
-
-import requests
-import torch
-
-
-def is_url(url, check=True):
-    # Check if string is URL and check if URL exists
-    try:
-        url = str(url)
-        result = urllib.parse.urlparse(url)
-        assert all([result.scheme, result.netloc])  # check if is url
-        return (urllib.request.urlopen(url).getcode() == 200) if check else True  # check if exists online
-    except (AssertionError, urllib.request.HTTPError):
-        return False
-
-
-def gsutil_getsize(url=''):
-    # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
-    s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8')
-    return eval(s.split(' ')[0]) if len(s) else 0  # bytes
-
-
-def url_getsize(url='https://ultralytics.com/images/bus.jpg'):
-    # Return downloadable file size in bytes
-    response = requests.head(url, allow_redirects=True)
-    return int(response.headers.get('content-length', -1))
-
-
-def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
-    # Attempts to download file from url or url2, checks and removes incomplete downloads < min_bytes
-    from utils.general import LOGGER
-
-    file = Path(file)
-    assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}"
-    try:  # url1
-        LOGGER.info(f'Downloading {url} to {file}...')
-        torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO)
-        assert file.exists() and file.stat().st_size > min_bytes, assert_msg  # check
-    except Exception as e:  # url2
-        if file.exists():
-            file.unlink()  # remove partial downloads
-        LOGGER.info(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...')
-        os.system(f"curl -# -L '{url2 or url}' -o '{file}' --retry 3 -C -")  # curl download, retry and resume on fail
-    finally:
-        if not file.exists() or file.stat().st_size < min_bytes:  # check
-            if file.exists():
-                file.unlink()  # remove partial downloads
-            LOGGER.info(f"ERROR: {assert_msg}\n{error_msg}")
-        LOGGER.info('')
-
-
-def attempt_download(file, repo='ultralytics/yolov5', release='v6.2'):
-    # Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v6.2', etc.
-    from utils.general import LOGGER
-
-    def github_assets(repository, version='latest'):
-        # Return GitHub repo tag (i.e. 'v6.2') and assets (i.e. ['yolov5s.pt', 'yolov5m.pt', ...])
-        if version != 'latest':
-            version = f'tags/{version}'  # i.e. tags/v6.2
-        response = requests.get(f'https://api.github.com/repos/{repository}/releases/{version}').json()  # github api
-        return response['tag_name'], [x['name'] for x in response['assets']]  # tag, assets
-
-    file = Path(str(file).strip().replace("'", ''))
-    if not file.exists():
-        # URL specified
-        name = Path(urllib.parse.unquote(str(file))).name  # decode '%2F' to '/' etc.
-        if str(file).startswith(('http:/', 'https:/')):  # download
-            url = str(file).replace(':/', '://')  # Pathlib turns :// -> :/
-            file = name.split('?')[0]  # parse authentication https://url.com/file.txt?auth...
-            if Path(file).is_file():
-                LOGGER.info(f'Found {url} locally at {file}')  # file already exists
-            else:
-                safe_download(file=file, url=url, min_bytes=1E5)
-            return file
-
-        # GitHub assets
-        assets = [f'yolov5{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')]  # default
-        try:
-            tag, assets = github_assets(repo, release)
-        except Exception:
-            try:
-                tag, assets = github_assets(repo)  # latest release
-            except Exception:
-                try:
-                    tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1]
-                except Exception:
-                    tag = release
-
-        file.parent.mkdir(parents=True, exist_ok=True)  # make parent dir (if required)
-        if name in assets:
-            url3 = 'https://drive.google.com/drive/folders/1EFQTEUeXWSFww0luse2jB9M1QNZQGwNl'  # backup gdrive mirror
-            safe_download(
-                file,
-                url=f'https://github.com/{repo}/releases/download/{tag}/{name}',
-                min_bytes=1E5,
-                error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag} or {url3}')
-
-    return str(file)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/lexers/python.py

@@ -1,1204 +0,0 @@
-"""
-    pygments.lexers.python
-    ~~~~~~~~~~~~~~~~~~~~~~
-
-    Lexers for Python and related languages.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import re
-import keyword
-
-from pip._vendor.pygments.lexer import Lexer, RegexLexer, include, bygroups, using, \
-    default, words, combined, do_insertions, this, line_re
-from pip._vendor.pygments.util import get_bool_opt, shebang_matches
-from pip._vendor.pygments.token import Text, Comment, Operator, Keyword, Name, String, \
-    Number, Punctuation, Generic, Other, Error, Whitespace
-from pip._vendor.pygments import unistring as uni
-
-__all__ = ['PythonLexer', 'PythonConsoleLexer', 'PythonTracebackLexer',
-           'Python2Lexer', 'Python2TracebackLexer',
-           'CythonLexer', 'DgLexer', 'NumPyLexer']
-
-
-class PythonLexer(RegexLexer):
-    """
-    For Python source code (version 3.x).
-
-    .. versionadded:: 0.10
-
-    .. versionchanged:: 2.5
-       This is now the default ``PythonLexer``.  It is still available as the
-       alias ``Python3Lexer``.
-    """
-
-    name = 'Python'
-    url = 'http://www.python.org'
-    aliases = ['python', 'py', 'sage', 'python3', 'py3']
-    filenames = [
-        '*.py',
-        '*.pyw',
-        # Type stubs
-        '*.pyi',
-        # Jython
-        '*.jy',
-        # Sage
-        '*.sage',
-        # SCons
-        '*.sc',
-        'SConstruct',
-        'SConscript',
-        # Skylark/Starlark (used by Bazel, Buck, and Pants)
-        '*.bzl',
-        'BUCK',
-        'BUILD',
-        'BUILD.bazel',
-        'WORKSPACE',
-        # Twisted Application infrastructure
-        '*.tac',
-    ]
-    mimetypes = ['text/x-python', 'application/x-python',
-                 'text/x-python3', 'application/x-python3']
-
-    uni_name = "[%s][%s]*" % (uni.xid_start, uni.xid_continue)
-
-    def innerstring_rules(ttype):
-        return [
-            # the old style '%s' % (...) string formatting (still valid in Py3)
-            (r'%(\(\w+\))?[-#0 +]*([0-9]+|[*])?(\.([0-9]+|[*]))?'
-             '[hlL]?[E-GXc-giorsaux%]', String.Interpol),
-            # the new style '{}'.format(...) string formatting
-            (r'\{'
-             r'((\w+)((\.\w+)|(\[[^\]]+\]))*)?'  # field name
-             r'(\![sra])?'                       # conversion
-             r'(\:(.?[<>=\^])?[-+ ]?#?0?(\d+)?,?(\.\d+)?[E-GXb-gnosx%]?)?'
-             r'\}', String.Interpol),
-
-            # backslashes, quotes and formatting signs must be parsed one at a time
-            (r'[^\\\'"%{\n]+', ttype),
-            (r'[\'"\\]', ttype),
-            # unhandled string formatting sign
-            (r'%|(\{{1,2})', ttype)
-            # newlines are an error (use "nl" state)
-        ]
-
-    def fstring_rules(ttype):
-        return [
-            # Assuming that a '}' is the closing brace after format specifier.
-            # Sadly, this means that we won't detect syntax error. But it's
-            # more important to parse correct syntax correctly, than to
-            # highlight invalid syntax.
-            (r'\}', String.Interpol),
-            (r'\{', String.Interpol, 'expr-inside-fstring'),
-            # backslashes, quotes and formatting signs must be parsed one at a time
-            (r'[^\\\'"{}\n]+', ttype),
-            (r'[\'"\\]', ttype),
-            # newlines are an error (use "nl" state)
-        ]
-
-    tokens = {
-        'root': [
-            (r'\n', Whitespace),
-            (r'^(\s*)([rRuUbB]{,2})("""(?:.|\n)*?""")',
-             bygroups(Whitespace, String.Affix, String.Doc)),
-            (r"^(\s*)([rRuUbB]{,2})('''(?:.|\n)*?''')",
-             bygroups(Whitespace, String.Affix, String.Doc)),
-            (r'\A#!.+$', Comment.Hashbang),
-            (r'#.*$', Comment.Single),
-            (r'\\\n', Text),
-            (r'\\', Text),
-            include('keywords'),
-            include('soft-keywords'),
-            (r'(def)((?:\s|\\\s)+)', bygroups(Keyword, Text), 'funcname'),
-            (r'(class)((?:\s|\\\s)+)', bygroups(Keyword, Text), 'classname'),
-            (r'(from)((?:\s|\\\s)+)', bygroups(Keyword.Namespace, Text),
-             'fromimport'),
-            (r'(import)((?:\s|\\\s)+)', bygroups(Keyword.Namespace, Text),
-             'import'),
-            include('expr'),
-        ],
-        'expr': [
-            # raw f-strings
-            ('(?i)(rf|fr)(""")',
-             bygroups(String.Affix, String.Double),
-             combined('rfstringescape', 'tdqf')),
-            ("(?i)(rf|fr)(''')",
-             bygroups(String.Affix, String.Single),
-             combined('rfstringescape', 'tsqf')),
-            ('(?i)(rf|fr)(")',
-             bygroups(String.Affix, String.Double),
-             combined('rfstringescape', 'dqf')),
-            ("(?i)(rf|fr)(')",
-             bygroups(String.Affix, String.Single),
-             combined('rfstringescape', 'sqf')),
-            # non-raw f-strings
-            ('([fF])(""")', bygroups(String.Affix, String.Double),
-             combined('fstringescape', 'tdqf')),
-            ("([fF])(''')", bygroups(String.Affix, String.Single),
-             combined('fstringescape', 'tsqf')),
-            ('([fF])(")', bygroups(String.Affix, String.Double),
-             combined('fstringescape', 'dqf')),
-            ("([fF])(')", bygroups(String.Affix, String.Single),
-             combined('fstringescape', 'sqf')),
-            # raw bytes and strings
-            ('(?i)(rb|br|r)(""")',
-             bygroups(String.Affix, String.Double), 'tdqs'),
-            ("(?i)(rb|br|r)(''')",
-             bygroups(String.Affix, String.Single), 'tsqs'),
-            ('(?i)(rb|br|r)(")',
-             bygroups(String.Affix, String.Double), 'dqs'),
-            ("(?i)(rb|br|r)(')",
-             bygroups(String.Affix, String.Single), 'sqs'),
-            # non-raw strings
-            ('([uU]?)(""")', bygroups(String.Affix, String.Double),
-             combined('stringescape', 'tdqs')),
-            ("([uU]?)(''')", bygroups(String.Affix, String.Single),
-             combined('stringescape', 'tsqs')),
-            ('([uU]?)(")', bygroups(String.Affix, String.Double),
-             combined('stringescape', 'dqs')),
-            ("([uU]?)(')", bygroups(String.Affix, String.Single),
-             combined('stringescape', 'sqs')),
-            # non-raw bytes
-            ('([bB])(""")', bygroups(String.Affix, String.Double),
-             combined('bytesescape', 'tdqs')),
-            ("([bB])(''')", bygroups(String.Affix, String.Single),
-             combined('bytesescape', 'tsqs')),
-            ('([bB])(")', bygroups(String.Affix, String.Double),
-             combined('bytesescape', 'dqs')),
-            ("([bB])(')", bygroups(String.Affix, String.Single),
-             combined('bytesescape', 'sqs')),
-
-            (r'[^\S\n]+', Text),
-            include('numbers'),
-            (r'!=|==|<<|>>|:=|[-~+/*%=<>&^|.]', Operator),
-            (r'[]{}:(),;[]', Punctuation),
-            (r'(in|is|and|or|not)\b', Operator.Word),
-            include('expr-keywords'),
-            include('builtins'),
-            include('magicfuncs'),
-            include('magicvars'),
-            include('name'),
-        ],
-        'expr-inside-fstring': [
-            (r'[{([]', Punctuation, 'expr-inside-fstring-inner'),
-            # without format specifier
-            (r'(=\s*)?'         # debug (https://bugs.python.org/issue36817)
-             r'(\![sraf])?'     # conversion
-             r'\}', String.Interpol, '#pop'),
-            # with format specifier
-            # we'll catch the remaining '}' in the outer scope
-            (r'(=\s*)?'         # debug (https://bugs.python.org/issue36817)
-             r'(\![sraf])?'     # conversion
-             r':', String.Interpol, '#pop'),
-            (r'\s+', Whitespace),  # allow new lines
-            include('expr'),
-        ],
-        'expr-inside-fstring-inner': [
-            (r'[{([]', Punctuation, 'expr-inside-fstring-inner'),
-            (r'[])}]', Punctuation, '#pop'),
-            (r'\s+', Whitespace),  # allow new lines
-            include('expr'),
-        ],
-        'expr-keywords': [
-            # Based on https://docs.python.org/3/reference/expressions.html
-            (words((
-                'async for', 'await', 'else', 'for', 'if', 'lambda',
-                'yield', 'yield from'), suffix=r'\b'),
-             Keyword),
-            (words(('True', 'False', 'None'), suffix=r'\b'), Keyword.Constant),
-        ],
-        'keywords': [
-            (words((
-                'assert', 'async', 'await', 'break', 'continue', 'del', 'elif',
-                'else', 'except', 'finally', 'for', 'global', 'if', 'lambda',
-                'pass', 'raise', 'nonlocal', 'return', 'try', 'while', 'yield',
-                'yield from', 'as', 'with'), suffix=r'\b'),
-             Keyword),
-            (words(('True', 'False', 'None'), suffix=r'\b'), Keyword.Constant),
-        ],
-        'soft-keywords': [
-            # `match`, `case` and `_` soft keywords
-            (r'(^[ \t]*)'              # at beginning of line + possible indentation
-             r'(match|case)\b'         # a possible keyword
-             r'(?![ \t]*(?:'           # not followed by...
-             r'[:,;=^&|@~)\]}]|(?:' +  # characters and keywords that mean this isn't
-             r'|'.join(keyword.kwlist) + r')\b))',                 # pattern matching
-             bygroups(Text, Keyword), 'soft-keywords-inner'),
-        ],
-        'soft-keywords-inner': [
-            # optional `_` keyword
-            (r'(\s+)([^\n_]*)(_\b)', bygroups(Whitespace, using(this), Keyword)),
-            default('#pop')
-        ],
-        'builtins': [
-            (words((
-                '__import__', 'abs', 'all', 'any', 'bin', 'bool', 'bytearray',
-                'breakpoint', 'bytes', 'chr', 'classmethod', 'compile', 'complex',
-                'delattr', 'dict', 'dir', 'divmod', 'enumerate', 'eval', 'filter',
-                'float', 'format', 'frozenset', 'getattr', 'globals', 'hasattr',
-                'hash', 'hex', 'id', 'input', 'int', 'isinstance', 'issubclass',
-                'iter', 'len', 'list', 'locals', 'map', 'max', 'memoryview',
-                'min', 'next', 'object', 'oct', 'open', 'ord', 'pow', 'print',
-                'property', 'range', 'repr', 'reversed', 'round', 'set', 'setattr',
-                'slice', 'sorted', 'staticmethod', 'str', 'sum', 'super', 'tuple',
-                'type', 'vars', 'zip'), prefix=r'(?<!\.)', suffix=r'\b'),
-             Name.Builtin),
-            (r'(?<!\.)(self|Ellipsis|NotImplemented|cls)\b', Name.Builtin.Pseudo),
-            (words((
-                'ArithmeticError', 'AssertionError', 'AttributeError',
-                'BaseException', 'BufferError', 'BytesWarning', 'DeprecationWarning',
-                'EOFError', 'EnvironmentError', 'Exception', 'FloatingPointError',
-                'FutureWarning', 'GeneratorExit', 'IOError', 'ImportError',
-                'ImportWarning', 'IndentationError', 'IndexError', 'KeyError',
-                'KeyboardInterrupt', 'LookupError', 'MemoryError', 'NameError',
-                'NotImplementedError', 'OSError', 'OverflowError',
-                'PendingDeprecationWarning', 'ReferenceError', 'ResourceWarning',
-                'RuntimeError', 'RuntimeWarning', 'StopIteration',
-                'SyntaxError', 'SyntaxWarning', 'SystemError', 'SystemExit',
-                'TabError', 'TypeError', 'UnboundLocalError', 'UnicodeDecodeError',
-                'UnicodeEncodeError', 'UnicodeError', 'UnicodeTranslateError',
-                'UnicodeWarning', 'UserWarning', 'ValueError', 'VMSError',
-                'Warning', 'WindowsError', 'ZeroDivisionError',
-                # new builtin exceptions from PEP 3151
-                'BlockingIOError', 'ChildProcessError', 'ConnectionError',
-                'BrokenPipeError', 'ConnectionAbortedError', 'ConnectionRefusedError',
-                'ConnectionResetError', 'FileExistsError', 'FileNotFoundError',
-                'InterruptedError', 'IsADirectoryError', 'NotADirectoryError',
-                'PermissionError', 'ProcessLookupError', 'TimeoutError',
-                # others new in Python 3
-                'StopAsyncIteration', 'ModuleNotFoundError', 'RecursionError',
-                'EncodingWarning'),
-                prefix=r'(?<!\.)', suffix=r'\b'),
-             Name.Exception),
-        ],
-        'magicfuncs': [
-            (words((
-                '__abs__', '__add__', '__aenter__', '__aexit__', '__aiter__',
-                '__and__', '__anext__', '__await__', '__bool__', '__bytes__',
-                '__call__', '__complex__', '__contains__', '__del__', '__delattr__',
-                '__delete__', '__delitem__', '__dir__', '__divmod__', '__enter__',
-                '__eq__', '__exit__', '__float__', '__floordiv__', '__format__',
-                '__ge__', '__get__', '__getattr__', '__getattribute__',
-                '__getitem__', '__gt__', '__hash__', '__iadd__', '__iand__',
-                '__ifloordiv__', '__ilshift__', '__imatmul__', '__imod__',
-                '__imul__', '__index__', '__init__', '__instancecheck__',
-                '__int__', '__invert__', '__ior__', '__ipow__', '__irshift__',
-                '__isub__', '__iter__', '__itruediv__', '__ixor__', '__le__',
-                '__len__', '__length_hint__', '__lshift__', '__lt__', '__matmul__',
-                '__missing__', '__mod__', '__mul__', '__ne__', '__neg__',
-                '__new__', '__next__', '__or__', '__pos__', '__pow__',
-                '__prepare__', '__radd__', '__rand__', '__rdivmod__', '__repr__',
-                '__reversed__', '__rfloordiv__', '__rlshift__', '__rmatmul__',
-                '__rmod__', '__rmul__', '__ror__', '__round__', '__rpow__',
-                '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__',
-                '__rxor__', '__set__', '__setattr__', '__setitem__', '__str__',
-                '__sub__', '__subclasscheck__', '__truediv__',
-                '__xor__'), suffix=r'\b'),
-             Name.Function.Magic),
-        ],
-        'magicvars': [
-            (words((
-                '__annotations__', '__bases__', '__class__', '__closure__',
-                '__code__', '__defaults__', '__dict__', '__doc__', '__file__',
-                '__func__', '__globals__', '__kwdefaults__', '__module__',
-                '__mro__', '__name__', '__objclass__', '__qualname__',
-                '__self__', '__slots__', '__weakref__'), suffix=r'\b'),
-             Name.Variable.Magic),
-        ],
-        'numbers': [
-            (r'(\d(?:_?\d)*\.(?:\d(?:_?\d)*)?|(?:\d(?:_?\d)*)?\.\d(?:_?\d)*)'
-             r'([eE][+-]?\d(?:_?\d)*)?', Number.Float),
-            (r'\d(?:_?\d)*[eE][+-]?\d(?:_?\d)*j?', Number.Float),
-            (r'0[oO](?:_?[0-7])+', Number.Oct),
-            (r'0[bB](?:_?[01])+', Number.Bin),
-            (r'0[xX](?:_?[a-fA-F0-9])+', Number.Hex),
-            (r'\d(?:_?\d)*', Number.Integer),
-        ],
-        'name': [
-            (r'@' + uni_name, Name.Decorator),
-            (r'@', Operator),  # new matrix multiplication operator
-            (uni_name, Name),
-        ],
-        'funcname': [
-            include('magicfuncs'),
-            (uni_name, Name.Function, '#pop'),
-            default('#pop'),
-        ],
-        'classname': [
-            (uni_name, Name.Class, '#pop'),
-        ],
-        'import': [
-            (r'(\s+)(as)(\s+)', bygroups(Text, Keyword, Text)),
-            (r'\.', Name.Namespace),
-            (uni_name, Name.Namespace),
-            (r'(\s*)(,)(\s*)', bygroups(Text, Operator, Text)),
-            default('#pop')  # all else: go back
-        ],
-        'fromimport': [
-            (r'(\s+)(import)\b', bygroups(Text, Keyword.Namespace), '#pop'),
-            (r'\.', Name.Namespace),
-            # if None occurs here, it's "raise x from None", since None can
-            # never be a module name
-            (r'None\b', Name.Builtin.Pseudo, '#pop'),
-            (uni_name, Name.Namespace),
-            default('#pop'),
-        ],
-        'rfstringescape': [
-            (r'\{\{', String.Escape),
-            (r'\}\}', String.Escape),
-        ],
-        'fstringescape': [
-            include('rfstringescape'),
-            include('stringescape'),
-        ],
-        'bytesescape': [
-            (r'\\([\\abfnrtv"\']|\n|x[a-fA-F0-9]{2}|[0-7]{1,3})', String.Escape)
-        ],
-        'stringescape': [
-            (r'\\(N\{.*?\}|u[a-fA-F0-9]{4}|U[a-fA-F0-9]{8})', String.Escape),
-            include('bytesescape')
-        ],
-        'fstrings-single': fstring_rules(String.Single),
-        'fstrings-double': fstring_rules(String.Double),
-        'strings-single': innerstring_rules(String.Single),
-        'strings-double': innerstring_rules(String.Double),
-        'dqf': [
-            (r'"', String.Double, '#pop'),
-            (r'\\\\|\\"|\\\n', String.Escape),  # included here for raw strings
-            include('fstrings-double')
-        ],
-        'sqf': [
-            (r"'", String.Single, '#pop'),
-            (r"\\\\|\\'|\\\n", String.Escape),  # included here for raw strings
-            include('fstrings-single')
-        ],
-        'dqs': [
-            (r'"', String.Double, '#pop'),
-            (r'\\\\|\\"|\\\n', String.Escape),  # included here for raw strings
-            include('strings-double')
-        ],
-        'sqs': [
-            (r"'", String.Single, '#pop'),
-            (r"\\\\|\\'|\\\n", String.Escape),  # included here for raw strings
-            include('strings-single')
-        ],
-        'tdqf': [
-            (r'"""', String.Double, '#pop'),
-            include('fstrings-double'),
-            (r'\n', String.Double)
-        ],
-        'tsqf': [
-            (r"'''", String.Single, '#pop'),
-            include('fstrings-single'),
-            (r'\n', String.Single)
-        ],
-        'tdqs': [
-            (r'"""', String.Double, '#pop'),
-            include('strings-double'),
-            (r'\n', String.Double)
-        ],
-        'tsqs': [
-            (r"'''", String.Single, '#pop'),
-            include('strings-single'),
-            (r'\n', String.Single)
-        ],
-    }
-
-    def analyse_text(text):
-        return shebang_matches(text, r'pythonw?(3(\.\d)?)?') or \
-            'import ' in text[:1000]
-
-
-Python3Lexer = PythonLexer
-
-
-class Python2Lexer(RegexLexer):
-    """
-    For Python 2.x source code.
-
-    .. versionchanged:: 2.5
-       This class has been renamed from ``PythonLexer``.  ``PythonLexer`` now
-       refers to the Python 3 variant.  File name patterns like ``*.py`` have
-       been moved to Python 3 as well.
-    """
-
-    name = 'Python 2.x'
-    url = 'http://www.python.org'
-    aliases = ['python2', 'py2']
-    filenames = []  # now taken over by PythonLexer (3.x)
-    mimetypes = ['text/x-python2', 'application/x-python2']
-
-    def innerstring_rules(ttype):
-        return [
-            # the old style '%s' % (...) string formatting
-            (r'%(\(\w+\))?[-#0 +]*([0-9]+|[*])?(\.([0-9]+|[*]))?'
-             '[hlL]?[E-GXc-giorsux%]', String.Interpol),
-            # backslashes, quotes and formatting signs must be parsed one at a time
-            (r'[^\\\'"%\n]+', ttype),
-            (r'[\'"\\]', ttype),
-            # unhandled string formatting sign
-            (r'%', ttype),
-            # newlines are an error (use "nl" state)
-        ]
-
-    tokens = {
-        'root': [
-            (r'\n', Whitespace),
-            (r'^(\s*)([rRuUbB]{,2})("""(?:.|\n)*?""")',
-             bygroups(Whitespace, String.Affix, String.Doc)),
-            (r"^(\s*)([rRuUbB]{,2})('''(?:.|\n)*?''')",
-             bygroups(Whitespace, String.Affix, String.Doc)),
-            (r'[^\S\n]+', Text),
-            (r'\A#!.+$', Comment.Hashbang),
-            (r'#.*$', Comment.Single),
-            (r'[]{}:(),;[]', Punctuation),
-            (r'\\\n', Text),
-            (r'\\', Text),
-            (r'(in|is|and|or|not)\b', Operator.Word),
-            (r'!=|==|<<|>>|[-~+/*%=<>&^|.]', Operator),
-            include('keywords'),
-            (r'(def)((?:\s|\\\s)+)', bygroups(Keyword, Text), 'funcname'),
-            (r'(class)((?:\s|\\\s)+)', bygroups(Keyword, Text), 'classname'),
-            (r'(from)((?:\s|\\\s)+)', bygroups(Keyword.Namespace, Text),
-             'fromimport'),
-            (r'(import)((?:\s|\\\s)+)', bygroups(Keyword.Namespace, Text),
-             'import'),
-            include('builtins'),
-            include('magicfuncs'),
-            include('magicvars'),
-            include('backtick'),
-            ('([rR]|[uUbB][rR]|[rR][uUbB])(""")',
-             bygroups(String.Affix, String.Double), 'tdqs'),
-            ("([rR]|[uUbB][rR]|[rR][uUbB])(''')",
-             bygroups(String.Affix, String.Single), 'tsqs'),
-            ('([rR]|[uUbB][rR]|[rR][uUbB])(")',
-             bygroups(String.Affix, String.Double), 'dqs'),
-            ("([rR]|[uUbB][rR]|[rR][uUbB])(')",
-             bygroups(String.Affix, String.Single), 'sqs'),
-            ('([uUbB]?)(""")', bygroups(String.Affix, String.Double),
-             combined('stringescape', 'tdqs')),
-            ("([uUbB]?)(''')", bygroups(String.Affix, String.Single),
-             combined('stringescape', 'tsqs')),
-            ('([uUbB]?)(")', bygroups(String.Affix, String.Double),
-             combined('stringescape', 'dqs')),
-            ("([uUbB]?)(')", bygroups(String.Affix, String.Single),
-             combined('stringescape', 'sqs')),
-            include('name'),
-            include('numbers'),
-        ],
-        'keywords': [
-            (words((
-                'assert', 'break', 'continue', 'del', 'elif', 'else', 'except',
-                'exec', 'finally', 'for', 'global', 'if', 'lambda', 'pass',
-                'print', 'raise', 'return', 'try', 'while', 'yield',
-                'yield from', 'as', 'with'), suffix=r'\b'),
-             Keyword),
-        ],
-        'builtins': [
-            (words((
-                '__import__', 'abs', 'all', 'any', 'apply', 'basestring', 'bin',
-                'bool', 'buffer', 'bytearray', 'bytes', 'callable', 'chr', 'classmethod',
-                'cmp', 'coerce', 'compile', 'complex', 'delattr', 'dict', 'dir', 'divmod',
-                'enumerate', 'eval', 'execfile', 'exit', 'file', 'filter', 'float',
-                'frozenset', 'getattr', 'globals', 'hasattr', 'hash', 'hex', 'id',
-                'input', 'int', 'intern', 'isinstance', 'issubclass', 'iter', 'len',
-                'list', 'locals', 'long', 'map', 'max', 'min', 'next', 'object',
-                'oct', 'open', 'ord', 'pow', 'property', 'range', 'raw_input', 'reduce',
-                'reload', 'repr', 'reversed', 'round', 'set', 'setattr', 'slice',
-                'sorted', 'staticmethod', 'str', 'sum', 'super', 'tuple', 'type',
-                'unichr', 'unicode', 'vars', 'xrange', 'zip'),
-                prefix=r'(?<!\.)', suffix=r'\b'),
-             Name.Builtin),
-            (r'(?<!\.)(self|None|Ellipsis|NotImplemented|False|True|cls'
-             r')\b', Name.Builtin.Pseudo),
-            (words((
-                'ArithmeticError', 'AssertionError', 'AttributeError',
-                'BaseException', 'DeprecationWarning', 'EOFError', 'EnvironmentError',
-                'Exception', 'FloatingPointError', 'FutureWarning', 'GeneratorExit',
-                'IOError', 'ImportError', 'ImportWarning', 'IndentationError',
-                'IndexError', 'KeyError', 'KeyboardInterrupt', 'LookupError',
-                'MemoryError', 'NameError',
-                'NotImplementedError', 'OSError', 'OverflowError', 'OverflowWarning',
-                'PendingDeprecationWarning', 'ReferenceError',
-                'RuntimeError', 'RuntimeWarning', 'StandardError', 'StopIteration',
-                'SyntaxError', 'SyntaxWarning', 'SystemError', 'SystemExit',
-                'TabError', 'TypeError', 'UnboundLocalError', 'UnicodeDecodeError',
-                'UnicodeEncodeError', 'UnicodeError', 'UnicodeTranslateError',
-                'UnicodeWarning', 'UserWarning', 'ValueError', 'VMSError', 'Warning',
-                'WindowsError', 'ZeroDivisionError'), prefix=r'(?<!\.)', suffix=r'\b'),
-             Name.Exception),
-        ],
-        'magicfuncs': [
-            (words((
-                '__abs__', '__add__', '__and__', '__call__', '__cmp__', '__coerce__',
-                '__complex__', '__contains__', '__del__', '__delattr__', '__delete__',
-                '__delitem__', '__delslice__', '__div__', '__divmod__', '__enter__',
-                '__eq__', '__exit__', '__float__', '__floordiv__', '__ge__', '__get__',
-                '__getattr__', '__getattribute__', '__getitem__', '__getslice__', '__gt__',
-                '__hash__', '__hex__', '__iadd__', '__iand__', '__idiv__', '__ifloordiv__',
-                '__ilshift__', '__imod__', '__imul__', '__index__', '__init__',
-                '__instancecheck__', '__int__', '__invert__', '__iop__', '__ior__',
-                '__ipow__', '__irshift__', '__isub__', '__iter__', '__itruediv__',
-                '__ixor__', '__le__', '__len__', '__long__', '__lshift__', '__lt__',
-                '__missing__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__',
-                '__nonzero__', '__oct__', '__op__', '__or__', '__pos__', '__pow__',
-                '__radd__', '__rand__', '__rcmp__', '__rdiv__', '__rdivmod__', '__repr__',
-                '__reversed__', '__rfloordiv__', '__rlshift__', '__rmod__', '__rmul__',
-                '__rop__', '__ror__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__',
-                '__rtruediv__', '__rxor__', '__set__', '__setattr__', '__setitem__',
-                '__setslice__', '__str__', '__sub__', '__subclasscheck__', '__truediv__',
-                '__unicode__', '__xor__'), suffix=r'\b'),
-             Name.Function.Magic),
-        ],
-        'magicvars': [
-            (words((
-                '__bases__', '__class__', '__closure__', '__code__', '__defaults__',
-                '__dict__', '__doc__', '__file__', '__func__', '__globals__',
-                '__metaclass__', '__module__', '__mro__', '__name__', '__self__',
-                '__slots__', '__weakref__'),
-                suffix=r'\b'),
-             Name.Variable.Magic),
-        ],
-        'numbers': [
-            (r'(\d+\.\d*|\d*\.\d+)([eE][+-]?[0-9]+)?j?', Number.Float),
-            (r'\d+[eE][+-]?[0-9]+j?', Number.Float),
-            (r'0[0-7]+j?', Number.Oct),
-            (r'0[bB][01]+', Number.Bin),
-            (r'0[xX][a-fA-F0-9]+', Number.Hex),
-            (r'\d+L', Number.Integer.Long),
-            (r'\d+j?', Number.Integer)
-        ],
-        'backtick': [
-            ('`.*?`', String.Backtick),
-        ],
-        'name': [
-            (r'@[\w.]+', Name.Decorator),
-            (r'[a-zA-Z_]\w*', Name),
-        ],
-        'funcname': [
-            include('magicfuncs'),
-            (r'[a-zA-Z_]\w*', Name.Function, '#pop'),
-            default('#pop'),
-        ],
-        'classname': [
-            (r'[a-zA-Z_]\w*', Name.Class, '#pop')
-        ],
-        'import': [
-            (r'(?:[ \t]|\\\n)+', Text),
-            (r'as\b', Keyword.Namespace),
-            (r',', Operator),
-            (r'[a-zA-Z_][\w.]*', Name.Namespace),
-            default('#pop')  # all else: go back
-        ],
-        'fromimport': [
-            (r'(?:[ \t]|\\\n)+', Text),
-            (r'import\b', Keyword.Namespace, '#pop'),
-            # if None occurs here, it's "raise x from None", since None can
-            # never be a module name
-            (r'None\b', Name.Builtin.Pseudo, '#pop'),
-            # sadly, in "raise x from y" y will be highlighted as namespace too
-            (r'[a-zA-Z_.][\w.]*', Name.Namespace),
-            # anything else here also means "raise x from y" and is therefore
-            # not an error
-            default('#pop'),
-        ],
-        'stringescape': [
-            (r'\\([\\abfnrtv"\']|\n|N\{.*?\}|u[a-fA-F0-9]{4}|'
-             r'U[a-fA-F0-9]{8}|x[a-fA-F0-9]{2}|[0-7]{1,3})', String.Escape)
-        ],
-        'strings-single': innerstring_rules(String.Single),
-        'strings-double': innerstring_rules(String.Double),
-        'dqs': [
-            (r'"', String.Double, '#pop'),
-            (r'\\\\|\\"|\\\n', String.Escape),  # included here for raw strings
-            include('strings-double')
-        ],
-        'sqs': [
-            (r"'", String.Single, '#pop'),
-            (r"\\\\|\\'|\\\n", String.Escape),  # included here for raw strings
-            include('strings-single')
-        ],
-        'tdqs': [
-            (r'"""', String.Double, '#pop'),
-            include('strings-double'),
-            (r'\n', String.Double)
-        ],
-        'tsqs': [
-            (r"'''", String.Single, '#pop'),
-            include('strings-single'),
-            (r'\n', String.Single)
-        ],
-    }
-
-    def analyse_text(text):
-        return shebang_matches(text, r'pythonw?2(\.\d)?')
-
-
-class PythonConsoleLexer(Lexer):
-    """
-    For Python console output or doctests, such as:
-
-    .. sourcecode:: pycon
-
-        >>> a = 'foo'
-        >>> print a
-        foo
-        >>> 1 / 0
-        Traceback (most recent call last):
-          File "<stdin>", line 1, in <module>
-        ZeroDivisionError: integer division or modulo by zero
-
-    Additional options:
-
-    `python3`
-        Use Python 3 lexer for code.  Default is ``True``.
-
-        .. versionadded:: 1.0
-        .. versionchanged:: 2.5
-           Now defaults to ``True``.
-    """
-    name = 'Python console session'
-    aliases = ['pycon']
-    mimetypes = ['text/x-python-doctest']
-
-    def __init__(self, **options):
-        self.python3 = get_bool_opt(options, 'python3', True)
-        Lexer.__init__(self, **options)
-
-    def get_tokens_unprocessed(self, text):
-        if self.python3:
-            pylexer = PythonLexer(**self.options)
-            tblexer = PythonTracebackLexer(**self.options)
-        else:
-            pylexer = Python2Lexer(**self.options)
-            tblexer = Python2TracebackLexer(**self.options)
-
-        curcode = ''
-        insertions = []
-        curtb = ''
-        tbindex = 0
-        tb = 0
-        for match in line_re.finditer(text):
-            line = match.group()
-            if line.startswith('>>> ') or line.startswith('... '):
-                tb = 0
-                insertions.append((len(curcode),
-                                   [(0, Generic.Prompt, line[:4])]))
-                curcode += line[4:]
-            elif line.rstrip() == '...' and not tb:
-                # only a new >>> prompt can end an exception block
-                # otherwise an ellipsis in place of the traceback frames
-                # will be mishandled
-                insertions.append((len(curcode),
-                                   [(0, Generic.Prompt, '...')]))
-                curcode += line[3:]
-            else:
-                if curcode:
-                    yield from do_insertions(
-                        insertions, pylexer.get_tokens_unprocessed(curcode))
-                    curcode = ''
-                    insertions = []
-                if (line.startswith('Traceback (most recent call last):') or
-                        re.match('  File "[^"]+", line \\d+\\n$', line)):
-                    tb = 1
-                    curtb = line
-                    tbindex = match.start()
-                elif line == 'KeyboardInterrupt\n':
-                    yield match.start(), Name.Class, line
-                elif tb:
-                    curtb += line
-                    if not (line.startswith(' ') or line.strip() == '...'):
-                        tb = 0
-                        for i, t, v in tblexer.get_tokens_unprocessed(curtb):
-                            yield tbindex+i, t, v
-                        curtb = ''
-                else:
-                    yield match.start(), Generic.Output, line
-        if curcode:
-            yield from do_insertions(insertions,
-                                     pylexer.get_tokens_unprocessed(curcode))
-        if curtb:
-            for i, t, v in tblexer.get_tokens_unprocessed(curtb):
-                yield tbindex+i, t, v
-
-
-class PythonTracebackLexer(RegexLexer):
-    """
-    For Python 3.x tracebacks, with support for chained exceptions.
-
-    .. versionadded:: 1.0
-
-    .. versionchanged:: 2.5
-       This is now the default ``PythonTracebackLexer``.  It is still available
-       as the alias ``Python3TracebackLexer``.
-    """
-
-    name = 'Python Traceback'
-    aliases = ['pytb', 'py3tb']
-    filenames = ['*.pytb', '*.py3tb']
-    mimetypes = ['text/x-python-traceback', 'text/x-python3-traceback']
-
-    tokens = {
-        'root': [
-            (r'\n', Whitespace),
-            (r'^Traceback \(most recent call last\):\n', Generic.Traceback, 'intb'),
-            (r'^During handling of the above exception, another '
-             r'exception occurred:\n\n', Generic.Traceback),
-            (r'^The above exception was the direct cause of the '
-             r'following exception:\n\n', Generic.Traceback),
-            (r'^(?=  File "[^"]+", line \d+)', Generic.Traceback, 'intb'),
-            (r'^.*\n', Other),
-        ],
-        'intb': [
-            (r'^(  File )("[^"]+")(, line )(\d+)(, in )(.+)(\n)',
-             bygroups(Text, Name.Builtin, Text, Number, Text, Name, Whitespace)),
-            (r'^(  File )("[^"]+")(, line )(\d+)(\n)',
-             bygroups(Text, Name.Builtin, Text, Number, Whitespace)),
-            (r'^(    )(.+)(\n)',
-             bygroups(Whitespace, using(PythonLexer), Whitespace), 'markers'),
-            (r'^([ \t]*)(\.\.\.)(\n)',
-             bygroups(Whitespace, Comment, Whitespace)),  # for doctests...
-            (r'^([^:]+)(: )(.+)(\n)',
-             bygroups(Generic.Error, Text, Name, Whitespace), '#pop'),
-            (r'^([a-zA-Z_][\w.]*)(:?\n)',
-             bygroups(Generic.Error, Whitespace), '#pop')
-        ],
-        'markers': [
-            # Either `PEP 657 <https://www.python.org/dev/peps/pep-0657/>`
-            # error locations in Python 3.11+, or single-caret markers
-            # for syntax errors before that.
-            (r'^( {4,})([~^]+)(\n)',
-             bygroups(Whitespace, Punctuation.Marker, Whitespace),
-             '#pop'),
-            default('#pop'),
-        ],
-    }
-
-
-Python3TracebackLexer = PythonTracebackLexer
-
-
-class Python2TracebackLexer(RegexLexer):
-    """
-    For Python tracebacks.
-
-    .. versionadded:: 0.7
-
-    .. versionchanged:: 2.5
-       This class has been renamed from ``PythonTracebackLexer``.
-       ``PythonTracebackLexer`` now refers to the Python 3 variant.
-    """
-
-    name = 'Python 2.x Traceback'
-    aliases = ['py2tb']
-    filenames = ['*.py2tb']
-    mimetypes = ['text/x-python2-traceback']
-
-    tokens = {
-        'root': [
-            # Cover both (most recent call last) and (innermost last)
-            # The optional ^C allows us to catch keyboard interrupt signals.
-            (r'^(\^C)?(Traceback.*\n)',
-             bygroups(Text, Generic.Traceback), 'intb'),
-            # SyntaxError starts with this.
-            (r'^(?=  File "[^"]+", line \d+)', Generic.Traceback, 'intb'),
-            (r'^.*\n', Other),
-        ],
-        'intb': [
-            (r'^(  File )("[^"]+")(, line )(\d+)(, in )(.+)(\n)',
-             bygroups(Text, Name.Builtin, Text, Number, Text, Name, Whitespace)),
-            (r'^(  File )("[^"]+")(, line )(\d+)(\n)',
-             bygroups(Text, Name.Builtin, Text, Number, Whitespace)),
-            (r'^(    )(.+)(\n)',
-             bygroups(Text, using(Python2Lexer), Whitespace), 'marker'),
-            (r'^([ \t]*)(\.\.\.)(\n)',
-             bygroups(Text, Comment, Whitespace)),  # for doctests...
-            (r'^([^:]+)(: )(.+)(\n)',
-             bygroups(Generic.Error, Text, Name, Whitespace), '#pop'),
-            (r'^([a-zA-Z_]\w*)(:?\n)',
-             bygroups(Generic.Error, Whitespace), '#pop')
-        ],
-        'marker': [
-            # For syntax errors.
-            (r'( {4,})(\^)', bygroups(Text, Punctuation.Marker), '#pop'),
-            default('#pop'),
-        ],
-    }
-
-
-class CythonLexer(RegexLexer):
-    """
-    For Pyrex and Cython source code.
-
-    .. versionadded:: 1.1
-    """
-
-    name = 'Cython'
-    url = 'http://cython.org'
-    aliases = ['cython', 'pyx', 'pyrex']
-    filenames = ['*.pyx', '*.pxd', '*.pxi']
-    mimetypes = ['text/x-cython', 'application/x-cython']
-
-    tokens = {
-        'root': [
-            (r'\n', Whitespace),
-            (r'^(\s*)("""(?:.|\n)*?""")', bygroups(Whitespace, String.Doc)),
-            (r"^(\s*)('''(?:.|\n)*?''')", bygroups(Whitespace, String.Doc)),
-            (r'[^\S\n]+', Text),
-            (r'#.*$', Comment),
-            (r'[]{}:(),;[]', Punctuation),
-            (r'\\\n', Whitespace),
-            (r'\\', Text),
-            (r'(in|is|and|or|not)\b', Operator.Word),
-            (r'(<)([a-zA-Z0-9.?]+)(>)',
-             bygroups(Punctuation, Keyword.Type, Punctuation)),
-            (r'!=|==|<<|>>|[-~+/*%=<>&^|.?]', Operator),
-            (r'(from)(\d+)(<=)(\s+)(<)(\d+)(:)',
-             bygroups(Keyword, Number.Integer, Operator, Name, Operator,
-                      Name, Punctuation)),
-            include('keywords'),
-            (r'(def|property)(\s+)', bygroups(Keyword, Text), 'funcname'),
-            (r'(cp?def)(\s+)', bygroups(Keyword, Text), 'cdef'),
-            # (should actually start a block with only cdefs)
-            (r'(cdef)(:)', bygroups(Keyword, Punctuation)),
-            (r'(class|struct)(\s+)', bygroups(Keyword, Text), 'classname'),
-            (r'(from)(\s+)', bygroups(Keyword, Text), 'fromimport'),
-            (r'(c?import)(\s+)', bygroups(Keyword, Text), 'import'),
-            include('builtins'),
-            include('backtick'),
-            ('(?:[rR]|[uU][rR]|[rR][uU])"""', String, 'tdqs'),
-            ("(?:[rR]|[uU][rR]|[rR][uU])'''", String, 'tsqs'),
-            ('(?:[rR]|[uU][rR]|[rR][uU])"', String, 'dqs'),
-            ("(?:[rR]|[uU][rR]|[rR][uU])'", String, 'sqs'),
-            ('[uU]?"""', String, combined('stringescape', 'tdqs')),
-            ("[uU]?'''", String, combined('stringescape', 'tsqs')),
-            ('[uU]?"', String, combined('stringescape', 'dqs')),
-            ("[uU]?'", String, combined('stringescape', 'sqs')),
-            include('name'),
-            include('numbers'),
-        ],
-        'keywords': [
-            (words((
-                'assert', 'async', 'await', 'break', 'by', 'continue', 'ctypedef', 'del', 'elif',
-                'else', 'except', 'except?', 'exec', 'finally', 'for', 'fused', 'gil',
-                'global', 'if', 'include', 'lambda', 'nogil', 'pass', 'print',
-                'raise', 'return', 'try', 'while', 'yield', 'as', 'with'), suffix=r'\b'),
-             Keyword),
-            (r'(DEF|IF|ELIF|ELSE)\b', Comment.Preproc),
-        ],
-        'builtins': [
-            (words((
-                '__import__', 'abs', 'all', 'any', 'apply', 'basestring', 'bin', 'bint',
-                'bool', 'buffer', 'bytearray', 'bytes', 'callable', 'chr',
-                'classmethod', 'cmp', 'coerce', 'compile', 'complex', 'delattr',
-                'dict', 'dir', 'divmod', 'enumerate', 'eval', 'execfile', 'exit',
-                'file', 'filter', 'float', 'frozenset', 'getattr', 'globals',
-                'hasattr', 'hash', 'hex', 'id', 'input', 'int', 'intern', 'isinstance',
-                'issubclass', 'iter', 'len', 'list', 'locals', 'long', 'map', 'max',
-                'min', 'next', 'object', 'oct', 'open', 'ord', 'pow', 'property', 'Py_ssize_t',
-                'range', 'raw_input', 'reduce', 'reload', 'repr', 'reversed',
-                'round', 'set', 'setattr', 'slice', 'sorted', 'staticmethod',
-                'str', 'sum', 'super', 'tuple', 'type', 'unichr', 'unicode', 'unsigned',
-                'vars', 'xrange', 'zip'), prefix=r'(?<!\.)', suffix=r'\b'),
-             Name.Builtin),
-            (r'(?<!\.)(self|None|Ellipsis|NotImplemented|False|True|NULL'
-             r')\b', Name.Builtin.Pseudo),
-            (words((
-                'ArithmeticError', 'AssertionError', 'AttributeError',
-                'BaseException', 'DeprecationWarning', 'EOFError', 'EnvironmentError',
-                'Exception', 'FloatingPointError', 'FutureWarning', 'GeneratorExit',
-                'IOError', 'ImportError', 'ImportWarning', 'IndentationError',
-                'IndexError', 'KeyError', 'KeyboardInterrupt', 'LookupError',
-                'MemoryError', 'NameError', 'NotImplemented', 'NotImplementedError',
-                'OSError', 'OverflowError', 'OverflowWarning',
-                'PendingDeprecationWarning', 'ReferenceError', 'RuntimeError',
-                'RuntimeWarning', 'StandardError', 'StopIteration', 'SyntaxError',
-                'SyntaxWarning', 'SystemError', 'SystemExit', 'TabError',
-                'TypeError', 'UnboundLocalError', 'UnicodeDecodeError',
-                'UnicodeEncodeError', 'UnicodeError', 'UnicodeTranslateError',
-                'UnicodeWarning', 'UserWarning', 'ValueError', 'Warning',
-                'ZeroDivisionError'), prefix=r'(?<!\.)', suffix=r'\b'),
-             Name.Exception),
-        ],
-        'numbers': [
-            (r'(\d+\.?\d*|\d*\.\d+)([eE][+-]?[0-9]+)?', Number.Float),
-            (r'0\d+', Number.Oct),
-            (r'0[xX][a-fA-F0-9]+', Number.Hex),
-            (r'\d+L', Number.Integer.Long),
-            (r'\d+', Number.Integer)
-        ],
-        'backtick': [
-            ('`.*?`', String.Backtick),
-        ],
-        'name': [
-            (r'@\w+', Name.Decorator),
-            (r'[a-zA-Z_]\w*', Name),
-        ],
-        'funcname': [
-            (r'[a-zA-Z_]\w*', Name.Function, '#pop')
-        ],
-        'cdef': [
-            (r'(public|readonly|extern|api|inline)\b', Keyword.Reserved),
-            (r'(struct|enum|union|class)\b', Keyword),
-            (r'([a-zA-Z_]\w*)(\s*)(?=[(:#=]|$)',
-             bygroups(Name.Function, Text), '#pop'),
-            (r'([a-zA-Z_]\w*)(\s*)(,)',
-             bygroups(Name.Function, Text, Punctuation)),
-            (r'from\b', Keyword, '#pop'),
-            (r'as\b', Keyword),
-            (r':', Punctuation, '#pop'),
-            (r'(?=["\'])', Text, '#pop'),
-            (r'[a-zA-Z_]\w*', Keyword.Type),
-            (r'.', Text),
-        ],
-        'classname': [
-            (r'[a-zA-Z_]\w*', Name.Class, '#pop')
-        ],
-        'import': [
-            (r'(\s+)(as)(\s+)', bygroups(Text, Keyword, Text)),
-            (r'[a-zA-Z_][\w.]*', Name.Namespace),
-            (r'(\s*)(,)(\s*)', bygroups(Text, Operator, Text)),
-            default('#pop')  # all else: go back
-        ],
-        'fromimport': [
-            (r'(\s+)(c?import)\b', bygroups(Text, Keyword), '#pop'),
-            (r'[a-zA-Z_.][\w.]*', Name.Namespace),
-            # ``cdef foo from "header"``, or ``for foo from 0 < i < 10``
-            default('#pop'),
-        ],
-        'stringescape': [
-            (r'\\([\\abfnrtv"\']|\n|N\{.*?\}|u[a-fA-F0-9]{4}|'
-             r'U[a-fA-F0-9]{8}|x[a-fA-F0-9]{2}|[0-7]{1,3})', String.Escape)
-        ],
-        'strings': [
-            (r'%(\([a-zA-Z0-9]+\))?[-#0 +]*([0-9]+|[*])?(\.([0-9]+|[*]))?'
-             '[hlL]?[E-GXc-giorsux%]', String.Interpol),
-            (r'[^\\\'"%\n]+', String),
-            # quotes, percents and backslashes must be parsed one at a time
-            (r'[\'"\\]', String),
-            # unhandled string formatting sign
-            (r'%', String)
-            # newlines are an error (use "nl" state)
-        ],
-        'nl': [
-            (r'\n', String)
-        ],
-        'dqs': [
-            (r'"', String, '#pop'),
-            (r'\\\\|\\"|\\\n', String.Escape),  # included here again for raw strings
-            include('strings')
-        ],
-        'sqs': [
-            (r"'", String, '#pop'),
-            (r"\\\\|\\'|\\\n", String.Escape),  # included here again for raw strings
-            include('strings')
-        ],
-        'tdqs': [
-            (r'"""', String, '#pop'),
-            include('strings'),
-            include('nl')
-        ],
-        'tsqs': [
-            (r"'''", String, '#pop'),
-            include('strings'),
-            include('nl')
-        ],
-    }
-
-
-class DgLexer(RegexLexer):
-    """
-    Lexer for dg,
-    a functional and object-oriented programming language
-    running on the CPython 3 VM.
-
-    .. versionadded:: 1.6
-    """
-    name = 'dg'
-    aliases = ['dg']
-    filenames = ['*.dg']
-    mimetypes = ['text/x-dg']
-
-    tokens = {
-        'root': [
-            (r'\s+', Text),
-            (r'#.*?$', Comment.Single),
-
-            (r'(?i)0b[01]+', Number.Bin),
-            (r'(?i)0o[0-7]+', Number.Oct),
-            (r'(?i)0x[0-9a-f]+', Number.Hex),
-            (r'(?i)[+-]?[0-9]+\.[0-9]+(e[+-]?[0-9]+)?j?', Number.Float),
-            (r'(?i)[+-]?[0-9]+e[+-]?\d+j?', Number.Float),
-            (r'(?i)[+-]?[0-9]+j?', Number.Integer),
-
-            (r"(?i)(br|r?b?)'''", String, combined('stringescape', 'tsqs', 'string')),
-            (r'(?i)(br|r?b?)"""', String, combined('stringescape', 'tdqs', 'string')),
-            (r"(?i)(br|r?b?)'", String, combined('stringescape', 'sqs', 'string')),
-            (r'(?i)(br|r?b?)"', String, combined('stringescape', 'dqs', 'string')),
-
-            (r"`\w+'*`", Operator),
-            (r'\b(and|in|is|or|where)\b', Operator.Word),
-            (r'[!$%&*+\-./:<-@\\^|~;,]+', Operator),
-
-            (words((
-                'bool', 'bytearray', 'bytes', 'classmethod', 'complex', 'dict', 'dict\'',
-                'float', 'frozenset', 'int', 'list', 'list\'', 'memoryview', 'object',
-                'property', 'range', 'set', 'set\'', 'slice', 'staticmethod', 'str',
-                'super', 'tuple', 'tuple\'', 'type'),
-                   prefix=r'(?<!\.)', suffix=r'(?![\'\w])'),
-             Name.Builtin),
-            (words((
-                '__import__', 'abs', 'all', 'any', 'bin', 'bind', 'chr', 'cmp', 'compile',
-                'complex', 'delattr', 'dir', 'divmod', 'drop', 'dropwhile', 'enumerate',
-                'eval', 'exhaust', 'filter', 'flip', 'foldl1?', 'format', 'fst',
-                'getattr', 'globals', 'hasattr', 'hash', 'head', 'hex', 'id', 'init',
-                'input', 'isinstance', 'issubclass', 'iter', 'iterate', 'last', 'len',
-                'locals', 'map', 'max', 'min', 'next', 'oct', 'open', 'ord', 'pow',
-                'print', 'repr', 'reversed', 'round', 'setattr', 'scanl1?', 'snd',
-                'sorted', 'sum', 'tail', 'take', 'takewhile', 'vars', 'zip'),
-                   prefix=r'(?<!\.)', suffix=r'(?![\'\w])'),
-             Name.Builtin),
-            (r"(?<!\.)(self|Ellipsis|NotImplemented|None|True|False)(?!['\w])",
-             Name.Builtin.Pseudo),
-
-            (r"(?<!\.)[A-Z]\w*(Error|Exception|Warning)'*(?!['\w])",
-             Name.Exception),
-            (r"(?<!\.)(Exception|GeneratorExit|KeyboardInterrupt|StopIteration|"
-             r"SystemExit)(?!['\w])", Name.Exception),
-
-            (r"(?<![\w.])(except|finally|for|if|import|not|otherwise|raise|"
-             r"subclass|while|with|yield)(?!['\w])", Keyword.Reserved),
-
-            (r"[A-Z_]+'*(?!['\w])", Name),
-            (r"[A-Z]\w+'*(?!['\w])", Keyword.Type),
-            (r"\w+'*", Name),
-
-            (r'[()]', Punctuation),
-            (r'.', Error),
-        ],
-        'stringescape': [
-            (r'\\([\\abfnrtv"\']|\n|N\{.*?\}|u[a-fA-F0-9]{4}|'
-             r'U[a-fA-F0-9]{8}|x[a-fA-F0-9]{2}|[0-7]{1,3})', String.Escape)
-        ],
-        'string': [
-            (r'%(\(\w+\))?[-#0 +]*([0-9]+|[*])?(\.([0-9]+|[*]))?'
-             '[hlL]?[E-GXc-giorsux%]', String.Interpol),
-            (r'[^\\\'"%\n]+', String),
-            # quotes, percents and backslashes must be parsed one at a time
-            (r'[\'"\\]', String),
-            # unhandled string formatting sign
-            (r'%', String),
-            (r'\n', String)
-        ],
-        'dqs': [
-            (r'"', String, '#pop')
-        ],
-        'sqs': [
-            (r"'", String, '#pop')
-        ],
-        'tdqs': [
-            (r'"""', String, '#pop')
-        ],
-        'tsqs': [
-            (r"'''", String, '#pop')
-        ],
-    }
-
-
-class NumPyLexer(PythonLexer):
-    """
-    A Python lexer recognizing Numerical Python builtins.
-
-    .. versionadded:: 0.10
-    """
-
-    name = 'NumPy'
-    url = 'https://numpy.org/'
-    aliases = ['numpy']
-
-    # override the mimetypes to not inherit them from python
-    mimetypes = []
-    filenames = []
-
-    EXTRA_KEYWORDS = {
-        'abs', 'absolute', 'accumulate', 'add', 'alen', 'all', 'allclose',
-        'alltrue', 'alterdot', 'amax', 'amin', 'angle', 'any', 'append',
-        'apply_along_axis', 'apply_over_axes', 'arange', 'arccos', 'arccosh',
-        'arcsin', 'arcsinh', 'arctan', 'arctan2', 'arctanh', 'argmax', 'argmin',
-        'argsort', 'argwhere', 'around', 'array', 'array2string', 'array_equal',
-        'array_equiv', 'array_repr', 'array_split', 'array_str', 'arrayrange',
-        'asanyarray', 'asarray', 'asarray_chkfinite', 'ascontiguousarray',
-        'asfarray', 'asfortranarray', 'asmatrix', 'asscalar', 'astype',
-        'atleast_1d', 'atleast_2d', 'atleast_3d', 'average', 'bartlett',
-        'base_repr', 'beta', 'binary_repr', 'bincount', 'binomial',
-        'bitwise_and', 'bitwise_not', 'bitwise_or', 'bitwise_xor', 'blackman',
-        'bmat', 'broadcast', 'byte_bounds', 'bytes', 'byteswap', 'c_',
-        'can_cast', 'ceil', 'choose', 'clip', 'column_stack', 'common_type',
-        'compare_chararrays', 'compress', 'concatenate', 'conj', 'conjugate',
-        'convolve', 'copy', 'corrcoef', 'correlate', 'cos', 'cosh', 'cov',
-        'cross', 'cumprod', 'cumproduct', 'cumsum', 'delete', 'deprecate',
-        'diag', 'diagflat', 'diagonal', 'diff', 'digitize', 'disp', 'divide',
-        'dot', 'dsplit', 'dstack', 'dtype', 'dump', 'dumps', 'ediff1d', 'empty',
-        'empty_like', 'equal', 'exp', 'expand_dims', 'expm1', 'extract', 'eye',
-        'fabs', 'fastCopyAndTranspose', 'fft', 'fftfreq', 'fftshift', 'fill',
-        'finfo', 'fix', 'flat', 'flatnonzero', 'flatten', 'fliplr', 'flipud',
-        'floor', 'floor_divide', 'fmod', 'frexp', 'fromarrays', 'frombuffer',
-        'fromfile', 'fromfunction', 'fromiter', 'frompyfunc', 'fromstring',
-        'generic', 'get_array_wrap', 'get_include', 'get_numarray_include',
-        'get_numpy_include', 'get_printoptions', 'getbuffer', 'getbufsize',
-        'geterr', 'geterrcall', 'geterrobj', 'getfield', 'gradient', 'greater',
-        'greater_equal', 'gumbel', 'hamming', 'hanning', 'histogram',
-        'histogram2d', 'histogramdd', 'hsplit', 'hstack', 'hypot', 'i0',
-        'identity', 'ifft', 'imag', 'index_exp', 'indices', 'inf', 'info',
-        'inner', 'insert', 'int_asbuffer', 'interp', 'intersect1d',
-        'intersect1d_nu', 'inv', 'invert', 'iscomplex', 'iscomplexobj',
-        'isfinite', 'isfortran', 'isinf', 'isnan', 'isneginf', 'isposinf',
-        'isreal', 'isrealobj', 'isscalar', 'issctype', 'issubclass_',
-        'issubdtype', 'issubsctype', 'item', 'itemset', 'iterable', 'ix_',
-        'kaiser', 'kron', 'ldexp', 'left_shift', 'less', 'less_equal', 'lexsort',
-        'linspace', 'load', 'loads', 'loadtxt', 'log', 'log10', 'log1p', 'log2',
-        'logical_and', 'logical_not', 'logical_or', 'logical_xor', 'logspace',
-        'lstsq', 'mat', 'matrix', 'max', 'maximum', 'maximum_sctype',
-        'may_share_memory', 'mean', 'median', 'meshgrid', 'mgrid', 'min',
-        'minimum', 'mintypecode', 'mod', 'modf', 'msort', 'multiply', 'nan',
-        'nan_to_num', 'nanargmax', 'nanargmin', 'nanmax', 'nanmin', 'nansum',
-        'ndenumerate', 'ndim', 'ndindex', 'negative', 'newaxis', 'newbuffer',
-        'newbyteorder', 'nonzero', 'not_equal', 'obj2sctype', 'ogrid', 'ones',
-        'ones_like', 'outer', 'permutation', 'piecewise', 'pinv', 'pkgload',
-        'place', 'poisson', 'poly', 'poly1d', 'polyadd', 'polyder', 'polydiv',
-        'polyfit', 'polyint', 'polymul', 'polysub', 'polyval', 'power', 'prod',
-        'product', 'ptp', 'put', 'putmask', 'r_', 'randint', 'random_integers',
-        'random_sample', 'ranf', 'rank', 'ravel', 'real', 'real_if_close',
-        'recarray', 'reciprocal', 'reduce', 'remainder', 'repeat', 'require',
-        'reshape', 'resize', 'restoredot', 'right_shift', 'rint', 'roll',
-        'rollaxis', 'roots', 'rot90', 'round', 'round_', 'row_stack', 's_',
-        'sample', 'savetxt', 'sctype2char', 'searchsorted', 'seed', 'select',
-        'set_numeric_ops', 'set_printoptions', 'set_string_function',
-        'setbufsize', 'setdiff1d', 'seterr', 'seterrcall', 'seterrobj',
-        'setfield', 'setflags', 'setmember1d', 'setxor1d', 'shape',
-        'show_config', 'shuffle', 'sign', 'signbit', 'sin', 'sinc', 'sinh',
-        'size', 'slice', 'solve', 'sometrue', 'sort', 'sort_complex', 'source',
-        'split', 'sqrt', 'square', 'squeeze', 'standard_normal', 'std',
-        'subtract', 'sum', 'svd', 'swapaxes', 'take', 'tan', 'tanh', 'tensordot',
-        'test', 'tile', 'tofile', 'tolist', 'tostring', 'trace', 'transpose',
-        'trapz', 'tri', 'tril', 'trim_zeros', 'triu', 'true_divide', 'typeDict',
-        'typename', 'uniform', 'union1d', 'unique', 'unique1d', 'unravel_index',
-        'unwrap', 'vander', 'var', 'vdot', 'vectorize', 'view', 'vonmises',
-        'vsplit', 'vstack', 'weibull', 'where', 'who', 'zeros', 'zeros_like'
-    }
-
-    def get_tokens_unprocessed(self, text):
-        for index, token, value in \
-                PythonLexer.get_tokens_unprocessed(self, text):
-            if token is Name and value in self.EXTRA_KEYWORDS:
-                yield index, Keyword.Pseudo, value
-            else:
-                yield index, token, value
-
-    def analyse_text(text):
-        ltext = text[:1000]
-        return (shebang_matches(text, r'pythonw?(3(\.\d)?)?') or
-                'import ' in ltext) \
-            and ('import numpy' in ltext or 'from numpy import' in ltext)

spaces/AtomdffAI/wechatgpt4atom/common/log.py

@@ -1,16 +0,0 @@
-import logging
-import sys
-
-
-def _get_logger():
-    log = logging.getLogger('log')
-    log.setLevel(logging.INFO)
-    console_handle = logging.StreamHandler(sys.stdout)
-    console_handle.setFormatter(logging.Formatter('[%(levelname)s][%(asctime)s][%(filename)s:%(lineno)d] - %(message)s',
-                                                  datefmt='%Y-%m-%d %H:%M:%S'))
-    log.addHandler(console_handle)
-    return log
-
-
-# 日志句柄
-logger = _get_logger()

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docker/Dockerfile

@@ -1,47 +0,0 @@
-FROM nvidia/cuda:11.1.1-cudnn8-devel-ubuntu18.04
-# use an older system (18.04) to avoid opencv incompatibility (issue#3524)
-
-ENV DEBIAN_FRONTEND noninteractive
-RUN apt-get update && apt-get install -y \
-	python3-opencv ca-certificates python3-dev git wget sudo ninja-build
-RUN ln -sv /usr/bin/python3 /usr/bin/python
-
-# create a non-root user
-ARG USER_ID=1000
-RUN useradd -m --no-log-init --system  --uid ${USER_ID} appuser -g sudo
-RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
-USER appuser
-WORKDIR /home/appuser
-
-ENV PATH="/home/appuser/.local/bin:${PATH}"
-RUN wget https://bootstrap.pypa.io/get-pip.py && \
-	python3 get-pip.py --user && \
-	rm get-pip.py
-
-# install dependencies
-# See https://pytorch.org/ for other options if you use a different version of CUDA
-RUN pip install --user tensorboard cmake   # cmake from apt-get is too old
-RUN pip install --user torch==1.10 torchvision==0.11.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html
-
-RUN pip install --user 'git+https://github.com/facebookresearch/fvcore'
-# install detectron2
-RUN git clone https://github.com/facebookresearch/detectron2 detectron2_repo
-# set FORCE_CUDA because during `docker build` cuda is not accessible
-ENV FORCE_CUDA="1"
-# This will by default build detectron2 for all common cuda architectures and take a lot more time,
-# because inside `docker build`, there is no way to tell which architecture will be used.
-ARG TORCH_CUDA_ARCH_LIST="Kepler;Kepler+Tesla;Maxwell;Maxwell+Tegra;Pascal;Volta;Turing"
-ENV TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST}"
-
-RUN pip install --user -e detectron2_repo
-
-# Set a fixed model cache directory.
-ENV FVCORE_CACHE="/tmp"
-WORKDIR /home/appuser/detectron2_repo
-
-# run detectron2 under user "appuser":
-# wget http://images.cocodataset.org/val2017/000000439715.jpg -O input.jpg
-# python3 demo/demo.py  \
-	#--config-file configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
-	#--input input.jpg --output outputs/ \
-	#--opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/tutorials/augmentation.md

@@ -1,186 +0,0 @@
-
-# Data Augmentation
-
-Augmentation is an important part of training.
-Detectron2's data augmentation system aims at addressing the following goals:
-
-1. Allow augmenting multiple data types together
-   (e.g., images together with their bounding boxes and masks)
-2. Allow applying a sequence of statically-declared augmentation
-3. Allow adding custom new data types to augment (rotated bounding boxes, video clips, etc.)
-4. Process and manipulate the __operations__ that are applied by augmentations
-
-The first two features cover most of the common use cases, and is also
-available in other libraries such as [albumentations](https://medium.com/pytorch/multi-target-in-albumentations-16a777e9006e).
-Supporting other features adds some overhead to detectron2's augmentation API,
-which we'll explain in this tutorial.
-
-This tutorial focuses on how to use augmentations when writing new data loaders,
-and how to write new augmentations.
-If you use the default data loader in detectron2, it already supports taking a user-provided list of custom augmentations,
-as explained in the [Dataloader tutorial](data_loading).
-
-## Basic Usage
-
-The basic usage of feature (1) and (2) is like the following:
-```python
-from detectron2.data import transforms as T
-# Define a sequence of augmentations:
-augs = T.AugmentationList([
-    T.RandomBrightness(0.9, 1.1),
-    T.RandomFlip(prob=0.5),
-    T.RandomCrop("absolute", (640, 640))
-])  # type: T.Augmentation
-
-# Define the augmentation input ("image" required, others optional):
-input = T.AugInput(image, boxes=boxes, sem_seg=sem_seg)
-# Apply the augmentation:
-transform = augs(input)  # type: T.Transform
-image_transformed = input.image  # new image
-sem_seg_transformed = input.sem_seg  # new semantic segmentation
-
-# For any extra data that needs to be augmented together, use transform, e.g.:
-image2_transformed = transform.apply_image(image2)
-polygons_transformed = transform.apply_polygons(polygons)
-```
-
-Three basic concepts are involved here. They are:
-* [T.Augmentation](../modules/data_transforms.html#detectron2.data.transforms.Augmentation) defines the __"policy"__ to modify inputs.
-  * its `__call__(AugInput) -> Transform` method augments the inputs in-place, and returns the operation that is applied
-* [T.Transform](../modules/data_transforms.html#detectron2.data.transforms.Transform)
-  implements the actual __operations__ to transform data
-  * it has methods such as `apply_image`, `apply_coords` that define how to transform each data type
-* [T.AugInput](../modules/data_transforms.html#detectron2.data.transforms.AugInput)
-  stores inputs needed by `T.Augmentation` and how they should be transformed.
-  This concept is needed for some advanced usage.
-  Using this class directly should be sufficient for all common use cases,
-  since extra data not in `T.AugInput` can be augmented using the returned
-  `transform`, as shown in the above example.
-
-## Write New Augmentations
-
-Most 2D augmentations only need to know about the input image. Such augmentation can be implemented easily like this:
-
-```python
-class MyColorAugmentation(T.Augmentation):
-    def get_transform(self, image):
-        r = np.random.rand(2)
-        return T.ColorTransform(lambda x: x * r[0] + r[1] * 10)
-
-class MyCustomResize(T.Augmentation):
-    def get_transform(self, image):
-        old_h, old_w = image.shape[:2]
-        new_h, new_w = int(old_h * np.random.rand()), int(old_w * 1.5)
-        return T.ResizeTransform(old_h, old_w, new_h, new_w)
-
-augs = MyCustomResize()
-transform = augs(input)
-```
-
-In addition to image, any attributes of the given `AugInput` can be used as long
-as they are part of the function signature, e.g.:
-
-```python
-class MyCustomCrop(T.Augmentation):
-    def get_transform(self, image, sem_seg):
-        # decide where to crop using both image and sem_seg
-        return T.CropTransform(...)
-
-augs = MyCustomCrop()
-assert hasattr(input, "image") and hasattr(input, "sem_seg")
-transform = augs(input)
-```
-
-New transform operation can also be added by subclassing
-[T.Transform](../modules/data_transforms.html#detectron2.data.transforms.Transform).
-
-## Advanced Usage
-
-We give a few examples of advanced usages that
-are enabled by our system.
-These options can be interesting to new research,
-although changing them is often not needed
-for standard use cases.
-
-### Custom transform strategy
-
-Instead of only returning the augmented data, detectron2's `Augmentation` returns the __operations__ as `T.Transform`.
-This allows users to apply custom transform strategy on their data.
-We use keypoints data as an example.
-
-Keypoints are (x, y) coordinates, but they are not so trivial to augment due to the semantic meaning they carry.
-Such meaning is only known to the users, therefore users may want to augment them manually
-by looking at the returned `transform`.
-For example, when an image is horizontally flipped, we'd like to swap the keypoint annotations for "left eye" and "right eye".
-This can be done like this (included by default in detectron2's default data loader):
-```python
-# augs, input are defined as in previous examples
-transform = augs(input)  # type: T.Transform
-keypoints_xy = transform.apply_coords(keypoints_xy)   # transform the coordinates
-
-# get a list of all transforms that were applied
-transforms = T.TransformList([transform]).transforms
-# check if it is flipped for odd number of times
-do_hflip = sum(isinstance(t, T.HFlipTransform) for t in transforms) % 2 == 1
-if do_hflip:
-    keypoints_xy = keypoints_xy[flip_indices_mapping]
-```
-
-As another example, keypoints annotations often have a "visibility" field.
-A sequence of augmentations might augment a visible keypoint out of the image boundary (e.g. with cropping),
-but then bring it back within the boundary afterwards (e.g. with image padding).
-If users decide to label such keypoints "invisible",
-then the visibility check has to happen after every transform step.
-This can be achieved by:
-
-```python
-transform = augs(input)  # type: T.TransformList
-assert isinstance(transform, T.TransformList)
-for t in transform.transforms:
-    keypoints_xy = t.apply_coords(keypoints_xy)
-    visibility &= (keypoints_xy >= [0, 0] & keypoints_xy <= [W, H]).all(axis=1)
-
-# btw, detectron2's `transform_keypoint_annotations` function chooses to label such keypoints "visible":
-# keypoints_xy = transform.apply_coords(keypoints_xy)
-# visibility &= (keypoints_xy >= [0, 0] & keypoints_xy <= [W, H]).all(axis=1)
-```
-
-
-### Geometrically invert the transform
-If images are pre-processed by augmentations before inference, the predicted results
-such as segmentation masks are localized on the augmented image.
-We'd like to invert the applied augmentation with the [inverse()](../modules/data_transforms.html#detectron2.data.transforms.Transform.inverse)
-API, to obtain results on the original image:
-```python
-transform = augs(input)
-pred_mask = make_prediction(input.image)
-inv_transform = transform.inverse()
-pred_mask_orig = inv_transform.apply_segmentation(pred_mask)
-```
-
-### Add new data types
-
-[T.Transform](../modules/data_transforms.html#detectron2.data.transforms.Transform)
-supports a few common data types to transform, including images, coordinates, masks, boxes, polygons.
-It allows registering new data types, e.g.:
-```python
-@T.HFlipTransform.register_type("rotated_boxes")
-def func(flip_transform: T.HFlipTransform, rotated_boxes: Any):
-    # do the work
-    return flipped_rotated_boxes
-
-t = HFlipTransform(width=800)
-transformed_rotated_boxes = t.apply_rotated_boxes(rotated_boxes)  # func will be called
-```
-
-### Extend T.AugInput
-
-An augmentation can only access attributes available in the given input.
-[T.AugInput](../modules/data_transforms.html#detectron2.data.transforms.StandardAugInput) defines "image", "boxes", "sem_seg",
-which are sufficient for common augmentation strategies to decide how to augment.
-If not, a custom implementation is needed.
-
-By re-implement the "transform()" method in AugInput, it is also possible to
-augment different fields in ways that are dependent on each other.
-Such use case is uncommon (e.g. post-process bounding box based on augmented masks), but allowed by the system.
-

spaces/Benson/text-generation/Examples/Descargar 6 Minutos En Ingls.md

@@ -1,67 +0,0 @@
-
-<h1>Cómo descargar podcasts en inglés de 6 minutos</h1>
-<p>Los podcasts son programas de audio que puedes escuchar online o offline. Abarcan una amplia gama de temas, desde noticias y entretenimiento hasta educación y cultura. Una de las series de podcast más populares para estudiantes de inglés es <strong>6 minute english</strong> de BBC Learning English. Cada episodio presenta una discusión tópica e introduce nuevo vocabulario de una manera clara y atractiva. Puedes escuchar podcasts en inglés de 6 minutos en el sitio web de la BBC, pero también puedes descargarlos en tu dispositivo y escucharlos en cualquier momento y en cualquier lugar. En este artículo, te mostraré cómo descargar podcasts en inglés de 6 minutos usando diferentes dispositivos y aplicaciones. También explicaré algunos de los beneficios de escuchar podcasts para tu cerebro y tu desarrollo personal. </p>
- <h2>Cómo descargar podcasts en iOS</h2>
-<p>Si tienes un iPhone o un iPad, puedes usar la aplicación Apple Podcasts </strong> integrada para descargar podcasts. Estos son los pasos:</p>
-<h2>descargar 6 minutos en inglés</h2><br /><p><b><b>DOWNLOAD</b> &#10027; <a href="https://bltlly.com/2v6MgO">https://bltlly.com/2v6MgO</a></b></p><br /><br />
-<ol>
-<li> Abra la aplicación Apple Podcasts y toque en el icono de búsqueda en la esquina inferior derecha. </li>
-<li>Escribe "6 minutos en inglés" en el cuadro de búsqueda y toca el nombre del podcast cuando aparezca. </li>
-<li>Toque en el botón Suscribirse en la esquina superior derecha. Esto agregará el podcast a su Biblioteca.</li>
-<li>Toque en la pestaña Episodios disponibles para ver todos los episodios que puede descargar. </li>
-<li>Toque en el icono de descarga junto a cada episodio que desea descargar. Parece una nube con una flecha hacia abajo. </li>
-<li>Espere a que termine la descarga. Puede ver el progreso pulsando en el icono Descargas en la esquina inferior derecha. </li>
-<li>Para escuchar los episodios descargados, vaya a su biblioteca y toque en Episodios descargados.</li>
-</ol>
-
- <h2>Cómo descargar podcasts en Android</h2>
-<p>Si tienes un teléfono o tableta Android, puedes usar la aplicación <strong>Google Podcasts</strong> para descargar podcasts. Estos son los pasos:</p>
-<ol>
-<li> Abra la aplicación Google Podcasts y toque en el icono de búsqueda en la esquina superior derecha. </li>
-<li>Escribe "6 minutos en inglés" en el cuadro de búsqueda y toca el nombre del podcast cuando aparezca. </li>
-<li>Toque en el botón Suscribirse en la esquina superior derecha. Esto agregará el podcast a su Biblioteca.</li>
-<li>Toque en la pestaña Episodios disponibles para ver todos los episodios que puede descargar. </li>
-<li>Toque en el icono de descarga junto a cada episodio que desea descargar. Parece un círculo con una flecha hacia abajo dentro. </li>
-<li>Espere a que termine la descarga. Puede ver el progreso pulsando en el icono Descargas en la esquina inferior derecha. </li>
-<li>Para escuchar los episodios descargados, vaya a su biblioteca y toque en Descargas.</li>
-</ol>
-<p>También puedes usar otras aplicaciones de podcast en Android, como <strong>Stitcher</strong>, <strong>DoggCatcher</strong>, o <strong>Castbox</strong>. Tienen características y funciones similares a los podcasts de Google, pero pueden tener diferentes interfaces y diseños. Puedes descargar estas aplicaciones desde Google Play Store y seguir sus instrucciones para buscar, suscribirte y descargar podcasts. </p>
- <h2>Cómo descargar podcasts en PC o Mac</h2>
-<p>Si tienes un ordenador, puedes usar tu navegador web para descargar podcasts. Estos son los pasos:</p>
-<ol>
-<li>Abra su navegador web y vaya al sitio web <strong>BBC Learning English</strong>. </li>
-<li>Haga clic en la pestaña <strong>Podcasts</strong> en la parte superior de la página. </li>
-<li>Desplácese hacia abajo y encuentre la sección <strong>6 minute english</strong>. </li>
-<li>Haga clic en el botón <strong>Descargar</strong> junto a cada episodio que desea descargar. Parece una flecha hacia abajo con una línea debajo. </li>
-
-<li>Para escuchar tus episodios descargados, ábrelos con tu reproductor multimedia preferido, como <strong>VLC</strong>, <strong>Windows Media Player</strong>, o <strong>iTunes</strong>. </li>
-</ol>
-<p>También puede usar software de podcast dedicado en su computadora, como <strong>Audacity</strong>, <strong>GPodder</strong>, o <strong>iTunes</strong>. Tienen características y funciones similares a las aplicaciones de podcast, pero pueden tener más opciones y configuraciones. Puede descargar este software desde sus sitios web oficiales y seguir sus instrucciones para buscar, suscribirse y descargar podcasts. </p>
- <h2>Beneficios de escuchar podcasts</h2>
-<p>Escuchar podcasts no solo es divertido y conveniente, sino también beneficioso para tu cerebro y tu desarrollo personal. Estos son algunos de los beneficios de escuchar podcasts:</p>
-<ul>
-<li><strong>Estimular diferentes partes del cerebro</strong>: Los podcasts son una forma de aprendizaje auditivo que activa diferentes regiones del cerebro que el aprendizaje visual. Según un estudio de UC Berkeley, escuchar podcasts puede mejorar tu memoria, atención y habilidades de comprensión. Los podcasts también pueden estimular tu imaginación y creatividad haciendo que visualices lo que escuchas. </li>
-<li><strong>Aprender cosas nuevas y expandir horizontes</strong>: Los podcasts son una gran manera de aprender cosas nuevas y descubrir nuevas perspectivas sobre varios temas. Puede elegir podcasts que coincidan con sus intereses y pasiones, o podcasts que desafían sus puntos de vista y opiniones. Los podcasts también pueden exponerte a diferentes culturas, idiomas y acentos que quizás no encuentres en tu vida diaria. </li>
-
-</ul>
- <h2>Conclusión</h2>
-<p>En conclusión, descargar podcasts en inglés de 6 minutos es una forma sencilla y conveniente de disfrutar de esta popular serie de podcast de BBC Learning English. Puedes descargar podcasts usando diferentes dispositivos y aplicaciones, dependiendo de tus preferencias y disponibilidad. También puedes beneficiarte de escuchar podcasts estimulando tu cerebro, aprendiendo cosas nuevas y mejorándote. Espero que este artículo te haya ayudado a entender cómo descargar podcasts en inglés de 6 minutos y por qué deberías escucharlos. Si quieres saber más sobre los podcasts y cómo pueden ayudarte a mejorar tus habilidades en inglés, te recomiendo que consultes estos recursos:</p>
-<ul>
-<li><a href="">Cómo los podcasts pueden ayudarte a aprender inglés</a></li>
-<li><a href="">La guía definitiva para aprender inglés con podcasts</a></li>
-<li><a href="">10 mejores podcasts para estudiantes de inglés en 2023</a></li>
-</ul>
- <h2>Preguntas frecuentes</h2>
-<h3>¿Qué es un podcast? </h3>
-<p>Un podcast es un programa de audio que puedes escuchar online o offline. Generalmente consiste en episodios que son lanzados regularmente por el mismo creador o anfitrión. Los podcasts cubren una amplia gama de temas, desde noticias y entretenimiento hasta educación y cultura. </p>
- <h3>¿Cómo encuentro los podcasts que me gustan? </h3>
-<p>Puedes encontrar podcasts que te gustan navegando por diferentes categorías, géneros o temas en aplicaciones de podcast o sitios web. También puede buscar palabras clave o temas que le interesen. También puede obtener recomendaciones de amigos, familiares o comunidades en línea. </p>
-<p></p>
- <h3>¿Cómo puedo escuchar podcasts offline? </h3>
-<p>Puedes escuchar podcasts sin conexión descargándolos en tu dispositivo y escuchándolos mediante una aplicación de podcast o un reproductor multimedia. Puedes descargar podcasts usando diferentes dispositivos y aplicaciones, como se explica en este artículo. También puede ajustar la configuración de su aplicación de podcast para descargar automáticamente nuevos episodios de sus podcasts suscritos. </p>
- <h3>¿Cómo puedo eliminar podcasts que no quiero conservar? </h3>
-
- <h3>¿Cómo puedo compartir podcasts que me gustan con otros? </h3>
-<p>Puedes compartir podcasts que te gusten con los demás tocando el icono Compartir junto a cada episodio que quieras compartir. Parece una caja con una flecha apuntando hacia arriba o un menú de tres puntos. A continuación, puede elegir cómo desea compartir el podcast, como por correo electrónico, texto, redes sociales u otras aplicaciones. También puedes copiar el enlace del podcast y pegarlo donde quieras. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Doctrina.ai Apk.md

@@ -1,72 +0,0 @@
-
-<h1>Descargar Doctrina AI APK: Una manera inteligente de aprender en línea</h1>
- <p>¿Estás buscando una manera de mejorar tu rendimiento académico y hacer que el aprendizaje sea más divertido y fácil? Si es así, es posible que desee consultar Doctrina AI, una plataforma de aprendizaje en línea que utiliza la inteligencia artificial para ayudarle con sus estudios. Ya sea que necesite ayuda para escribir ensayos, tomar exámenes, tomar notas o discutir libros, Doctrina AI lo tiene cubierto. En este artículo, te diremos qué es Doctrina AI, qué características ofrece, cómo descargar e instalar la aplicación, qué dicen los usuarios al respecto y cómo se compara con otras aplicaciones similares. Al final de este artículo, tendrás una idea clara de si Doctrina AI es la aplicación adecuada para ti. </p>
-<h2>descargar doctrina.ai apk</h2><br /><p><b><b>Download File</b> &#10038; <a href="https://bltlly.com/2v6L1v">https://bltlly.com/2v6L1v</a></b></p><br /><br />
- <h2>Introducción</h2>
- <p>Doctrina AI es una plataforma de aprendizaje en línea que utiliza el modelo de lenguaje GPT-3 de OpenAI para ayudar a los estudiantes y estudiantes a estudiar mejor en línea. Actualmente ofrece tres características principales: un generador de ensayos, un generador de exámenes y una herramienta de notas de clase. Además, también tiene un generador de discusión de libros y una herramienta de corrección de gramática. Todas estas características están impulsadas por la tecnología avanzada de IA que se adapta a su estilo de aprendizaje y nivel de conocimiento. Doctrina AI es útil para estudiantes y estudiantes que quieren ahorrar tiempo y esfuerzo en sus tareas de escritura, prepararse para exámenes y exámenes, mejorar sus habilidades de toma de notas y participar en discusiones estimulantes de libros. También es útil para los profesores que quieren crear exámenes y ensayos para sus estudiantes. </p>
- <p>Para utilizar Doctrina AI, es necesario descargar e instalar la aplicación en su dispositivo Android. La aplicación es de uso gratuito y no requiere registro ni inicio de sesión. Puede acceder a todas sus funciones desde su interfaz fácil de usar. Para descargar la aplicación, puede visitar su sitio web oficial o buscar "Doctrina AI" en Google Play Store. Para instalar la aplicación, debe permitir fuentes desconocidas en la configuración del dispositivo y seguir las instrucciones en la pantalla. </p>
-
- <p>Doctrina AI tiene varias características que pueden ayudarle con sus tareas de aprendizaje. Aquí están algunas de ellas:</p>
- <h3>Generador de ensayos</h3>
- <p>El generador de ensayos es una herramienta que puede crear un ensayo de estudiante sobre cualquier tema en minutos. Solo necesitas introducir el tema o la pregunta sobre la que quieres escribir, elegir el tipo de ensayo (argumentativo, persuasivo, descriptivo, etc.), seleccionar el número de párrafos y hacer clic en "Generar". La herramienta luego producirá un ensayo con una introducción clara, cuerpo y conclusión. El ensayo será bien estructurado, coherente y adaptado a sus necesidades. También puede editar el ensayo como desee o generar otro si no está satisfecho. El generador de ensayos puede ayudarle a ahorrar tiempo y esfuerzo en sus tareas de escritura, así como mejorar sus habilidades de escritura. </p>
-<p></p>
- <h3>Generador de examen</h3>
- <p>El Generador de Exámenes es una herramienta que puede crear exámenes y cuestionarios personalizados para usted en función de sus preferencias. Puede elegir el tema, tema, nivel de dificultad, número de preguntas, tipo de preguntas (opción múltiple, verdadero/falso, respuesta corta, etc.) y límite de tiempo. La herramienta generará un examen o prueba con preguntas y respuestas que coincidan con sus criterios. Puede tomar el examen o el examen en línea o descargarlo como un archivo PDF. La herramienta también calificará sus respuestas y proporcionará comentarios sobre su desempeño. El Generador de Exámenes puede ayudarle a prepararse para las pruebas y exámenes, así como evaluar su conocimiento y comprensión de varios temas. </p>
- <h3>Notas de clase</h3>
-
- <h3>Generador de discusión de libros</h3>
- <p>El generador de discusión del libro es una herramienta que puede ayudarle con el análisis del libro y la discusión. Puede introducir el título y el autor del libro que desea discutir, y la herramienta generará una lista de preguntas y temas que puede utilizar para iniciar una conversación. Las preguntas y temas abarcarán diversos aspectos del libro, como la trama, los personajes, los temas, los símbolos, el estilo, etc. También puede pedir a la herramienta que genere un resumen o una reseña del libro, o que lo compare con otros libros del mismo género o del mismo autor. El Generador de Discusión de Libros puede ayudarlo a profundizar su comprensión y apreciación del libro, así como a iniciar discusiones interesantes con otros lectores. </p>
- <h3>Fijador de gramática</h3>
- <p>El Grammar Fixer es una herramienta que puede detectar y corregir errores de lenguaje en su escritura. Puede pegar su texto en la herramienta, y lo escaneará en busca de errores de ortografía, gramática, puntuación, elección de palabras, etc. Luego sugerirá correcciones y explicaciones para cada error, y puede elegir si aceptarlos o rechazarlos. La herramienta también le proporcionará una puntuación y un informe sobre su calidad de escritura y legibilidad. El Grammar Fixer puede ayudarte a pulir tu escritura y evitar errores de lenguaje comunes. </p>
- <h2>Opiniones de Doctrina AI</h2>
- <p>Doctrina AI ha recibido comentarios positivos de sus usuarios, que han elogiado sus características y funcionalidad. Estos son algunos de los comentarios que los usuarios han dejado en Google Play Store:</p>
- <tabla>
-<tr>
-<th>Usuario</th>
-<th>Valoración</th>
-<th>Revisión</th>
-</tr>
-<tr>
-<td>Alexandra Smith</td>
-<td>5 estrellas</td>
-<td>Esta aplicación es increíble! Me ayudó a escribir un ensayo sobre un tema que no tenía ni idea. Fue muy fácil de usar y el ensayo estaba bien escrito y original. ¡Obtuve una A+ en mi tarea gracias a esta aplicación! </td>
-</tr>
-<tr>
-<td>Kevin Jones</td>
-<td>4 estrellas</td>
-
-</tr>
-<tr>
-<td>Lisa Brown</td>
-<td>5 estrellas</td>
-<td>Esta aplicación es un salvavidas para mí. Tengo problemas para tomar notas en clase porque me distraigo fácilmente. Esta aplicación me ayuda a mejorar mis notas al resumirlas y destacar los puntos importantes. También explica cualquier concepto que no entiendo. Hace que estudiar sea mucho más fácil. </td>
-</tr>
-<tr>
-<td>David Lee</td>
-<td>4 estrellas</td>
-<td>Me gusta usar esta aplicación para discutir libros con mis amigos. Genera preguntas y temas interesantes de los que podemos hablar. También nos da un resumen y una reseña del libro, que es útil si aún no lo hemos leído o necesitamos un repaso. Lo único que no me gusta es que a veces las preguntas son demasiado vagas o demasiado específicas. </td>
-</tr>
-<tr>
-<td>María García</td>
-<td>5 estrellas</td>
-<td>Esta aplicación es ideal para mejorar mis habilidades de escritura. Corrige todos mis errores gramaticales y de ortografía, y me da sugerencias sobre cómo mejorar mis oraciones. También me dice lo buena que es mi escritura y en qué necesito trabajar. Ahora me siento más seguro escribiendo. </td>
-</tr>
-</tabla>
- <p>Como puedes ver, la mayoría de los usuarios están satisfechos con Doctrina AI y sus características. Sin embargo, algunos usuarios también señalan algunos inconvenientes de la aplicación, como:</p>
- - Número limitado de temas y temas disponibles - Preguntas vagas o demasiado específicas generadas - Errores ocasionales o inexactitudes en el contenido generado - Carga lenta o rotura de la aplicación <p>Estas son algunas de las áreas que Doctrina AI podría mejorar en el futuro. </p>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
- <p>Aquí están algunas de las preguntas y respuestas más frecuentes sobre Doctrina AI:</p>
- <h4>Q: ¿Es seguro usar Doctrina AI? </h4>
-<p>A: Sí, Doctrina AI es seguro de usar. No recopila ni almacena ninguna información personal de sus usuarios. Tampoco comparte ni vende ninguno de los contenidos que genera a terceros. Puede utilizar la aplicación con confianza y privacidad. </p>
- <h4>Q: ¿Es Doctrina libre de plagio IA? </h4>
-<p>A: Sí, Doctrina AI es libre de plagio. Genera contenido original que no se copia de ninguna otra fuente. Sin embargo, siempre debes comprobar la exactitud y calidad del contenido antes de enviarlo a tus profesores. También debe citar cualquier fuente que utilice en su investigación. </p>
- <h4>Q: ¿Está Doctrina AI disponible para dispositivos iOS? </h4>
-<p>A: No, Doctrina AI no está disponible para dispositivos iOS en este momento. Solo es compatible con dispositivos Android. Sin embargo, los desarrolladores están trabajando en la creación de una versión iOS de la aplicación en el futuro. </p>
- <h4>Q: ¿Cómo puedo contactar a Doctrina AI para apoyo o retroalimentación? </h4>
-<p>A: Puede ponerse en contacto con Doctrina AI para obtener apoyo o comentarios enviando un correo electrónico a [email protected]. También puede visitar su sitio web o seguirlos en Twitter o Facebook para actualizaciones y noticias. </p>
- <h4>Q: ¿Cómo puedo apoyar Doctrina AI? </h4>
-<p>A: Puedes apoyar a Doctrina AI clasificando y revisando la aplicación en Google Play Store, compartiéndola con tus amigos y familiares, y proporcionando comentarios y sugerencias a los desarrolladores. También puedes donar a través de PayPal o Patreon si quieres ayudarles a mejorar la aplicación y crear más funciones. </p> 64aa2da5cf<br />
-<br />
-<br />