Update parquet files (step 3 of 296)

spaces/1368565466ki/Satdia/commons.py

@@ -1,172 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
-  return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
-  return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm

spaces/17TheWord/RealESRGAN/Training.md

@@ -1,100 +0,0 @@
-# :computer: How to Train Real-ESRGAN
-
-The training codes have been released. <br>
-Note that the codes have a lot of refactoring. So there may be some bugs/performance drops. Welcome to report issues and I will also retrain the models.
-
-## Overview
-
-The training has been divided into two stages. These two stages have the same data synthesis process and training pipeline, except for the loss functions. Specifically,
-
-1. We first train Real-ESRNet with L1 loss from the pre-trained model ESRGAN.
-1. We then use the trained Real-ESRNet model as an initialization of the generator, and train the Real-ESRGAN with a combination of L1 loss, perceptual loss and GAN loss.
-
-## Dataset Preparation
-
-We use DF2K (DIV2K and Flickr2K) + OST datasets for our training. Only HR images are required. <br>
-You can download from :
-
-1. DIV2K: http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip
-2. Flickr2K: https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar
-3. OST: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/datasets/OST_dataset.zip
-
-For the DF2K dataset, we use a multi-scale strategy, *i.e.*, we downsample HR images to obtain several Ground-Truth images with different scales.
-
-We then crop DF2K images into sub-images for faster IO and processing.
-
-You need to prepare a txt file containing the image paths. The following are some examples in `meta_info_DF2Kmultiscale+OST_sub.txt` (As different users may have different sub-images partitions, this file is not suitable for your purpose and you need to prepare your own txt file):
-
-```txt
-DF2K_HR_sub/000001_s001.png
-DF2K_HR_sub/000001_s002.png
-DF2K_HR_sub/000001_s003.png
-...
-```
-
-## Train Real-ESRNet
-
-1. Download pre-trained model [ESRGAN](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth) into `experiments/pretrained_models`.
-    ```bash
-    wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth -P experiments/pretrained_models
-    ```
-1. Modify the content in the option file `options/train_realesrnet_x4plus.yml` accordingly:
-    ```yml
-    train:
-        name: DF2K+OST
-        type: RealESRGANDataset
-        dataroot_gt: datasets/DF2K  # modify to the root path of your folder
-        meta_info: realesrgan/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt  # modify to your own generate meta info txt
-        io_backend:
-            type: disk
-    ```
-1. If you want to perform validation during training, uncomment those lines and modify accordingly:
-    ```yml
-      # Uncomment these for validation
-      # val:
-      #   name: validation
-      #   type: PairedImageDataset
-      #   dataroot_gt: path_to_gt
-      #   dataroot_lq: path_to_lq
-      #   io_backend:
-      #     type: disk
-
-    ...
-
-      # Uncomment these for validation
-      # validation settings
-      # val:
-      #   val_freq: !!float 5e3
-      #   save_img: True
-
-      #   metrics:
-      #     psnr: # metric name, can be arbitrary
-      #       type: calculate_psnr
-      #       crop_border: 4
-      #       test_y_channel: false
-    ```
-1. Before the formal training, you may run in the `--debug` mode to see whether everything is OK. We use four GPUs for training:
-    ```bash
-    CUDA_VISIBLE_DEVICES=0,1,2,3 \
-    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --debug
-    ```
-1. The formal training. We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary.
-    ```bash
-    CUDA_VISIBLE_DEVICES=0,1,2,3 \
-    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --auto_resume
-    ```
-
-## Train Real-ESRGAN
-
-1. After the training of Real-ESRNet, you now have the file `experiments/train_RealESRNetx4plus_1000k_B12G4_fromESRGAN/model/net_g_1000000.pth`. If you need to specify the pre-trained path to other files, modify the `pretrain_network_g` value in the option file `train_realesrgan_x4plus.yml`.
-1. Modify the option file `train_realesrgan_x4plus.yml` accordingly. Most modifications are similar to those listed above.
-1. Before the formal training, you may run in the `--debug` mode to see whether everything is OK. We use four GPUs for training:
-    ```bash
-    CUDA_VISIBLE_DEVICES=0,1,2,3 \
-    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --debug
-    ```
-1. The formal training. We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary.
-    ```bash
-    CUDA_VISIBLE_DEVICES=0,1,2,3 \
-    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --auto_resume
-    ```

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Adobe After Effects CS6 Free Download with Crack 64 Bit Kickass Learn the Secrets of Professional Video Editing and Motion Graphics.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Game Shark Ps2 V6 Iso717 The Best Way to Cheat in PS2 Games.md

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- <p>If you are a fan of PlayStation 2 games, you might have heard of Game Shark Ps2 V6 Iso717. This is a cheat device or software that allows you to modify or enhance your gaming experience by unlocking hidden features, codes, or cheats for your PS2 games. With Game Shark Ps2 V6 Iso717, you can access unlimited lives, ammo, health, money, weapons, items, and more in your favorite PS2 games.</p>
- <p>Game Shark Ps2 V6 Iso717 is an ISO file that you can download and burn onto a CD or DVD. You can then insert the disc into your PS2 console and run the software. The software will scan your memory card and detect the games that you have saved. You can then select the game that you want to play and choose from a list of cheats that are available for that game. You can also create your own custom cheats by using the code generator feature.</p>
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- <h2>How to use Game Shark Ps2 V6 Iso717?</h2>
- <p>Using Game Shark Ps2 V6 Iso717 is easy and simple. Here are the steps that you need to follow:</p>
- <ol>
-<li>Download Game Shark Ps2 V6 Iso717 from one of the sources that we will mention later in this article.</li>
-<li>Burn the ISO file onto a CD or DVD using a software like Nero or ImgBurn.</li>
-<li>Insert the disc into your PS2 console and turn it on.</li>
-<li>The software will load automatically and display a menu with various options.</li>
-<li>Select "Start Game" and then choose "With Codes" or "Without Codes".</li>
-<li>The software will scan your memory card and show you a list of games that you have saved.</li>
-<li>Select the game that you want to play and press X.</li>
-<li>The software will show you a list of cheats that are available for that game.</li>
-<li>Select the cheats that you want to activate and press X.</li>
-<li>Press Start to begin playing the game with the cheats enabled.</li>
-</ol>
- <p>You can also create your own custom cheats by using the code generator feature. To do this, follow these steps:</p>
- <ol>
-<li>Select "Expert Mode" from the main menu.</li>
-<li>Select "Code Generator" from the sub-menu.</li>
-<li>Select the game that you want to create cheats for and press X.</li>
-<li>The software will show you a list of values that correspond to different aspects of the game, such as health, ammo, money, etc.</li>
-<li>Select the value that you want to modify and press X.</li>
-<li>The software will show you a list of possible codes that can change that value.</li>
-<li>Select the code that you want to use and press X.</li>
-<li>The software will ask you to name your cheat and save it on your memory card.</li>
-<li>You can then activate your custom cheat by selecting it from the list of cheats for that game.</li>
-</ol>
- <h3>What are the benefits of using Game Shark Ps2 V6 Iso717?</h3>
- <p>There are many benefits of using Game Shark Ps2 V6 Iso717 for your PS2 games. Some of them are:</p>
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-<li>You can enjoy playing your PS2 games without any limitations or restrictions.</li>
-<li>You can unlock hidden features, modes, levels, characters, or items that are normally inaccessible in your PS2 games.</li>
-<li>You can enhance your gaming experience by increasing your performance, skills, abilities, or stats in your PS2 games.</li>
-<li>You can experiment with different combinations of cheats and codes to create new and fun scenarios in your PS2 games.</li>
-<li>You can save time and effort by skipping difficult or boring parts of your PS2 games.</li>
-</ul>
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- <p>While using Game Shark Ps2 V6 Iso717 can be fun and exciting, there are also some drawbacks or risks that you should be aware of. Some of them are:</p>
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-<li>You might lose interest or challenge in playing your PS2 games if you use too many cheats or codes.</li>
-<li>You might encounter glitches, errors, bugs, or crashes in your PS2 games if you use incompatible or faulty cheats or codes.</li>
-<li>You might damage your PS2 console or memory card if you use low-quality or corrupted discs or files.</li>
-<li>You might violate the terms and conditions of your PS2 games or console if you use unauthorized or illegal cheats or codes.</li>
-</ul>
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- <p>There are many sources where you can download Game Shark Ps2 V6 Iso717 online. However, not all of them are reliable or safe. Some of them might contain viruses, malware, spyware, adware, or other harmful programs that can harm your computer or device. Some of them might also contain fake, incomplete, outdated, or corrupted files that can damage your PS2 console or memory card. Therefore, you should be careful and selective when choosing where to download Game Shark Ps2 V6 Iso717 from. Here is a table of some of the best sources where you can download Game Shark Ps2 V6 Iso717 from:</p>
- <table style="border-collapse: collapse; width: 100%;">
-<tbody>
-<tr style="height: 23px;">
-<td style="width: 33.3333%; height: 23px; text-align: center;"><strong>Source</strong></td>
-<td style="width: 33.3333%; height: 23px; text-align: center;"><strong>Pros</strong></td>
-<td style="width: 33.3333%; height: 23px; text-align: center;"><strong>Cons</strong></td>
-</tr>
-<tr style="height: 23px;">
-<td style="width: 33.3333%; height: 23px; text-align: center;">CoolROM.com</td>
-<td style="width: 33.3333%; height: 23px; text-align: center;">- One of the most popular and trusted sites for downloading ROMs and ISOs.<br />- Has a large collection of PS2 games and cheat devices.<br />- Provides detailed information and screenshots for each file.<br />- Allows users to rate and review each file.<br />- Has a fast and easy download process.<br /></td>
-<td style="width: 33.3333%; height: 23px; text-align: center;">- Some files might require additional software or tools to extract or burn.<br />- Some files might have broken links or missing parts.<br />- Some files might be region-locked or incompatible with certain consoles.<br /></td>
-</tr>
-<tr style="height: 23px;">
-<td style="width: 33.3333%; height: 23px; text-align: center;">OpenSea.io</td>
-width: 33.3333%; height: 23px; text-align: center;">- A platform for buying and selling digital collectibles and NFTs.<br />- Has a collection of Game Shark Ps2 V6 Iso717 NFTs that are verified and authentic.<br />- Provides a secure and transparent transaction process.<br />- Allows users to bid and negotiate prices.<br />- Has a user-friendly and interactive interface.<br /></td>
-<td style="width: 33.3333%; height: 23px; text-align: center;">- Requires users to have a cryptocurrency wallet and account.<br />- Charges fees for each transaction.<br />- Has a limited supply and availability of Game Shark Ps2 V6 Iso717 NFTs.<br /></td>
-</tr>
-<tr style="height: 23px;">
-<td style="width: 33.3333%; height: 23px; text-align: center;">Netlify.app</td>
-<td style="width: 33.3333%; height: 23px; text-align: center;">- A platform for hosting and deploying websites and web applications.<br />- Has a collection of Game Shark Ps2 V6 Iso717 files that are hosted and shared by other users.<br />- Provides a fast and reliable download speed.<br />- Allows users to preview and test the files before downloading.<br />- Has a simple and minimalist design.<br /></td>
-<td style="width: 33.3333%; height: 23px; text-align: center;">- Some files might be unverified or unsafe.<br />- Some files might be outdated or incompatible with certain consoles.<br />- Some files might have low quality or resolution.<br /></td>
-</tr>
-</tbody>
-</table>
- <h3>How to verify the authenticity and safety of the download?</h3>
- <p>Before you download Game Shark Ps2 V6 Iso717 from any source, you should verify the authenticity and safety of the file. This will help you avoid downloading fake, incomplete, corrupted, or infected files that can harm your computer or device. Here are some tips on how to verify the authenticity and safety of the download:</p>
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-<li>Check the file size, format, and name. The file size should be around 700 MB, the format should be ISO, and the name should be Game Shark Ps2 V6 Iso717 or something similar.</li>
-<li>Check the source reputation, reviews, ratings, and feedback. The source should have a good reputation, positive reviews, high ratings, and helpful feedback from other users.</li>
-<li>Check the virus scan, malware scan, spyware scan, and adware scan. The file should be free of any viruses, malware, spyware, or adware that can harm your computer or device.</li>
-<li>Check the compatibility, region-lock, and update status. The file should be compatible with your PS2 console model and region, not region-locked or restricted to certain countries or areas, and updated to the latest version or patch.</li>
-</ul>
- <h4>What are some alternatives to Game Shark Ps2 V6 Iso717?</h4>
- <p>If you are looking for some alternatives to Game Shark Ps2 V6 Iso717, there are other cheat devices or software that you can use for your PS2 games. Some of them are:</p>
- <table style="border-collapse: collapse; width: 100%;">
-<tbody>
-<tr style="height: 23px;">
-<td style="width: 33.3333%; height: 23px; text-align: center;"><strong>Cheat Device/Software</strong></td>
-<td style="width: 33.3333%; height: 23px; text-align: center;"><strong>Features</strong></td>
-<td style="width: 33.3333%; height: 23px; text-align: center;"><strong>Pros</strong></td>
-<td style="width: 33.3333%; height: 23px; text-align: center;"><strong>Cons</strong></td>
-</tr>
-<tr style="height: 23px;">
-<td style="width: 33.3333%; height: 23px; text-align: center;">Code Breaker</td>
-<td style="width: 33.3333%; height: 23px; text-align: center;">- A cheat device that allows you to modify or enhance your PS2 games by using codes or cheats.<br />- Has a large database of codes for over 1500 PS2 games.<br />- Has a code generator feature that allows you to create your own custom codes.<br />- Has a code saver feature that allows you to save your codes on your memory card.<br /></td>
-<td style="width: 33.3333%; height: 23px; text-align: center;">- Has a user-friendly and intuitive interface.<br />- Has a fast and easy installation and operation process.<br />- Has a high compatibility rate with most PS2 games and consoles.<br /></td>
-<td style="width: 33.3333%; height: 23px; text-align: center;">- Some codes might not work properly or cause glitches in some games.<br />- Some codes might require additional hardware or software to activate.<br />- Some codes might be region-locked or incompatible with certain consoles.<br /></td>
-</tr>
-<tr style="height: 23px;">
-<td style="width: 33.3333%; height: 23px; text-align: center;">Action Replay Max</td>
-<td style="width: 33.3333%; height: 23px; text-align: center;">- A cheat device that allows you to modify or enhance your PS2 games by using codes or cheats.<br />- Has a large database of codes for over 2000 PS2 games.<br />- Has a code generator feature that allows you to create your own custom codes.<br />- Has a code saver feature that allows you to save your codes on your memory card.<br />- Has an online mode that allows you to download new codes from the internet.<br /></td>
-, and alternatives. You can download it from various sources, but you should verify the authenticity and safety of the file before downloading. You can also contact customer support for any help or assistance with the software. You can also update the software to the latest version or patch by following the steps that we explained in this article. We hope that this article has helped you learn more about Game Shark Ps2 V6 Iso717 and how to use it for your PS2 games. <h1>FAQs</h1>
- <p>Here are some of the frequently asked questions about Game Shark Ps2 V6 Iso717:</p>
- <ol>
-<li><strong>What is the difference between Game Shark Ps2 V6 Iso717 and Game Shark Ps2 V4?</strong><br />Game Shark Ps2 V6 Iso717 is an updated version of Game Shark Ps2 V4. It has more codes, features, and compatibility than Game Shark Ps2 V4. It also has a code generator feature that allows you to create your own custom codes.</li>
-<li><strong>Can I use Game Shark Ps2 V6 Iso717 on my PS3 or PS4 console?</strong><br />No, you cannot use Game Shark Ps2 V6 Iso717 on your PS3 or PS4 console. It is only compatible with PS2 consoles and games.</li>
-<li><strong>Can I use Game Shark Ps2 V6 Iso717 on my PC or laptop?</strong><br />Yes, you can use Game Shark Ps2 V6 Iso717 on your PC or laptop if you have a PS2 emulator installed on your device. A PS2 emulator is a software that allows you to run PS2 games on your PC or laptop. You can download a PS2 emulator from various sources online, but you should verify the authenticity and safety of the file before downloading.</li>
-<li><strong>Can I use Game Shark Ps2 V6 Iso717 online or offline?</strong><br />You can use Game Shark Ps2 V6 Iso717 both online and offline. However, you should be careful when using it online, as some games or servers might detect or ban you for using cheats or codes. You should also respect the rules and etiquette of online gaming and not ruin the fun or experience for other players.</li>
-<li><strong>Can I use Game Shark Ps2 V6 Iso717 with other cheat devices or software?</strong><br />Yes, you can use Game Shark Ps2 V6 Iso717 with other cheat devices or software, such as Code Breaker, Action Replay Max, or Free McBoot. However, you should be careful when using multiple cheat devices or software at once, as this might cause conflicts or errors in your PS2 console or games.</li>
-</ol>
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-<p>If you are looking for a collection of songs that are suitable for young people and youth activities, you might be interested in the Sda Youth Song Book. This book contains 214 songs selected especially for Adventist youth ministries, including hymns, choruses, and contemporary songs. All songs are arranged in four-part harmony and are chorded for guitar.</p>
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-import os
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-    consumer_secret = os.environ.get("TW_CONSUMER_SECRET")
-    access_token = os.environ.get("TW_ACCESS_TOKEN")
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-    # Authenticate to Twitter
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-    auth.set_access_token(access_token, access_token_secret)
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-    # Create API object
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-        print("Tweet sent successfully!")
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-<li><strong>More content and value</strong>: You will be able to access more content and features that are not available in the original game. You can extend your gameplay time and get more value for your money.</li>
-</ul>
- <h2>What are the drawbacks of the Beast Quest mod apk 2023?</h2>
-<p>The Beast Quest mod apk 2023 will also have some drawbacks that you should be aware of before downloading it, such as:</p>
-<ul>
-<li><strong>Potential security risks</strong>: You will be downloading and installing an unofficial version of the game from an unknown source. This could expose your device to malware, viruses, or other harmful software. You should always scan any file before opening it.</li>
-<li><strong>Possible compatibility issues</strong>: You will be using a modified version of the game that may not work properly on your device or with your operating system. This could cause crashes, glitches, or errors. You should always backup your data before installing any mod apk.</li>
-<li><strong>Lack of support and updates</strong>: You will not receive any support or updates from the official developers or publishers of the game. This could affect your gameplay experience or cause problems with future versions of the game. You should always check for updates from the mod apk source.</li>
-</ul>
- <h2>How to download and install the Beast Quest mod apk 2023?</h2>
-<p>If you want to try the Beast Quest mod apk 2023, you will need to follow these steps:</p>
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-<ol>
-<li><strong>Find a reliable source</strong>: You will need to find a website or a platform that offers the Beast Quest mod apk 2023 for download. You can search online or ask for recommendations from other players. You should always check the reviews, ratings, and feedback of the source before downloading anything.</li>
-<li><strong>Download the file</strong>: You will need to download the Beast Quest mod apk 2023 file to your device. You should always scan the file for any malware or viruses before opening it. You should also make sure that you have enough storage space on your device.</li>
-<li><strong>Enable unknown sources</strong>: You will need to enable the option to install apps from unknown sources on your device. This will allow you to install the Beast Quest mod apk 2023 without any restrictions. You can usually find this option in your device settings, security settings, or developer options.</li>
-<li><strong>Install the app</strong>: You will need to install the Beast Quest mod apk 2023 on your device. You should follow the instructions on the screen and agree to the terms and conditions. You should also allow the app to access any permissions or resources that it needs.</li>
-<li><strong>Launch the game</strong>: You will need to launch the Beast Quest mod apk 2023 on your device. You should see the modded features and options in the game menu. You can now enjoy the game with the mod apk version.</li>
-</ol>
- <h2>How to uninstall the Beast Quest mod apk 2023?</h2>
-<p>If you want to uninstall the Beast Quest mod apk 2023, you will need to follow these steps:</p>
-<ol>
-<li><strong>Delete the app</strong>: You will need to delete the Beast Quest mod apk 2023 from your device. You can usually do this by long-pressing the app icon and selecting the uninstall option. You can also go to your device settings, apps, and find the app and uninstall it.</li>
-<li><strong>Clear the cache and data</strong>: You will need to clear the cache and data of the Beast Quest mod apk 2023 from your device. This will remove any residual files or settings that may affect your device performance or storage. You can usually do this by going to your device settings, apps, and finding the app and clearing its cache and data.</li>
-<li><strong>Restore the original game</strong>: You will need to restore the original game of Beast Quest on your device. You can do this by downloading and installing it from the official app store. You should be able to play the game without any modded features or options.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>The Beast Quest mod apk 2023 is a modified version of the original game that offers some features, benefits, and drawbacks for players who want to enjoy the game more. It is not an official version of the game and it may have some security, compatibility, or support issues. It is up to you whether you want to try it or not, but you should always be careful and responsible when downloading and installing any mod apk.</p>
- <h2>FAQs</h2>
-<h3>What is Beast Quest?</h3>
-<p>Beast Quest is a mobile game based on the best-selling fantasy novels by Adam Blade. It is an action-adventure game that lets you explore the open world of Avantia, fight against dangerous creatures and giant beasts, collect treasures and artifacts, and upgrade your equipment.</p>
- <h3>Is Beast Quest free to play?</h3>
-<p>Beast Quest is free to download and play, but it also offers some in-app purchases that can enhance your gameplay experience or unlock some premium content.</p>
- <h3>Is Beast Quest mod apk safe?</h3>
-<p>Beast Quest mod apk is not an official version of the game and it may have some security risks. You should always download and install it from a reliable source and scan it for any malware or viruses before opening it.</p>
- <h3>Is Beast Quest mod apk legal?</h3>
-<p>Beast Quest mod apk is not an authorized version of the game and it may violate some terms and conditions of the original game developers or publishers. You should always respect their intellectual property rights and use their products in a fair and ethical way.</p>
- <h3>How can I contact Beast Quest support?</h3>
-<p>If you have any questions or issues with Beast Quest, you can contact their support team by emailing them at [email protected] or visiting their website at https://support.miniclip.com/.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Enjoy the Best Vegas Casino Experience with Lucky Play Casino - Download Now!.md

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spaces/1toTree/lora_test/ppdiffusers/utils/dummy_paddle_and_paddlenlp_and_k_diffusion_objects.py

@@ -1,33 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-# flake8: noqa
-
-from ..utils import DummyObject, requires_backends
-
-
-class StableDiffusionKDiffusionPipeline(metaclass=DummyObject):
-    _backends = ["paddle", "paddlenlp", "k_diffusion"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle", "paddlenlp", "k_diffusion"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle", "paddlenlp", "k_diffusion"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle", "paddlenlp", "k_diffusion"])

spaces/1vash/demo-flask-docker-template/static/style.css

@@ -1,45 +0,0 @@
-body {
-  --text: hsl(0 0% 15%);
-  padding: 2.5rem;
-  font-family: sans-serif;
-  color: var(--text);
-}
-
-body.dark-theme {
-  --text: hsl(0 0% 90%);
-  background-color: hsl(223 39% 7%);
-}
-
-main {
-  max-width: 80rem;
-  text-align: center;
-}
-
-section {
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-}
-
-a {
-  color: var(--text);
-}
-
-form {
-  width: 30rem;
-  margin: 0 auto;
-}
-
-input {
-  width: 100%;
-}
-
-button {
-  cursor: pointer;
-}
-
-.text-gen-output {
-  min-height: 1.2rem;
-  margin: 1rem;
-  border: 0.5px solid grey;
-}

spaces/232labs/VToonify/vtoonify/model/raft/core/utils/augmentor.py

@@ -1,246 +0,0 @@
-import numpy as np
-import random
-import math
-from PIL import Image
-
-import cv2
-cv2.setNumThreads(0)
-cv2.ocl.setUseOpenCL(False)
-
-import torch
-from torchvision.transforms import ColorJitter
-import torch.nn.functional as F
-
-
-class FlowAugmentor:
-    def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=True):
-        
-        # spatial augmentation params
-        self.crop_size = crop_size
-        self.min_scale = min_scale
-        self.max_scale = max_scale
-        self.spatial_aug_prob = 0.8
-        self.stretch_prob = 0.8
-        self.max_stretch = 0.2
-
-        # flip augmentation params
-        self.do_flip = do_flip
-        self.h_flip_prob = 0.5
-        self.v_flip_prob = 0.1
-
-        # photometric augmentation params
-        self.photo_aug = ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.5/3.14)
-        self.asymmetric_color_aug_prob = 0.2
-        self.eraser_aug_prob = 0.5
-
-    def color_transform(self, img1, img2):
-        """ Photometric augmentation """
-
-        # asymmetric
-        if np.random.rand() < self.asymmetric_color_aug_prob:
-            img1 = np.array(self.photo_aug(Image.fromarray(img1)), dtype=np.uint8)
-            img2 = np.array(self.photo_aug(Image.fromarray(img2)), dtype=np.uint8)
-
-        # symmetric
-        else:
-            image_stack = np.concatenate([img1, img2], axis=0)
-            image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
-            img1, img2 = np.split(image_stack, 2, axis=0)
-
-        return img1, img2
-
-    def eraser_transform(self, img1, img2, bounds=[50, 100]):
-        """ Occlusion augmentation """
-
-        ht, wd = img1.shape[:2]
-        if np.random.rand() < self.eraser_aug_prob:
-            mean_color = np.mean(img2.reshape(-1, 3), axis=0)
-            for _ in range(np.random.randint(1, 3)):
-                x0 = np.random.randint(0, wd)
-                y0 = np.random.randint(0, ht)
-                dx = np.random.randint(bounds[0], bounds[1])
-                dy = np.random.randint(bounds[0], bounds[1])
-                img2[y0:y0+dy, x0:x0+dx, :] = mean_color
-
-        return img1, img2
-
-    def spatial_transform(self, img1, img2, flow):
-        # randomly sample scale
-        ht, wd = img1.shape[:2]
-        min_scale = np.maximum(
-            (self.crop_size[0] + 8) / float(ht), 
-            (self.crop_size[1] + 8) / float(wd))
-
-        scale = 2 ** np.random.uniform(self.min_scale, self.max_scale)
-        scale_x = scale
-        scale_y = scale
-        if np.random.rand() < self.stretch_prob:
-            scale_x *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch)
-            scale_y *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch)
-        
-        scale_x = np.clip(scale_x, min_scale, None)
-        scale_y = np.clip(scale_y, min_scale, None)
-
-        if np.random.rand() < self.spatial_aug_prob:
-            # rescale the images
-            img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
-            img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
-            flow = cv2.resize(flow, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
-            flow = flow * [scale_x, scale_y]
-
-        if self.do_flip:
-            if np.random.rand() < self.h_flip_prob: # h-flip
-                img1 = img1[:, ::-1]
-                img2 = img2[:, ::-1]
-                flow = flow[:, ::-1] * [-1.0, 1.0]
-
-            if np.random.rand() < self.v_flip_prob: # v-flip
-                img1 = img1[::-1, :]
-                img2 = img2[::-1, :]
-                flow = flow[::-1, :] * [1.0, -1.0]
-
-        y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0])
-        x0 = np.random.randint(0, img1.shape[1] - self.crop_size[1])
-        
-        img1 = img1[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
-        img2 = img2[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
-        flow = flow[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
-
-        return img1, img2, flow
-
-    def __call__(self, img1, img2, flow):
-        img1, img2 = self.color_transform(img1, img2)
-        img1, img2 = self.eraser_transform(img1, img2)
-        img1, img2, flow = self.spatial_transform(img1, img2, flow)
-
-        img1 = np.ascontiguousarray(img1)
-        img2 = np.ascontiguousarray(img2)
-        flow = np.ascontiguousarray(flow)
-
-        return img1, img2, flow
-
-class SparseFlowAugmentor:
-    def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=False):
-        # spatial augmentation params
-        self.crop_size = crop_size
-        self.min_scale = min_scale
-        self.max_scale = max_scale
-        self.spatial_aug_prob = 0.8
-        self.stretch_prob = 0.8
-        self.max_stretch = 0.2
-
-        # flip augmentation params
-        self.do_flip = do_flip
-        self.h_flip_prob = 0.5
-        self.v_flip_prob = 0.1
-
-        # photometric augmentation params
-        self.photo_aug = ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.3/3.14)
-        self.asymmetric_color_aug_prob = 0.2
-        self.eraser_aug_prob = 0.5
-        
-    def color_transform(self, img1, img2):
-        image_stack = np.concatenate([img1, img2], axis=0)
-        image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
-        img1, img2 = np.split(image_stack, 2, axis=0)
-        return img1, img2
-
-    def eraser_transform(self, img1, img2):
-        ht, wd = img1.shape[:2]
-        if np.random.rand() < self.eraser_aug_prob:
-            mean_color = np.mean(img2.reshape(-1, 3), axis=0)
-            for _ in range(np.random.randint(1, 3)):
-                x0 = np.random.randint(0, wd)
-                y0 = np.random.randint(0, ht)
-                dx = np.random.randint(50, 100)
-                dy = np.random.randint(50, 100)
-                img2[y0:y0+dy, x0:x0+dx, :] = mean_color
-
-        return img1, img2
-
-    def resize_sparse_flow_map(self, flow, valid, fx=1.0, fy=1.0):
-        ht, wd = flow.shape[:2]
-        coords = np.meshgrid(np.arange(wd), np.arange(ht))
-        coords = np.stack(coords, axis=-1)
-
-        coords = coords.reshape(-1, 2).astype(np.float32)
-        flow = flow.reshape(-1, 2).astype(np.float32)
-        valid = valid.reshape(-1).astype(np.float32)
-
-        coords0 = coords[valid>=1]
-        flow0 = flow[valid>=1]
-
-        ht1 = int(round(ht * fy))
-        wd1 = int(round(wd * fx))
-
-        coords1 = coords0 * [fx, fy]
-        flow1 = flow0 * [fx, fy]
-
-        xx = np.round(coords1[:,0]).astype(np.int32)
-        yy = np.round(coords1[:,1]).astype(np.int32)
-
-        v = (xx > 0) & (xx < wd1) & (yy > 0) & (yy < ht1)
-        xx = xx[v]
-        yy = yy[v]
-        flow1 = flow1[v]
-
-        flow_img = np.zeros([ht1, wd1, 2], dtype=np.float32)
-        valid_img = np.zeros([ht1, wd1], dtype=np.int32)
-
-        flow_img[yy, xx] = flow1
-        valid_img[yy, xx] = 1
-
-        return flow_img, valid_img
-
-    def spatial_transform(self, img1, img2, flow, valid):
-        # randomly sample scale
-
-        ht, wd = img1.shape[:2]
-        min_scale = np.maximum(
-            (self.crop_size[0] + 1) / float(ht), 
-            (self.crop_size[1] + 1) / float(wd))
-
-        scale = 2 ** np.random.uniform(self.min_scale, self.max_scale)
-        scale_x = np.clip(scale, min_scale, None)
-        scale_y = np.clip(scale, min_scale, None)
-
-        if np.random.rand() < self.spatial_aug_prob:
-            # rescale the images
-            img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
-            img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
-            flow, valid = self.resize_sparse_flow_map(flow, valid, fx=scale_x, fy=scale_y)
-
-        if self.do_flip:
-            if np.random.rand() < 0.5: # h-flip
-                img1 = img1[:, ::-1]
-                img2 = img2[:, ::-1]
-                flow = flow[:, ::-1] * [-1.0, 1.0]
-                valid = valid[:, ::-1]
-
-        margin_y = 20
-        margin_x = 50
-
-        y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0] + margin_y)
-        x0 = np.random.randint(-margin_x, img1.shape[1] - self.crop_size[1] + margin_x)
-
-        y0 = np.clip(y0, 0, img1.shape[0] - self.crop_size[0])
-        x0 = np.clip(x0, 0, img1.shape[1] - self.crop_size[1])
-
-        img1 = img1[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
-        img2 = img2[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
-        flow = flow[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
-        valid = valid[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
-        return img1, img2, flow, valid
-
-
-    def __call__(self, img1, img2, flow, valid):
-        img1, img2 = self.color_transform(img1, img2)
-        img1, img2 = self.eraser_transform(img1, img2)
-        img1, img2, flow, valid = self.spatial_transform(img1, img2, flow, valid)
-
-        img1 = np.ascontiguousarray(img1)
-        img2 = np.ascontiguousarray(img2)
-        flow = np.ascontiguousarray(flow)
-        valid = np.ascontiguousarray(valid)
-
-        return img1, img2, flow, valid

spaces/2ndelement/voicevox/test/test_mora_list.py

@@ -1,20 +0,0 @@
-from unittest import TestCase
-
-from voicevox_engine.mora_list import openjtalk_mora2text
-
-
-class TestOpenJTalkMoraList(TestCase):
-    def test_mora2text(self):
-        self.assertEqual("ッ", openjtalk_mora2text["cl"])
-        self.assertEqual("ティ", openjtalk_mora2text["ti"])
-        self.assertEqual("トゥ", openjtalk_mora2text["tu"])
-        self.assertEqual("ディ", openjtalk_mora2text["di"])
-        # GitHub issue #60
-        self.assertEqual("ギェ", openjtalk_mora2text["gye"])
-        self.assertEqual("イェ", openjtalk_mora2text["ye"])
-
-    def test_mora2text_injective(self):
-        """異なるモーラが同じ読みがなに対応しないか確認する"""
-        values = list(openjtalk_mora2text.values())
-        uniq_values = list(set(values))
-        self.assertCountEqual(values, uniq_values)

spaces/4Taps/SadTalker/src/utils/text2speech.py

@@ -1,12 +0,0 @@
-import os
-
-def text2speech(txt, audio_path):
-    print(txt)
-    cmd = f'tts --text "{txt}" --out_path {audio_path}'
-    print(cmd)
-    try:
-        os.system(cmd)
-        return audio_path
-    except:
-        print("Error: Failed convert txt to audio")
-        return None

spaces/AICODER009/food_detection/model.py

@@ -1,36 +0,0 @@
-import torch
-import torchvision
-
-from torch import nn
-
-
-def create_effnetb2_model(num_classes:int=3, 
-                          seed:int=42):
-    """Creates an EfficientNetB2 feature extractor model and transforms.
-
-    Args:
-        num_classes (int, optional): number of classes in the classifier head. 
-            Defaults to 3.
-        seed (int, optional): random seed value. Defaults to 42.
-
-    Returns:
-        model (torch.nn.Module): EffNetB2 feature extractor model. 
-        transforms (torchvision.transforms): EffNetB2 image transforms.
-    """
-    # Create EffNetB2 pretrained weights, transforms and model
-    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
-    transforms = weights.transforms()
-    model = torchvision.models.efficientnet_b2(weights=weights)
-
-    # Freeze all layers in base model
-    for param in model.parameters():
-        param.requires_grad = False
-
-    # Change classifier head with random seed for reproducibility
-    torch.manual_seed(seed)
-    model.classifier = nn.Sequential(
-        nn.Dropout(p=0.3, inplace=True),
-        nn.Linear(in_features=1408, out_features=num_classes),
-    )
-    
-    return model, transforms

spaces/AIGC-Audio/AudioGPT/audio_to_text/captioning/utils/train_util.py

@@ -1,178 +0,0 @@
-# -*- coding: utf-8 -*-
-#!/usr/bin/env python3
-import os
-import sys
-import logging
-from typing import Callable, Dict, Union
-import yaml
-import torch
-from torch.optim.swa_utils import AveragedModel as torch_average_model
-import numpy as np
-import pandas as pd
-from pprint import pformat
-
-
-def load_dict_from_csv(csv, cols):
-    df = pd.read_csv(csv, sep="\t")
-    output = dict(zip(df[cols[0]], df[cols[1]]))
-    return output
-
-
-def init_logger(filename, level="INFO"):
-    formatter = logging.Formatter(
-        "[ %(levelname)s : %(asctime)s ] - %(message)s")
-    logger = logging.getLogger(__name__ + "." + filename)
-    logger.setLevel(getattr(logging, level))
-    # Log results to std
-    # stdhandler = logging.StreamHandler(sys.stdout)
-    # stdhandler.setFormatter(formatter)
-    # Dump log to file
-    filehandler = logging.FileHandler(filename)
-    filehandler.setFormatter(formatter)
-    logger.addHandler(filehandler)
-    # logger.addHandler(stdhandler)
-    return logger
-
-
-def init_obj(module, config, **kwargs):# 'captioning.models.encoder'
-    obj_args = config["args"].copy()
-    obj_args.update(kwargs)
-    return getattr(module, config["type"])(**obj_args)
-
-
-def pprint_dict(in_dict, outputfun=sys.stdout.write, formatter='yaml'):
-    """pprint_dict
-
-    :param outputfun: function to use, defaults to sys.stdout
-    :param in_dict: dict to print
-    """
-    if formatter == 'yaml':
-        format_fun = yaml.dump
-    elif formatter == 'pretty':
-        format_fun = pformat
-    for line in format_fun(in_dict).split('\n'):
-        outputfun(line)
-
-
-def merge_a_into_b(a, b):
-    # merge dict a into dict b. values in a will overwrite b.
-    for k, v in a.items():
-        if isinstance(v, dict) and k in b:
-            assert isinstance(
-                b[k], dict
-            ), "Cannot inherit key '{}' from base!".format(k)
-            merge_a_into_b(v, b[k])
-        else:
-            b[k] = v
-
-
-def load_config(config_file):
-    with open(config_file, "r") as reader:
-        config = yaml.load(reader, Loader=yaml.FullLoader)
-    if "inherit_from" in config:
-        base_config_file = config["inherit_from"]
-        base_config_file = os.path.join(
-            os.path.dirname(config_file), base_config_file
-        )
-        assert not os.path.samefile(config_file, base_config_file), \
-            "inherit from itself"
-        base_config = load_config(base_config_file)
-        del config["inherit_from"]
-        merge_a_into_b(config, base_config)
-        return base_config
-    return config
-
-
-def parse_config_or_kwargs(config_file, **kwargs):
-    yaml_config = load_config(config_file)
-    # passed kwargs will override yaml config
-    args = dict(yaml_config, **kwargs)
-    return args
-
-
-def store_yaml(config, config_file):
-    with open(config_file, "w") as con_writer:
-        yaml.dump(config, con_writer, indent=4, default_flow_style=False)
-
-
-class MetricImprover:
-
-    def __init__(self, mode):
-        assert mode in ("min", "max")
-        self.mode = mode
-        # min: lower -> better; max: higher -> better
-        self.best_value = np.inf if mode == "min" else -np.inf
-
-    def compare(self, x, best_x):
-        return x < best_x if self.mode == "min" else x > best_x
-
-    def __call__(self, x):
-        if self.compare(x, self.best_value):
-            self.best_value = x
-            return True
-        return False
-
-    def state_dict(self):
-        return self.__dict__
-
-    def load_state_dict(self, state_dict):
-        self.__dict__.update(state_dict)
-
-
-def fix_batchnorm(model: torch.nn.Module):
-    def inner(module):
-        class_name = module.__class__.__name__
-        if class_name.find("BatchNorm") != -1:
-            module.eval()
-    model.apply(inner)
-
-
-def load_pretrained_model(model: torch.nn.Module,
-                          pretrained: Union[str, Dict],
-                          output_fn: Callable = sys.stdout.write):
-    if not isinstance(pretrained, dict) and not os.path.exists(pretrained):
-        output_fn(f"pretrained {pretrained} not exist!")
-        return
-    
-    if hasattr(model, "load_pretrained"):
-        model.load_pretrained(pretrained)
-        return
-
-    if isinstance(pretrained, dict):
-        state_dict = pretrained
-    else:
-        state_dict = torch.load(pretrained, map_location="cpu")
-
-    if "model" in state_dict:
-        state_dict = state_dict["model"]
-    model_dict = model.state_dict()
-    pretrained_dict = {
-        k: v for k, v in state_dict.items() if (k in model_dict) and (
-            model_dict[k].shape == v.shape)
-    }
-    output_fn(f"Loading pretrained keys {pretrained_dict.keys()}")
-    model_dict.update(pretrained_dict)
-    model.load_state_dict(model_dict, strict=True)
-
-
-class AveragedModel(torch_average_model):
-
-    def update_parameters(self, model):
-        for p_swa, p_model in zip(self.parameters(), model.parameters()):
-            device = p_swa.device
-            p_model_ = p_model.detach().to(device)
-            if self.n_averaged == 0:
-                p_swa.detach().copy_(p_model_)
-            else:
-                p_swa.detach().copy_(self.avg_fn(p_swa.detach(), p_model_,
-                                                 self.n_averaged.to(device)))
-
-        for b_swa, b_model in zip(list(self.buffers())[1:], model.buffers()):
-            device = b_swa.device
-            b_model_ = b_model.detach().to(device)
-            if self.n_averaged == 0:
-                b_swa.detach().copy_(b_model_)
-            else:
-                b_swa.detach().copy_(self.avg_fn(b_swa.detach(), b_model_,
-                                                 self.n_averaged.to(device)))
-        self.n_averaged += 1

spaces/AIxPha/Real-CUGAN/upcunet_v3.py

@@ -1,714 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-import os, sys
-import numpy as np
-
-root_path = os.path.abspath('.')
-sys.path.append(root_path)
-
-
-class SEBlock(nn.Module):
-    def __init__(self, in_channels, reduction=8, bias=False):
-        super(SEBlock, self).__init__()
-        self.conv1 = nn.Conv2d(in_channels, in_channels // reduction, 1, 1, 0, bias=bias)
-        self.conv2 = nn.Conv2d(in_channels // reduction, in_channels, 1, 1, 0, bias=bias)
-
-    def forward(self, x):
-        if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-            x0 = torch.mean(x.float(), dim=(2, 3), keepdim=True).half()
-        else:
-            x0 = torch.mean(x, dim=(2, 3), keepdim=True)
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-    def forward_mean(self, x, x0):
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-
-class UNetConv(nn.Module):
-    def __init__(self, in_channels, mid_channels, out_channels, se):
-        super(UNetConv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(in_channels, mid_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-            nn.Conv2d(mid_channels, out_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-        )
-        if se:
-            self.seblock = SEBlock(out_channels, reduction=8, bias=True)
-        else:
-            self.seblock = None
-
-    def forward(self, x):
-        z = self.conv(x)
-        if self.seblock is not None:
-            z = self.seblock(z)
-        return z
-
-
-class UNet1(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet1x3(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1x3, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 5, 3, 2)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet2(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet2, self).__init__()
-
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 64, 128, se=True)
-        self.conv2_down = nn.Conv2d(128, 128, 2, 2, 0)
-        self.conv3 = UNetConv(128, 256, 128, se=True)
-        self.conv3_up = nn.ConvTranspose2d(128, 128, 2, 2, 0)
-        self.conv4 = UNetConv(128, 64, 64, se=True)
-        self.conv4_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv5 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3(x3)
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4(x2 + x3)
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-    def forward_a(self, x):  # conv234结尾有se
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x2):  # conv234结尾有se
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3.conv(x3)
-        return x3
-
-    def forward_c(self, x2, x3):  # conv234结尾有se
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4.conv(x2 + x3)
-        return x4
-
-    def forward_d(self, x1, x4):  # conv234结尾有se
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-
-class UpCunet2x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet2x, self).__init__()
-        self.unet1 = UNet1(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 2, :w0 * 2]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分���一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 36, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 36, j:j + crop_size[1] + 36]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 36, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 2 - 72, w * 2 - 72)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                res[:, :, i * 2:i * 2 + h1 * 2 - 72, j * 2:j * 2 + w1 * 2 - 72] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 2, :w0 * 2]
-        return res  #
-
-
-class UpCunet3x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet3x, self).__init__()
-        self.unet1 = UNet1x3(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 4 + 1) * 4
-            pw = ((w0 - 1) // 4 + 1) * 4
-            x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 3, :w0 * 3]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_h = (h0 - 1) // 4 * 4 + 4  # 能被4整除
-            else:
-                crop_size_h = ((h0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_w = (w0 - 1) // 4 * 4 + 4  # 能被4整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 2, ((w0 - 1) // 8 * 8 + 8) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 12 * 12 + 12) // 3, ((w0 - 1) // 12 * 12 + 12) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 16 * 16 + 16) // 4, ((w0 - 1) // 16 * 16 + 16) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 28, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 28, j:j + crop_size[1] + 28]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 28, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop  #
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 3 - 84, w * 3 - 84)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                res[:, :, i * 3:i * 3 + h1 * 3 - 84, j * 3:j * 3 + w1 * 3 - 84] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 3, :w0 * 3]
-        return res
-
-
-class UpCunet4x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet4x, self).__init__()
-        self.unet1 = UNet1(in_channels, 64, deconv=True)
-        self.unet2 = UNet2(64, 64, deconv=False)
-        self.ps = nn.PixelShuffle(2)
-        self.conv_final = nn.Conv2d(64, 12, 3, 1, padding=0, bias=True)
-
-    def forward(self, x, tile_mode):
-        n, c, h0, w0 = x.shape
-        x00 = x
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            x = self.conv_final(x)
-            x = F.pad(x, (-1, -1, -1, -1))
-            x = self.ps(x)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 4, :w0 * 4]
-            x += F.interpolate(x00, scale_factor=4, mode='nearest')
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.1G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 38, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 38, j:j + crop_size[1] + 38]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 38, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                x_crop = self.conv_final(x_crop)
-                x_crop = F.pad(x_crop, (-1, -1, -1, -1))
-                x_crop = self.ps(x_crop)
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 4 - 152, w * 4 - 152)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                # print(opt_res_dict[i][j].shape,res[:, :, i * 4:i * 4 + h1 * 4 - 144, j * 4:j * 4 + w1 * 4 - 144].shape)
-                res[:, :, i * 4:i * 4 + h1 * 4 - 152, j * 4:j * 4 + w1 * 4 - 152] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 4, :w0 * 4]
-        res += F.interpolate(x00, scale_factor=4, mode='nearest')
-        return res  #
-
-
-class RealWaifuUpScaler(object):
-    def __init__(self, scale, weight_path, half, device):
-        weight = torch.load(weight_path, map_location="cpu")
-        self.model = eval("UpCunet%sx" % scale)()
-        if (half == True):
-            self.model = self.model.half().to(device)
-        else:
-            self.model = self.model.to(device)
-        self.model.load_state_dict(weight, strict=True)
-        self.model.eval()
-        self.half = half
-        self.device = device
-
-    def np2tensor(self, np_frame):
-        if (self.half == False):
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).float() / 255
-        else:
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).half() / 255
-
-    def tensor2np(self, tensor):
-        if (self.half == False):
-            return (
-                np.transpose((tensor.data.squeeze() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(), (1, 2, 0)))
-        else:
-            return (np.transpose((tensor.data.squeeze().float() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(),
-                                 (1, 2, 0)))
-
-    def __call__(self, frame, tile_mode):
-        with torch.no_grad():
-            tensor = self.np2tensor(frame)
-            result = self.tensor2np(self.model(tensor, tile_mode))
-        return result
-
-
-if __name__ == "__main__":
-    ###########inference_img
-    import time, cv2, sys
-    from time import time as ttime
-
-    for weight_path, scale in [("weights_v3/up2x-latest-denoise3x.pth", 2), ("weights_v3/up3x-latest-denoise3x.pth", 3),
-                               ("weights_v3/up4x-latest-denoise3x.pth", 4)]:
-        for tile_mode in [0, 1, 2, 3, 4]:
-            upscaler2x = RealWaifuUpScaler(scale, weight_path, half=True, device="cuda:0")
-            input_dir = "%s/input_dir1" % root_path
-            output_dir = "%s/opt-dir-all-test" % root_path
-            os.makedirs(output_dir, exist_ok=True)
-            for name in os.listdir(input_dir):
-                print(name)
-                tmp = name.split(".")
-                inp_path = os.path.join(input_dir, name)
-                suffix = tmp[-1]
-                prefix = ".".join(tmp[:-1])
-                tmp_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                print(inp_path, tmp_path)
-                # 支持中文路径
-                # os.link(inp_path, tmp_path)#win用硬链接
-                os.symlink(inp_path, tmp_path)  # linux用软链接
-                frame = cv2.imread(tmp_path)[:, :, [2, 1, 0]]
-                t0 = ttime()
-                result = upscaler2x(frame, tile_mode=tile_mode)[:, :, ::-1]
-                t1 = ttime()
-                print(prefix, "done", t1 - t0)
-                tmp_opt_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                cv2.imwrite(tmp_opt_path, result)
-                n = 0
-                while (1):
-                    if (n == 0):
-                        suffix = "_%sx_tile%s.png" % (scale, tile_mode)
-                    else:
-                        suffix = "_%sx_tile%s_%s.png" % (scale, tile_mode, n)  #
-                    if (os.path.exists(os.path.join(output_dir, prefix + suffix)) == False):
-                        break
-                    else:
-                        n += 1
-                final_opt_path = os.path.join(output_dir, prefix + suffix)
-                os.rename(tmp_opt_path, final_opt_path)
-                os.remove(tmp_path)

spaces/Ababababababbababa/Ashaar/poetry_diacritizer/models/cbhg.py

@@ -1,121 +0,0 @@
-"""
-The CBHG model implementation
-"""
-from typing import List, Optional
-
-from torch import nn
-import torch
-
-from poetry_diacritizer.modules.tacotron_modules import CBHG, Prenet
-
-
-class CBHGModel(nn.Module):
-    """CBHG model implementation as described in the paper:
-     https://ieeexplore.ieee.org/document/9274427
-
-    Args:
-    inp_vocab_size (int): the number of the input symbols
-    targ_vocab_size (int): the number of the target symbols (diacritics)
-    embedding_dim (int): the embedding  size
-    use_prenet (bool): whether to use prenet or not
-    prenet_sizes (List[int]): the sizes of the prenet networks
-    cbhg_gru_units (int): the number of units of the CBHG GRU, which is the last
-    layer of the CBHG Model.
-    cbhg_filters (int): number of filters used in the CBHG module
-    cbhg_projections: projections used in the CBHG module
-
-    Returns:
-    diacritics Dict[str, Tensor]:
-    """
-
-    def __init__(
-        self,
-        inp_vocab_size: int,
-        targ_vocab_size: int,
-        embedding_dim: int = 512,
-        use_prenet: bool = True,
-        prenet_sizes: List[int] = [512, 256],
-        cbhg_gru_units: int = 512,
-        cbhg_filters: int = 16,
-        cbhg_projections: List[int] = [128, 256],
-        post_cbhg_layers_units: List[int] = [256, 256],
-        post_cbhg_use_batch_norm: bool = True
-    ):
-        super().__init__()
-        self.use_prenet = use_prenet
-        self.embedding = nn.Embedding(inp_vocab_size, embedding_dim)
-        if self.use_prenet:
-            self.prenet = Prenet(embedding_dim, prenet_depth=prenet_sizes)
-
-        self.cbhg = CBHG(
-            prenet_sizes[-1] if self.use_prenet else embedding_dim,
-            cbhg_gru_units,
-            K=cbhg_filters,
-            projections=cbhg_projections,
-        )
-
-        layers = []
-        post_cbhg_layers_units = [cbhg_gru_units] + post_cbhg_layers_units
-
-        for i in range(1, len(post_cbhg_layers_units)):
-            layers.append(
-                nn.LSTM(
-                    post_cbhg_layers_units[i - 1] * 2,
-                    post_cbhg_layers_units[i],
-                    bidirectional=True,
-                    batch_first=True,
-                )
-            )
-            if post_cbhg_use_batch_norm:
-                layers.append(nn.BatchNorm1d(post_cbhg_layers_units[i] * 2))
-
-        self.post_cbhg_layers = nn.ModuleList(layers)
-        self.projections = nn.Linear(post_cbhg_layers_units[-1] * 2, targ_vocab_size)
-        self.post_cbhg_layers_units = post_cbhg_layers_units
-        self.post_cbhg_use_batch_norm = post_cbhg_use_batch_norm
-
-
-    def forward(
-        self,
-        src: torch.Tensor,
-        lengths: Optional[torch.Tensor] = None,
-        target: Optional[torch.Tensor] = None,  # not required in this model
-    ):
-        """Compute forward propagation"""
-
-        # src = [batch_size, src len]
-        # lengths = [batch_size]
-        # target = [batch_size, trg len]
-
-        embedding_out = self.embedding(src)
-        # embedding_out; [batch_size, src_len, embedding_dim]
-
-        cbhg_input = embedding_out
-        if self.use_prenet:
-            cbhg_input = self.prenet(embedding_out)
-
-            # cbhg_input = [batch_size, src_len, prenet_sizes[-1]]
-
-        outputs = self.cbhg(cbhg_input, lengths)
-
-        hn = torch.zeros((2, 2, 2))
-        cn = torch.zeros((2, 2, 2))
-
-        for i, layer in enumerate(self.post_cbhg_layers):
-            if isinstance(layer, nn.BatchNorm1d):
-                outputs = layer(outputs.permute(0, 2, 1))
-                outputs = outputs.permute(0, 2, 1)
-                continue
-            if i > 0:
-                outputs, (hn, cn) = layer(outputs, (hn, cn))
-            else:
-                outputs, (hn, cn) = layer(outputs)
-
-
-        predictions = self.projections(outputs)
-
-        # predictions = [batch_size, src len, targ_vocab_size]
-
-        output = {"diacritics": predictions}
-
-        return output

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/live2d.d.ts

@@ -1,11 +0,0 @@
-import {
-    Live2dCoreScriptFileCallback,
-    Live2dFileCallback,
-    Live2dGameObject
-} from './gameobjects/live2d/index';
-
-export {
-    Live2dCoreScriptFileCallback,
-    Live2dFileCallback,
-    Live2dGameObject
-};

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/board/chess/RandomSymobl.js

@@ -1,38 +0,0 @@
-const GetRandom = Phaser.Utils.Array.GetRandom;
-
-var RandomSymbol = function (board, tileX, tileY, callback, scope, excluded) {
-    var symbol;
-    if (Array.isArray(callback)) {
-        // pick random symbol from symbol array
-        var symbols = callback;
-        // excluded: undefined or a symbol array
-        if (excluded !== undefined) {
-            for (var i = 0, cnt = symbols.length; i < cnt; i++) {
-                symbol = symbols[i];
-                if (excluded.indexOf(symbol) !== -1) {
-                    continue;
-                }
-                tmpSymbolArray.push(symbol);
-            }
-            symbol = GetRandom(tmpSymbolArray);
-            tmpSymbolArray.length = 0;
-        } else {
-            symbol = GetRandom(symbols);
-        }
-
-    } else if (typeof (obj) === 'function') {
-        // symbols from return of callback
-        if (scope) {
-            symbol = callback.call(scope, board, tileX, tileY, excluded);
-        } else {
-            symbol = callback(board, tileX, tileY, excluded);
-        }
-    } else {
-        // symbol value
-        symbol = callback;
-    }
-    return symbol;
-}
-
-var tmpSymbolArray = [];
-export default RandomSymbol;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/press/Factory.js

@@ -1,16 +0,0 @@
-import Press from './Press.js';
-import ObjectFactory from '../ObjectFactory.js';
-import IsGameObject from '../../../plugins/utils/system/IsGameObject.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('press', function (gameObject, config) {
-    if (!IsGameObject(gameObject)) {
-        config = gameObject;
-        gameObject = this.scene;
-    }
-    return new Press(gameObject, config);
-});
-
-SetValue(window, 'RexPlugins.UI.Press', Press);
-
-export default Press;

spaces/AjulorC/question_answering_bot_deployed_with_Gradio/app.py

@@ -1,39 +0,0 @@
-import tensorflow as tf
-
-#!pip install transformers
-
-from transformers import pipeline
-
-# importing necessary libraries
-from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering
-
-
-tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
-model = TFAutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad",return_dict=False)
-
-nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)
-
-#!pip install gradio
-import gradio as gr
-
-# creating the function
-def func(context, question):
-  result = nlp(question = question, context=context)
-  return result['answer']
-
-example_1 = "(1) My name is Ajulor Christian, I am a data scientist and machine learning engineer"
-qst_1 =  "what is christian's profession?"
-
-example_2 = "(2) Natural Language Processing (NLP) allows machines to break down and interpret human language. It's at the core of tools we use every day – from translation software, chatbots, spam filters, and search engines, to grammar correction software, voice assistants, and social media monitoring tools."
-qst_2 =  "What is NLP used for?"
-
-# creating the interface
-app = gr.Interface(fn=func, inputs = ['textbox', 'text'], outputs = 'textbox', 
-                   title = 'Question Answering bot', theme = 'dark-grass',
-                   description = 'Input context and question, then get answers!',
-                   examples = [[example_1, qst_1],
-                               [example_2, qst_2]]
-                   )
-
-# launching the app
-app.launch(inline=False)

spaces/Akmyradov/TurkmenTTSweSTT/vits/modules.py

@@ -1,390 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-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/AlexWang/lama/bin/mask_example.py

@@ -1,14 +0,0 @@
-import matplotlib.pyplot as plt
-from skimage import io
-from skimage.transform import resize
-
-from saicinpainting.evaluation.masks.mask import SegmentationMask
-
-im = io.imread('imgs/ex4.jpg')
-im = resize(im, (512, 1024), anti_aliasing=True)
-mask_seg = SegmentationMask(num_variants_per_mask=10)
-mask_examples = mask_seg.get_masks(im)
-for i, example in enumerate(mask_examples):
-    plt.imshow(example)
-    plt.show()
-    plt.imsave(f'tmp/img_masks/{i}.png', example)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/training/text_inversion.md

@@ -1,277 +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.
--->
-
-
-
-# Textual Inversion
-
-[Textual Inversion](https://arxiv.org/abs/2208.01618) is a technique for capturing novel concepts from a small number of example images. While the technique was originally demonstrated with a [latent diffusion model](https://github.com/CompVis/latent-diffusion), it has since been applied to other model variants like [Stable Diffusion](https://huggingface.co/docs/diffusers/main/en/conceptual/stable_diffusion). The learned concepts can be used to better control the images generated from text-to-image pipelines. It learns new "words" in the text encoder's embedding space, which are used within text prompts for personalized image generation.
-
-![Textual Inversion example](https://textual-inversion.github.io/static/images/editing/colorful_teapot.JPG)
-<small>By using just 3-5 images you can teach new concepts to a model such as Stable Diffusion for personalized image generation <a href="https://github.com/rinongal/textual_inversion">(image source)</a>.</small>
-
-This guide will show you how to train a [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) model with Textual Inversion. All the training scripts for Textual Inversion used in this guide can be found [here](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) if you're interested in taking a closer look at how things work under the hood.
-
-<Tip>
-
-There is a community-created collection of trained Textual Inversion models in the [Stable Diffusion Textual Inversion Concepts Library](https://huggingface.co/sd-concepts-library) which are readily available for inference. Over time, this'll hopefully grow into a useful resource as more concepts are added!
-
-</Tip>
-
-Before you begin, make sure you install the library's training dependencies:
-
-```bash
-pip install diffusers accelerate transformers
-```
-
-After all the dependencies have been set up, initialize a [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-To setup a default 🤗 Accelerate environment without choosing any configurations:
-
-```bash
-accelerate config default
-```
-
-Or if your environment doesn't support an interactive shell like a notebook, you can use:
-
-```bash
-from accelerate.utils import write_basic_config
-
-write_basic_config()
-```
-
-Finally, you try and [install xFormers](https://huggingface.co/docs/diffusers/main/en/training/optimization/xformers) to reduce your memory footprint with xFormers memory-efficient attention. Once you have xFormers installed, add the `--enable_xformers_memory_efficient_attention` argument to the training script. xFormers is not supported for Flax.
-
-## Upload model to Hub
-
-If you want to store your model on the Hub, add the following argument to the training script:
-
-```bash
---push_to_hub
-```
-
-## Save and load checkpoints
-
-It is often a good idea to regularly save checkpoints of your model during training. This way, you can resume training from a saved checkpoint if your training is interrupted for any reason. To save a checkpoint, pass the following argument to the training script to save the full training state in a subfolder in `output_dir` every 500 steps:
-
-```bash
---checkpointing_steps=500
-```
-
-To resume training from a saved checkpoint, pass the following argument to the training script and the specific checkpoint you'd like to resume from:
-
-```bash
---resume_from_checkpoint="checkpoint-1500"
-```
-
-## Finetuning
-
-For your training dataset, download these [images of a cat toy](https://huggingface.co/datasets/diffusers/cat_toy_example) and store them in a directory. To use your own dataset, take a look at the [Create a dataset for training](create_dataset) guide.
-
-```py
-from huggingface_hub import snapshot_download
-
-local_dir = "./cat"
-snapshot_download(
-    "diffusers/cat_toy_example", local_dir=local_dir, repo_type="dataset", ignore_patterns=".gitattributes"
-)
-```
-
-Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) argument, and the `DATA_DIR` environment variable to the path of the directory containing the images. 
-
-Now you can launch the [training script](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py). The script creates and saves the following files to your repository: `learned_embeds.bin`, `token_identifier.txt`, and `type_of_concept.txt`.
-
-<Tip>
-
-💡 A full training run takes ~1 hour on one V100 GPU. While you're waiting for the training to complete, feel free to check out [how Textual Inversion works](#how-it-works) in the section below if you're curious!
-
-</Tip>
-
-<frameworkcontent>
-<pt>
-```bash
-export MODEL_NAME="runwayml/stable-diffusion-v1-5"
-export DATA_DIR="./cat"
-
-accelerate launch textual_inversion.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --train_data_dir=$DATA_DIR \
-  --learnable_property="object" \
-  --placeholder_token="<cat-toy>" --initializer_token="toy" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=4 \
-  --max_train_steps=3000 \
-  --learning_rate=5.0e-04 --scale_lr \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --output_dir="textual_inversion_cat" \
-  --push_to_hub
-```
-
-<Tip>
-
-💡 If you want to increase the trainable capacity, you can associate your placeholder token, *e.g.* `<cat-toy>` to 
-multiple embedding vectors. This can help the model to better capture the style of more (complex) images. 
-To enable training multiple embedding vectors, simply pass:
-
-```bash
---num_vectors=5
-```
-
-</Tip>
-</pt>
-<jax>
-If you have access to TPUs, try out the [Flax training script](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion_flax.py) to train even faster (this'll also work for GPUs). With the same configuration settings, the Flax training script should be at least 70% faster than the PyTorch training script! ⚡️
-
-Before you begin, make sure you install the Flax specific dependencies:
-
-```bash
-pip install -U -r requirements_flax.txt
-```
-
-Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) argument.
-
-Then you can launch the [training script](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion_flax.py):
-
-```bash
-export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
-export DATA_DIR="./cat"
-
-python textual_inversion_flax.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --train_data_dir=$DATA_DIR \
-  --learnable_property="object" \
-  --placeholder_token="<cat-toy>" --initializer_token="toy" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --max_train_steps=3000 \
-  --learning_rate=5.0e-04 --scale_lr \
-  --output_dir="textual_inversion_cat" \
-  --push_to_hub
-```
-</jax>
-</frameworkcontent>
-
-### Intermediate logging
-
-If you're interested in following along with your model training progress, you can save the generated images from the training process. Add the following arguments to the training script to enable intermediate logging:
-
-- `validation_prompt`, the prompt used to generate samples (this is set to `None` by default and intermediate logging is disabled)
-- `num_validation_images`, the number of sample images to generate
-- `validation_steps`, the number of steps before generating `num_validation_images` from the `validation_prompt`
-
-```bash
---validation_prompt="A <cat-toy> backpack"
---num_validation_images=4
---validation_steps=100
-```
-
-## Inference
-
-Once you have trained a model, you can use it for inference with the [`StableDiffusionPipeline`].
-
-The textual inversion script will by default only save the textual inversion embedding vector(s) that have 
-been added to the text encoder embedding matrix and consequently been trained.
-
-<frameworkcontent>
-<pt>
-<Tip>
-
-💡 The community has created a large library of different textual inversion embedding vectors, called [sd-concepts-library](https://huggingface.co/sd-concepts-library).
-Instead of training textual inversion embeddings from scratch you can also see whether a fitting textual inversion embedding has already been added to the libary.
-
-</Tip>
-
-To load the textual inversion embeddings you first need to load the base model that was used when training 
-your textual inversion embedding vectors. Here we assume that [`runwayml/stable-diffusion-v1-5`](runwayml/stable-diffusion-v1-5)
-was used as a base model so we load it first:
-```python
-from diffusers import StableDiffusionPipeline
-import torch
-
-model_id = "runwayml/stable-diffusion-v1-5"
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
-```
-
-Next, we need to load the textual inversion embedding vector which can be done via the [`TextualInversionLoaderMixin.load_textual_inversion`]
-function. Here we'll load the embeddings of the "<cat-toy>" example from before.
-```python
-pipe.load_textual_inversion("sd-concepts-library/cat-toy")
-```
-
-Now we can run the pipeline making sure that the placeholder token `<cat-toy>` is used in our prompt.
-
-```python
-prompt = "A <cat-toy> backpack"
-
-image = pipe(prompt, num_inference_steps=50).images[0]
-image.save("cat-backpack.png")
-```
-
-The function [`TextualInversionLoaderMixin.load_textual_inversion`] can not only 
-load textual embedding vectors saved in Diffusers' format, but also embedding vectors
-saved in [Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) format.
-To do so, you can first download an embedding vector from [civitAI](https://civitai.com/models/3036?modelVersionId=8387)
-and then load it locally:
-```python
-pipe.load_textual_inversion("./charturnerv2.pt")
-```
-</pt>
-<jax>
-Currently there is no `load_textual_inversion` function for Flax so one has to make sure the textual inversion
-embedding vector is saved as part of the model after training.
-
-The model can then be run just like any other Flax model:
-
-```python
-import jax
-import numpy as np
-from flax.jax_utils import replicate
-from flax.training.common_utils import shard
-from diffusers import FlaxStableDiffusionPipeline
-
-model_path = "path-to-your-trained-model"
-pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(model_path, dtype=jax.numpy.bfloat16)
-
-prompt = "A <cat-toy> backpack"
-prng_seed = jax.random.PRNGKey(0)
-num_inference_steps = 50
-
-num_samples = jax.device_count()
-prompt = num_samples * [prompt]
-prompt_ids = pipeline.prepare_inputs(prompt)
-
-# shard inputs and rng
-params = replicate(params)
-prng_seed = jax.random.split(prng_seed, jax.device_count())
-prompt_ids = shard(prompt_ids)
-
-images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
-images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
-image.save("cat-backpack.png")
-```
-</jax>
-</frameworkcontent>
-
-## How it works
-
-![Diagram from the paper showing overview](https://textual-inversion.github.io/static/images/training/training.JPG)
-<small>Architecture overview from the Textual Inversion <a href="https://textual-inversion.github.io/">blog post.</a></small>
-
-Usually, text prompts are tokenized into an embedding before being passed to a model, which is often a transformer. Textual Inversion does something similar, but it learns a new token embedding, `v*`, from a special token `S*` in the diagram above. The model output is used to condition the diffusion model, which helps the diffusion model understand the prompt and new concepts from just a few example images.
-
-To do this, Textual Inversion uses a generator model and noisy versions of the training images. The generator tries to predict less noisy versions of the images, and the token embedding `v*` is optimized based on how well the generator does. If the token embedding successfully captures the new concept, it gives more useful information to the diffusion model and helps create clearer images with less noise. This optimization process typically occurs after several thousand steps of exposure to a variety of prompt and image variants.

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/controlnet/test_controlnet.py

@@ -1,1002 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# 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 gc
-import tempfile
-import traceback
-import unittest
-
-import numpy as np
-import torch
-from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
-
-from diffusers import (
-    AutoencoderKL,
-    ControlNetModel,
-    DDIMScheduler,
-    EulerDiscreteScheduler,
-    StableDiffusionControlNetPipeline,
-    UNet2DConditionModel,
-)
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
-from diffusers.utils import load_image, load_numpy, randn_tensor, slow, torch_device
-from diffusers.utils.import_utils import is_xformers_available
-from diffusers.utils.testing_utils import (
-    enable_full_determinism,
-    require_torch_2,
-    require_torch_gpu,
-    run_test_in_subprocess,
-)
-
-from ..pipeline_params import (
-    IMAGE_TO_IMAGE_IMAGE_PARAMS,
-    TEXT_TO_IMAGE_BATCH_PARAMS,
-    TEXT_TO_IMAGE_IMAGE_PARAMS,
-    TEXT_TO_IMAGE_PARAMS,
-)
-from ..test_pipelines_common import (
-    PipelineKarrasSchedulerTesterMixin,
-    PipelineLatentTesterMixin,
-    PipelineTesterMixin,
-)
-
-
-enable_full_determinism()
-
-
-# Will be run via run_test_in_subprocess
-def _test_stable_diffusion_compile(in_queue, out_queue, timeout):
-    error = None
-    try:
-        _ = in_queue.get(timeout=timeout)
-
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.to("cuda")
-        pipe.set_progress_bar_config(disable=None)
-
-        pipe.unet.to(memory_format=torch.channels_last)
-        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-
-        pipe.controlnet.to(memory_format=torch.channels_last)
-        pipe.controlnet = torch.compile(pipe.controlnet, mode="reduce-overhead", fullgraph=True)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "bird"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
-        )
-
-        output = pipe(prompt, image, generator=generator, output_type="np")
-        image = output.images[0]
-
-        assert image.shape == (768, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny_out_full.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 1.0
-
-    except Exception:
-        error = f"{traceback.format_exc()}"
-
-    results = {"error": error}
-    out_queue.put(results, timeout=timeout)
-    out_queue.join()
-
-
-class ControlNetPipelineFastTests(
-    PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
-):
-    pipeline_class = StableDiffusionControlNetPipeline
-    params = TEXT_TO_IMAGE_PARAMS
-    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
-    image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
-    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
-
-    def get_dummy_components(self):
-        torch.manual_seed(0)
-        unet = UNet2DConditionModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            sample_size=32,
-            in_channels=4,
-            out_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
-            cross_attention_dim=32,
-        )
-        torch.manual_seed(0)
-        controlnet = ControlNetModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            in_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            cross_attention_dim=32,
-            conditioning_embedding_out_channels=(16, 32),
-        )
-        torch.manual_seed(0)
-        scheduler = DDIMScheduler(
-            beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="scaled_linear",
-            clip_sample=False,
-            set_alpha_to_one=False,
-        )
-        torch.manual_seed(0)
-        vae = AutoencoderKL(
-            block_out_channels=[32, 64],
-            in_channels=3,
-            out_channels=3,
-            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            latent_channels=4,
-        )
-        torch.manual_seed(0)
-        text_encoder_config = CLIPTextConfig(
-            bos_token_id=0,
-            eos_token_id=2,
-            hidden_size=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            pad_token_id=1,
-            vocab_size=1000,
-        )
-        text_encoder = CLIPTextModel(text_encoder_config)
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
-        components = {
-            "unet": unet,
-            "controlnet": controlnet,
-            "scheduler": scheduler,
-            "vae": vae,
-            "text_encoder": text_encoder,
-            "tokenizer": tokenizer,
-            "safety_checker": None,
-            "feature_extractor": None,
-        }
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-
-        controlnet_embedder_scale_factor = 2
-        image = randn_tensor(
-            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
-            generator=generator,
-            device=torch.device(device),
-        )
-
-        inputs = {
-            "prompt": "A painting of a squirrel eating a burger",
-            "generator": generator,
-            "num_inference_steps": 2,
-            "guidance_scale": 6.0,
-            "output_type": "numpy",
-            "image": image,
-        }
-
-        return inputs
-
-    def test_attention_slicing_forward_pass(self):
-        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
-
-    @unittest.skipIf(
-        torch_device != "cuda" or not is_xformers_available(),
-        reason="XFormers attention is only available with CUDA and `xformers` installed",
-    )
-    def test_xformers_attention_forwardGenerator_pass(self):
-        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
-
-    def test_inference_batch_single_identical(self):
-        self._test_inference_batch_single_identical(expected_max_diff=2e-3)
-
-
-class StableDiffusionMultiControlNetPipelineFastTests(
-    PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
-):
-    pipeline_class = StableDiffusionControlNetPipeline
-    params = TEXT_TO_IMAGE_PARAMS
-    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
-    image_params = frozenset([])  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
-
-    def get_dummy_components(self):
-        torch.manual_seed(0)
-        unet = UNet2DConditionModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            sample_size=32,
-            in_channels=4,
-            out_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
-            cross_attention_dim=32,
-        )
-        torch.manual_seed(0)
-
-        def init_weights(m):
-            if isinstance(m, torch.nn.Conv2d):
-                torch.nn.init.normal(m.weight)
-                m.bias.data.fill_(1.0)
-
-        controlnet1 = ControlNetModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            in_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            cross_attention_dim=32,
-            conditioning_embedding_out_channels=(16, 32),
-        )
-        controlnet1.controlnet_down_blocks.apply(init_weights)
-
-        torch.manual_seed(0)
-        controlnet2 = ControlNetModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            in_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            cross_attention_dim=32,
-            conditioning_embedding_out_channels=(16, 32),
-        )
-        controlnet2.controlnet_down_blocks.apply(init_weights)
-
-        torch.manual_seed(0)
-        scheduler = DDIMScheduler(
-            beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="scaled_linear",
-            clip_sample=False,
-            set_alpha_to_one=False,
-        )
-        torch.manual_seed(0)
-        vae = AutoencoderKL(
-            block_out_channels=[32, 64],
-            in_channels=3,
-            out_channels=3,
-            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            latent_channels=4,
-        )
-        torch.manual_seed(0)
-        text_encoder_config = CLIPTextConfig(
-            bos_token_id=0,
-            eos_token_id=2,
-            hidden_size=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            pad_token_id=1,
-            vocab_size=1000,
-        )
-        text_encoder = CLIPTextModel(text_encoder_config)
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
-        controlnet = MultiControlNetModel([controlnet1, controlnet2])
-
-        components = {
-            "unet": unet,
-            "controlnet": controlnet,
-            "scheduler": scheduler,
-            "vae": vae,
-            "text_encoder": text_encoder,
-            "tokenizer": tokenizer,
-            "safety_checker": None,
-            "feature_extractor": None,
-        }
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-
-        controlnet_embedder_scale_factor = 2
-
-        images = [
-            randn_tensor(
-                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
-                generator=generator,
-                device=torch.device(device),
-            ),
-            randn_tensor(
-                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
-                generator=generator,
-                device=torch.device(device),
-            ),
-        ]
-
-        inputs = {
-            "prompt": "A painting of a squirrel eating a burger",
-            "generator": generator,
-            "num_inference_steps": 2,
-            "guidance_scale": 6.0,
-            "output_type": "numpy",
-            "image": images,
-        }
-
-        return inputs
-
-    def test_control_guidance_switch(self):
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe.to(torch_device)
-
-        scale = 10.0
-        steps = 4
-
-        inputs = self.get_dummy_inputs(torch_device)
-        inputs["num_inference_steps"] = steps
-        inputs["controlnet_conditioning_scale"] = scale
-        output_1 = pipe(**inputs)[0]
-
-        inputs = self.get_dummy_inputs(torch_device)
-        inputs["num_inference_steps"] = steps
-        inputs["controlnet_conditioning_scale"] = scale
-        output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0]
-
-        inputs = self.get_dummy_inputs(torch_device)
-        inputs["num_inference_steps"] = steps
-        inputs["controlnet_conditioning_scale"] = scale
-        output_3 = pipe(**inputs, control_guidance_start=[0.1, 0.3], control_guidance_end=[0.2, 0.7])[0]
-
-        inputs = self.get_dummy_inputs(torch_device)
-        inputs["num_inference_steps"] = steps
-        inputs["controlnet_conditioning_scale"] = scale
-        output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5, 0.8])[0]
-
-        # make sure that all outputs are different
-        assert np.sum(np.abs(output_1 - output_2)) > 1e-3
-        assert np.sum(np.abs(output_1 - output_3)) > 1e-3
-        assert np.sum(np.abs(output_1 - output_4)) > 1e-3
-
-    def test_attention_slicing_forward_pass(self):
-        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
-
-    @unittest.skipIf(
-        torch_device != "cuda" or not is_xformers_available(),
-        reason="XFormers attention is only available with CUDA and `xformers` installed",
-    )
-    def test_xformers_attention_forwardGenerator_pass(self):
-        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
-
-    def test_inference_batch_single_identical(self):
-        self._test_inference_batch_single_identical(expected_max_diff=2e-3)
-
-    def test_save_pretrained_raise_not_implemented_exception(self):
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        with tempfile.TemporaryDirectory() as tmpdir:
-            try:
-                # save_pretrained is not implemented for Multi-ControlNet
-                pipe.save_pretrained(tmpdir)
-            except NotImplementedError:
-                pass
-
-
-class StableDiffusionMultiControlNetOneModelPipelineFastTests(
-    PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
-):
-    pipeline_class = StableDiffusionControlNetPipeline
-    params = TEXT_TO_IMAGE_PARAMS
-    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
-    image_params = frozenset([])  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
-
-    def get_dummy_components(self):
-        torch.manual_seed(0)
-        unet = UNet2DConditionModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            sample_size=32,
-            in_channels=4,
-            out_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
-            cross_attention_dim=32,
-        )
-        torch.manual_seed(0)
-
-        def init_weights(m):
-            if isinstance(m, torch.nn.Conv2d):
-                torch.nn.init.normal(m.weight)
-                m.bias.data.fill_(1.0)
-
-        controlnet = ControlNetModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            in_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            cross_attention_dim=32,
-            conditioning_embedding_out_channels=(16, 32),
-        )
-        controlnet.controlnet_down_blocks.apply(init_weights)
-
-        torch.manual_seed(0)
-        scheduler = DDIMScheduler(
-            beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="scaled_linear",
-            clip_sample=False,
-            set_alpha_to_one=False,
-        )
-        torch.manual_seed(0)
-        vae = AutoencoderKL(
-            block_out_channels=[32, 64],
-            in_channels=3,
-            out_channels=3,
-            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            latent_channels=4,
-        )
-        torch.manual_seed(0)
-        text_encoder_config = CLIPTextConfig(
-            bos_token_id=0,
-            eos_token_id=2,
-            hidden_size=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            pad_token_id=1,
-            vocab_size=1000,
-        )
-        text_encoder = CLIPTextModel(text_encoder_config)
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
-        controlnet = MultiControlNetModel([controlnet])
-
-        components = {
-            "unet": unet,
-            "controlnet": controlnet,
-            "scheduler": scheduler,
-            "vae": vae,
-            "text_encoder": text_encoder,
-            "tokenizer": tokenizer,
-            "safety_checker": None,
-            "feature_extractor": None,
-        }
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-
-        controlnet_embedder_scale_factor = 2
-
-        images = [
-            randn_tensor(
-                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
-                generator=generator,
-                device=torch.device(device),
-            ),
-        ]
-
-        inputs = {
-            "prompt": "A painting of a squirrel eating a burger",
-            "generator": generator,
-            "num_inference_steps": 2,
-            "guidance_scale": 6.0,
-            "output_type": "numpy",
-            "image": images,
-        }
-
-        return inputs
-
-    def test_control_guidance_switch(self):
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe.to(torch_device)
-
-        scale = 10.0
-        steps = 4
-
-        inputs = self.get_dummy_inputs(torch_device)
-        inputs["num_inference_steps"] = steps
-        inputs["controlnet_conditioning_scale"] = scale
-        output_1 = pipe(**inputs)[0]
-
-        inputs = self.get_dummy_inputs(torch_device)
-        inputs["num_inference_steps"] = steps
-        inputs["controlnet_conditioning_scale"] = scale
-        output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0]
-
-        inputs = self.get_dummy_inputs(torch_device)
-        inputs["num_inference_steps"] = steps
-        inputs["controlnet_conditioning_scale"] = scale
-        output_3 = pipe(
-            **inputs,
-            control_guidance_start=[0.1],
-            control_guidance_end=[0.2],
-        )[0]
-
-        inputs = self.get_dummy_inputs(torch_device)
-        inputs["num_inference_steps"] = steps
-        inputs["controlnet_conditioning_scale"] = scale
-        output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5])[0]
-
-        # make sure that all outputs are different
-        assert np.sum(np.abs(output_1 - output_2)) > 1e-3
-        assert np.sum(np.abs(output_1 - output_3)) > 1e-3
-        assert np.sum(np.abs(output_1 - output_4)) > 1e-3
-
-    def test_attention_slicing_forward_pass(self):
-        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
-
-    @unittest.skipIf(
-        torch_device != "cuda" or not is_xformers_available(),
-        reason="XFormers attention is only available with CUDA and `xformers` installed",
-    )
-    def test_xformers_attention_forwardGenerator_pass(self):
-        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
-
-    def test_inference_batch_single_identical(self):
-        self._test_inference_batch_single_identical(expected_max_diff=2e-3)
-
-    def test_save_pretrained_raise_not_implemented_exception(self):
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        with tempfile.TemporaryDirectory() as tmpdir:
-            try:
-                # save_pretrained is not implemented for Multi-ControlNet
-                pipe.save_pretrained(tmpdir)
-            except NotImplementedError:
-                pass
-
-
-@slow
-@require_torch_gpu
-class ControlNetPipelineSlowTests(unittest.TestCase):
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def test_canny(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "bird"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
-        )
-
-        output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (768, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 9e-2
-
-    def test_depth(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-depth")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "Stormtrooper's lecture"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png"
-        )
-
-        output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (512, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 8e-1
-
-    def test_hed(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-hed")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "oil painting of handsome old man, masterpiece"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/man_hed.png"
-        )
-
-        output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (704, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/man_hed_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 8e-2
-
-    def test_mlsd(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-mlsd")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "room"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/room_mlsd.png"
-        )
-
-        output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (704, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/room_mlsd_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 5e-2
-
-    def test_normal(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-normal")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "cute toy"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/cute_toy_normal.png"
-        )
-
-        output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (512, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/cute_toy_normal_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 5e-2
-
-    def test_openpose(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "Chef in the kitchen"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"
-        )
-
-        output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (768, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/chef_pose_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 8e-2
-
-    def test_scribble(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(5)
-        prompt = "bag"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bag_scribble.png"
-        )
-
-        output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (640, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bag_scribble_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 8e-2
-
-    def test_seg(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(5)
-        prompt = "house"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg.png"
-        )
-
-        output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (512, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 8e-2
-
-    def test_sequential_cpu_offloading(self):
-        torch.cuda.empty_cache()
-        torch.cuda.reset_max_memory_allocated()
-        torch.cuda.reset_peak_memory_stats()
-
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.set_progress_bar_config(disable=None)
-        pipe.enable_attention_slicing()
-        pipe.enable_sequential_cpu_offload()
-
-        prompt = "house"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg.png"
-        )
-
-        _ = pipe(
-            prompt,
-            image,
-            num_inference_steps=2,
-            output_type="np",
-        )
-
-        mem_bytes = torch.cuda.max_memory_allocated()
-        # make sure that less than 7 GB is allocated
-        assert mem_bytes < 4 * 10**9
-
-    def test_canny_guess_mode(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = ""
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
-        )
-
-        output = pipe(
-            prompt,
-            image,
-            generator=generator,
-            output_type="np",
-            num_inference_steps=3,
-            guidance_scale=3.0,
-            guess_mode=True,
-        )
-
-        image = output.images[0]
-        assert image.shape == (768, 512, 3)
-
-        image_slice = image[-3:, -3:, -1]
-        expected_slice = np.array([0.2724, 0.2846, 0.2724, 0.3843, 0.3682, 0.2736, 0.4675, 0.3862, 0.2887])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_canny_guess_mode_euler(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = ""
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
-        )
-
-        output = pipe(
-            prompt,
-            image,
-            generator=generator,
-            output_type="np",
-            num_inference_steps=3,
-            guidance_scale=3.0,
-            guess_mode=True,
-        )
-
-        image = output.images[0]
-        assert image.shape == (768, 512, 3)
-
-        image_slice = image[-3:, -3:, -1]
-        expected_slice = np.array([0.1655, 0.1721, 0.1623, 0.1685, 0.1711, 0.1646, 0.1651, 0.1631, 0.1494])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    @require_torch_2
-    def test_stable_diffusion_compile(self):
-        run_test_in_subprocess(test_case=self, target_func=_test_stable_diffusion_compile, inputs=None)
-
-    def test_v11_shuffle_global_pool_conditions(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11e_sd15_shuffle")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "New York"
-        image = load_image(
-            "https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"
-        )
-
-        output = pipe(
-            prompt,
-            image,
-            generator=generator,
-            output_type="np",
-            num_inference_steps=3,
-            guidance_scale=7.0,
-        )
-
-        image = output.images[0]
-        assert image.shape == (512, 640, 3)
-
-        image_slice = image[-3:, -3:, -1]
-        expected_slice = np.array([0.1338, 0.1597, 0.1202, 0.1687, 0.1377, 0.1017, 0.2070, 0.1574, 0.1348])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_load_local(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
-        pipe_1 = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-
-        controlnet = ControlNetModel.from_single_file(
-            "https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"
-        )
-        pipe_2 = StableDiffusionControlNetPipeline.from_single_file(
-            "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors",
-            safety_checker=None,
-            controlnet=controlnet,
-        )
-        pipes = [pipe_1, pipe_2]
-        images = []
-
-        for pipe in pipes:
-            pipe.enable_model_cpu_offload()
-            pipe.set_progress_bar_config(disable=None)
-
-            generator = torch.Generator(device="cpu").manual_seed(0)
-            prompt = "bird"
-            image = load_image(
-                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
-            )
-
-            output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-            images.append(output.images[0])
-
-            del pipe
-            gc.collect()
-            torch.cuda.empty_cache()
-
-        assert np.abs(images[0] - images[1]).sum() < 1e-3
-
-
-@slow
-@require_torch_gpu
-class StableDiffusionMultiControlNetPipelineSlowTests(unittest.TestCase):
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def test_pose_and_canny(self):
-        controlnet_canny = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
-        controlnet_pose = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=[controlnet_pose, controlnet_canny]
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "bird and Chef"
-        image_canny = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
-        )
-        image_pose = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"
-        )
-
-        output = pipe(prompt, [image_pose, image_canny], generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (768, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose_canny_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 5e-2

spaces/AnimaLab/bias-test-gpt-pairs/README.md

@@ -1,14 +0,0 @@
----
-title: Bias Test Gpt Pairs
-emoji: 🦀
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: RKocielnik/bias-test-gpt-pairs
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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

@@ -1,737 +0,0 @@
-import os
-
-os.environ["WANDB_MODE"] = "offline"
-# os.environ["WANDB_DISABLED"] = "true"
-
-import json
-import math
-import random
-import shutil
-import sys
-import threading
-import time
-import traceback
-from datetime import datetime
-from pathlib import Path
-
-import gradio as gr
-import torch
-import transformers
-from datasets import Dataset, load_dataset
-from peft import (
-    LoraConfig,
-    get_peft_model,
-    prepare_model_for_kbit_training,
-    set_peft_model_state_dict
-)
-from peft.utils.other import \
-    TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING as model_to_lora_modules
-from transformers.models.auto.modeling_auto import (
-    MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
-)
-
-from modules import shared, ui, utils
-from modules.evaluate import (
-    calculate_perplexity,
-    generate_markdown_table,
-    save_past_evaluations
-)
-from modules.logging_colors import logger
-from modules.models import reload_model
-from modules.utils import natural_keys
-
-MODEL_CLASSES = {v[1]: v[0] for v in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.items()}
-PARAMETERS = ["lora_name", "always_override", "save_steps", "micro_batch_size", "batch_size", "epochs", "learning_rate", "lr_scheduler_type", "lora_rank", "lora_alpha", "lora_dropout", "cutoff_len", "dataset", "eval_dataset", "format", "eval_steps", "raw_text_file", "overlap_len", "newline_favor_len", "higher_rank_limit", "warmup_steps", "optimizer", "hard_cut_string", "train_only_after", "stop_at_loss", "add_eos_token", "min_chars", "report_to"]
-WANT_INTERRUPT = False
-
-train_log = {}
-train_template = {}
-
-
-def create_ui():
-    mu = shared.args.multi_user
-    with gr.Tab("Training", elem_id="training-tab"):
-        with gr.Tab('Train LoRA', elem_id='lora-train-tab'):
-            tmp = gr.State('')
-            with gr.Row():
-                with gr.Column():
-                    gr.Markdown("[Tutorial](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Training-LoRAs.md)")
-
-                    with gr.Row():
-                        copy_from = gr.Dropdown(label='Copy parameters from', value='None', choices=utils.get_available_loras(), elem_classes=['slim-dropdown'], interactive=not mu)
-                        ui.create_refresh_button(copy_from, lambda: None, lambda: {'choices': utils.get_available_loras()}, 'refresh-button', interactive=not mu)
-
-                    with gr.Row():
-                        with gr.Column(scale=5):
-                            lora_name = gr.Textbox(label='Name', info='The name of your new LoRA file')
-                        with gr.Column():
-                            always_override = gr.Checkbox(label='Override Existing Files', value=False, info='If the name is the same, checking will replace the existing file, and unchecking will load and continue from it (the rank must be the same).', elem_classes=['no-background'])
-
-                    with gr.Row():
-                        with gr.Column():
-                            lora_rank = gr.Slider(label='LoRA Rank', value=32, minimum=0, maximum=1024, step=4, info='Also called dimension count. Higher values = larger file, more content control. Smaller values = smaller file, less control. Use 4 or 8 for style, 128 or 256 to teach, 1024+ for fine-detail on big data. More VRAM is needed for higher ranks.')
-                            lora_alpha = gr.Slider(label='LoRA Alpha', value=64, minimum=0, maximum=2048, step=4, info='This divided by the rank becomes the scaling of the LoRA. Higher means stronger. A good standard value is twice your Rank.')
-                            batch_size = gr.Slider(label='Batch Size', value=128, minimum=0, maximum=1024, step=4, info='Global batch size. The two batch sizes together determine gradient accumulation (gradientAccum = batch / microBatch). Higher gradient accum values lead to better quality training.')
-                            micro_batch_size = gr.Slider(label='Micro Batch Size', value=4, minimum=1, maximum=128, step=1, info='Per-device batch size (NOTE: multiple devices not yet implemented). Increasing this will increase VRAM usage.')
-                            cutoff_len = gr.Slider(label='Cutoff Length', minimum=0, maximum=2048, value=256, step=32, info='Cutoff length for text input. Essentially, how long of a line of text to feed in at a time. Higher values require drastically more VRAM.')
-
-                        with gr.Column():
-                            save_steps = gr.Number(label='Save every n steps', value=0, info='If above 0, a checkpoint of the LoRA will be saved every time this many steps pass.')
-
-                            epochs = gr.Number(label='Epochs', value=3, info='Number of times every entry in the dataset should be fed into training. So 1 means feed each item in once, 5 means feed it in five times, etc.')
-                            learning_rate = gr.Textbox(label='Learning Rate', value='3e-4', info='In scientific notation. 3e-4 is a good starting base point. 1e-2 is extremely high, 1e-6 is extremely low.')
-                            lr_scheduler_type = gr.Dropdown(label='LR Scheduler', value='linear', choices=['linear', 'constant', 'constant_with_warmup', 'cosine', 'cosine_with_restarts', 'polynomial', 'inverse_sqrt'], info='Learning rate scheduler - defines how the learning rate changes over time. "Constant" means never change, "linear" means to go in a straight line from the learning rate down to 0, cosine follows a curve, etc.', elem_classes=['slim-dropdown'])
-
-                    with gr.Accordion(label='Advanced Options', open=False):
-                        with gr.Row():
-                            with gr.Column():
-                                lora_dropout = gr.Slider(label='LoRA Dropout', minimum=0.0, maximum=1.0, step=0.025, value=0.05, info='Percentage probability for dropout of LoRA layers. This can help reduce overfitting. Most users should leave at default.')
-                                stop_at_loss = gr.Slider(label='Stop at loss', minimum=0.0, maximum=3.0, step=0.1, value=0.00, info='The process will automatically stop once the desired loss value is reached. (reasonable numbers are 1.5-1.8)')
-                                optimizer = gr.Dropdown(label='Optimizer', value='adamw_torch', choices=['adamw_hf', 'adamw_torch', 'adamw_torch_fused', 'adamw_torch_xla', 'adamw_apex_fused', 'adafactor', 'adamw_bnb_8bit', 'adamw_anyprecision', 'sgd', 'adagrad'], info='Different optimizer implementation options, for advanced users. Effects of different options are not well documented yet.', elem_classes=['slim-dropdown'])
-
-                            with gr.Column():
-                                warmup_steps = gr.Number(label='Warmup Steps', value=100, info='For this many steps at the start, the learning rate will be lower than normal. This helps the trainer prepare the model and precompute statistics to improve the quality of training after the start.')
-                                train_only_after = gr.Textbox(label='Train Only After', value='', info='Only consider text *after* this string in any given chunk for training. For Alpaca datasets, use "### Response:" to only train the response and ignore the input.')
-
-                                add_eos_token = gr.Checkbox(label='Add EOS token', value=False, info="Adds EOS token for each dataset item. In case of raw text, the EOS will be added at the Hard Cut")
-
-                                higher_rank_limit = gr.Checkbox(label='Enable higher ranks', value=False, info='If checked, changes Rank/Alpha slider above to go much higher. This will not work without a datacenter-class GPU.')
-                                report_to = gr.Radio(label="Save detailed logs with", value="None", choices=["None", "wandb", "tensorboard"], interactive=True)
-
-                with gr.Column():
-                    with gr.Tab(label='Formatted Dataset'):
-                        with gr.Row():
-                            format = gr.Dropdown(choices=utils.get_datasets('training/formats', 'json'), value='None', label='Data Format', info='The format file used to decide how to format the dataset input.', elem_classes=['slim-dropdown'], interactive=not mu)
-                            ui.create_refresh_button(format, lambda: None, lambda: {'choices': utils.get_datasets('training/formats', 'json')}, 'refresh-button', interactive=not mu)
-
-                        with gr.Row():
-                            dataset = gr.Dropdown(choices=utils.get_datasets('training/datasets', 'json'), value='None', label='Dataset', info='The dataset file to use for training.', elem_classes=['slim-dropdown'], interactive=not mu)
-                            ui.create_refresh_button(dataset, lambda: None, lambda: {'choices': utils.get_datasets('training/datasets', 'json')}, 'refresh-button', interactive=not mu)
-
-                        with gr.Row():
-                            eval_dataset = gr.Dropdown(choices=utils.get_datasets('training/datasets', 'json'), value='None', label='Evaluation Dataset', info='The (optional) dataset file used to evaluate the model after training.', elem_classes=['slim-dropdown'], interactive=not mu)
-                            ui.create_refresh_button(eval_dataset, lambda: None, lambda: {'choices': utils.get_datasets('training/datasets', 'json')}, 'refresh-button', interactive=not mu)
-
-                        eval_steps = gr.Number(label='Evaluate every n steps', value=100, info='If an evaluation dataset is given, test it every time this many steps pass.')
-
-                    with gr.Tab(label="Raw text file"):
-                        with gr.Row():
-                            raw_text_file = gr.Dropdown(choices=utils.get_datasets('training/datasets', 'txt'), value='None', label='Text file', info='The raw text file to use for training.', elem_classes=['slim-dropdown'], interactive=not mu)
-                            ui.create_refresh_button(raw_text_file, lambda: None, lambda: {'choices': utils.get_datasets('training/datasets', 'txt')}, 'refresh-button', interactive=not mu)
-
-                        with gr.Row():
-                            with gr.Column():
-                                overlap_len = gr.Slider(label='Overlap Length', minimum=0, maximum=512, value=128, step=16, info='How many tokens from the prior chunk of text to include into the next chunk. (The chunks themselves will be of a size determined by Cutoff Length). Setting overlap to exactly half the cutoff length may be ideal.')
-                                newline_favor_len = gr.Slider(label='Prefer Newline Cut Length', minimum=0, maximum=512, value=128, step=16, info='Length (in characters, not tokens) of the maximum distance to shift an overlap cut by to ensure chunks cut at newlines. If too low, cuts may occur in the middle of lines.')
-
-                            with gr.Column():
-                                hard_cut_string = gr.Textbox(label='Hard Cut String', value='\\n\\n\\n', info='String that indicates a hard cut between text parts. Helps prevent unwanted overlap.')
-                                min_chars = gr.Number(label='Ignore small blocks', value=0, info='Ignore Hard Cut blocks that have less or equal characters than this number')
-
-                    with gr.Row():
-                        start_button = gr.Button("Start LoRA Training", variant='primary', interactive=not mu)
-                        stop_button = gr.Button("Interrupt", interactive=not mu)
-
-                    output = gr.Markdown(value="Ready")
-
-        with gr.Tab('Perplexity evaluation', elem_id='evaluate-tab'):
-            with gr.Row():
-                with gr.Column():
-                    models = gr.Dropdown(utils.get_available_models(), label='Models', multiselect=True, interactive=not mu)
-                    evaluate_text_file = gr.Dropdown(choices=['wikitext', 'ptb', 'ptb_new'] + utils.get_datasets('training/datasets', 'txt')[1:], value='wikitext', label='Input dataset', info='The raw text file on which the model will be evaluated. The first options are automatically downloaded: wikitext, ptb, and ptb_new. The next options are your local text files under training/datasets.', interactive=not mu)
-                    with gr.Row():
-                        with gr.Column():
-                            stride_length = gr.Slider(label='Stride', minimum=0, maximum=32768, value=512, step=256, info='Used to make the evaluation faster at the cost of accuracy. 1 = slowest but most accurate. 512 is a common value.')
-
-                        with gr.Column():
-                            max_length = gr.Slider(label='max_length', minimum=0, maximum=32768, value=0, step=256, info='The context for each evaluation. If set to 0, the maximum context length for the model will be used.')
-
-                    with gr.Row():
-                        start_current_evaluation = gr.Button("Evaluate loaded model", interactive=not mu)
-                        start_evaluation = gr.Button("Evaluate selected models", interactive=not mu)
-                        stop_evaluation = gr.Button("Interrupt", interactive=not mu)
-
-                with gr.Column():
-                    evaluation_log = gr.Markdown(value='')
-
-            evaluation_table = gr.Dataframe(value=generate_markdown_table(), interactive=True)
-            with gr.Row():
-                save_comments = gr.Button('Save comments', elem_classes="small-button", interactive=not mu)
-                refresh_table = gr.Button('Refresh the table', elem_classes="small-button", interactive=not mu)
-
-    # Training events
-    all_params = [lora_name, always_override, save_steps, micro_batch_size, batch_size, epochs, learning_rate, lr_scheduler_type, lora_rank, lora_alpha, lora_dropout, cutoff_len, dataset, eval_dataset, format, eval_steps, raw_text_file, overlap_len, newline_favor_len, higher_rank_limit, warmup_steps, optimizer, hard_cut_string, train_only_after, stop_at_loss, add_eos_token, min_chars, report_to]
-
-    copy_from.change(do_copy_params, [copy_from] + all_params, all_params)
-    start_button.click(do_train, all_params, output)
-    stop_button.click(do_interrupt, None, None, queue=False)
-    higher_rank_limit.change(change_rank_limit, [higher_rank_limit], [lora_rank, lora_alpha])
-
-    # Evaluation events. For some reason, the interrupt event
-    # doesn't work with the .then() syntax, so I write them one
-    # by one in this ugly but functional way.
-    ev = start_evaluation.click(calculate_perplexity, [models, evaluate_text_file, stride_length, max_length], evaluation_log, show_progress=False)
-    start_evaluation.click(generate_markdown_table, None, evaluation_table, show_progress=False)
-
-    start_current_evaluation.click(lambda: ['current model'], None, tmp)
-    ev_cur = start_current_evaluation.click(calculate_perplexity, [tmp, evaluate_text_file, stride_length, max_length], evaluation_log, show_progress=False)
-    start_current_evaluation.click(generate_markdown_table, None, evaluation_table, show_progress=False)
-
-    stop_evaluation.click(None, None, None, cancels=[ev, ev_cur], queue=False)
-    refresh_table.click(generate_markdown_table, None, evaluation_table, show_progress=True)
-    save_comments.click(
-        save_past_evaluations, evaluation_table, None).then(
-        lambda: "Comments saved.", None, evaluation_log, show_progress=False)
-
-
-def do_interrupt():
-    global WANT_INTERRUPT
-    WANT_INTERRUPT = True
-
-
-def do_copy_params(lora_name: str, *args):
-    f_name = f"{shared.args.lora_dir}/{clean_path(None, lora_name)}/training_parameters.json"
-    if Path(f_name).is_file():
-        with open(f_name, 'r', encoding='utf-8') as format_file:
-            params: dict[str, str] = json.load(format_file)
-    else:
-        params = {}
-
-    result = list()
-    for i in range(0, len(PARAMETERS)):
-        key = PARAMETERS[i]
-        if key in params:
-            result.append(params[key])
-        else:
-            result.append(args[i])
-
-    return result
-
-
-def change_rank_limit(use_higher_ranks: bool):
-    mult = 2 if use_higher_ranks else 1
-    return {"maximum": 1024 * mult, "__type__": "update"}, {"maximum": 2048 * mult, "__type__": "update"}
-
-
-def clean_path(base_path: str, path: str):
-    """Strips unusual symbols and forcibly builds a path as relative to the intended directory."""
-    path = path.replace('\\', '/').replace('..', '_')
-    if base_path is None:
-        return path
-
-    return f'{Path(base_path).absolute()}/{path}'
-
-
-def backup_adapter(input_folder):
-    # Get the creation date of the file adapter_model.bin
-    try:
-        adapter_file = Path(f"{input_folder}/adapter_model.bin")
-        if adapter_file.is_file():
-
-            logger.info("Backing up existing LoRA adapter...")
-            creation_date = datetime.fromtimestamp(adapter_file.stat().st_ctime)
-            creation_date_str = creation_date.strftime("Backup-%Y-%m-%d")
-
-            # Create the new subfolder
-            subfolder_path = Path(f"{input_folder}/{creation_date_str}")
-            subfolder_path.mkdir(parents=True, exist_ok=True)
-
-            # Check if the file already exists in the subfolder
-            backup_adapter_file = Path(f"{input_folder}/{creation_date_str}/adapter_model.bin")
-            if backup_adapter_file.is_file():
-                print(" - Backup already exists. Skipping backup process.")
-                return
-
-            # Copy existing files to the new subfolder
-            existing_files = Path(input_folder).iterdir()
-            for file in existing_files:
-                if file.is_file():
-                    shutil.copy2(file, subfolder_path)
-    except Exception as e:
-        print("An error occurred in backup_adapter:", str(e))
-
-
-def calc_trainable_parameters(model):
-    trainable_params = 0
-    all_param = 0
-    for _, param in model.named_parameters():
-        num_params = param.numel()
-        # if using DS Zero 3 and the weights are initialized empty
-        if num_params == 0 and hasattr(param, "ds_numel"):
-            num_params = param.ds_numel
-
-        all_param += num_params
-        if param.requires_grad:
-            trainable_params += num_params
-
-    return trainable_params, all_param
-
-
-def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lr_scheduler_type: str, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, eval_steps: int, raw_text_file: str, overlap_len: int, newline_favor_len: int, higher_rank_limit: bool, warmup_steps: int, optimizer: str, hard_cut_string: str, train_only_after: str, stop_at_loss: float, add_eos_token: bool, min_chars: int, report_to: str):
-
-    if shared.args.monkey_patch:
-        from alpaca_lora_4bit.monkeypatch.peft_tuners_lora_monkey_patch import (
-            replace_peft_model_with_int4_lora_model
-        )
-        replace_peft_model_with_int4_lora_model()
-
-    global WANT_INTERRUPT
-    WANT_INTERRUPT = False
-
-    # == Input validation / processing ==
-    yield "Preparing the input..."
-    lora_file_path = clean_path(None, lora_name)
-    if lora_file_path.strip() == '':
-        yield "Missing or invalid LoRA file name input."
-        return
-
-    lora_file_path = f"{Path(shared.args.lora_dir)}/{lora_file_path}"
-    actual_lr = float(learning_rate)
-    model_type = type(shared.model).__name__
-
-    if model_type in MODEL_CLASSES:
-        model_id = MODEL_CLASSES[model_type]
-    else:
-        model_id = "llama"
-        if model_type == "PeftModelForCausalLM":
-            if len(shared.lora_names) > 0:
-                yield "You are trying to train a LoRA while you already have another LoRA loaded. This will work, but may have unexpected effects. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*"
-                logger.warning("Training LoRA over top of another LoRA. May have unexpected effects.")
-            else:
-                yield "Model ID not matched due to LoRA loading. Consider reloading base model. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*"
-                logger.warning("Model ID not matched due to LoRA loading. Consider reloading base model.")
-        else:
-            yield "LoRA training has only currently been validated for LLaMA, OPT, GPT-J, and GPT-NeoX models. Unexpected errors may follow. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*"
-            logger.warning(f"LoRA training has only currently been validated for LLaMA, OPT, GPT-J, and GPT-NeoX models. (Found model type: {model_type})")
-
-        time.sleep(5)
-
-    if shared.args.loader == 'GPTQ-for-LLaMa' and not shared.args.monkey_patch:
-        yield "LoRA training with GPTQ-for-LLaMa requires loading with `--monkey-patch`"
-        return
-
-    if cutoff_len <= 0 or micro_batch_size <= 0 or batch_size <= 0 or actual_lr <= 0 or lora_rank <= 0 or lora_alpha <= 0:
-        yield "Cannot input zeroes."
-        return
-
-    gradient_accumulation_steps = batch_size // micro_batch_size
-    shared.tokenizer.pad_token_id = 0
-    shared.tokenizer.padding_side = "left"
-
-    def encode(text, add_bos_token):
-        result = shared.tokenizer.encode(text, truncation=True, max_length=cutoff_len)
-        # Check if the first two tokens are BOS
-        if len(result) >= 2 and result[:2] == [shared.tokenizer.bos_token_id, shared.tokenizer.bos_token_id]:
-            result = result[1:]
-
-        if not add_bos_token and result[0] == shared.tokenizer.bos_token_id:
-            result = result[1:]
-        return result
-
-    def tokenize(prompt, append_eos_token=False):
-
-        if train_only_after == '' or train_only_after not in prompt:
-            input_ids = encode(prompt, True)
-
-            if append_eos_token and input_ids[-1] != shared.tokenizer.eos_token_id and len(input_ids) < cutoff_len:
-                input_ids.append(shared.tokenizer.eos_token_id)
-
-            input_ids = [shared.tokenizer.pad_token_id] * (cutoff_len - len(input_ids)) + input_ids
-            labels = [1] * len(input_ids)
-
-        else:
-            ind = prompt.index(train_only_after) + len(train_only_after)
-            before_tokens = encode(prompt[:ind], True)
-            after_tokens = encode(prompt[ind:], False)
-
-            if append_eos_token and after_tokens[-1] != shared.tokenizer.eos_token_id:
-                after_tokens.append(shared.tokenizer.eos_token_id)
-
-            full_length = len(after_tokens) + len(before_tokens)
-            if full_length > cutoff_len:
-                after_tokens = after_tokens[:cutoff_len - len(before_tokens)]
-            else:
-                before_tokens = [shared.tokenizer.pad_token_id] * (cutoff_len - full_length) + before_tokens
-
-            input_ids = before_tokens + after_tokens
-            labels = [-100] * len(before_tokens) + [1] * len(after_tokens)
-
-        input_ids = torch.tensor(input_ids)
-        return {
-            "input_ids": input_ids,
-            "labels": labels,
-            "attention_mask": input_ids.ne(shared.tokenizer.pad_token_id),
-        }
-
-    train_template.clear()
-
-    # == Prep the dataset, format, etc ==
-    if raw_text_file not in ['None', '']:
-        train_template["template_type"] = "raw_text"
-        logger.info("Loading raw text file dataset...")
-        fullpath = clean_path('training/datasets', f'{raw_text_file}')
-        fullpath = Path(fullpath)
-        if fullpath.is_dir():
-            logger.info('Training path directory {}'.format(raw_text_file))
-            raw_text = ""
-            file_paths = sorted(fullpath.glob('*.txt'), key=lambda path: natural_keys(path.name))
-            for file_path in file_paths:
-                if file_path.is_file():
-                    with file_path.open('r', encoding='utf-8') as file:
-                        raw_text += file.read().replace('\r', '')
-
-                    logger.info(f"Loaded training file: {file_path.name}")
-        else:
-            with open(clean_path('training/datasets', f'{raw_text_file}.txt'), 'r', encoding='utf-8') as file:
-                raw_text = file.read().replace('\r', '')
-
-        cut_string = hard_cut_string.replace('\\n', '\n')
-        eos_added = 0
-        out_tokens = []
-        for text_part in raw_text.split(cut_string):
-            if len(text_part.strip()) <= min_chars:
-                continue
-
-            tokens = shared.tokenizer.encode(text_part)
-            if add_eos_token:
-                tokens.append(shared.tokenizer.eos_token_id)
-                eos_added += 1
-
-            step = cutoff_len - overlap_len
-            if step <= 0:
-                yield f"Error: overlap_len ({overlap_len}) cannot be greater than or equal to cutoff_len ({cutoff_len})"
-                return
-
-            out_tokens.extend(split_chunks(tokens, cutoff_len, step))
-
-        if eos_added > 0:
-            print(f"EOS added to {eos_added} text blocks")
-
-        del raw_text  # Note: could be a gig for a large dataset, so delete redundant data as we go to be safe on RAM
-        text_chunks = [shared.tokenizer.decode(x) for x in out_tokens]
-        del out_tokens
-        if newline_favor_len > 0:
-            text_chunks = [cut_chunk_for_newline(x, newline_favor_len) for x in text_chunks]
-
-        train_data = Dataset.from_list([tokenize(x) for x in text_chunks])
-        del text_chunks
-        eval_data = None
-    else:
-        if dataset in ['None', '']:
-            yield "Missing dataset choice input, cannot continue."
-            return
-
-        if format in ['None', '']:
-            yield "Missing format choice input, cannot continue."
-            return
-
-        train_template["template_type"] = "dataset"
-
-        with open(clean_path('training/formats', f'{format}.json'), 'r', encoding='utf-8-sig') as formatFile:
-            format_data: dict[str, str] = json.load(formatFile)
-
-        # == store training prompt ==
-        for _, value in format_data.items():
-            prompt_key = f"template_{len(train_template)}"
-            train_template[prompt_key] = value
-
-        def generate_prompt(data_point: dict[str, str]):
-            for options, data in format_data.items():
-                if set(options.split(',')) == set(x[0] for x in data_point.items() if (type(x[1]) is str and len(x[1].strip()) > 0)):
-                    for key, val in data_point.items():
-                        if type(val) is str:
-                            data = data.replace(f'%{key}%', val)
-                    return data
-            raise RuntimeError(f'Data-point "{data_point}" has no keyset match within format "{list(format_data.keys())}"')
-
-        def generate_and_tokenize_prompt(data_point):
-            prompt = generate_prompt(data_point)
-            return tokenize(prompt, add_eos_token)
-
-        logger.info("Loading JSON datasets...")
-        data = load_dataset("json", data_files=clean_path('training/datasets', f'{dataset}.json'))
-        train_data = data['train'].map(generate_and_tokenize_prompt, new_fingerprint='%030x' % random.randrange(16**30))
-
-        if eval_dataset == 'None':
-            eval_data = None
-        else:
-            eval_data = load_dataset("json", data_files=clean_path('training/datasets', f'{eval_dataset}.json'))
-            eval_data = eval_data['train'].map(generate_and_tokenize_prompt, new_fingerprint='%030x' % random.randrange(16**30))
-
-    # == We MUST reload model if it went through any previous training, even failed one ==
-    if shared.model_dirty_from_training:
-        selected_model = shared.model_name
-        if selected_model:
-            print("\033[1;31;1m(Model has been modified by previous training, it needs to be reloaded...)\033[0;37;0m")
-            try:
-                yield f"Reloading {selected_model}..."
-                reload_model()
-                if shared.model is not None:
-                    print("Model reloaded OK, continue with training.")
-                else:
-                    return f"Failed to load {selected_model}."
-            except:
-                exc = traceback.format_exc()
-                logger.error('Failed to reload the model.')
-                print(exc)
-                return exc.replace('\n', '\n\n')
-
-    # == Start prepping the model itself ==
-    if not hasattr(shared.model, 'lm_head') or hasattr(shared.model.lm_head, 'weight'):
-        logger.info("Getting model ready...")
-        prepare_model_for_kbit_training(shared.model)
-
-    # base model is now frozen and should not be reused for any other LoRA training than this one
-    shared.model_dirty_from_training = True
-
-    logger.info("Preparing for training...")
-    config = LoraConfig(
-        r=lora_rank,
-        lora_alpha=lora_alpha,
-        target_modules=model_to_lora_modules[model_id],
-        lora_dropout=lora_dropout,
-        bias="none",
-        task_type="CAUSAL_LM"
-    )
-
-    # == Backup the existing adapter ==
-    if not always_override:
-        backup_adapter(lora_file_path)
-
-    # == get model trainable params
-    model_trainable_params, model_all_params = calc_trainable_parameters(shared.model)
-
-    try:
-        logger.info("Creating LoRA model...")
-        lora_model = get_peft_model(shared.model, config)
-        if not always_override and Path(f"{lora_file_path}/adapter_model.bin").is_file():
-            logger.info("Loading existing LoRA data...")
-            state_dict_peft = torch.load(f"{lora_file_path}/adapter_model.bin")
-            set_peft_model_state_dict(lora_model, state_dict_peft)
-    except:
-        yield traceback.format_exc().replace('\n', '\n\n')
-        return
-
-    if shared.args.monkey_patch:
-        from alpaca_lora_4bit.autograd_4bit import Autograd4bitQuantLinear
-        from alpaca_lora_4bit.models import Linear4bitLt
-        for _, m in lora_model.named_modules():
-            if isinstance(m, Autograd4bitQuantLinear) or isinstance(m, Linear4bitLt):
-                if m.is_v1_model:
-                    m.zeros = m.zeros.half()
-                m.scales = m.scales.half()
-
-    class Tracked():
-        def __init__(self):
-            self.current_steps = 0
-            self.max_steps = 0
-            self.did_save = False
-
-    tracked = Tracked()
-    actual_save_steps = math.ceil(save_steps / gradient_accumulation_steps)
-
-    class Callbacks(transformers.TrainerCallback):
-        def on_step_begin(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, **kwargs):
-            tracked.current_steps = state.global_step * gradient_accumulation_steps
-            tracked.max_steps = state.max_steps * gradient_accumulation_steps
-            if WANT_INTERRUPT:
-                control.should_epoch_stop = True
-                control.should_training_stop = True
-            elif state.global_step > 0 and actual_save_steps > 0 and state.global_step % actual_save_steps == 0:
-                lora_model.save_pretrained(f"{lora_file_path}/checkpoint-{tracked.current_steps}/")
-                # Save log
-                with open(f"{lora_file_path}/checkpoint-{tracked.current_steps}/training_log.json", 'w', encoding='utf-8') as file:
-                    json.dump(train_log, file, indent=2)
-                # == Save training prompt ==
-                with open(f"{lora_file_path}/checkpoint-{tracked.current_steps}/training_prompt.json", 'w', encoding='utf-8') as file:
-                    json.dump(train_template, file, indent=2)
-
-        def on_substep_end(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, **kwargs):
-            tracked.current_steps += 1
-            if WANT_INTERRUPT:
-                control.should_epoch_stop = True
-                control.should_training_stop = True
-
-        def on_log(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, logs, **kwargs):
-            train_log.update(logs)
-            train_log.update({"current_steps": tracked.current_steps})
-            if WANT_INTERRUPT:
-                print("\033[1;31;1mInterrupted by user\033[0;37;0m")
-
-            print(f"\033[1;30;40mStep: {tracked.current_steps} \033[0;37;0m", end='')
-            if 'loss' in logs:
-                loss = float(logs['loss'])
-                if loss <= stop_at_loss:
-                    control.should_epoch_stop = True
-                    control.should_training_stop = True
-                    print(f"\033[1;31;1mStop Loss {stop_at_loss} reached.\033[0;37;0m")
-
-    trainer = transformers.Trainer(
-        model=lora_model,
-        train_dataset=train_data,
-        eval_dataset=eval_data,
-        args=transformers.TrainingArguments(
-            report_to=report_to if report_to != "None" else None,
-            per_device_train_batch_size=micro_batch_size,
-            gradient_accumulation_steps=gradient_accumulation_steps,
-            warmup_steps=math.ceil(warmup_steps / gradient_accumulation_steps),
-            num_train_epochs=epochs,
-            learning_rate=actual_lr,
-            fp16=False if shared.args.cpu else True,
-            optim=optimizer,
-            logging_steps=2 if stop_at_loss > 0 else 5,
-            evaluation_strategy="steps" if eval_data is not None else "no",
-            eval_steps=math.ceil(eval_steps / gradient_accumulation_steps) if eval_data is not None else None,
-            save_strategy="steps" if eval_data is not None else "no",
-            output_dir=lora_file_path,
-            lr_scheduler_type=lr_scheduler_type,
-            load_best_model_at_end=eval_data is not None,
-            # TODO: Enable multi-device support
-            ddp_find_unused_parameters=None,
-            no_cuda=shared.args.cpu,
-        ),
-        data_collator=transformers.DataCollatorForLanguageModeling(shared.tokenizer, mlm=False),
-        callbacks=list([Callbacks()])
-    )
-
-    lora_model.config.use_cache = False
-
-    if torch.__version__ >= "2" and sys.platform != "win32":
-        lora_model = torch.compile(lora_model)
-
-    # == Save parameters for reuse ==
-    with open(f"{lora_file_path}/training_parameters.json", 'w', encoding='utf-8') as file:
-        vars = locals()
-        json.dump({x: vars[x] for x in PARAMETERS}, file, indent=2)
-
-    # == Save training prompt ==
-    with open(f"{lora_file_path}/training_prompt.json", 'w', encoding='utf-8') as file:
-        json.dump(train_template, file, indent=2)
-
-    # == Main run and monitor loop ==
-    logger.info("Starting training...")
-    yield "Starting..."
-
-    lora_trainable_param, lora_all_param = calc_trainable_parameters(lora_model)
-
-    projections_string = ", ".join([projection.replace("_proj", "") for projection in model_to_lora_modules[model_id]])
-
-    print(f"Training '{model_id}' model using ({projections_string}) projections")
-
-    if lora_all_param > 0:
-        print(f"Trainable params: {lora_trainable_param:,d} ({100 * lora_trainable_param / lora_all_param:.4f} %), All params: {lora_all_param:,d} (Model: {model_all_params:,d})")
-
-    train_log.update({"base_model_name": shared.model_name})
-    train_log.update({"base_model_class": shared.model.__class__.__name__})
-    train_log.update({"base_loaded_in_4bit": getattr(lora_model, "is_loaded_in_4bit", False)})
-    train_log.update({"base_loaded_in_8bit": getattr(lora_model, "is_loaded_in_8bit", False)})
-    train_log.update({"projections": projections_string})
-
-    if stop_at_loss > 0:
-        print(f"Monitoring loss \033[1;31;1m(Auto-Stop at: {stop_at_loss})\033[0;37;0m")
-
-    if WANT_INTERRUPT:
-        yield "Interrupted before start."
-        return
-
-    def log_train_dataset(trainer):
-        decoded_entries = []
-        # Try to decode the entries and write the log file
-        try:
-            # Iterate over the first 10 elements in the dataset (or fewer if there are less than 10)
-            for i in range(min(10, len(trainer.train_dataset))):
-                decoded_text = shared.tokenizer.decode(trainer.train_dataset[i]['input_ids'])
-                decoded_entries.append({"value": decoded_text})
-
-            # Write the log file
-            Path('logs').mkdir(exist_ok=True)
-            with open(Path('logs/train_dataset_sample.json'), 'w') as json_file:
-                json.dump(decoded_entries, json_file, indent=4)
-
-            logger.info("Log file 'train_dataset_sample.json' created in the 'logs' directory.")
-        except Exception as e:
-            logger.error(f"Failed to create log file due to error: {e}")
-
-    def threaded_run():
-        log_train_dataset(trainer)
-        trainer.train()
-        # Note: save in the thread in case the gradio thread breaks (eg browser closed)
-        lora_model.save_pretrained(lora_file_path)
-        logger.info("LoRA training run is completed and saved.")
-        # Save log
-        with open(f"{lora_file_path}/training_log.json", 'w', encoding='utf-8') as file:
-            json.dump(train_log, file, indent=2)
-
-    thread = threading.Thread(target=threaded_run)
-    thread.start()
-    last_step = 0
-    start_time = time.perf_counter()
-
-    while thread.is_alive():
-        time.sleep(0.5)
-        if WANT_INTERRUPT:
-            yield "Interrupting, please wait... *(Run will stop after the current training step completes.)*"
-
-        elif tracked.current_steps != last_step:
-            last_step = tracked.current_steps
-            time_elapsed = time.perf_counter() - start_time
-            if time_elapsed <= 0:
-                timer_info = ""
-                total_time_estimate = 999
-            else:
-                its = tracked.current_steps / time_elapsed
-                if its > 1:
-                    timer_info = f"`{its:.2f}` it/s"
-                else:
-                    timer_info = f"`{1.0/its:.2f}` s/it"
-
-                total_time_estimate = (1.0 / its) * (tracked.max_steps)
-
-            yield f"Running... **{tracked.current_steps}** / **{tracked.max_steps}** ... {timer_info}, {format_time(time_elapsed)} / {format_time(total_time_estimate)} ... {format_time(total_time_estimate - time_elapsed)} remaining"
-
-    # Saving in the train thread might fail if an error occurs, so save here if so.
-    if not tracked.did_save:
-        logger.info("Training complete, saving...")
-        lora_model.save_pretrained(lora_file_path)
-
-    if WANT_INTERRUPT:
-        logger.info("Training interrupted.")
-        yield f"Interrupted. Incomplete LoRA saved to `{lora_file_path}`."
-    else:
-        logger.info("Training complete!")
-        yield f"Done! LoRA saved to `{lora_file_path}`.\n\nBefore testing your new LoRA, make sure to first reload the model, as it is currently dirty from training."
-
-
-def split_chunks(arr, size, step):
-    for i in range(0, len(arr), step):
-        yield arr[i:i + size]
-
-
-def cut_chunk_for_newline(chunk: str, max_length: int):
-    if '\n' not in chunk:
-        return chunk
-
-    first_newline = chunk.index('\n')
-    if first_newline < max_length:
-        chunk = chunk[first_newline + 1:]
-
-    if '\n' not in chunk:
-        return chunk
-
-    last_newline = chunk.rindex('\n')
-    if len(chunk) - last_newline < max_length:
-        chunk = chunk[:last_newline]
-
-    return chunk
-
-
-def format_time(seconds: float):
-    if seconds < 120:
-        return f"`{seconds:.0f}` seconds"
-
-    minutes = seconds / 60
-    if minutes < 120:
-        return f"`{minutes:.0f}` minutes"
-
-    hours = minutes / 60
-    return f"`{hours:.0f}` hours"

spaces/AnnasBlackHat/Image-Similarity/src/model/simlarity_model.py

@@ -1,9 +0,0 @@
-from dataclasses import dataclass
-from .similarity_interface import SimilarityInterface
-
-@dataclass
-class SimilarityModel:
-    name: str 
-    image_size: int
-    model_cls: SimilarityInterface
-    image_input_type: str = 'array'

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/bbox.py

@@ -1,72 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', ['bbox_overlaps'])
-
-
-def bbox_overlaps(bboxes1, bboxes2, mode='iou', aligned=False, offset=0):
-    """Calculate overlap between two set of bboxes.
-
-    If ``aligned`` is ``False``, then calculate the ious between each bbox
-    of bboxes1 and bboxes2, otherwise the ious between each aligned pair of
-    bboxes1 and bboxes2.
-
-    Args:
-        bboxes1 (Tensor): shape (m, 4) in <x1, y1, x2, y2> format or empty.
-        bboxes2 (Tensor): shape (n, 4) in <x1, y1, x2, y2> format or empty.
-            If aligned is ``True``, then m and n must be equal.
-        mode (str): "iou" (intersection over union) or iof (intersection over
-            foreground).
-
-    Returns:
-        ious(Tensor): shape (m, n) if aligned == False else shape (m, 1)
-
-    Example:
-        >>> bboxes1 = torch.FloatTensor([
-        >>>     [0, 0, 10, 10],
-        >>>     [10, 10, 20, 20],
-        >>>     [32, 32, 38, 42],
-        >>> ])
-        >>> bboxes2 = torch.FloatTensor([
-        >>>     [0, 0, 10, 20],
-        >>>     [0, 10, 10, 19],
-        >>>     [10, 10, 20, 20],
-        >>> ])
-        >>> bbox_overlaps(bboxes1, bboxes2)
-        tensor([[0.5000, 0.0000, 0.0000],
-                [0.0000, 0.0000, 1.0000],
-                [0.0000, 0.0000, 0.0000]])
-
-    Example:
-        >>> empty = torch.FloatTensor([])
-        >>> nonempty = torch.FloatTensor([
-        >>>     [0, 0, 10, 9],
-        >>> ])
-        >>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1)
-        >>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0)
-        >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0)
-    """
-
-    mode_dict = {'iou': 0, 'iof': 1}
-    assert mode in mode_dict.keys()
-    mode_flag = mode_dict[mode]
-    # Either the boxes are empty or the length of boxes' last dimension is 4
-    assert (bboxes1.size(-1) == 4 or bboxes1.size(0) == 0)
-    assert (bboxes2.size(-1) == 4 or bboxes2.size(0) == 0)
-    assert offset == 1 or offset == 0
-
-    rows = bboxes1.size(0)
-    cols = bboxes2.size(0)
-    if aligned:
-        assert rows == cols
-
-    if rows * cols == 0:
-        return bboxes1.new(rows, 1) if aligned else bboxes1.new(rows, cols)
-
-    if aligned:
-        ious = bboxes1.new_zeros(rows)
-    else:
-        ious = bboxes1.new_zeros((rows, cols))
-    ext_module.bbox_overlaps(
-        bboxes1, bboxes2, ious, mode=mode_flag, aligned=aligned, offset=offset)
-    return ious

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/platformdirs/windows.py

@@ -1,195 +0,0 @@
-from __future__ import annotations
-
-import ctypes
-import os
-import sys
-from functools import lru_cache
-from typing import Callable
-
-from .api import PlatformDirsABC
-
-
-class Windows(PlatformDirsABC):
-    """`MSDN on where to store app data files
-    <http://support.microsoft.com/default.aspx?scid=kb;en-us;310294#XSLTH3194121123120121120120>`_.
-    Makes use of the
-    `appname <platformdirs.api.PlatformDirsABC.appname>`,
-    `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`,
-    `version <platformdirs.api.PlatformDirsABC.version>`,
-    `roaming <platformdirs.api.PlatformDirsABC.roaming>`,
-    `opinion <platformdirs.api.PlatformDirsABC.opinion>`,
-    `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
-    """
-
-    @property
-    def user_data_dir(self) -> str:
-        """
-        :return: data directory tied to the user, e.g.
-         ``%USERPROFILE%\\AppData\\Local\\$appauthor\\$appname`` (not roaming) or
-         ``%USERPROFILE%\\AppData\\Roaming\\$appauthor\\$appname`` (roaming)
-        """
-        const = "CSIDL_APPDATA" if self.roaming else "CSIDL_LOCAL_APPDATA"
-        path = os.path.normpath(get_win_folder(const))
-        return self._append_parts(path)
-
-    def _append_parts(self, path: str, *, opinion_value: str | None = None) -> str:
-        params = []
-        if self.appname:
-            if self.appauthor is not False:
-                author = self.appauthor or self.appname
-                params.append(author)
-            params.append(self.appname)
-            if opinion_value is not None and self.opinion:
-                params.append(opinion_value)
-            if self.version:
-                params.append(self.version)
-        path = os.path.join(path, *params)
-        self._optionally_create_directory(path)
-        return path
-
-    @property
-    def site_data_dir(self) -> str:
-        """:return: data directory shared by users, e.g. ``C:\\ProgramData\\$appauthor\\$appname``"""
-        path = os.path.normpath(get_win_folder("CSIDL_COMMON_APPDATA"))
-        return self._append_parts(path)
-
-    @property
-    def user_config_dir(self) -> str:
-        """:return: config directory tied to the user, same as `user_data_dir`"""
-        return self.user_data_dir
-
-    @property
-    def site_config_dir(self) -> str:
-        """:return: config directory shared by the users, same as `site_data_dir`"""
-        return self.site_data_dir
-
-    @property
-    def user_cache_dir(self) -> str:
-        """
-        :return: cache directory tied to the user (if opinionated with ``Cache`` folder within ``$appname``) e.g.
-         ``%USERPROFILE%\\AppData\\Local\\$appauthor\\$appname\\Cache\\$version``
-        """
-        path = os.path.normpath(get_win_folder("CSIDL_LOCAL_APPDATA"))
-        return self._append_parts(path, opinion_value="Cache")
-
-    @property
-    def site_cache_dir(self) -> str:
-        """:return: cache directory shared by users, e.g. ``C:\\ProgramData\\$appauthor\\$appname\\Cache\\$version``"""
-        path = os.path.normpath(get_win_folder("CSIDL_COMMON_APPDATA"))
-        return self._append_parts(path, opinion_value="Cache")
-
-    @property
-    def user_state_dir(self) -> str:
-        """:return: state directory tied to the user, same as `user_data_dir`"""
-        return self.user_data_dir
-
-    @property
-    def user_log_dir(self) -> str:
-        """
-        :return: log directory tied to the user, same as `user_data_dir` if not opinionated else ``Logs`` in it
-        """
-        path = self.user_data_dir
-        if self.opinion:
-            path = os.path.join(path, "Logs")
-            self._optionally_create_directory(path)
-        return path
-
-    @property
-    def user_documents_dir(self) -> str:
-        """
-        :return: documents directory tied to the user e.g. ``%USERPROFILE%\\Documents``
-        """
-        return os.path.normpath(get_win_folder("CSIDL_PERSONAL"))
-
-    @property
-    def user_runtime_dir(self) -> str:
-        """
-        :return: runtime directory tied to the user, e.g.
-         ``%USERPROFILE%\\AppData\\Local\\Temp\\$appauthor\\$appname``
-        """
-        path = os.path.normpath(os.path.join(get_win_folder("CSIDL_LOCAL_APPDATA"), "Temp"))
-        return self._append_parts(path)
-
-
-def get_win_folder_from_env_vars(csidl_name: str) -> str:
-    """Get folder from environment variables."""
-    if csidl_name == "CSIDL_PERSONAL":  # does not have an environment name
-        return os.path.join(os.path.normpath(os.environ["USERPROFILE"]), "Documents")
-
-    env_var_name = {
-        "CSIDL_APPDATA": "APPDATA",
-        "CSIDL_COMMON_APPDATA": "ALLUSERSPROFILE",
-        "CSIDL_LOCAL_APPDATA": "LOCALAPPDATA",
-    }.get(csidl_name)
-    if env_var_name is None:
-        raise ValueError(f"Unknown CSIDL name: {csidl_name}")
-    result = os.environ.get(env_var_name)
-    if result is None:
-        raise ValueError(f"Unset environment variable: {env_var_name}")
-    return result
-
-
-def get_win_folder_from_registry(csidl_name: str) -> str:
-    """Get folder from the registry.
-
-    This is a fallback technique at best. I'm not sure if using the
-    registry for this guarantees us the correct answer for all CSIDL_*
-    names.
-    """
-    shell_folder_name = {
-        "CSIDL_APPDATA": "AppData",
-        "CSIDL_COMMON_APPDATA": "Common AppData",
-        "CSIDL_LOCAL_APPDATA": "Local AppData",
-        "CSIDL_PERSONAL": "Personal",
-    }.get(csidl_name)
-    if shell_folder_name is None:
-        raise ValueError(f"Unknown CSIDL name: {csidl_name}")
-    if sys.platform != "win32":  # only needed for mypy type checker to know that this code runs only on Windows
-        raise NotImplementedError
-    import winreg
-
-    key = winreg.OpenKey(winreg.HKEY_CURRENT_USER, r"Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders")
-    directory, _ = winreg.QueryValueEx(key, shell_folder_name)
-    return str(directory)
-
-
-def get_win_folder_via_ctypes(csidl_name: str) -> str:
-    """Get folder with ctypes."""
-    csidl_const = {
-        "CSIDL_APPDATA": 26,
-        "CSIDL_COMMON_APPDATA": 35,
-        "CSIDL_LOCAL_APPDATA": 28,
-        "CSIDL_PERSONAL": 5,
-    }.get(csidl_name)
-    if csidl_const is None:
-        raise ValueError(f"Unknown CSIDL name: {csidl_name}")
-
-    buf = ctypes.create_unicode_buffer(1024)
-    windll = getattr(ctypes, "windll")  # noqa: B009 # using getattr to avoid false positive with mypy type checker
-    windll.shell32.SHGetFolderPathW(None, csidl_const, None, 0, buf)
-
-    # Downgrade to short path name if it has highbit chars.
-    if any(ord(c) > 255 for c in buf):
-        buf2 = ctypes.create_unicode_buffer(1024)
-        if windll.kernel32.GetShortPathNameW(buf.value, buf2, 1024):
-            buf = buf2
-
-    return buf.value
-
-
-def _pick_get_win_folder() -> Callable[[str], str]:
-    if hasattr(ctypes, "windll"):
-        return get_win_folder_via_ctypes
-    try:
-        import winreg  # noqa: F401
-    except ImportError:
-        return get_win_folder_from_env_vars
-    else:
-        return get_win_folder_from_registry
-
-
-get_win_folder = lru_cache(maxsize=None)(_pick_get_win_folder())
-
-__all__ = [
-    "Windows",
-]

spaces/Atualli/yoloxTeste/yoloxdetect2/configs/yolox_l.py

@@ -1,15 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) Megvii, Inc. and its affiliates.
-
-import os
-
-from yolox.exp import Exp as MyExp
-
-
-class Exp(MyExp):
-    def __init__(self):
-        super(Exp, self).__init__()
-        self.depth = 1.0
-        self.width = 1.0
-        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

spaces/Audio-AGI/AudioSep/data/audiotext_dataset.py

@@ -1,91 +0,0 @@
-import json
-import random
-import torch
-import torchaudio
-from torch.utils.data import Dataset
-
-
-class AudioTextDataset(Dataset):
-    """Can sample data from audio-text databases
-    Params:
-    sampling_rate: audio sampling rate
-    max_clip_len: max length (seconds) of audio clip to be sampled
-    """
-    def __init__(
-        self,
-        datafiles=[''], 
-        sampling_rate=32000, 
-        max_clip_len=5,
-    ):
-        all_data_json = []
-        for datafile in datafiles:
-            with open(datafile, 'r') as fp:
-                data_json = json.load(fp)['data']
-                all_data_json.extend(data_json)
-        self.all_data_json = all_data_json
-
-        self.sampling_rate = sampling_rate
-        self.max_length = max_clip_len * sampling_rate
-
-    def __len__(self):
-        return len(self.all_data_json)
-
-    def _cut_or_randomcrop(self, waveform):
-        # waveform: [1, samples]
-        # random crop
-        if waveform.size(1) > self.max_length:
-            random_idx = random.randint(0, waveform.size(1)-self.max_length)
-            waveform = waveform[:, random_idx:random_idx+self.max_length]
-        else:
-            temp_wav = torch.zeros(1, self.max_length)
-            temp_wav[:, 0:waveform.size(1)] = waveform
-            waveform = temp_wav
-
-        assert waveform.size(1) == self.max_length, \
-            f"number of audio samples is {waveform.size(1)}"
-
-        return waveform
-
-    def _read_audio(self, index):
-        try:
-            audio_path = self.all_data_json[index]['wav']
-            audio_data, audio_rate = torchaudio.load(audio_path, channels_first=True)
-            text = self.all_data_json[index]['caption']
-
-            # drop short utterance
-            if audio_data.size(1) < self.sampling_rate * 1:
-                raise Exception(f'{audio_path} is too short, drop it ...') 
-            
-            return text, audio_data, audio_rate
-        
-        except Exception as e:
-            print(f'error: {e} occurs, when loading {audio_path}')
-            random_index = random.randint(0, len(self.all_data_json)-1)
-            return self._read_audio(index=random_index)
-
-    def __getitem__(self, index):
-        # create a audio tensor  
-        text, audio_data, audio_rate = self._read_audio(index)
-        audio_len = audio_data.shape[1] / audio_rate
-        # convert stero to single channel
-        if audio_data.shape[0] > 1:
-            # audio_data: [samples]
-            audio_data = (audio_data[0] + audio_data[1]) / 2
-        else:
-            audio_data = audio_data.squeeze(0)
-        
-        # resample audio clip
-        if audio_rate != self.sampling_rate:
-            audio_data = torchaudio.functional.resample(audio_data, orig_freq=audio_rate, new_freq=self.sampling_rate)
-        
-        audio_data = audio_data.unsqueeze(0)
-        
-        audio_data = self._cut_or_randomcrop(audio_data)            
-
-        data_dict = {
-            'text': text, 
-            'waveform': audio_data,  
-            'modality': 'audio_text'
-        }
-
-        return data_dict

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/__init__.py

@@ -1,19 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from . import transforms  # isort:skip
-
-from .build import (
-    build_batch_data_loader,
-    build_detection_test_loader,
-    build_detection_train_loader,
-    get_detection_dataset_dicts,
-    load_proposals_into_dataset,
-    print_instances_class_histogram,
-)
-from .catalog import DatasetCatalog, MetadataCatalog, Metadata
-from .common import DatasetFromList, MapDataset, ToIterableDataset
-from .dataset_mapper import DatasetMapper
-
-# ensure the builtin datasets are registered
-from . import datasets, samplers  # isort:skip
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]

spaces/Bart92/RVC_HF/lib/infer_pack/commons.py

@@ -1,166 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-    """KL(P||Q)"""
-    kl = (logs_q - logs_p) - 0.5
-    kl += (
-        0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
-    )
-    return kl
-
-
-def rand_gumbel(shape):
-    """Sample from the Gumbel distribution, protect from overflows."""
-    uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-    return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-    g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-    return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, :, idx_str:idx_end]
-    return ret
-
-
-def slice_segments2(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, idx_str:idx_end]
-    return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-    b, d, t = x.size()
-    if x_lengths is None:
-        x_lengths = t
-    ids_str_max = x_lengths - segment_size + 1
-    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-    ret = slice_segments(x, ids_str, segment_size)
-    return ret, ids_str
-
-
-def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
-    position = torch.arange(length, dtype=torch.float)
-    num_timescales = channels // 2
-    log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
-        num_timescales - 1
-    )
-    inv_timescales = min_timescale * torch.exp(
-        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
-    )
-    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-    signal = F.pad(signal, [0, 0, 0, channels % 2])
-    signal = signal.view(1, channels, length)
-    return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-    return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-    n_channels_int = n_channels[0]
-    in_act = input_a + input_b
-    t_act = torch.tanh(in_act[:, :n_channels_int, :])
-    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-    acts = t_act * s_act
-    return acts
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def shift_1d(x):
-    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-    return x
-
-
-def sequence_mask(length, max_length=None):
-    if max_length is None:
-        max_length = length.max()
-    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-    return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-    """
-    duration: [b, 1, t_x]
-    mask: [b, 1, t_y, t_x]
-    """
-    device = duration.device
-
-    b, _, t_y, t_x = mask.shape
-    cum_duration = torch.cumsum(duration, -1)
-
-    cum_duration_flat = cum_duration.view(b * t_x)
-    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-    path = path.view(b, t_x, t_y)
-    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-    path = path.unsqueeze(1).transpose(2, 3) * mask
-    return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-    if isinstance(parameters, torch.Tensor):
-        parameters = [parameters]
-    parameters = list(filter(lambda p: p.grad is not None, parameters))
-    norm_type = float(norm_type)
-    if clip_value is not None:
-        clip_value = float(clip_value)
-
-    total_norm = 0
-    for p in parameters:
-        param_norm = p.grad.data.norm(norm_type)
-        total_norm += param_norm.item() ** norm_type
-        if clip_value is not None:
-            p.grad.data.clamp_(min=-clip_value, max=clip_value)
-    total_norm = total_norm ** (1.0 / norm_type)
-    return total_norm

spaces/BetterAPI/BetterChat/svelte.config.js

@@ -1,26 +0,0 @@
-import adapter from "@sveltejs/adapter-node";
-import { vitePreprocess } from "@sveltejs/kit/vite";
-import dotenv from "dotenv";
-import pkg from "./package.json" assert { type: "json" };
-
-dotenv.config({ path: "./.env.local" });
-dotenv.config({ path: "./.env" });
-
-process.env.PUBLIC_VERSION = pkg.version.replace(/\.0\b/g, "");
-
-/** @type {import('@sveltejs/kit').Config} */
-const config = {
-	// Consult https://kit.svelte.dev/docs/integrations#preprocessors
-	// for more information about preprocessors
-	preprocess: vitePreprocess(),
-
-	kit: {
-		adapter: adapter(),
-
-		paths: {
-			base: process.env.APP_BASE || "",
-		},
-	},
-};
-
-export default config;

spaces/BetterAPI/BetterChat_new/src/lib/actions/snapScrollToBottom.ts

@@ -1,54 +0,0 @@
-import { navigating } from "$app/stores";
-import { tick } from "svelte";
-import { get } from "svelte/store";
-
-const detachedOffset = 10;
-
-/**
- * @param node element to snap scroll to bottom
- * @param dependency pass in a dependency to update scroll on changes.
- */
-export const snapScrollToBottom = (node: HTMLElement, dependency: any) => {
-	let prevScrollValue = node.scrollTop;
-	let isDetached = false;
-
-	const handleScroll = () => {
-		// if user scrolled up, we detach
-		if (node.scrollTop < prevScrollValue) {
-			isDetached = true;
-		}
-
-		// if user scrolled back to within 10px of bottom, we reattach
-		if (node.scrollTop - (node.scrollHeight - node.clientHeight) >= -detachedOffset) {
-			isDetached = false;
-		}
-
-		prevScrollValue = node.scrollTop;
-	};
-
-	const updateScroll = async (_options: { force?: boolean } = {}) => {
-		const defaultOptions = { force: false };
-		const options = { ...defaultOptions, ..._options };
-		const { force } = options;
-
-		if (!force && isDetached && !get(navigating)) return;
-
-		// wait for next tick to ensure that the DOM is updated
-		await tick();
-
-		node.scrollTo({ top: node.scrollHeight });
-	};
-
-	node.addEventListener("scroll", handleScroll);
-
-	if (dependency) {
-		updateScroll({ force: true });
-	}
-
-	return {
-		update: updateScroll,
-		destroy: () => {
-			node.removeEventListener("scroll", handleScroll);
-		},
-	};
-};

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distro/distro.py

@@ -1,1399 +0,0 @@
-#!/usr/bin/env python
-# Copyright 2015,2016,2017 Nir Cohen
-#
-# 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.
-
-"""
-The ``distro`` package (``distro`` stands for Linux Distribution) provides
-information about the Linux distribution it runs on, such as a reliable
-machine-readable distro ID, or version information.
-
-It is the recommended replacement for Python's original
-:py:func:`platform.linux_distribution` function, but it provides much more
-functionality. An alternative implementation became necessary because Python
-3.5 deprecated this function, and Python 3.8 removed it altogether. Its
-predecessor function :py:func:`platform.dist` was already deprecated since
-Python 2.6 and removed in Python 3.8. Still, there are many cases in which
-access to OS distribution information is needed. See `Python issue 1322
-<https://bugs.python.org/issue1322>`_ for more information.
-"""
-
-import argparse
-import json
-import logging
-import os
-import re
-import shlex
-import subprocess
-import sys
-import warnings
-from typing import (
-    Any,
-    Callable,
-    Dict,
-    Iterable,
-    Optional,
-    Sequence,
-    TextIO,
-    Tuple,
-    Type,
-)
-
-try:
-    from typing import TypedDict
-except ImportError:
-    # Python 3.7
-    TypedDict = dict
-
-__version__ = "1.8.0"
-
-
-class VersionDict(TypedDict):
-    major: str
-    minor: str
-    build_number: str
-
-
-class InfoDict(TypedDict):
-    id: str
-    version: str
-    version_parts: VersionDict
-    like: str
-    codename: str
-
-
-_UNIXCONFDIR = os.environ.get("UNIXCONFDIR", "/etc")
-_UNIXUSRLIBDIR = os.environ.get("UNIXUSRLIBDIR", "/usr/lib")
-_OS_RELEASE_BASENAME = "os-release"
-
-#: Translation table for normalizing the "ID" attribute defined in os-release
-#: files, for use by the :func:`distro.id` method.
-#:
-#: * Key: Value as defined in the os-release file, translated to lower case,
-#:   with blanks translated to underscores.
-#:
-#: * Value: Normalized value.
-NORMALIZED_OS_ID = {
-    "ol": "oracle",  # Oracle Linux
-    "opensuse-leap": "opensuse",  # Newer versions of OpenSuSE report as opensuse-leap
-}
-
-#: Translation table for normalizing the "Distributor ID" attribute returned by
-#: the lsb_release command, for use by the :func:`distro.id` method.
-#:
-#: * Key: Value as returned by the lsb_release command, translated to lower
-#:   case, with blanks translated to underscores.
-#:
-#: * Value: Normalized value.
-NORMALIZED_LSB_ID = {
-    "enterpriseenterpriseas": "oracle",  # Oracle Enterprise Linux 4
-    "enterpriseenterpriseserver": "oracle",  # Oracle Linux 5
-    "redhatenterpriseworkstation": "rhel",  # RHEL 6, 7 Workstation
-    "redhatenterpriseserver": "rhel",  # RHEL 6, 7 Server
-    "redhatenterprisecomputenode": "rhel",  # RHEL 6 ComputeNode
-}
-
-#: Translation table for normalizing the distro ID derived from the file name
-#: of distro release files, for use by the :func:`distro.id` method.
-#:
-#: * Key: Value as derived from the file name of a distro release file,
-#:   translated to lower case, with blanks translated to underscores.
-#:
-#: * Value: Normalized value.
-NORMALIZED_DISTRO_ID = {
-    "redhat": "rhel",  # RHEL 6.x, 7.x
-}
-
-# Pattern for content of distro release file (reversed)
-_DISTRO_RELEASE_CONTENT_REVERSED_PATTERN = re.compile(
-    r"(?:[^)]*\)(.*)\()? *(?:STL )?([\d.+\-a-z]*\d) *(?:esaeler *)?(.+)"
-)
-
-# Pattern for base file name of distro release file
-_DISTRO_RELEASE_BASENAME_PATTERN = re.compile(r"(\w+)[-_](release|version)$")
-
-# Base file names to be looked up for if _UNIXCONFDIR is not readable.
-_DISTRO_RELEASE_BASENAMES = [
-    "SuSE-release",
-    "arch-release",
-    "base-release",
-    "centos-release",
-    "fedora-release",
-    "gentoo-release",
-    "mageia-release",
-    "mandrake-release",
-    "mandriva-release",
-    "mandrivalinux-release",
-    "manjaro-release",
-    "oracle-release",
-    "redhat-release",
-    "rocky-release",
-    "sl-release",
-    "slackware-version",
-]
-
-# Base file names to be ignored when searching for distro release file
-_DISTRO_RELEASE_IGNORE_BASENAMES = (
-    "debian_version",
-    "lsb-release",
-    "oem-release",
-    _OS_RELEASE_BASENAME,
-    "system-release",
-    "plesk-release",
-    "iredmail-release",
-)
-
-
-def linux_distribution(full_distribution_name: bool = True) -> Tuple[str, str, str]:
-    """
-    .. deprecated:: 1.6.0
-
-        :func:`distro.linux_distribution()` is deprecated. It should only be
-        used as a compatibility shim with Python's
-        :py:func:`platform.linux_distribution()`. Please use :func:`distro.id`,
-        :func:`distro.version` and :func:`distro.name` instead.
-
-    Return information about the current OS distribution as a tuple
-    ``(id_name, version, codename)`` with items as follows:
-
-    * ``id_name``:  If *full_distribution_name* is false, the result of
-      :func:`distro.id`. Otherwise, the result of :func:`distro.name`.
-
-    * ``version``:  The result of :func:`distro.version`.
-
-    * ``codename``:  The extra item (usually in parentheses) after the
-      os-release version number, or the result of :func:`distro.codename`.
-
-    The interface of this function is compatible with the original
-    :py:func:`platform.linux_distribution` function, supporting a subset of
-    its parameters.
-
-    The data it returns may not exactly be the same, because it uses more data
-    sources than the original function, and that may lead to different data if
-    the OS distribution is not consistent across multiple data sources it
-    provides (there are indeed such distributions ...).
-
-    Another reason for differences is the fact that the :func:`distro.id`
-    method normalizes the distro ID string to a reliable machine-readable value
-    for a number of popular OS distributions.
-    """
-    warnings.warn(
-        "distro.linux_distribution() is deprecated. It should only be used as a "
-        "compatibility shim with Python's platform.linux_distribution(). Please use "
-        "distro.id(), distro.version() and distro.name() instead.",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-    return _distro.linux_distribution(full_distribution_name)
-
-
-def id() -> str:
-    """
-    Return the distro ID of the current distribution, as a
-    machine-readable string.
-
-    For a number of OS distributions, the returned distro ID value is
-    *reliable*, in the sense that it is documented and that it does not change
-    across releases of the distribution.
-
-    This package maintains the following reliable distro ID values:
-
-    ==============  =========================================
-    Distro ID       Distribution
-    ==============  =========================================
-    "ubuntu"        Ubuntu
-    "debian"        Debian
-    "rhel"          RedHat Enterprise Linux
-    "centos"        CentOS
-    "fedora"        Fedora
-    "sles"          SUSE Linux Enterprise Server
-    "opensuse"      openSUSE
-    "amzn"          Amazon Linux
-    "arch"          Arch Linux
-    "buildroot"     Buildroot
-    "cloudlinux"    CloudLinux OS
-    "exherbo"       Exherbo Linux
-    "gentoo"        GenToo Linux
-    "ibm_powerkvm"  IBM PowerKVM
-    "kvmibm"        KVM for IBM z Systems
-    "linuxmint"     Linux Mint
-    "mageia"        Mageia
-    "mandriva"      Mandriva Linux
-    "parallels"     Parallels
-    "pidora"        Pidora
-    "raspbian"      Raspbian
-    "oracle"        Oracle Linux (and Oracle Enterprise Linux)
-    "scientific"    Scientific Linux
-    "slackware"     Slackware
-    "xenserver"     XenServer
-    "openbsd"       OpenBSD
-    "netbsd"        NetBSD
-    "freebsd"       FreeBSD
-    "midnightbsd"   MidnightBSD
-    "rocky"         Rocky Linux
-    "aix"           AIX
-    "guix"          Guix System
-    ==============  =========================================
-
-    If you have a need to get distros for reliable IDs added into this set,
-    or if you find that the :func:`distro.id` function returns a different
-    distro ID for one of the listed distros, please create an issue in the
-    `distro issue tracker`_.
-
-    **Lookup hierarchy and transformations:**
-
-    First, the ID is obtained from the following sources, in the specified
-    order. The first available and non-empty value is used:
-
-    * the value of the "ID" attribute of the os-release file,
-
-    * the value of the "Distributor ID" attribute returned by the lsb_release
-      command,
-
-    * the first part of the file name of the distro release file,
-
-    The so determined ID value then passes the following transformations,
-    before it is returned by this method:
-
-    * it is translated to lower case,
-
-    * blanks (which should not be there anyway) are translated to underscores,
-
-    * a normalization of the ID is performed, based upon
-      `normalization tables`_. The purpose of this normalization is to ensure
-      that the ID is as reliable as possible, even across incompatible changes
-      in the OS distributions. A common reason for an incompatible change is
-      the addition of an os-release file, or the addition of the lsb_release
-      command, with ID values that differ from what was previously determined
-      from the distro release file name.
-    """
-    return _distro.id()
-
-
-def name(pretty: bool = False) -> str:
-    """
-    Return the name of the current OS distribution, as a human-readable
-    string.
-
-    If *pretty* is false, the name is returned without version or codename.
-    (e.g. "CentOS Linux")
-
-    If *pretty* is true, the version and codename are appended.
-    (e.g. "CentOS Linux 7.1.1503 (Core)")
-
-    **Lookup hierarchy:**
-
-    The name is obtained from the following sources, in the specified order.
-    The first available and non-empty value is used:
-
-    * If *pretty* is false:
-
-      - the value of the "NAME" attribute of the os-release file,
-
-      - the value of the "Distributor ID" attribute returned by the lsb_release
-        command,
-
-      - the value of the "<name>" field of the distro release file.
-
-    * If *pretty* is true:
-
-      - the value of the "PRETTY_NAME" attribute of the os-release file,
-
-      - the value of the "Description" attribute returned by the lsb_release
-        command,
-
-      - the value of the "<name>" field of the distro release file, appended
-        with the value of the pretty version ("<version_id>" and "<codename>"
-        fields) of the distro release file, if available.
-    """
-    return _distro.name(pretty)
-
-
-def version(pretty: bool = False, best: bool = False) -> str:
-    """
-    Return the version of the current OS distribution, as a human-readable
-    string.
-
-    If *pretty* is false, the version is returned without codename (e.g.
-    "7.0").
-
-    If *pretty* is true, the codename in parenthesis is appended, if the
-    codename is non-empty (e.g. "7.0 (Maipo)").
-
-    Some distributions provide version numbers with different precisions in
-    the different sources of distribution information. Examining the different
-    sources in a fixed priority order does not always yield the most precise
-    version (e.g. for Debian 8.2, or CentOS 7.1).
-
-    Some other distributions may not provide this kind of information. In these
-    cases, an empty string would be returned. This behavior can be observed
-    with rolling releases distributions (e.g. Arch Linux).
-
-    The *best* parameter can be used to control the approach for the returned
-    version:
-
-    If *best* is false, the first non-empty version number in priority order of
-    the examined sources is returned.
-
-    If *best* is true, the most precise version number out of all examined
-    sources is returned.
-
-    **Lookup hierarchy:**
-
-    In all cases, the version number is obtained from the following sources.
-    If *best* is false, this order represents the priority order:
-
-    * the value of the "VERSION_ID" attribute of the os-release file,
-    * the value of the "Release" attribute returned by the lsb_release
-      command,
-    * the version number parsed from the "<version_id>" field of the first line
-      of the distro release file,
-    * the version number parsed from the "PRETTY_NAME" attribute of the
-      os-release file, if it follows the format of the distro release files.
-    * the version number parsed from the "Description" attribute returned by
-      the lsb_release command, if it follows the format of the distro release
-      files.
-    """
-    return _distro.version(pretty, best)
-
-
-def version_parts(best: bool = False) -> Tuple[str, str, str]:
-    """
-    Return the version of the current OS distribution as a tuple
-    ``(major, minor, build_number)`` with items as follows:
-
-    * ``major``:  The result of :func:`distro.major_version`.
-
-    * ``minor``:  The result of :func:`distro.minor_version`.
-
-    * ``build_number``:  The result of :func:`distro.build_number`.
-
-    For a description of the *best* parameter, see the :func:`distro.version`
-    method.
-    """
-    return _distro.version_parts(best)
-
-
-def major_version(best: bool = False) -> str:
-    """
-    Return the major version of the current OS distribution, as a string,
-    if provided.
-    Otherwise, the empty string is returned. The major version is the first
-    part of the dot-separated version string.
-
-    For a description of the *best* parameter, see the :func:`distro.version`
-    method.
-    """
-    return _distro.major_version(best)
-
-
-def minor_version(best: bool = False) -> str:
-    """
-    Return the minor version of the current OS distribution, as a string,
-    if provided.
-    Otherwise, the empty string is returned. The minor version is the second
-    part of the dot-separated version string.
-
-    For a description of the *best* parameter, see the :func:`distro.version`
-    method.
-    """
-    return _distro.minor_version(best)
-
-
-def build_number(best: bool = False) -> str:
-    """
-    Return the build number of the current OS distribution, as a string,
-    if provided.
-    Otherwise, the empty string is returned. The build number is the third part
-    of the dot-separated version string.
-
-    For a description of the *best* parameter, see the :func:`distro.version`
-    method.
-    """
-    return _distro.build_number(best)
-
-
-def like() -> str:
-    """
-    Return a space-separated list of distro IDs of distributions that are
-    closely related to the current OS distribution in regards to packaging
-    and programming interfaces, for example distributions the current
-    distribution is a derivative from.
-
-    **Lookup hierarchy:**
-
-    This information item is only provided by the os-release file.
-    For details, see the description of the "ID_LIKE" attribute in the
-    `os-release man page
-    <http://www.freedesktop.org/software/systemd/man/os-release.html>`_.
-    """
-    return _distro.like()
-
-
-def codename() -> str:
-    """
-    Return the codename for the release of the current OS distribution,
-    as a string.
-
-    If the distribution does not have a codename, an empty string is returned.
-
-    Note that the returned codename is not always really a codename. For
-    example, openSUSE returns "x86_64". This function does not handle such
-    cases in any special way and just returns the string it finds, if any.
-
-    **Lookup hierarchy:**
-
-    * the codename within the "VERSION" attribute of the os-release file, if
-      provided,
-
-    * the value of the "Codename" attribute returned by the lsb_release
-      command,
-
-    * the value of the "<codename>" field of the distro release file.
-    """
-    return _distro.codename()
-
-
-def info(pretty: bool = False, best: bool = False) -> InfoDict:
-    """
-    Return certain machine-readable information items about the current OS
-    distribution in a dictionary, as shown in the following example:
-
-    .. sourcecode:: python
-
-        {
-            'id': 'rhel',
-            'version': '7.0',
-            'version_parts': {
-                'major': '7',
-                'minor': '0',
-                'build_number': ''
-            },
-            'like': 'fedora',
-            'codename': 'Maipo'
-        }
-
-    The dictionary structure and keys are always the same, regardless of which
-    information items are available in the underlying data sources. The values
-    for the various keys are as follows:
-
-    * ``id``:  The result of :func:`distro.id`.
-
-    * ``version``:  The result of :func:`distro.version`.
-
-    * ``version_parts -> major``:  The result of :func:`distro.major_version`.
-
-    * ``version_parts -> minor``:  The result of :func:`distro.minor_version`.
-
-    * ``version_parts -> build_number``:  The result of
-      :func:`distro.build_number`.
-
-    * ``like``:  The result of :func:`distro.like`.
-
-    * ``codename``:  The result of :func:`distro.codename`.
-
-    For a description of the *pretty* and *best* parameters, see the
-    :func:`distro.version` method.
-    """
-    return _distro.info(pretty, best)
-
-
-def os_release_info() -> Dict[str, str]:
-    """
-    Return a dictionary containing key-value pairs for the information items
-    from the os-release file data source of the current OS distribution.
-
-    See `os-release file`_ for details about these information items.
-    """
-    return _distro.os_release_info()
-
-
-def lsb_release_info() -> Dict[str, str]:
-    """
-    Return a dictionary containing key-value pairs for the information items
-    from the lsb_release command data source of the current OS distribution.
-
-    See `lsb_release command output`_ for details about these information
-    items.
-    """
-    return _distro.lsb_release_info()
-
-
-def distro_release_info() -> Dict[str, str]:
-    """
-    Return a dictionary containing key-value pairs for the information items
-    from the distro release file data source of the current OS distribution.
-
-    See `distro release file`_ for details about these information items.
-    """
-    return _distro.distro_release_info()
-
-
-def uname_info() -> Dict[str, str]:
-    """
-    Return a dictionary containing key-value pairs for the information items
-    from the distro release file data source of the current OS distribution.
-    """
-    return _distro.uname_info()
-
-
-def os_release_attr(attribute: str) -> str:
-    """
-    Return a single named information item from the os-release file data source
-    of the current OS distribution.
-
-    Parameters:
-
-    * ``attribute`` (string): Key of the information item.
-
-    Returns:
-
-    * (string): Value of the information item, if the item exists.
-      The empty string, if the item does not exist.
-
-    See `os-release file`_ for details about these information items.
-    """
-    return _distro.os_release_attr(attribute)
-
-
-def lsb_release_attr(attribute: str) -> str:
-    """
-    Return a single named information item from the lsb_release command output
-    data source of the current OS distribution.
-
-    Parameters:
-
-    * ``attribute`` (string): Key of the information item.
-
-    Returns:
-
-    * (string): Value of the information item, if the item exists.
-      The empty string, if the item does not exist.
-
-    See `lsb_release command output`_ for details about these information
-    items.
-    """
-    return _distro.lsb_release_attr(attribute)
-
-
-def distro_release_attr(attribute: str) -> str:
-    """
-    Return a single named information item from the distro release file
-    data source of the current OS distribution.
-
-    Parameters:
-
-    * ``attribute`` (string): Key of the information item.
-
-    Returns:
-
-    * (string): Value of the information item, if the item exists.
-      The empty string, if the item does not exist.
-
-    See `distro release file`_ for details about these information items.
-    """
-    return _distro.distro_release_attr(attribute)
-
-
-def uname_attr(attribute: str) -> str:
-    """
-    Return a single named information item from the distro release file
-    data source of the current OS distribution.
-
-    Parameters:
-
-    * ``attribute`` (string): Key of the information item.
-
-    Returns:
-
-    * (string): Value of the information item, if the item exists.
-                The empty string, if the item does not exist.
-    """
-    return _distro.uname_attr(attribute)
-
-
-try:
-    from functools import cached_property
-except ImportError:
-    # Python < 3.8
-    class cached_property:  # type: ignore
-        """A version of @property which caches the value.  On access, it calls the
-        underlying function and sets the value in `__dict__` so future accesses
-        will not re-call the property.
-        """
-
-        def __init__(self, f: Callable[[Any], Any]) -> None:
-            self._fname = f.__name__
-            self._f = f
-
-        def __get__(self, obj: Any, owner: Type[Any]) -> Any:
-            assert obj is not None, f"call {self._fname} on an instance"
-            ret = obj.__dict__[self._fname] = self._f(obj)
-            return ret
-
-
-class LinuxDistribution:
-    """
-    Provides information about a OS distribution.
-
-    This package creates a private module-global instance of this class with
-    default initialization arguments, that is used by the
-    `consolidated accessor functions`_ and `single source accessor functions`_.
-    By using default initialization arguments, that module-global instance
-    returns data about the current OS distribution (i.e. the distro this
-    package runs on).
-
-    Normally, it is not necessary to create additional instances of this class.
-    However, in situations where control is needed over the exact data sources
-    that are used, instances of this class can be created with a specific
-    distro release file, or a specific os-release file, or without invoking the
-    lsb_release command.
-    """
-
-    def __init__(
-        self,
-        include_lsb: Optional[bool] = None,
-        os_release_file: str = "",
-        distro_release_file: str = "",
-        include_uname: Optional[bool] = None,
-        root_dir: Optional[str] = None,
-        include_oslevel: Optional[bool] = None,
-    ) -> None:
-        """
-        The initialization method of this class gathers information from the
-        available data sources, and stores that in private instance attributes.
-        Subsequent access to the information items uses these private instance
-        attributes, so that the data sources are read only once.
-
-        Parameters:
-
-        * ``include_lsb`` (bool): Controls whether the
-          `lsb_release command output`_ is included as a data source.
-
-          If the lsb_release command is not available in the program execution
-          path, the data source for the lsb_release command will be empty.
-
-        * ``os_release_file`` (string): The path name of the
-          `os-release file`_ that is to be used as a data source.
-
-          An empty string (the default) will cause the default path name to
-          be used (see `os-release file`_ for details).
-
-          If the specified or defaulted os-release file does not exist, the
-          data source for the os-release file will be empty.
-
-        * ``distro_release_file`` (string): The path name of the
-          `distro release file`_ that is to be used as a data source.
-
-          An empty string (the default) will cause a default search algorithm
-          to be used (see `distro release file`_ for details).
-
-          If the specified distro release file does not exist, or if no default
-          distro release file can be found, the data source for the distro
-          release file will be empty.
-
-        * ``include_uname`` (bool): Controls whether uname command output is
-          included as a data source. If the uname command is not available in
-          the program execution path the data source for the uname command will
-          be empty.
-
-        * ``root_dir`` (string): The absolute path to the root directory to use
-          to find distro-related information files. Note that ``include_*``
-          parameters must not be enabled in combination with ``root_dir``.
-
-        * ``include_oslevel`` (bool): Controls whether (AIX) oslevel command
-          output is included as a data source. If the oslevel command is not
-          available in the program execution path the data source will be
-          empty.
-
-        Public instance attributes:
-
-        * ``os_release_file`` (string): The path name of the
-          `os-release file`_ that is actually used as a data source. The
-          empty string if no distro release file is used as a data source.
-
-        * ``distro_release_file`` (string): The path name of the
-          `distro release file`_ that is actually used as a data source. The
-          empty string if no distro release file is used as a data source.
-
-        * ``include_lsb`` (bool): The result of the ``include_lsb`` parameter.
-          This controls whether the lsb information will be loaded.
-
-        * ``include_uname`` (bool): The result of the ``include_uname``
-          parameter. This controls whether the uname information will
-          be loaded.
-
-        * ``include_oslevel`` (bool): The result of the ``include_oslevel``
-          parameter. This controls whether (AIX) oslevel information will be
-          loaded.
-
-        * ``root_dir`` (string): The result of the ``root_dir`` parameter.
-          The absolute path to the root directory to use to find distro-related
-          information files.
-
-        Raises:
-
-        * :py:exc:`ValueError`: Initialization parameters combination is not
-           supported.
-
-        * :py:exc:`OSError`: Some I/O issue with an os-release file or distro
-          release file.
-
-        * :py:exc:`UnicodeError`: A data source has unexpected characters or
-          uses an unexpected encoding.
-        """
-        self.root_dir = root_dir
-        self.etc_dir = os.path.join(root_dir, "etc") if root_dir else _UNIXCONFDIR
-        self.usr_lib_dir = (
-            os.path.join(root_dir, "usr/lib") if root_dir else _UNIXUSRLIBDIR
-        )
-
-        if os_release_file:
-            self.os_release_file = os_release_file
-        else:
-            etc_dir_os_release_file = os.path.join(self.etc_dir, _OS_RELEASE_BASENAME)
-            usr_lib_os_release_file = os.path.join(
-                self.usr_lib_dir, _OS_RELEASE_BASENAME
-            )
-
-            # NOTE: The idea is to respect order **and** have it set
-            #       at all times for API backwards compatibility.
-            if os.path.isfile(etc_dir_os_release_file) or not os.path.isfile(
-                usr_lib_os_release_file
-            ):
-                self.os_release_file = etc_dir_os_release_file
-            else:
-                self.os_release_file = usr_lib_os_release_file
-
-        self.distro_release_file = distro_release_file or ""  # updated later
-
-        is_root_dir_defined = root_dir is not None
-        if is_root_dir_defined and (include_lsb or include_uname or include_oslevel):
-            raise ValueError(
-                "Including subprocess data sources from specific root_dir is disallowed"
-                " to prevent false information"
-            )
-        self.include_lsb = (
-            include_lsb if include_lsb is not None else not is_root_dir_defined
-        )
-        self.include_uname = (
-            include_uname if include_uname is not None else not is_root_dir_defined
-        )
-        self.include_oslevel = (
-            include_oslevel if include_oslevel is not None else not is_root_dir_defined
-        )
-
-    def __repr__(self) -> str:
-        """Return repr of all info"""
-        return (
-            "LinuxDistribution("
-            "os_release_file={self.os_release_file!r}, "
-            "distro_release_file={self.distro_release_file!r}, "
-            "include_lsb={self.include_lsb!r}, "
-            "include_uname={self.include_uname!r}, "
-            "include_oslevel={self.include_oslevel!r}, "
-            "root_dir={self.root_dir!r}, "
-            "_os_release_info={self._os_release_info!r}, "
-            "_lsb_release_info={self._lsb_release_info!r}, "
-            "_distro_release_info={self._distro_release_info!r}, "
-            "_uname_info={self._uname_info!r}, "
-            "_oslevel_info={self._oslevel_info!r})".format(self=self)
-        )
-
-    def linux_distribution(
-        self, full_distribution_name: bool = True
-    ) -> Tuple[str, str, str]:
-        """
-        Return information about the OS distribution that is compatible
-        with Python's :func:`platform.linux_distribution`, supporting a subset
-        of its parameters.
-
-        For details, see :func:`distro.linux_distribution`.
-        """
-        return (
-            self.name() if full_distribution_name else self.id(),
-            self.version(),
-            self._os_release_info.get("release_codename") or self.codename(),
-        )
-
-    def id(self) -> str:
-        """Return the distro ID of the OS distribution, as a string.
-
-        For details, see :func:`distro.id`.
-        """
-
-        def normalize(distro_id: str, table: Dict[str, str]) -> str:
-            distro_id = distro_id.lower().replace(" ", "_")
-            return table.get(distro_id, distro_id)
-
-        distro_id = self.os_release_attr("id")
-        if distro_id:
-            return normalize(distro_id, NORMALIZED_OS_ID)
-
-        distro_id = self.lsb_release_attr("distributor_id")
-        if distro_id:
-            return normalize(distro_id, NORMALIZED_LSB_ID)
-
-        distro_id = self.distro_release_attr("id")
-        if distro_id:
-            return normalize(distro_id, NORMALIZED_DISTRO_ID)
-
-        distro_id = self.uname_attr("id")
-        if distro_id:
-            return normalize(distro_id, NORMALIZED_DISTRO_ID)
-
-        return ""
-
-    def name(self, pretty: bool = False) -> str:
-        """
-        Return the name of the OS distribution, as a string.
-
-        For details, see :func:`distro.name`.
-        """
-        name = (
-            self.os_release_attr("name")
-            or self.lsb_release_attr("distributor_id")
-            or self.distro_release_attr("name")
-            or self.uname_attr("name")
-        )
-        if pretty:
-            name = self.os_release_attr("pretty_name") or self.lsb_release_attr(
-                "description"
-            )
-            if not name:
-                name = self.distro_release_attr("name") or self.uname_attr("name")
-                version = self.version(pretty=True)
-                if version:
-                    name = f"{name} {version}"
-        return name or ""
-
-    def version(self, pretty: bool = False, best: bool = False) -> str:
-        """
-        Return the version of the OS distribution, as a string.
-
-        For details, see :func:`distro.version`.
-        """
-        versions = [
-            self.os_release_attr("version_id"),
-            self.lsb_release_attr("release"),
-            self.distro_release_attr("version_id"),
-            self._parse_distro_release_content(self.os_release_attr("pretty_name")).get(
-                "version_id", ""
-            ),
-            self._parse_distro_release_content(
-                self.lsb_release_attr("description")
-            ).get("version_id", ""),
-            self.uname_attr("release"),
-        ]
-        if self.uname_attr("id").startswith("aix"):
-            # On AIX platforms, prefer oslevel command output.
-            versions.insert(0, self.oslevel_info())
-        elif self.id() == "debian" or "debian" in self.like().split():
-            # On Debian-like, add debian_version file content to candidates list.
-            versions.append(self._debian_version)
-        version = ""
-        if best:
-            # This algorithm uses the last version in priority order that has
-            # the best precision. If the versions are not in conflict, that
-            # does not matter; otherwise, using the last one instead of the
-            # first one might be considered a surprise.
-            for v in versions:
-                if v.count(".") > version.count(".") or version == "":
-                    version = v
-        else:
-            for v in versions:
-                if v != "":
-                    version = v
-                    break
-        if pretty and version and self.codename():
-            version = f"{version} ({self.codename()})"
-        return version
-
-    def version_parts(self, best: bool = False) -> Tuple[str, str, str]:
-        """
-        Return the version of the OS distribution, as a tuple of version
-        numbers.
-
-        For details, see :func:`distro.version_parts`.
-        """
-        version_str = self.version(best=best)
-        if version_str:
-            version_regex = re.compile(r"(\d+)\.?(\d+)?\.?(\d+)?")
-            matches = version_regex.match(version_str)
-            if matches:
-                major, minor, build_number = matches.groups()
-                return major, minor or "", build_number or ""
-        return "", "", ""
-
-    def major_version(self, best: bool = False) -> str:
-        """
-        Return the major version number of the current distribution.
-
-        For details, see :func:`distro.major_version`.
-        """
-        return self.version_parts(best)[0]
-
-    def minor_version(self, best: bool = False) -> str:
-        """
-        Return the minor version number of the current distribution.
-
-        For details, see :func:`distro.minor_version`.
-        """
-        return self.version_parts(best)[1]
-
-    def build_number(self, best: bool = False) -> str:
-        """
-        Return the build number of the current distribution.
-
-        For details, see :func:`distro.build_number`.
-        """
-        return self.version_parts(best)[2]
-
-    def like(self) -> str:
-        """
-        Return the IDs of distributions that are like the OS distribution.
-
-        For details, see :func:`distro.like`.
-        """
-        return self.os_release_attr("id_like") or ""
-
-    def codename(self) -> str:
-        """
-        Return the codename of the OS distribution.
-
-        For details, see :func:`distro.codename`.
-        """
-        try:
-            # Handle os_release specially since distros might purposefully set
-            # this to empty string to have no codename
-            return self._os_release_info["codename"]
-        except KeyError:
-            return (
-                self.lsb_release_attr("codename")
-                or self.distro_release_attr("codename")
-                or ""
-            )
-
-    def info(self, pretty: bool = False, best: bool = False) -> InfoDict:
-        """
-        Return certain machine-readable information about the OS
-        distribution.
-
-        For details, see :func:`distro.info`.
-        """
-        return dict(
-            id=self.id(),
-            version=self.version(pretty, best),
-            version_parts=dict(
-                major=self.major_version(best),
-                minor=self.minor_version(best),
-                build_number=self.build_number(best),
-            ),
-            like=self.like(),
-            codename=self.codename(),
-        )
-
-    def os_release_info(self) -> Dict[str, str]:
-        """
-        Return a dictionary containing key-value pairs for the information
-        items from the os-release file data source of the OS distribution.
-
-        For details, see :func:`distro.os_release_info`.
-        """
-        return self._os_release_info
-
-    def lsb_release_info(self) -> Dict[str, str]:
-        """
-        Return a dictionary containing key-value pairs for the information
-        items from the lsb_release command data source of the OS
-        distribution.
-
-        For details, see :func:`distro.lsb_release_info`.
-        """
-        return self._lsb_release_info
-
-    def distro_release_info(self) -> Dict[str, str]:
-        """
-        Return a dictionary containing key-value pairs for the information
-        items from the distro release file data source of the OS
-        distribution.
-
-        For details, see :func:`distro.distro_release_info`.
-        """
-        return self._distro_release_info
-
-    def uname_info(self) -> Dict[str, str]:
-        """
-        Return a dictionary containing key-value pairs for the information
-        items from the uname command data source of the OS distribution.
-
-        For details, see :func:`distro.uname_info`.
-        """
-        return self._uname_info
-
-    def oslevel_info(self) -> str:
-        """
-        Return AIX' oslevel command output.
-        """
-        return self._oslevel_info
-
-    def os_release_attr(self, attribute: str) -> str:
-        """
-        Return a single named information item from the os-release file data
-        source of the OS distribution.
-
-        For details, see :func:`distro.os_release_attr`.
-        """
-        return self._os_release_info.get(attribute, "")
-
-    def lsb_release_attr(self, attribute: str) -> str:
-        """
-        Return a single named information item from the lsb_release command
-        output data source of the OS distribution.
-
-        For details, see :func:`distro.lsb_release_attr`.
-        """
-        return self._lsb_release_info.get(attribute, "")
-
-    def distro_release_attr(self, attribute: str) -> str:
-        """
-        Return a single named information item from the distro release file
-        data source of the OS distribution.
-
-        For details, see :func:`distro.distro_release_attr`.
-        """
-        return self._distro_release_info.get(attribute, "")
-
-    def uname_attr(self, attribute: str) -> str:
-        """
-        Return a single named information item from the uname command
-        output data source of the OS distribution.
-
-        For details, see :func:`distro.uname_attr`.
-        """
-        return self._uname_info.get(attribute, "")
-
-    @cached_property
-    def _os_release_info(self) -> Dict[str, str]:
-        """
-        Get the information items from the specified os-release file.
-
-        Returns:
-            A dictionary containing all information items.
-        """
-        if os.path.isfile(self.os_release_file):
-            with open(self.os_release_file, encoding="utf-8") as release_file:
-                return self._parse_os_release_content(release_file)
-        return {}
-
-    @staticmethod
-    def _parse_os_release_content(lines: TextIO) -> Dict[str, str]:
-        """
-        Parse the lines of an os-release file.
-
-        Parameters:
-
-        * lines: Iterable through the lines in the os-release file.
-                 Each line must be a unicode string or a UTF-8 encoded byte
-                 string.
-
-        Returns:
-            A dictionary containing all information items.
-        """
-        props = {}
-        lexer = shlex.shlex(lines, posix=True)
-        lexer.whitespace_split = True
-
-        tokens = list(lexer)
-        for token in tokens:
-            # At this point, all shell-like parsing has been done (i.e.
-            # comments processed, quotes and backslash escape sequences
-            # processed, multi-line values assembled, trailing newlines
-            # stripped, etc.), so the tokens are now either:
-            # * variable assignments: var=value
-            # * commands or their arguments (not allowed in os-release)
-            # Ignore any tokens that are not variable assignments
-            if "=" in token:
-                k, v = token.split("=", 1)
-                props[k.lower()] = v
-
-        if "version" in props:
-            # extract release codename (if any) from version attribute
-            match = re.search(r"\((\D+)\)|,\s*(\D+)", props["version"])
-            if match:
-                release_codename = match.group(1) or match.group(2)
-                props["codename"] = props["release_codename"] = release_codename
-
-        if "version_codename" in props:
-            # os-release added a version_codename field.  Use that in
-            # preference to anything else Note that some distros purposefully
-            # do not have code names.  They should be setting
-            # version_codename=""
-            props["codename"] = props["version_codename"]
-        elif "ubuntu_codename" in props:
-            # Same as above but a non-standard field name used on older Ubuntus
-            props["codename"] = props["ubuntu_codename"]
-
-        return props
-
-    @cached_property
-    def _lsb_release_info(self) -> Dict[str, str]:
-        """
-        Get the information items from the lsb_release command output.
-
-        Returns:
-            A dictionary containing all information items.
-        """
-        if not self.include_lsb:
-            return {}
-        try:
-            cmd = ("lsb_release", "-a")
-            stdout = subprocess.check_output(cmd, stderr=subprocess.DEVNULL)
-        # Command not found or lsb_release returned error
-        except (OSError, subprocess.CalledProcessError):
-            return {}
-        content = self._to_str(stdout).splitlines()
-        return self._parse_lsb_release_content(content)
-
-    @staticmethod
-    def _parse_lsb_release_content(lines: Iterable[str]) -> Dict[str, str]:
-        """
-        Parse the output of the lsb_release command.
-
-        Parameters:
-
-        * lines: Iterable through the lines of the lsb_release output.
-                 Each line must be a unicode string or a UTF-8 encoded byte
-                 string.
-
-        Returns:
-            A dictionary containing all information items.
-        """
-        props = {}
-        for line in lines:
-            kv = line.strip("\n").split(":", 1)
-            if len(kv) != 2:
-                # Ignore lines without colon.
-                continue
-            k, v = kv
-            props.update({k.replace(" ", "_").lower(): v.strip()})
-        return props
-
-    @cached_property
-    def _uname_info(self) -> Dict[str, str]:
-        if not self.include_uname:
-            return {}
-        try:
-            cmd = ("uname", "-rs")
-            stdout = subprocess.check_output(cmd, stderr=subprocess.DEVNULL)
-        except OSError:
-            return {}
-        content = self._to_str(stdout).splitlines()
-        return self._parse_uname_content(content)
-
-    @cached_property
-    def _oslevel_info(self) -> str:
-        if not self.include_oslevel:
-            return ""
-        try:
-            stdout = subprocess.check_output("oslevel", stderr=subprocess.DEVNULL)
-        except (OSError, subprocess.CalledProcessError):
-            return ""
-        return self._to_str(stdout).strip()
-
-    @cached_property
-    def _debian_version(self) -> str:
-        try:
-            with open(
-                os.path.join(self.etc_dir, "debian_version"), encoding="ascii"
-            ) as fp:
-                return fp.readline().rstrip()
-        except FileNotFoundError:
-            return ""
-
-    @staticmethod
-    def _parse_uname_content(lines: Sequence[str]) -> Dict[str, str]:
-        if not lines:
-            return {}
-        props = {}
-        match = re.search(r"^([^\s]+)\s+([\d\.]+)", lines[0].strip())
-        if match:
-            name, version = match.groups()
-
-            # This is to prevent the Linux kernel version from
-            # appearing as the 'best' version on otherwise
-            # identifiable distributions.
-            if name == "Linux":
-                return {}
-            props["id"] = name.lower()
-            props["name"] = name
-            props["release"] = version
-        return props
-
-    @staticmethod
-    def _to_str(bytestring: bytes) -> str:
-        encoding = sys.getfilesystemencoding()
-        return bytestring.decode(encoding)
-
-    @cached_property
-    def _distro_release_info(self) -> Dict[str, str]:
-        """
-        Get the information items from the specified distro release file.
-
-        Returns:
-            A dictionary containing all information items.
-        """
-        if self.distro_release_file:
-            # If it was specified, we use it and parse what we can, even if
-            # its file name or content does not match the expected pattern.
-            distro_info = self._parse_distro_release_file(self.distro_release_file)
-            basename = os.path.basename(self.distro_release_file)
-            # The file name pattern for user-specified distro release files
-            # is somewhat more tolerant (compared to when searching for the
-            # file), because we want to use what was specified as best as
-            # possible.
-            match = _DISTRO_RELEASE_BASENAME_PATTERN.match(basename)
-        else:
-            try:
-                basenames = [
-                    basename
-                    for basename in os.listdir(self.etc_dir)
-                    if basename not in _DISTRO_RELEASE_IGNORE_BASENAMES
-                    and os.path.isfile(os.path.join(self.etc_dir, basename))
-                ]
-                # We sort for repeatability in cases where there are multiple
-                # distro specific files; e.g. CentOS, Oracle, Enterprise all
-                # containing `redhat-release` on top of their own.
-                basenames.sort()
-            except OSError:
-                # This may occur when /etc is not readable but we can't be
-                # sure about the *-release files. Check common entries of
-                # /etc for information. If they turn out to not be there the
-                # error is handled in `_parse_distro_release_file()`.
-                basenames = _DISTRO_RELEASE_BASENAMES
-            for basename in basenames:
-                match = _DISTRO_RELEASE_BASENAME_PATTERN.match(basename)
-                if match is None:
-                    continue
-                filepath = os.path.join(self.etc_dir, basename)
-                distro_info = self._parse_distro_release_file(filepath)
-                # The name is always present if the pattern matches.
-                if "name" not in distro_info:
-                    continue
-                self.distro_release_file = filepath
-                break
-            else:  # the loop didn't "break": no candidate.
-                return {}
-
-        if match is not None:
-            distro_info["id"] = match.group(1)
-
-        # CloudLinux < 7: manually enrich info with proper id.
-        if "cloudlinux" in distro_info.get("name", "").lower():
-            distro_info["id"] = "cloudlinux"
-
-        return distro_info
-
-    def _parse_distro_release_file(self, filepath: str) -> Dict[str, str]:
-        """
-        Parse a distro release file.
-
-        Parameters:
-
-        * filepath: Path name of the distro release file.
-
-        Returns:
-            A dictionary containing all information items.
-        """
-        try:
-            with open(filepath, encoding="utf-8") as fp:
-                # Only parse the first line. For instance, on SLES there
-                # are multiple lines. We don't want them...
-                return self._parse_distro_release_content(fp.readline())
-        except OSError:
-            # Ignore not being able to read a specific, seemingly version
-            # related file.
-            # See https://github.com/python-distro/distro/issues/162
-            return {}
-
-    @staticmethod
-    def _parse_distro_release_content(line: str) -> Dict[str, str]:
-        """
-        Parse a line from a distro release file.
-
-        Parameters:
-        * line: Line from the distro release file. Must be a unicode string
-                or a UTF-8 encoded byte string.
-
-        Returns:
-            A dictionary containing all information items.
-        """
-        matches = _DISTRO_RELEASE_CONTENT_REVERSED_PATTERN.match(line.strip()[::-1])
-        distro_info = {}
-        if matches:
-            # regexp ensures non-None
-            distro_info["name"] = matches.group(3)[::-1]
-            if matches.group(2):
-                distro_info["version_id"] = matches.group(2)[::-1]
-            if matches.group(1):
-                distro_info["codename"] = matches.group(1)[::-1]
-        elif line:
-            distro_info["name"] = line.strip()
-        return distro_info
-
-
-_distro = LinuxDistribution()
-
-
-def main() -> None:
-    logger = logging.getLogger(__name__)
-    logger.setLevel(logging.DEBUG)
-    logger.addHandler(logging.StreamHandler(sys.stdout))
-
-    parser = argparse.ArgumentParser(description="OS distro info tool")
-    parser.add_argument(
-        "--json", "-j", help="Output in machine readable format", action="store_true"
-    )
-
-    parser.add_argument(
-        "--root-dir",
-        "-r",
-        type=str,
-        dest="root_dir",
-        help="Path to the root filesystem directory (defaults to /)",
-    )
-
-    args = parser.parse_args()
-
-    if args.root_dir:
-        dist = LinuxDistribution(
-            include_lsb=False,
-            include_uname=False,
-            include_oslevel=False,
-            root_dir=args.root_dir,
-        )
-    else:
-        dist = _distro
-
-    if args.json:
-        logger.info(json.dumps(dist.info(), indent=4, sort_keys=True))
-    else:
-        logger.info("Name: %s", dist.name(pretty=True))
-        distribution_version = dist.version(pretty=True)
-        logger.info("Version: %s", distribution_version)
-        distribution_codename = dist.codename()
-        logger.info("Codename: %s", distribution_codename)
-
-
-if __name__ == "__main__":
-    main()

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/command/config.py

@@ -1,377 +0,0 @@
-"""distutils.command.config
-
-Implements the Distutils 'config' command, a (mostly) empty command class
-that exists mainly to be sub-classed by specific module distributions and
-applications.  The idea is that while every "config" command is different,
-at least they're all named the same, and users always see "config" in the
-list of standard commands.  Also, this is a good place to put common
-configure-like tasks: "try to compile this C code", or "figure out where
-this header file lives".
-"""
-
-import os
-import re
-
-from distutils.core import Command
-from distutils.errors import DistutilsExecError
-from distutils.sysconfig import customize_compiler
-from distutils import log
-
-LANG_EXT = {"c": ".c", "c++": ".cxx"}
-
-
-class config(Command):
-
-    description = "prepare to build"
-
-    user_options = [
-        ('compiler=', None, "specify the compiler type"),
-        ('cc=', None, "specify the compiler executable"),
-        ('include-dirs=', 'I', "list of directories to search for header files"),
-        ('define=', 'D', "C preprocessor macros to define"),
-        ('undef=', 'U', "C preprocessor macros to undefine"),
-        ('libraries=', 'l', "external C libraries to link with"),
-        ('library-dirs=', 'L', "directories to search for external C libraries"),
-        ('noisy', None, "show every action (compile, link, run, ...) taken"),
-        (
-            'dump-source',
-            None,
-            "dump generated source files before attempting to compile them",
-        ),
-    ]
-
-    # The three standard command methods: since the "config" command
-    # does nothing by default, these are empty.
-
-    def initialize_options(self):
-        self.compiler = None
-        self.cc = None
-        self.include_dirs = None
-        self.libraries = None
-        self.library_dirs = None
-
-        # maximal output for now
-        self.noisy = 1
-        self.dump_source = 1
-
-        # list of temporary files generated along-the-way that we have
-        # to clean at some point
-        self.temp_files = []
-
-    def finalize_options(self):
-        if self.include_dirs is None:
-            self.include_dirs = self.distribution.include_dirs or []
-        elif isinstance(self.include_dirs, str):
-            self.include_dirs = self.include_dirs.split(os.pathsep)
-
-        if self.libraries is None:
-            self.libraries = []
-        elif isinstance(self.libraries, str):
-            self.libraries = [self.libraries]
-
-        if self.library_dirs is None:
-            self.library_dirs = []
-        elif isinstance(self.library_dirs, str):
-            self.library_dirs = self.library_dirs.split(os.pathsep)
-
-    def run(self):
-        pass
-
-    # Utility methods for actual "config" commands.  The interfaces are
-    # loosely based on Autoconf macros of similar names.  Sub-classes
-    # may use these freely.
-
-    def _check_compiler(self):
-        """Check that 'self.compiler' really is a CCompiler object;
-        if not, make it one.
-        """
-        # We do this late, and only on-demand, because this is an expensive
-        # import.
-        from distutils.ccompiler import CCompiler, new_compiler
-
-        if not isinstance(self.compiler, CCompiler):
-            self.compiler = new_compiler(
-                compiler=self.compiler, dry_run=self.dry_run, force=1
-            )
-            customize_compiler(self.compiler)
-            if self.include_dirs:
-                self.compiler.set_include_dirs(self.include_dirs)
-            if self.libraries:
-                self.compiler.set_libraries(self.libraries)
-            if self.library_dirs:
-                self.compiler.set_library_dirs(self.library_dirs)
-
-    def _gen_temp_sourcefile(self, body, headers, lang):
-        filename = "_configtest" + LANG_EXT[lang]
-        with open(filename, "w") as file:
-            if headers:
-                for header in headers:
-                    file.write("#include <%s>\n" % header)
-                file.write("\n")
-            file.write(body)
-            if body[-1] != "\n":
-                file.write("\n")
-        return filename
-
-    def _preprocess(self, body, headers, include_dirs, lang):
-        src = self._gen_temp_sourcefile(body, headers, lang)
-        out = "_configtest.i"
-        self.temp_files.extend([src, out])
-        self.compiler.preprocess(src, out, include_dirs=include_dirs)
-        return (src, out)
-
-    def _compile(self, body, headers, include_dirs, lang):
-        src = self._gen_temp_sourcefile(body, headers, lang)
-        if self.dump_source:
-            dump_file(src, "compiling '%s':" % src)
-        (obj,) = self.compiler.object_filenames([src])
-        self.temp_files.extend([src, obj])
-        self.compiler.compile([src], include_dirs=include_dirs)
-        return (src, obj)
-
-    def _link(self, body, headers, include_dirs, libraries, library_dirs, lang):
-        (src, obj) = self._compile(body, headers, include_dirs, lang)
-        prog = os.path.splitext(os.path.basename(src))[0]
-        self.compiler.link_executable(
-            [obj],
-            prog,
-            libraries=libraries,
-            library_dirs=library_dirs,
-            target_lang=lang,
-        )
-
-        if self.compiler.exe_extension is not None:
-            prog = prog + self.compiler.exe_extension
-        self.temp_files.append(prog)
-
-        return (src, obj, prog)
-
-    def _clean(self, *filenames):
-        if not filenames:
-            filenames = self.temp_files
-            self.temp_files = []
-        log.info("removing: %s", ' '.join(filenames))
-        for filename in filenames:
-            try:
-                os.remove(filename)
-            except OSError:
-                pass
-
-    # XXX these ignore the dry-run flag: what to do, what to do? even if
-    # you want a dry-run build, you still need some sort of configuration
-    # info.  My inclination is to make it up to the real config command to
-    # consult 'dry_run', and assume a default (minimal) configuration if
-    # true.  The problem with trying to do it here is that you'd have to
-    # return either true or false from all the 'try' methods, neither of
-    # which is correct.
-
-    # XXX need access to the header search path and maybe default macros.
-
-    def try_cpp(self, body=None, headers=None, include_dirs=None, lang="c"):
-        """Construct a source file from 'body' (a string containing lines
-        of C/C++ code) and 'headers' (a list of header files to include)
-        and run it through the preprocessor.  Return true if the
-        preprocessor succeeded, false if there were any errors.
-        ('body' probably isn't of much use, but what the heck.)
-        """
-        from distutils.ccompiler import CompileError
-
-        self._check_compiler()
-        ok = True
-        try:
-            self._preprocess(body, headers, include_dirs, lang)
-        except CompileError:
-            ok = False
-
-        self._clean()
-        return ok
-
-    def search_cpp(self, pattern, body=None, headers=None, include_dirs=None, lang="c"):
-        """Construct a source file (just like 'try_cpp()'), run it through
-        the preprocessor, and return true if any line of the output matches
-        'pattern'.  'pattern' should either be a compiled regex object or a
-        string containing a regex.  If both 'body' and 'headers' are None,
-        preprocesses an empty file -- which can be useful to determine the
-        symbols the preprocessor and compiler set by default.
-        """
-        self._check_compiler()
-        src, out = self._preprocess(body, headers, include_dirs, lang)
-
-        if isinstance(pattern, str):
-            pattern = re.compile(pattern)
-
-        with open(out) as file:
-            match = False
-            while True:
-                line = file.readline()
-                if line == '':
-                    break
-                if pattern.search(line):
-                    match = True
-                    break
-
-        self._clean()
-        return match
-
-    def try_compile(self, body, headers=None, include_dirs=None, lang="c"):
-        """Try to compile a source file built from 'body' and 'headers'.
-        Return true on success, false otherwise.
-        """
-        from distutils.ccompiler import CompileError
-
-        self._check_compiler()
-        try:
-            self._compile(body, headers, include_dirs, lang)
-            ok = True
-        except CompileError:
-            ok = False
-
-        log.info(ok and "success!" or "failure.")
-        self._clean()
-        return ok
-
-    def try_link(
-        self,
-        body,
-        headers=None,
-        include_dirs=None,
-        libraries=None,
-        library_dirs=None,
-        lang="c",
-    ):
-        """Try to compile and link a source file, built from 'body' and
-        'headers', to executable form.  Return true on success, false
-        otherwise.
-        """
-        from distutils.ccompiler import CompileError, LinkError
-
-        self._check_compiler()
-        try:
-            self._link(body, headers, include_dirs, libraries, library_dirs, lang)
-            ok = True
-        except (CompileError, LinkError):
-            ok = False
-
-        log.info(ok and "success!" or "failure.")
-        self._clean()
-        return ok
-
-    def try_run(
-        self,
-        body,
-        headers=None,
-        include_dirs=None,
-        libraries=None,
-        library_dirs=None,
-        lang="c",
-    ):
-        """Try to compile, link to an executable, and run a program
-        built from 'body' and 'headers'.  Return true on success, false
-        otherwise.
-        """
-        from distutils.ccompiler import CompileError, LinkError
-
-        self._check_compiler()
-        try:
-            src, obj, exe = self._link(
-                body, headers, include_dirs, libraries, library_dirs, lang
-            )
-            self.spawn([exe])
-            ok = True
-        except (CompileError, LinkError, DistutilsExecError):
-            ok = False
-
-        log.info(ok and "success!" or "failure.")
-        self._clean()
-        return ok
-
-    # -- High-level methods --------------------------------------------
-    # (these are the ones that are actually likely to be useful
-    # when implementing a real-world config command!)
-
-    def check_func(
-        self,
-        func,
-        headers=None,
-        include_dirs=None,
-        libraries=None,
-        library_dirs=None,
-        decl=0,
-        call=0,
-    ):
-        """Determine if function 'func' is available by constructing a
-        source file that refers to 'func', and compiles and links it.
-        If everything succeeds, returns true; otherwise returns false.
-
-        The constructed source file starts out by including the header
-        files listed in 'headers'.  If 'decl' is true, it then declares
-        'func' (as "int func()"); you probably shouldn't supply 'headers'
-        and set 'decl' true in the same call, or you might get errors about
-        a conflicting declarations for 'func'.  Finally, the constructed
-        'main()' function either references 'func' or (if 'call' is true)
-        calls it.  'libraries' and 'library_dirs' are used when
-        linking.
-        """
-        self._check_compiler()
-        body = []
-        if decl:
-            body.append("int %s ();" % func)
-        body.append("int main () {")
-        if call:
-            body.append("  %s();" % func)
-        else:
-            body.append("  %s;" % func)
-        body.append("}")
-        body = "\n".join(body) + "\n"
-
-        return self.try_link(body, headers, include_dirs, libraries, library_dirs)
-
-    def check_lib(
-        self,
-        library,
-        library_dirs=None,
-        headers=None,
-        include_dirs=None,
-        other_libraries=[],
-    ):
-        """Determine if 'library' is available to be linked against,
-        without actually checking that any particular symbols are provided
-        by it.  'headers' will be used in constructing the source file to
-        be compiled, but the only effect of this is to check if all the
-        header files listed are available.  Any libraries listed in
-        'other_libraries' will be included in the link, in case 'library'
-        has symbols that depend on other libraries.
-        """
-        self._check_compiler()
-        return self.try_link(
-            "int main (void) { }",
-            headers,
-            include_dirs,
-            [library] + other_libraries,
-            library_dirs,
-        )
-
-    def check_header(self, header, include_dirs=None, library_dirs=None, lang="c"):
-        """Determine if the system header file named by 'header_file'
-        exists and can be found by the preprocessor; return true if so,
-        false otherwise.
-        """
-        return self.try_cpp(
-            body="/* No body */", headers=[header], include_dirs=include_dirs
-        )
-
-
-def dump_file(filename, head=None):
-    """Dumps a file content into log.info.
-
-    If head is not None, will be dumped before the file content.
-    """
-    if head is None:
-        log.info('%s', filename)
-    else:
-        log.info(head)
-    file = open(filename)
-    try:
-        log.info(file.read())
-    finally:
-        file.close()

spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/README.md

@@ -1,12 +0,0 @@
----
-title: Image Animation Using Thin Plate Spline Motion Model
-emoji: 👁
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.48.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/WALT/mmdet/models/dense_heads/cascade_rpn_head.py

@@ -1,784 +0,0 @@
-from __future__ import division
-import copy
-import warnings
-
-import torch
-import torch.nn as nn
-from mmcv import ConfigDict
-from mmcv.cnn import normal_init
-from mmcv.ops import DeformConv2d, batched_nms
-
-from mmdet.core import (RegionAssigner, build_assigner, build_sampler,
-                        images_to_levels, multi_apply)
-from ..builder import HEADS, build_head
-from .base_dense_head import BaseDenseHead
-from .rpn_head import RPNHead
-
-
-class AdaptiveConv(nn.Module):
-    """AdaptiveConv used to adapt the sampling location with the anchors.
-
-    Args:
-        in_channels (int): Number of channels in the input image
-        out_channels (int): Number of channels produced by the convolution
-        kernel_size (int or tuple): Size of the conv kernel. Default: 3
-        stride (int or tuple, optional): Stride of the convolution. Default: 1
-        padding (int or tuple, optional): Zero-padding added to both sides of
-            the input. Default: 1
-        dilation (int or tuple, optional): Spacing between kernel elements.
-            Default: 3
-        groups (int, optional): Number of blocked connections from input
-            channels to output channels. Default: 1
-        bias (bool, optional): If set True, adds a learnable bias to the
-            output. Default: False.
-        type (str, optional): Type of adaptive conv, can be either 'offset'
-            (arbitrary anchors) or 'dilation' (uniform anchor).
-            Default: 'dilation'.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size=3,
-                 stride=1,
-                 padding=1,
-                 dilation=3,
-                 groups=1,
-                 bias=False,
-                 type='dilation'):
-        super(AdaptiveConv, self).__init__()
-        assert type in ['offset', 'dilation']
-        self.adapt_type = type
-
-        assert kernel_size == 3, 'Adaptive conv only supports kernels 3'
-        if self.adapt_type == 'offset':
-            assert stride == 1 and padding == 1 and groups == 1, \
-                'Adaptive conv offset mode only supports padding: {1}, ' \
-                f'stride: {1}, groups: {1}'
-            self.conv = DeformConv2d(
-                in_channels,
-                out_channels,
-                kernel_size,
-                padding=padding,
-                stride=stride,
-                groups=groups,
-                bias=bias)
-        else:
-            self.conv = nn.Conv2d(
-                in_channels,
-                out_channels,
-                kernel_size,
-                padding=dilation,
-                dilation=dilation)
-
-    def init_weights(self):
-        """Init weights."""
-        normal_init(self.conv, std=0.01)
-
-    def forward(self, x, offset):
-        """Forward function."""
-        if self.adapt_type == 'offset':
-            N, _, H, W = x.shape
-            assert offset is not None
-            assert H * W == offset.shape[1]
-            # reshape [N, NA, 18] to (N, 18, H, W)
-            offset = offset.permute(0, 2, 1).reshape(N, -1, H, W)
-            offset = offset.contiguous()
-            x = self.conv(x, offset)
-        else:
-            assert offset is None
-            x = self.conv(x)
-        return x
-
-
-@HEADS.register_module()
-class StageCascadeRPNHead(RPNHead):
-    """Stage of CascadeRPNHead.
-
-    Args:
-        in_channels (int): Number of channels in the input feature map.
-        anchor_generator (dict): anchor generator config.
-        adapt_cfg (dict): adaptation config.
-        bridged_feature (bool, optional): whether update rpn feature.
-            Default: False.
-        with_cls (bool, optional): wheather use classification branch.
-            Default: True.
-        sampling (bool, optional): wheather use sampling. Default: True.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 anchor_generator=dict(
-                     type='AnchorGenerator',
-                     scales=[8],
-                     ratios=[1.0],
-                     strides=[4, 8, 16, 32, 64]),
-                 adapt_cfg=dict(type='dilation', dilation=3),
-                 bridged_feature=False,
-                 with_cls=True,
-                 sampling=True,
-                 **kwargs):
-        self.with_cls = with_cls
-        self.anchor_strides = anchor_generator['strides']
-        self.anchor_scales = anchor_generator['scales']
-        self.bridged_feature = bridged_feature
-        self.adapt_cfg = adapt_cfg
-        super(StageCascadeRPNHead, self).__init__(
-            in_channels, anchor_generator=anchor_generator, **kwargs)
-
-        # override sampling and sampler
-        self.sampling = sampling
-        if self.train_cfg:
-            self.assigner = build_assigner(self.train_cfg.assigner)
-            # use PseudoSampler when sampling is False
-            if self.sampling and hasattr(self.train_cfg, 'sampler'):
-                sampler_cfg = self.train_cfg.sampler
-            else:
-                sampler_cfg = dict(type='PseudoSampler')
-            self.sampler = build_sampler(sampler_cfg, context=self)
-
-    def _init_layers(self):
-        """Init layers of a CascadeRPN stage."""
-        self.rpn_conv = AdaptiveConv(self.in_channels, self.feat_channels,
-                                     **self.adapt_cfg)
-        if self.with_cls:
-            self.rpn_cls = nn.Conv2d(self.feat_channels,
-                                     self.num_anchors * self.cls_out_channels,
-                                     1)
-        self.rpn_reg = nn.Conv2d(self.feat_channels, self.num_anchors * 4, 1)
-        self.relu = nn.ReLU(inplace=True)
-
-    def init_weights(self):
-        """Init weights of a CascadeRPN stage."""
-        self.rpn_conv.init_weights()
-        normal_init(self.rpn_reg, std=0.01)
-        if self.with_cls:
-            normal_init(self.rpn_cls, std=0.01)
-
-    def forward_single(self, x, offset):
-        """Forward function of single scale."""
-        bridged_x = x
-        x = self.relu(self.rpn_conv(x, offset))
-        if self.bridged_feature:
-            bridged_x = x  # update feature
-        cls_score = self.rpn_cls(x) if self.with_cls else None
-        bbox_pred = self.rpn_reg(x)
-        return bridged_x, cls_score, bbox_pred
-
-    def forward(self, feats, offset_list=None):
-        """Forward function."""
-        if offset_list is None:
-            offset_list = [None for _ in range(len(feats))]
-        return multi_apply(self.forward_single, feats, offset_list)
-
-    def _region_targets_single(self,
-                               anchors,
-                               valid_flags,
-                               gt_bboxes,
-                               gt_bboxes_ignore,
-                               gt_labels,
-                               img_meta,
-                               featmap_sizes,
-                               label_channels=1):
-        """Get anchor targets based on region for single level."""
-        assign_result = self.assigner.assign(
-            anchors,
-            valid_flags,
-            gt_bboxes,
-            img_meta,
-            featmap_sizes,
-            self.anchor_scales[0],
-            self.anchor_strides,
-            gt_bboxes_ignore=gt_bboxes_ignore,
-            gt_labels=None,
-            allowed_border=self.train_cfg.allowed_border)
-        flat_anchors = torch.cat(anchors)
-        sampling_result = self.sampler.sample(assign_result, flat_anchors,
-                                              gt_bboxes)
-
-        num_anchors = flat_anchors.shape[0]
-        bbox_targets = torch.zeros_like(flat_anchors)
-        bbox_weights = torch.zeros_like(flat_anchors)
-        labels = flat_anchors.new_zeros(num_anchors, dtype=torch.long)
-        label_weights = flat_anchors.new_zeros(num_anchors, dtype=torch.float)
-
-        pos_inds = sampling_result.pos_inds
-        neg_inds = sampling_result.neg_inds
-        if len(pos_inds) > 0:
-            if not self.reg_decoded_bbox:
-                pos_bbox_targets = self.bbox_coder.encode(
-                    sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
-            else:
-                pos_bbox_targets = sampling_result.pos_gt_bboxes
-            bbox_targets[pos_inds, :] = pos_bbox_targets
-            bbox_weights[pos_inds, :] = 1.0
-            if gt_labels is None:
-                labels[pos_inds] = 1
-            else:
-                labels[pos_inds] = gt_labels[
-                    sampling_result.pos_assigned_gt_inds]
-            if self.train_cfg.pos_weight <= 0:
-                label_weights[pos_inds] = 1.0
-            else:
-                label_weights[pos_inds] = self.train_cfg.pos_weight
-        if len(neg_inds) > 0:
-            label_weights[neg_inds] = 1.0
-
-        return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
-                neg_inds)
-
-    def region_targets(self,
-                       anchor_list,
-                       valid_flag_list,
-                       gt_bboxes_list,
-                       img_metas,
-                       featmap_sizes,
-                       gt_bboxes_ignore_list=None,
-                       gt_labels_list=None,
-                       label_channels=1,
-                       unmap_outputs=True):
-        """See :func:`StageCascadeRPNHead.get_targets`."""
-        num_imgs = len(img_metas)
-        assert len(anchor_list) == len(valid_flag_list) == num_imgs
-
-        # anchor number of multi levels
-        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-
-        # compute targets for each image
-        if gt_bboxes_ignore_list is None:
-            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
-        if gt_labels_list is None:
-            gt_labels_list = [None for _ in range(num_imgs)]
-        (all_labels, all_label_weights, all_bbox_targets, all_bbox_weights,
-         pos_inds_list, neg_inds_list) = multi_apply(
-             self._region_targets_single,
-             anchor_list,
-             valid_flag_list,
-             gt_bboxes_list,
-             gt_bboxes_ignore_list,
-             gt_labels_list,
-             img_metas,
-             featmap_sizes=featmap_sizes,
-             label_channels=label_channels)
-        # no valid anchors
-        if any([labels is None for labels in all_labels]):
-            return None
-        # sampled anchors of all images
-        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
-        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
-        # split targets to a list w.r.t. multiple levels
-        labels_list = images_to_levels(all_labels, num_level_anchors)
-        label_weights_list = images_to_levels(all_label_weights,
-                                              num_level_anchors)
-        bbox_targets_list = images_to_levels(all_bbox_targets,
-                                             num_level_anchors)
-        bbox_weights_list = images_to_levels(all_bbox_weights,
-                                             num_level_anchors)
-        return (labels_list, label_weights_list, bbox_targets_list,
-                bbox_weights_list, num_total_pos, num_total_neg)
-
-    def get_targets(self,
-                    anchor_list,
-                    valid_flag_list,
-                    gt_bboxes,
-                    img_metas,
-                    featmap_sizes,
-                    gt_bboxes_ignore=None,
-                    label_channels=1):
-        """Compute regression and classification targets for anchors.
-
-        Args:
-            anchor_list (list[list]): Multi level anchors of each image.
-            valid_flag_list (list[list]): Multi level valid flags of each
-                image.
-            gt_bboxes (list[Tensor]): Ground truth bboxes of each image.
-            img_metas (list[dict]): Meta info of each image.
-            featmap_sizes (list[Tensor]): Feature mapsize each level
-            gt_bboxes_ignore (list[Tensor]): Ignore bboxes of each images
-            label_channels (int): Channel of label.
-
-        Returns:
-            cls_reg_targets (tuple)
-        """
-        if isinstance(self.assigner, RegionAssigner):
-            cls_reg_targets = self.region_targets(
-                anchor_list,
-                valid_flag_list,
-                gt_bboxes,
-                img_metas,
-                featmap_sizes,
-                gt_bboxes_ignore_list=gt_bboxes_ignore,
-                label_channels=label_channels)
-        else:
-            cls_reg_targets = super(StageCascadeRPNHead, self).get_targets(
-                anchor_list,
-                valid_flag_list,
-                gt_bboxes,
-                img_metas,
-                gt_bboxes_ignore_list=gt_bboxes_ignore,
-                label_channels=label_channels)
-        return cls_reg_targets
-
-    def anchor_offset(self, anchor_list, anchor_strides, featmap_sizes):
-        """ Get offest for deformable conv based on anchor shape
-        NOTE: currently support deformable kernel_size=3 and dilation=1
-
-        Args:
-            anchor_list (list[list[tensor])): [NI, NLVL, NA, 4] list of
-                multi-level anchors
-            anchor_strides (list[int]): anchor stride of each level
-
-        Returns:
-            offset_list (list[tensor]): [NLVL, NA, 2, 18]: offset of DeformConv
-                kernel.
-        """
-
-        def _shape_offset(anchors, stride, ks=3, dilation=1):
-            # currently support kernel_size=3 and dilation=1
-            assert ks == 3 and dilation == 1
-            pad = (ks - 1) // 2
-            idx = torch.arange(-pad, pad + 1, dtype=dtype, device=device)
-            yy, xx = torch.meshgrid(idx, idx)  # return order matters
-            xx = xx.reshape(-1)
-            yy = yy.reshape(-1)
-            w = (anchors[:, 2] - anchors[:, 0]) / stride
-            h = (anchors[:, 3] - anchors[:, 1]) / stride
-            w = w / (ks - 1) - dilation
-            h = h / (ks - 1) - dilation
-            offset_x = w[:, None] * xx  # (NA, ks**2)
-            offset_y = h[:, None] * yy  # (NA, ks**2)
-            return offset_x, offset_y
-
-        def _ctr_offset(anchors, stride, featmap_size):
-            feat_h, feat_w = featmap_size
-            assert len(anchors) == feat_h * feat_w
-
-            x = (anchors[:, 0] + anchors[:, 2]) * 0.5
-            y = (anchors[:, 1] + anchors[:, 3]) * 0.5
-            # compute centers on feature map
-            x = x / stride
-            y = y / stride
-            # compute predefine centers
-            xx = torch.arange(0, feat_w, device=anchors.device)
-            yy = torch.arange(0, feat_h, device=anchors.device)
-            yy, xx = torch.meshgrid(yy, xx)
-            xx = xx.reshape(-1).type_as(x)
-            yy = yy.reshape(-1).type_as(y)
-
-            offset_x = x - xx  # (NA, )
-            offset_y = y - yy  # (NA, )
-            return offset_x, offset_y
-
-        num_imgs = len(anchor_list)
-        num_lvls = len(anchor_list[0])
-        dtype = anchor_list[0][0].dtype
-        device = anchor_list[0][0].device
-        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-
-        offset_list = []
-        for i in range(num_imgs):
-            mlvl_offset = []
-            for lvl in range(num_lvls):
-                c_offset_x, c_offset_y = _ctr_offset(anchor_list[i][lvl],
-                                                     anchor_strides[lvl],
-                                                     featmap_sizes[lvl])
-                s_offset_x, s_offset_y = _shape_offset(anchor_list[i][lvl],
-                                                       anchor_strides[lvl])
-
-                # offset = ctr_offset + shape_offset
-                offset_x = s_offset_x + c_offset_x[:, None]
-                offset_y = s_offset_y + c_offset_y[:, None]
-
-                # offset order (y0, x0, y1, x2, .., y8, x8, y9, x9)
-                offset = torch.stack([offset_y, offset_x], dim=-1)
-                offset = offset.reshape(offset.size(0), -1)  # [NA, 2*ks**2]
-                mlvl_offset.append(offset)
-            offset_list.append(torch.cat(mlvl_offset))  # [totalNA, 2*ks**2]
-        offset_list = images_to_levels(offset_list, num_level_anchors)
-        return offset_list
-
-    def loss_single(self, cls_score, bbox_pred, anchors, labels, label_weights,
-                    bbox_targets, bbox_weights, num_total_samples):
-        """Loss function on single scale."""
-        # classification loss
-        if self.with_cls:
-            labels = labels.reshape(-1)
-            label_weights = label_weights.reshape(-1)
-            cls_score = cls_score.permute(0, 2, 3,
-                                          1).reshape(-1, self.cls_out_channels)
-            loss_cls = self.loss_cls(
-                cls_score, labels, label_weights, avg_factor=num_total_samples)
-        # regression loss
-        bbox_targets = bbox_targets.reshape(-1, 4)
-        bbox_weights = bbox_weights.reshape(-1, 4)
-        bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
-        if self.reg_decoded_bbox:
-            # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
-            # is applied directly on the decoded bounding boxes, it
-            # decodes the already encoded coordinates to absolute format.
-            anchors = anchors.reshape(-1, 4)
-            bbox_pred = self.bbox_coder.decode(anchors, bbox_pred)
-        loss_reg = self.loss_bbox(
-            bbox_pred,
-            bbox_targets,
-            bbox_weights,
-            avg_factor=num_total_samples)
-        if self.with_cls:
-            return loss_cls, loss_reg
-        return None, loss_reg
-
-    def loss(self,
-             anchor_list,
-             valid_flag_list,
-             cls_scores,
-             bbox_preds,
-             gt_bboxes,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute losses of the head.
-
-        Args:
-            anchor_list (list[list]): Multi level anchors of each image.
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W)
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
-                boxes can be ignored when computing the loss. Default: None
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        featmap_sizes = [featmap.size()[-2:] for featmap in bbox_preds]
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            featmap_sizes,
-            gt_bboxes_ignore=gt_bboxes_ignore,
-            label_channels=label_channels)
-        if cls_reg_targets is None:
-            return None
-        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
-         num_total_pos, num_total_neg) = cls_reg_targets
-        if self.sampling:
-            num_total_samples = num_total_pos + num_total_neg
-        else:
-            # 200 is hard-coded average factor,
-            # which follows guided anchoring.
-            num_total_samples = sum([label.numel()
-                                     for label in labels_list]) / 200.0
-
-        # change per image, per level anchor_list to per_level, per_image
-        mlvl_anchor_list = list(zip(*anchor_list))
-        # concat mlvl_anchor_list
-        mlvl_anchor_list = [
-            torch.cat(anchors, dim=0) for anchors in mlvl_anchor_list
-        ]
-
-        losses = multi_apply(
-            self.loss_single,
-            cls_scores,
-            bbox_preds,
-            mlvl_anchor_list,
-            labels_list,
-            label_weights_list,
-            bbox_targets_list,
-            bbox_weights_list,
-            num_total_samples=num_total_samples)
-        if self.with_cls:
-            return dict(loss_rpn_cls=losses[0], loss_rpn_reg=losses[1])
-        return dict(loss_rpn_reg=losses[1])
-
-    def get_bboxes(self,
-                   anchor_list,
-                   cls_scores,
-                   bbox_preds,
-                   img_metas,
-                   cfg,
-                   rescale=False):
-        """Get proposal predict."""
-        assert len(cls_scores) == len(bbox_preds)
-        num_levels = len(cls_scores)
-
-        result_list = []
-        for img_id in range(len(img_metas)):
-            cls_score_list = [
-                cls_scores[i][img_id].detach() for i in range(num_levels)
-            ]
-            bbox_pred_list = [
-                bbox_preds[i][img_id].detach() for i in range(num_levels)
-            ]
-            img_shape = img_metas[img_id]['img_shape']
-            scale_factor = img_metas[img_id]['scale_factor']
-            proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list,
-                                                anchor_list[img_id], img_shape,
-                                                scale_factor, cfg, rescale)
-            result_list.append(proposals)
-        return result_list
-
-    def refine_bboxes(self, anchor_list, bbox_preds, img_metas):
-        """Refine bboxes through stages."""
-        num_levels = len(bbox_preds)
-        new_anchor_list = []
-        for img_id in range(len(img_metas)):
-            mlvl_anchors = []
-            for i in range(num_levels):
-                bbox_pred = bbox_preds[i][img_id].detach()
-                bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
-                img_shape = img_metas[img_id]['img_shape']
-                bboxes = self.bbox_coder.decode(anchor_list[img_id][i],
-                                                bbox_pred, img_shape)
-                mlvl_anchors.append(bboxes)
-            new_anchor_list.append(mlvl_anchors)
-        return new_anchor_list
-
-    # TODO: temporary plan
-    def _get_bboxes_single(self,
-                           cls_scores,
-                           bbox_preds,
-                           mlvl_anchors,
-                           img_shape,
-                           scale_factor,
-                           cfg,
-                           rescale=False):
-        """Transform outputs for a single batch item into bbox predictions.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (num_anchors * num_classes, H, W).
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (num_anchors * 4, H, W).
-            mlvl_anchors (list[Tensor]): Box reference for each scale level
-                with shape (num_total_anchors, 4).
-            img_shape (tuple[int]): Shape of the input image,
-                (height, width, 3).
-            scale_factor (ndarray): Scale factor of the image arange as
-                (w_scale, h_scale, w_scale, h_scale).
-            cfg (mmcv.Config): Test / postprocessing configuration,
-                if None, test_cfg would be used.
-            rescale (bool): If True, return boxes in original image space.
-
-        Returns:
-            Tensor: Labeled boxes have the shape of (n,5), where the
-                first 4 columns are bounding box positions
-                (tl_x, tl_y, br_x, br_y) and the 5-th column is a score
-                between 0 and 1.
-        """
-        cfg = self.test_cfg if cfg is None else cfg
-        cfg = copy.deepcopy(cfg)
-        # bboxes from different level should be independent during NMS,
-        # level_ids are used as labels for batched NMS to separate them
-        level_ids = []
-        mlvl_scores = []
-        mlvl_bbox_preds = []
-        mlvl_valid_anchors = []
-        for idx in range(len(cls_scores)):
-            rpn_cls_score = cls_scores[idx]
-            rpn_bbox_pred = bbox_preds[idx]
-            assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
-            rpn_cls_score = rpn_cls_score.permute(1, 2, 0)
-            if self.use_sigmoid_cls:
-                rpn_cls_score = rpn_cls_score.reshape(-1)
-                scores = rpn_cls_score.sigmoid()
-            else:
-                rpn_cls_score = rpn_cls_score.reshape(-1, 2)
-                # We set FG labels to [0, num_class-1] and BG label to
-                # num_class in RPN head since mmdet v2.5, which is unified to
-                # be consistent with other head since mmdet v2.0. In mmdet v2.0
-                # to v2.4 we keep BG label as 0 and FG label as 1 in rpn head.
-                scores = rpn_cls_score.softmax(dim=1)[:, 0]
-            rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1, 4)
-            anchors = mlvl_anchors[idx]
-            if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre:
-                # sort is faster than topk
-                # _, topk_inds = scores.topk(cfg.nms_pre)
-                if torch.onnx.is_in_onnx_export():
-                    # sort op will be converted to TopK in onnx
-                    # and k<=3480 in TensorRT
-                    _, topk_inds = scores.topk(cfg.nms_pre)
-                    scores = scores[topk_inds]
-                else:
-                    ranked_scores, rank_inds = scores.sort(descending=True)
-                    topk_inds = rank_inds[:cfg.nms_pre]
-                    scores = ranked_scores[:cfg.nms_pre]
-                rpn_bbox_pred = rpn_bbox_pred[topk_inds, :]
-                anchors = anchors[topk_inds, :]
-            mlvl_scores.append(scores)
-            mlvl_bbox_preds.append(rpn_bbox_pred)
-            mlvl_valid_anchors.append(anchors)
-            level_ids.append(
-                scores.new_full((scores.size(0), ), idx, dtype=torch.long))
-
-        scores = torch.cat(mlvl_scores)
-        anchors = torch.cat(mlvl_valid_anchors)
-        rpn_bbox_pred = torch.cat(mlvl_bbox_preds)
-        proposals = self.bbox_coder.decode(
-            anchors, rpn_bbox_pred, max_shape=img_shape)
-        ids = torch.cat(level_ids)
-
-        # Skip nonzero op while exporting to ONNX
-        if cfg.min_bbox_size > 0 and (not torch.onnx.is_in_onnx_export()):
-            w = proposals[:, 2] - proposals[:, 0]
-            h = proposals[:, 3] - proposals[:, 1]
-            valid_inds = torch.nonzero(
-                (w >= cfg.min_bbox_size)
-                & (h >= cfg.min_bbox_size),
-                as_tuple=False).squeeze()
-            if valid_inds.sum().item() != len(proposals):
-                proposals = proposals[valid_inds, :]
-                scores = scores[valid_inds]
-                ids = ids[valid_inds]
-
-        # deprecate arguments warning
-        if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg:
-            warnings.warn(
-                'In rpn_proposal or test_cfg, '
-                'nms_thr has been moved to a dict named nms as '
-                'iou_threshold, max_num has been renamed as max_per_img, '
-                'name of original arguments and the way to specify '
-                'iou_threshold of NMS will be deprecated.')
-        if 'nms' not in cfg:
-            cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr))
-        if 'max_num' in cfg:
-            if 'max_per_img' in cfg:
-                assert cfg.max_num == cfg.max_per_img, f'You ' \
-                    f'set max_num and ' \
-                    f'max_per_img at the same time, but get {cfg.max_num} ' \
-                    f'and {cfg.max_per_img} respectively' \
-                    'Please delete max_num which will be deprecated.'
-            else:
-                cfg.max_per_img = cfg.max_num
-        if 'nms_thr' in cfg:
-            assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set' \
-                f' iou_threshold in nms and ' \
-                f'nms_thr at the same time, but get' \
-                f' {cfg.nms.iou_threshold} and {cfg.nms_thr}' \
-                f' respectively. Please delete the nms_thr ' \
-                f'which will be deprecated.'
-
-        dets, keep = batched_nms(proposals, scores, ids, cfg.nms)
-        return dets[:cfg.max_per_img]
-
-
-@HEADS.register_module()
-class CascadeRPNHead(BaseDenseHead):
-    """The CascadeRPNHead will predict more accurate region proposals, which is
-    required for two-stage detectors (such as Fast/Faster R-CNN). CascadeRPN
-    consists of a sequence of RPNStage to progressively improve the accuracy of
-    the detected proposals.
-
-    More details can be found in ``https://arxiv.org/abs/1909.06720``.
-
-    Args:
-        num_stages (int): number of CascadeRPN stages.
-        stages (list[dict]): list of configs to build the stages.
-        train_cfg (list[dict]): list of configs at training time each stage.
-        test_cfg (dict): config at testing time.
-    """
-
-    def __init__(self, num_stages, stages, train_cfg, test_cfg):
-        super(CascadeRPNHead, self).__init__()
-        assert num_stages == len(stages)
-        self.num_stages = num_stages
-        self.stages = nn.ModuleList()
-        for i in range(len(stages)):
-            train_cfg_i = train_cfg[i] if train_cfg is not None else None
-            stages[i].update(train_cfg=train_cfg_i)
-            stages[i].update(test_cfg=test_cfg)
-            self.stages.append(build_head(stages[i]))
-        self.train_cfg = train_cfg
-        self.test_cfg = test_cfg
-
-    def init_weights(self):
-        """Init weight of CascadeRPN."""
-        for i in range(self.num_stages):
-            self.stages[i].init_weights()
-
-    def loss(self):
-        """loss() is implemented in StageCascadeRPNHead."""
-        pass
-
-    def get_bboxes(self):
-        """get_bboxes() is implemented in StageCascadeRPNHead."""
-        pass
-
-    def forward_train(self,
-                      x,
-                      img_metas,
-                      gt_bboxes,
-                      gt_labels=None,
-                      gt_bboxes_ignore=None,
-                      proposal_cfg=None):
-        """Forward train function."""
-        assert gt_labels is None, 'RPN does not require gt_labels'
-
-        featmap_sizes = [featmap.size()[-2:] for featmap in x]
-        device = x[0].device
-        anchor_list, valid_flag_list = self.stages[0].get_anchors(
-            featmap_sizes, img_metas, device=device)
-
-        losses = dict()
-
-        for i in range(self.num_stages):
-            stage = self.stages[i]
-
-            if stage.adapt_cfg['type'] == 'offset':
-                offset_list = stage.anchor_offset(anchor_list,
-                                                  stage.anchor_strides,
-                                                  featmap_sizes)
-            else:
-                offset_list = None
-            x, cls_score, bbox_pred = stage(x, offset_list)
-            rpn_loss_inputs = (anchor_list, valid_flag_list, cls_score,
-                               bbox_pred, gt_bboxes, img_metas)
-            stage_loss = stage.loss(*rpn_loss_inputs)
-            for name, value in stage_loss.items():
-                losses['s{}.{}'.format(i, name)] = value
-
-            # refine boxes
-            if i < self.num_stages - 1:
-                anchor_list = stage.refine_bboxes(anchor_list, bbox_pred,
-                                                  img_metas)
-        if proposal_cfg is None:
-            return losses
-        else:
-            proposal_list = self.stages[-1].get_bboxes(anchor_list, cls_score,
-                                                       bbox_pred, img_metas,
-                                                       self.test_cfg)
-            return losses, proposal_list
-
-    def simple_test_rpn(self, x, img_metas):
-        """Simple forward test function."""
-        featmap_sizes = [featmap.size()[-2:] for featmap in x]
-        device = x[0].device
-        anchor_list, _ = self.stages[0].get_anchors(
-            featmap_sizes, img_metas, device=device)
-
-        for i in range(self.num_stages):
-            stage = self.stages[i]
-            if stage.adapt_cfg['type'] == 'offset':
-                offset_list = stage.anchor_offset(anchor_list,
-                                                  stage.anchor_strides,
-                                                  featmap_sizes)
-            else:
-                offset_list = None
-            x, cls_score, bbox_pred = stage(x, offset_list)
-            if i < self.num_stages - 1:
-                anchor_list = stage.refine_bboxes(anchor_list, bbox_pred,
-                                                  img_metas)
-
-        proposal_list = self.stages[-1].get_bboxes(anchor_list, cls_score,
-                                                   bbox_pred, img_metas,
-                                                   self.test_cfg)
-        return proposal_list
-
-    def aug_test_rpn(self, x, img_metas):
-        """Augmented forward test function."""
-        raise NotImplementedError

spaces/CVPR/drawings-to-human/static/index.html

@@ -1,209 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-	<head>
-		<meta charset="utf-8" />
-		<link rel="icon" href="/static/favicon.png" />
-		<meta name="viewport" content="width=device-width, initial-scale=1" />
-		<script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script>
-		<meta http-equiv="content-security-policy" content="">
-	<link rel="stylesheet" href="/static/_app/immutable/assets/pages/__layout.svelte-cc9dd261.css">
-	<link rel="stylesheet" href="/static/_app/immutable/assets/pages/index.svelte-7bf249dc.css">
-	<link rel="modulepreload" href="/static/_app/immutable/start-62e3dfe2.js">
-	<link rel="modulepreload" href="/static/_app/immutable/chunks/index-bcf2726a.js">
-	<link rel="modulepreload" href="/static/_app/immutable/chunks/paths-d3bcbd10.js">
-	<link rel="modulepreload" href="/static/_app/immutable/pages/__layout.svelte-d07d8fed.js">
-	<link rel="modulepreload" href="/static/_app/immutable/pages/index.svelte-b5d75a5f.js">
-	</head>
-	<body class="dark:bg-[rgb(11,15,25)] bg-white dark:text-white text-black">
-		
-
-
-<div class="max-w-screen-md mx-auto px-3 py-5 relative z-0"><article class="prose dark:prose-invert"><h1>Drawings to Human</h1>
-		<p>This is an unofficial drawing tool to explore the generative human generator <a href="https://github.com/yumingj/Text2Human" target="_blank"><span>Text2Human</span></a>. Please check all the model features on this
-			<a href="https://huggingface.co/spaces/CVPR/Text2Human" target="_blank">Space</a>.
-		</p>
-		<small><h4 id="thanks-to">Thanks to</h4>
-			<p>Authors: <a href="https://yumingj.github.io/" target="_blank">Yuming Jiang</a>,
-				<a href="https://williamyang1991.github.io/" target="_blank">Shuai Yang</a>,
-				<a href="http://haonanqiu.com/" target="_blank">Haonan Qiu</a>,
-				<a href="https://wywu.github.io/" target="_blank">Wayne Wu</a>,
-				<a href="https://www.mmlab-ntu.com/person/ccloy/" target="_blank">Chen Change Loy</a>
-				and <a href="https://liuziwei7.github.io/" target="_blank">Ziwei Liu</a><br></p>
-			<p><a href="https://huggingface.co/hysts" target="_blank">@hysts</a> for the original Space implementation
-			</p></small>
-		<details><summary class="cursor-pointer"><small>More</small></summary>
-			<p>The backend is powered by a <a href="https://gradio.app/" target="_blank">Gradio</a>
-				application running on
-				<a href="https://huggingface.co/spaces/CVPR/Text2Human" target="_blank">Spaces</a>. You can
-				also check the source code and clone it locally if you want:
-			</p>
-
-			<p><code class="block whitespace-pre overflow-x-scroll">git clone https://huggingface.co/spaces/CVPR/Text2Human
-				</code></p></details></article>
-	<form><h4 class="font-bold mt-6 mb-2 leading-6 my-3">Set the Brush Type</h4>
-	<div class="colors svelte-1oy4poo" name="colors"><div class="snap-always snap-start"><input name="color"  type="radio" id="color-0" value="0" class="svelte-1oy4poo">
-				<label for="color-0" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(0,0,0)"></rect></svg>
-					<span class="svelte-1oy4poo">background</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-1" value="1" class="svelte-1oy4poo">
-				<label for="color-1" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(255,140,0)"></rect></svg>
-					<span class="svelte-1oy4poo">bag</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-2" value="2" class="svelte-1oy4poo">
-				<label for="color-2" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(255,255,0)"></rect></svg>
-					<span class="svelte-1oy4poo">belt</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-3" value="3" class="svelte-1oy4poo">
-				<label for="color-3" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(255,250,205)"></rect></svg>
-					<span class="svelte-1oy4poo">dress</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-4" value="4" class="svelte-1oy4poo">
-				<label for="color-4" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(130,165,180)"></rect></svg>
-					<span class="svelte-1oy4poo">earrings</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-5" value="5" class="svelte-1oy4poo">
-				<label for="color-5" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(0,100,0)"></rect></svg>
-					<span class="svelte-1oy4poo">eyeglass</span></label>
-			</div><div class="snap-always snap-start"><input name="color" checked type="radio" id="color-6" value="6" class="svelte-1oy4poo">
-				<label for="color-6" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(16,78,139)"></rect></svg>
-					<span class="svelte-1oy4poo">face</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-7" value="7" class="svelte-1oy4poo">
-				<label for="color-7" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(245,222,179)"></rect></svg>
-					<span class="svelte-1oy4poo">footwear</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-8" value="8" class="svelte-1oy4poo">
-				<label for="color-8" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(213,140,88)"></rect></svg>
-					<span class="svelte-1oy4poo">gloves</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-9" value="9" class="svelte-1oy4poo">
-				<label for="color-9" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(255,0,0)"></rect></svg>
-					<span class="svelte-1oy4poo">hair</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-10" value="10" class="svelte-1oy4poo">
-				<label for="color-10" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(127,255,212)"></rect></svg>
-					<span class="svelte-1oy4poo">headwear</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-11" value="11" class="svelte-1oy4poo">
-				<label for="color-11" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(70,130,180)"></rect></svg>
-					<span class="svelte-1oy4poo">leggings</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-12" value="12" class="svelte-1oy4poo">
-				<label for="color-12" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(90,140,90)"></rect></svg>
-					<span class="svelte-1oy4poo">necklace</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-13" value="13" class="svelte-1oy4poo">
-				<label for="color-13" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(50,205,50)"></rect></svg>
-					<span class="svelte-1oy4poo">neckwear</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-14" value="14" class="svelte-1oy4poo">
-				<label for="color-14" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(220,220,220)"></rect></svg>
-					<span class="svelte-1oy4poo">outer</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-15" value="15" class="svelte-1oy4poo">
-				<label for="color-15" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(211,211,211)"></rect></svg>
-					<span class="svelte-1oy4poo">pants</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-16" value="16" class="svelte-1oy4poo">
-				<label for="color-16" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(50,205,174)"></rect></svg>
-					<span class="svelte-1oy4poo">ring</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-17" value="17" class="svelte-1oy4poo">
-				<label for="color-17" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(185,210,205)"></rect></svg>
-					<span class="svelte-1oy4poo">rompers</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-18" value="18" class="svelte-1oy4poo">
-				<label for="color-18" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(144,238,144)"></rect></svg>
-					<span class="svelte-1oy4poo">skin</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-19" value="19" class="svelte-1oy4poo">
-				<label for="color-19" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(250,235,215)"></rect></svg>
-					<span class="svelte-1oy4poo">skirt</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-20" value="20" class="svelte-1oy4poo">
-				<label for="color-20" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(160,140,88)"></rect></svg>
-					<span class="svelte-1oy4poo">socks</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-21" value="21" class="svelte-1oy4poo">
-				<label for="color-21" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(225,141,151)"></rect></svg>
-					<span class="svelte-1oy4poo">tie</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-22" value="22" class="svelte-1oy4poo">
-				<label for="color-22" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(255,250,250)"></rect></svg>
-					<span class="svelte-1oy4poo">top</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-23" value="23" class="svelte-1oy4poo">
-				<label for="color-23" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(50,155,250)"></rect></svg>
-					<span class="svelte-1oy4poo">wrist wearing</span></label>
-			</div></div>
-	<h4 class="font-bold mt-6 mb-2 my-6 leading-6">Set the Brush Size</h4>
-	<div class="brush svelte-1oy4poo"><input value="10" min="1" max="50" step="1" name="brush" type="range">
-		<label class="pl-2 svelte-1oy4poo" for="brush">40</label></div>
-</form>
-	<div><h4 class="font-bold mt-6 mb-2 my-6 leading-6">Select a Template</h4>
-	<form class="svelte-1gwcbp"><div class="samples  svelte-1gwcbp"><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-0" value="0"  class="svelte-1gwcbp">
-					<label for="sample-0" class="svelte-1gwcbp"><img src="/static/samples/WOMEN-Skirts-id_00004406-02_7_additional_segm.png" alt="/samples/WOMEN-Skirts-id_00004406-02_7_additional_segm.png" class="svelte-1gwcbp"></label>
-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-1" value="1"  class="svelte-1gwcbp">
-					<label for="sample-1" class="svelte-1gwcbp"><img src="/static/samples/MEN-Pants-id_00002565-02_1_front_segm.png" alt="/samples/MEN-Pants-id_00002565-02_1_front_segm.png" class="svelte-1gwcbp"></label>
-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-2" value="2"  class="svelte-1gwcbp">
-					<label for="sample-2" class="svelte-1gwcbp"><img src="/static/samples/MEN-Pants-id_00005213-02_4_full_segm.png" alt="/samples/MEN-Pants-id_00005213-02_4_full_segm.png" class="svelte-1gwcbp"></label>
-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-3" value="3"  class="svelte-1gwcbp">
-					<label for="sample-3" class="svelte-1gwcbp"><img src="/static/samples/WOMEN-Blouses_Shirts-id_00002356-02_4_full_segm.png" alt="/samples/WOMEN-Blouses_Shirts-id_00002356-02_4_full_segm.png" class="svelte-1gwcbp"></label>
-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-4" value="4"  class="svelte-1gwcbp">
-					<label for="sample-4" class="svelte-1gwcbp"><img src="/static/samples/WOMEN-Blouses_Shirts-id_00004090-03_7_additional_segm.png" alt="/samples/WOMEN-Blouses_Shirts-id_00004090-03_7_additional_segm.png" class="svelte-1gwcbp"></label>
-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-5" value="5"  class="svelte-1gwcbp">
-					<label for="sample-5" class="svelte-1gwcbp"><img src="/static/samples/WOMEN-Cardigans-id_00000853-01_2_side_segm.png" alt="/samples/WOMEN-Cardigans-id_00000853-01_2_side_segm.png" class="svelte-1gwcbp"></label>
-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-6" value="6"  class="svelte-1gwcbp">
-					<label for="sample-6" class="svelte-1gwcbp"><img src="/static/samples/WOMEN-Cardigans-id_00000899-02_1_front_segm.png" alt="/samples/WOMEN-Cardigans-id_00000899-02_1_front_segm.png" class="svelte-1gwcbp"></label>
-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-7" value="7"  class="svelte-1gwcbp">
-					<label for="sample-7" class="svelte-1gwcbp"><img src="/static/samples/WOMEN-Cardigans-id_00006462-02_7_additional_segm.png" alt="/samples/WOMEN-Cardigans-id_00006462-02_7_additional_segm.png" class="svelte-1gwcbp"></label>
-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-8" value="8"  class="svelte-1gwcbp">
-					<label for="sample-8" class="svelte-1gwcbp"><img src="/static/samples/WOMEN-Dresses-id_00000021-05_1_front_segm.png" alt="/samples/WOMEN-Dresses-id_00000021-05_1_front_segm.png" class="svelte-1gwcbp"></label>
-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-9" value="9"  class="svelte-1gwcbp">
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-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-13" value="13"  class="svelte-1gwcbp">
-					<label for="sample-13" class="svelte-1gwcbp"><img src="/static/samples/WOMEN-Jackets_Coats-id_00005263-06_1_front_segm.png" alt="/samples/WOMEN-Jackets_Coats-id_00005263-06_1_front_segm.png" class="svelte-1gwcbp"></label>
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-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-15" value="15"  class="svelte-1gwcbp">
-					<label for="sample-15" class="svelte-1gwcbp"><img src="/static/samples/WOMEN-Rompers_Jumpsuits-id_00004575-02_1_front_segm.png" alt="/samples/WOMEN-Rompers_Jumpsuits-id_00004575-02_1_front_segm.png" class="svelte-1gwcbp"></label>
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-				</div></div></form>
-</div>
-	<div class="drawings py-3 -mx-3 svelte-237ry5"><div><div class="relative overflow-clip"><canvas class="canvas svelte-1k5plc8" width="256" height="512"></canvas>
-		<canvas class="brush svelte-1k5plc8" width="10" height="10"></canvas>
-		<span class="label svelte-1k5plc8">face</span>
-		<button class="absolute bottom-0 left-0 p-3" disabled><svg xmlns="http://www.w3.org/2000/svg" width="20" viewBox="0 0 512 512" class=""><path fill="white" stroke="black" stroke-width="30" d="M480 256c0 123.4-100.5 223.9-223.9 223.9c-48.84 0-95.17-15.58-134.2-44.86c-14.12-10.59-16.97-30.66-6.375-44.81c10.59-14.12 30.62-16.94 44.81-6.375c27.84 20.91 61 31.94 95.88 31.94C344.3 415.8 416 344.1 416 256s-71.69-159.8-159.8-159.8c-37.46 0-73.09 13.49-101.3 36.64l45.12 45.14c17.01 17.02 4.955 46.1-19.1 46.1H35.17C24.58 224.1 16 215.5 16 204.9V59.04c0-24.04 29.07-36.08 46.07-19.07l47.6 47.63C149.9 52.71 201.5 32.11 256.1 32.11C379.5 32.11 480 132.6 480 256z"></path></svg></button></div>
-</div>
-		<div class="relative overflow-clip flex flex-col justify-center items-center w-full h-full">
-	</div>
-
-</div>
-	<button  class="svelte-237ry5">Generate Human
-	</button>
-	<button disabled class="svelte-237ry5">Save Result
-	</button>
-	<form><h4 class="font-bold mt-6 mb-2 my-6 leading-6">Texture Description</h4>
-	<div class="sections svelte-uoay71"><select name="texture0"  class="svelte-uoay71"><option disabled selected value="upper clothing texture">upper clothing texture</option><option value="pure color">pure color</option>`<option value="stripe/spline">stripe/spline</option>`<option value="plaid/lattice">plaid/lattice</option>`<option value="floral">floral</option>`<option value="denim">denim</option>`</select><select name="texture1"  class="svelte-uoay71"><option disabled selected value="lower clothing texture">lower clothing texture</option><option value="pure color">pure color</option>`<option value="stripe/spline">stripe/spline</option>`<option value="plaid/lattice">plaid/lattice</option>`<option value="floral">floral</option>`<option value="denim">denim</option>`</select><select name="texture2"  class="svelte-uoay71"><option disabled selected value="outer clothing texture">outer clothing texture</option><option value="pure color">pure color</option>`<option value="stripe/spline">stripe/spline</option>`<option value="plaid/lattice">plaid/lattice</option>`<option value="floral">floral</option>`<option value="denim">denim</option>`</select></div>
-	<h4 class="font-bold mt-6 mb-2 my-6 leading-6">Random Seed</h4>
-	<input type="Number" name="seed" placeholder="Integer Seed"  class="svelte-uoay71" value="861014016">
-	<button  class="svelte-uoay71">Random
-	</button>
-	<h4 class="font-bold mt-6 mb-2 my-6 leading-6">Sample Steps</h4>
-	<div class="flex"><input type="range" name="steps" min="10" max="300" step="1"  class="svelte-uoay71" value="10">
-		<label class="pl-2 svelte-uoay71" for="steps">10</label></div>
-</form>
-</div>
-
-
-		<script type="module" data-sveltekit-hydrate="1lpy11h">
-		import { start } from "/static/_app/immutable/start-62e3dfe2.js";
-		start({
-			target: document.querySelector('[data-sveltekit-hydrate="1lpy11h"]').parentNode,
-			paths: {"base":"/static","assets":"/static"},
-			session: {},
-			route: true,
-			spa: false,
-			trailing_slash: "never",
-			hydrate: {
-				status: 200,
-				error: null,
-				nodes: [0, 2],
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-				routeId: ""
-			}
-		});
-	</script>
-	</body>
-</html>

spaces/CVPR/regionclip-demo/detectron2/evaluation/__init__.py

@@ -1,13 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .cityscapes_evaluation import CityscapesInstanceEvaluator, CityscapesSemSegEvaluator
-from .coco_evaluation import COCOEvaluator
-from .rotated_coco_evaluation import RotatedCOCOEvaluator
-from .evaluator import DatasetEvaluator, DatasetEvaluators, inference_context, inference_on_dataset
-from .lvis_evaluation import LVISEvaluator
-from .panoptic_evaluation import COCOPanopticEvaluator
-from .pascal_voc_evaluation import PascalVOCDetectionEvaluator
-from .sem_seg_evaluation import SemSegEvaluator
-from .testing import print_csv_format, verify_results
-from .flickr30k_evaluation import FLICKR30KEvaluator
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]

spaces/Caoyunkang/Segment-Any-Anomaly/utils/metrics.py

@@ -1,219 +0,0 @@
-import numpy as np
-from skimage import measure
-from sklearn.metrics import auc
-from sklearn.metrics import precision_recall_curve
-from sklearn.metrics import roc_auc_score
-from sklearn.metrics import roc_curve
-
-def calculate_max_f1(gt, scores):
-    precision, recall, thresholds = precision_recall_curve(gt, scores)
-    a = 2 * precision * recall
-    b = precision + recall
-    f1s = np.divide(a, b, out=np.zeros_like(a), where=b != 0)
-    index = np.argmax(f1s)
-    max_f1 = f1s[index]
-    threshold = thresholds[index]
-    return max_f1, threshold
-
-def metric_cal(scores, gt_list, gt_mask_list, cal_pro=False):
-    # calculate image-level ROC AUC score
-    img_scores = scores.reshape(scores.shape[0], -1).max(axis=1)
-    gt_list = np.asarray(gt_list, dtype=int)
-    fpr, tpr, _ = roc_curve(gt_list, img_scores)
-    img_roc_auc = roc_auc_score(gt_list, img_scores)
-    # print('INFO: image ROCAUC: %.3f' % (img_roc_auc))
-
-    img_f1, img_threshold = calculate_max_f1(gt_list, img_scores)
-
-    gt_mask = np.asarray(gt_mask_list, dtype=int)
-    pxl_f1, pxl_threshold = calculate_max_f1(gt_mask.flatten(), scores.flatten())
-
-    # calculate per-pixel level ROCAUC
-    fpr, tpr, _ = roc_curve(gt_mask.flatten(), scores.flatten())
-    per_pixel_rocauc = roc_auc_score(gt_mask.flatten(), scores.flatten())
-
-
-    # calculate max-f1 region
-    if cal_pro:
-        # pro_auc_score = cal_pro_metric(gt_mask_list, scores, fpr_thresh=0.3)
-        # calculate max-f1 region
-        max_f1_region = calculate_max_f1_region(gt_mask_list, scores)
-
-    else:
-        # pro_auc_score = 0
-        # calculate max-f1 region
-        max_f1_region = 0
-
-    result_dict = {'i_roc': img_roc_auc * 100, 'p_roc': per_pixel_rocauc * 100,
-     'i_f1': img_f1 * 100, 'i_thresh': img_threshold, 'p_f1': pxl_f1 * 100, 'p_thresh': pxl_threshold, 'r_f1': max_f1_region * 100}
-
-    return result_dict
-
-
-def rescale(x):
-    return (x - x.min()) / (x.max() - x.min())
-
-
-def cal_pro_metric(labeled_imgs, score_imgs, fpr_thresh=0.3, max_steps=200):
-    labeled_imgs = np.array(labeled_imgs)
-    labeled_imgs[labeled_imgs <= 0.45] = 0
-    labeled_imgs[labeled_imgs > 0.45] = 1
-    labeled_imgs = labeled_imgs.astype(np.bool)
-
-    max_th = score_imgs.max()
-    min_th = score_imgs.min()
-    delta = (max_th - min_th) / max_steps
-
-    ious_mean = []
-    ious_std = []
-    pros_mean = []
-    pros_std = []
-    threds = []
-    fprs = []
-    binary_score_maps = np.zeros_like(score_imgs, dtype=bool)
-    for step in range(max_steps):
-        thred = max_th - step * delta
-        # segmentation
-        binary_score_maps[score_imgs <= thred] = 0
-        binary_score_maps[score_imgs > thred] = 1
-
-        pro = []  # per region overlap
-        iou = []  # per image iou
-        # pro: find each connected gt region, compute the overlapped pixels between the gt region and predicted region
-        # iou: for each image, compute the ratio, i.e. intersection/union between the gt and predicted binary map
-        for i in range(len(binary_score_maps)):  # for i th image
-            # pro (per region level)
-            label_map = measure.label(labeled_imgs[i], connectivity=2)
-            props = measure.regionprops(label_map)
-            for prop in props:
-                x_min, y_min, x_max, y_max = prop.bbox
-                cropped_pred_label = binary_score_maps[i][x_min:x_max, y_min:y_max]
-                # cropped_mask = masks[i][x_min:x_max, y_min:y_max]
-                cropped_mask = prop.filled_image  # corrected!
-                intersection = np.logical_and(cropped_pred_label, cropped_mask).astype(np.float32).sum()
-                pro.append(intersection / prop.area)
-            # iou (per image level)
-            intersection = np.logical_and(binary_score_maps[i], labeled_imgs[i]).astype(np.float32).sum()
-            union = np.logical_or(binary_score_maps[i], labeled_imgs[i]).astype(np.float32).sum()
-            if labeled_imgs[i].any() > 0:  # when the gt have no anomaly pixels, skip it
-                iou.append(intersection / union)
-        # against steps and average metrics on the testing data
-        ious_mean.append(np.array(iou).mean())
-        #             print("per image mean iou:", np.array(iou).mean())
-        ious_std.append(np.array(iou).std())
-        pros_mean.append(np.array(pro).mean())
-        pros_std.append(np.array(pro).std())
-        # fpr for pro-auc
-        masks_neg = ~labeled_imgs
-        fpr = np.logical_and(masks_neg, binary_score_maps).sum() / masks_neg.sum()
-        fprs.append(fpr)
-        threds.append(thred)
-
-    # as array
-    threds = np.array(threds)
-    pros_mean = np.array(pros_mean)
-    pros_std = np.array(pros_std)
-    fprs = np.array(fprs)
-
-    # default 30% fpr vs pro, pro_auc
-    idx = fprs <= fpr_thresh  # find the indexs of fprs that is less than expect_fpr (default 0.3)
-    fprs_selected = fprs[idx]
-    fprs_selected = rescale(fprs_selected)  # rescale fpr [0,0.3] -> [0, 1]
-    pros_mean_selected = pros_mean[idx]
-    pro_auc_score = auc(fprs_selected, pros_mean_selected)
-    # print("pro auc ({}% FPR):".format(int(expect_fpr * 100)), pro_auc_score)
-    return pro_auc_score
-
-def calculate_max_f1_region(labeled_imgs, score_imgs, pro_thresh=0.6, max_steps=200):
-    labeled_imgs = np.array(labeled_imgs)
-    # labeled_imgs[labeled_imgs <= 0.1] = 0
-    # labeled_imgs[labeled_imgs > 0.1] = 1
-    labeled_imgs = labeled_imgs.astype(bool)
-
-    max_th = score_imgs.max()
-    min_th = score_imgs.min()
-    delta = (max_th - min_th) / max_steps
-
-    f1_list = []
-    recall_list = []
-    precision_list = []
-
-    binary_score_maps = np.zeros_like(score_imgs, dtype=bool)
-    for step in range(max_steps):
-        thred = max_th - step * delta
-        # segmentation
-        binary_score_maps[score_imgs <= thred] = 0
-        binary_score_maps[score_imgs > thred] = 1
-
-        pro = []  # per region overlap
-
-        predict_region_number = 0
-        gt_region_number = 0
-
-        # pro: find each connected gt region, compute the overlapped pixels between the gt region and predicted region
-        # iou: for each image, compute the ratio, i.e. intersection/union between the gt and predicted binary map
-        for i in range(len(binary_score_maps)):  # for i th image
-            # pro (per region level)
-            label_map = measure.label(labeled_imgs[i], connectivity=2)
-            props = measure.regionprops(label_map)
-
-            score_map = measure.label(binary_score_maps[i], connectivity=2)
-            score_props = measure.regionprops(score_map)
-
-            predict_region_number += len(score_props)
-            gt_region_number += len(props)
-
-            # if len(score_props) == 0 or len(props) == 0:
-            #     pro.append(0)
-            #     continue
-
-            for score_prop in score_props:
-                x_min_0, y_min_0, x_max_0, y_max_0 = score_prop.bbox
-                cur_pros = [0]
-                for prop in props:
-                    x_min_1, y_min_1, x_max_1, y_max_1 = prop.bbox
-
-                    x_min = min(x_min_0, x_min_1)
-                    y_min = min(y_min_0, y_min_1)
-                    x_max = max(x_max_0, x_max_1)
-                    y_max = max(y_max_0, y_max_1)
-
-                    cropped_pred_label = binary_score_maps[i][x_min:x_max, y_min:y_max]
-                    cropped_gt_label = labeled_imgs[i][x_min:x_max, y_min:y_max]
-
-                    # cropped_mask = masks[i][x_min:x_max, y_min:y_max]
-                    # cropped_mask = prop.filled_image  # corrected!
-                    intersection = np.logical_and(cropped_pred_label, cropped_gt_label).astype(np.float32).sum()
-                    union = np.logical_or(cropped_pred_label, cropped_gt_label).astype(np.float32).sum()
-                    cur_pros.append(intersection / union)
-
-                pro.append(max(cur_pros))
-
-        pro = np.array(pro)
-
-        if gt_region_number == 0 or predict_region_number == 0:
-            print(f'gt_number: {gt_region_number}, pred_number: {predict_region_number}')
-            recall = 0
-            precision = 0
-            f1 = 0
-        else:
-            recall = np.array(pro >= pro_thresh).astype(np.float32).sum() / gt_region_number
-            precision = np.array(pro >= pro_thresh).astype(np.float32).sum() / predict_region_number
-
-            if recall == 0 or precision == 0:
-                f1 = 0
-            else:
-                f1 = 2 * recall * precision / (recall + precision)
-
-
-        f1_list.append(f1)
-        recall_list.append(recall)
-        precision_list.append(precision)
-
-    # as array
-    f1_list = np.array(f1_list)
-    max_f1 = f1_list.max()
-    cor_recall = recall_list[f1_list.argmax()]
-    cor_precision = precision_list[f1_list.argmax()]
-    print(f'cor recall: {cor_recall}, cor precision: {cor_precision}')
-    return max_f1

spaces/CarlDennis/HYTTS/text/mandarin.py

@@ -1,170 +0,0 @@
-import os
-import re
-import sys
-
-import jieba
-import cn2an
-import logging
-from pypinyin import lazy_pinyin, BOPOMOFO
-
-logging.getLogger('jieba').setLevel(logging.WARNING)
-
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', 'ㄟˉ'),
-    ('b', 'ㄅㄧˋ'),
-    ('c', 'ㄙㄧˉ'),
-    ('d', 'ㄉㄧˋ'),
-    ('e', 'ㄧˋ'),
-    ('f', 'ㄝˊㄈㄨˋ'),
-    ('g', 'ㄐㄧˋ'),
-    ('h', 'ㄝˇㄑㄩˋ'),
-    ('i', 'ㄞˋ'),
-    ('j', 'ㄐㄟˋ'),
-    ('k', 'ㄎㄟˋ'),
-    ('l', 'ㄝˊㄛˋ'),
-    ('m', 'ㄝˊㄇㄨˋ'),
-    ('n', 'ㄣˉ'),
-    ('o', 'ㄡˉ'),
-    ('p', 'ㄆㄧˉ'),
-    ('q', 'ㄎㄧㄡˉ'),
-    ('r', 'ㄚˋ'),
-    ('s', 'ㄝˊㄙˋ'),
-    ('t', 'ㄊㄧˋ'),
-    ('u', 'ㄧㄡˉ'),
-    ('v', 'ㄨㄧˉ'),
-    ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
-    ('x', 'ㄝˉㄎㄨˋㄙˋ'),
-    ('y', 'ㄨㄞˋ'),
-    ('z', 'ㄗㄟˋ')
-]]
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄅㄛ', 'p⁼wo'),
-    ('ㄆㄛ', 'pʰwo'),
-    ('ㄇㄛ', 'mwo'),
-    ('ㄈㄛ', 'fwo'),
-    ('ㄅ', 'p⁼'),
-    ('ㄆ', 'pʰ'),
-    ('ㄇ', 'm'),
-    ('ㄈ', 'f'),
-    ('ㄉ', 't⁼'),
-    ('ㄊ', 'tʰ'),
-    ('ㄋ', 'n'),
-    ('ㄌ', 'l'),
-    ('ㄍ', 'k⁼'),
-    ('ㄎ', 'kʰ'),
-    ('ㄏ', 'h'),
-    ('ㄐ', 'ʧ⁼'),
-    ('ㄑ', 'ʧʰ'),
-    ('ㄒ', 'ʃ'),
-    ('ㄓ', 'ʦ`⁼'),
-    ('ㄔ', 'ʦ`ʰ'),
-    ('ㄕ', 's`'),
-    ('ㄖ', 'ɹ`'),
-    ('ㄗ', 'ʦ⁼'),
-    ('ㄘ', 'ʦʰ'),
-    ('ㄙ', 's'),
-    ('ㄚ', 'a'),
-    ('ㄛ', 'o'),
-    ('ㄜ', 'ə'),
-    ('ㄝ', 'e'),
-    ('ㄞ', 'ai'),
-    ('ㄟ', 'ei'),
-    ('ㄠ', 'au'),
-    ('ㄡ', 'ou'),
-    ('ㄧㄢ', 'yeNN'),
-    ('ㄢ', 'aNN'),
-    ('ㄧㄣ', 'iNN'),
-    ('ㄣ', 'əNN'),
-    ('ㄤ', 'aNg'),
-    ('ㄧㄥ', 'iNg'),
-    ('ㄨㄥ', 'uNg'),
-    ('ㄩㄥ', 'yuNg'),
-    ('ㄥ', 'əNg'),
-    ('ㄦ', 'əɻ'),
-    ('ㄧ', 'i'),
-    ('ㄨ', 'u'),
-    ('ㄩ', 'ɥ'),
-    ('ˉ', '→'),
-    ('ˊ', '↑'),
-    ('ˇ', '↓↑'),
-    ('ˋ', '↓'),
-    ('˙', ''),
-    ('，', ','),
-    ('。', '.'),
-    ('！', '!'),
-    ('？', '?'),
-    ('—', '-')
-]]
-
-# List of (romaji, ipa) pairs:
-_romaji_to_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('ʃy', 'ʃ'),
-    ('ʧʰy', 'ʧʰ'),
-    ('ʧ⁼y', 'ʧ⁼'),
-    ('NN', 'n'),
-    ('Ng', 'ŋ'),
-    ('y', 'j'),
-    ('h', 'x')
-]]
-
-
-def number_to_chinese(text):
-    numbers = re.findall(r'\d+(?:\.?\d+)?', text)
-    for number in numbers:
-        text = text.replace(number, cn2an.an2cn(number), 1)
-    return text
-
-
-def chinese_to_bopomofo(text):
-    text = text.replace('、', '，').replace('；', '，').replace('：', '，')
-    words = jieba.lcut(text, cut_all=False)
-    text = ''
-    for word in words:
-        bopomofos = lazy_pinyin(word, BOPOMOFO)
-        if not re.search('[\u4e00-\u9fff]', word):
-            text += word
-            continue
-        for i in range(len(bopomofos)):
-            if re.match('[\u3105-\u3129]', bopomofos[i][-1]):
-                bopomofos[i] += 'ˉ'
-        if text != '':
-            text += ' '
-        text += ''.join(bopomofos)
-    return text
-
-
-def latin_to_bopomofo(text):
-    for regex, replacement in _latin_to_bopomofo:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def bopomofo_to_romaji(text):
-    for regex, replacement in _bopomofo_to_romaji:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def chinese_to_romaji(text):
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = bopomofo_to_romaji(text)
-    text = re.sub('i[aoe]', lambda x: 'y' + x.group(0)[1:], text)
-    text = re.sub('u[aoəe]', lambda x: 'w' + x.group(0)[1:], text)
-    text = re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑ ]+|$)', lambda x: x.group(1) +
-                                                        'ɹ`' + x.group(2), text).replace('ɻ', 'ɹ`')
-    text = re.sub('([ʦs][⁼ʰ]?)([→↓↑ ]+|$)',
-                  lambda x: x.group(1) + 'ɹ' + x.group(2), text)
-    return text
-
-
-def chinese_to_lazy_ipa(text):
-    text = chinese_to_romaji(text)
-    for regex, replacement in _romaji_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text

spaces/CikeyQI/meme-api/meme_generator/memes/beat_head/__init__.py

@@ -1,47 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from PIL.Image import Image as IMG
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.exception import TextOverLength
-from meme_generator.utils import save_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def beat_head(images: List[BuildImage], texts: List[str], args):
-    text = texts[0] if texts else "怎么说话的你"
-    img = images[0].convert("RGBA")
-    locs = [(160, 121, 76, 76), (172, 124, 69, 69), (208, 166, 52, 52)]
-    frames: List[IMG] = []
-    for i in range(3):
-        x, y, w, h = locs[i]
-        head = img.resize((w, h), keep_ratio=True).circle()
-        frame = BuildImage.open(img_dir / f"{i}.png")
-        frame.paste(head, (x, y), below=True)
-        try:
-            frame.draw_text(
-                (175, 28, 316, 82),
-                text,
-                max_fontsize=50,
-                min_fontsize=10,
-                allow_wrap=True,
-            )
-        except ValueError:
-            raise TextOverLength(text)
-
-        frames.append(frame.image)
-    return save_gif(frames, 0.05)
-
-
-add_meme(
-    "beat_head",
-    beat_head,
-    min_images=1,
-    max_images=1,
-    min_texts=0,
-    max_texts=1,
-    keywords=["拍头"],
-)

spaces/Cong723/gpt-academic-public/crazy_functions/高级功能函数模板.py

@@ -1,29 +0,0 @@
-from toolbox import CatchException, update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-import datetime
-@CatchException
-def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append(("这是什么功能？", "[Local Message] 请注意，您正在调用一个[函数插件]的模板，该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板（该函数只有20多行代码）。此外我们也提供可同步处理大量文件的多线程Demo供您参考。您若希望分享新的功能模组，请不吝PR！"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-    for i in range(5):
-        currentMonth = (datetime.date.today() + datetime.timedelta(days=i)).month
-        currentDay = (datetime.date.today() + datetime.timedelta(days=i)).day
-        i_say = f'历史中哪些事件发生在{currentMonth}月{currentDay}日？列举两条并发送相关图片。发送图片时，请使用Markdown，将Unsplash API中的PUT_YOUR_QUERY_HERE替换成描述该事件的一个最重要的单词。'
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=i_say, inputs_show_user=i_say, 
-            llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], 
-            sys_prompt="当你想发送一张照片时，请使用Markdown, 并且不要有反斜线, 不要用代码块。使用 Unsplash API (https://source.unsplash.com/1280x720/? < PUT_YOUR_QUERY_HERE >)。"
-        )
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say);history.append(gpt_say)
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新

spaces/CosmoAI/ChitChat/README.md

@@ -1,13 +0,0 @@
----
-title: ChitChat
-emoji: 🔥
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/utils/collect_env.py

@@ -1,14 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import PIL
-
-from torch.utils.collect_env import get_pretty_env_info
-
-
-def get_pil_version():
-    return "\n        Pillow ({})".format(PIL.__version__)
-
-
-def collect_env_info():
-    env_str = get_pretty_env_info()
-    env_str += get_pil_version()
-    return env_str