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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Artensoft Photo Collage Maker Pro 2.0.135 Key How to Make Stunning Photo Collages in Minutes.md

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-<br> - Features: What are the main features of Artensoft Photo Collage Maker Pro? <br> - Benefits: How can Artensoft Photo Collage Maker Pro help you create amazing photo collages? <br> - How to use: How can you download, install and use Artensoft Photo Collage Maker Pro? <br> - Pros and cons: What are the advantages and disadvantages of Artensoft Photo Collage Maker Pro? <br> - Conclusion: A summary of the main points and a call to action. | | H2: Introduction | - Explain what Artensoft Photo Collage Maker Pro is and what it does. <br> - Mention that it is a software that allows you to create photo collages from your own photos. <br> - Give some examples of photo collages that you can create with Artensoft Photo Collage Maker Pro. | | H2: Features | - List the main features of Artensoft Photo Collage Maker Pro, such as: <br> - It can create photo collages from any number of photos. <br> - It can automatically adjust the size, orientation and color of the photos to create a seamless collage. <br> - It can use any photo as a source for the collage, such as a portrait, a landscape or a logo. <br> - It can save the collage as a high-resolution image or print it directly from the software. <br> - It can edit the collage by adding, removing or moving photos, changing the background color or applying filters. | | H2: Benefits | - Explain how Artensoft Photo Collage Maker Pro can help you create amazing photo collages, such as: <br> - It can help you preserve your memories in a creative way. <br> - It can help you showcase your photos in a unique way. <br> - It can help you express your personality and style. <br> - It can help you make personalized gifts for your friends and family. | | H2: How to use | - Provide a step-by-step guide on how to download, install and use Artensoft Photo Collage Maker Pro, such as: <br> - Visit the official website of Artensoft Photo Collage Maker Pro and click on the download button. <br> - Run the installer and follow the instructions to complete the installation process. <br> - Launch the software and select the photos that you want to use for your collage. <br> - Choose a source photo for your collage and adjust the settings according to your preferences. <br> - Preview the collage and make any changes if needed. <br> - Save or print your collage and enjoy your masterpiece. | | H2: Pros and cons | - Compare the advantages and disadvantages of Artensoft Photo Collage Maker Pro, such as: <br> - Pros: <br>  - It is easy to use and has a user-friendly interface. <br>  - It has a lot of options and features to customize your collage. <br>  - It can create high-quality and realistic collages from any photos. <br>  - It is compatible with Windows XP, Vista, 7, 8 and 10. <br>  - It has a free trial version that you can try before buying. <br> - Cons: <br>  - It is not available for Mac or Linux users. <br>  - It requires a lot of disk space and memory to run smoothly. <br>  - It may take some time to process large numbers of photos or complex collages. | | H2: Conclusion | - Summarize the main points of the article and provide a call to action, such as: <br> - Artensoft Photo Collage Maker Pro is a powerful and versatile software that allows you to create stunning photo collages from your own photos. <br> - It has many features and benefits that make it stand out from other photo collage makers. <br> - It is easy to use and has a free trial version that you can download from their website. <br> - If you want to unleash your creativity and turn your photos into amazing artworks, you should try Artensoft Photo Collage Maker Pro today! | # Article with HTML formatting <h1><b>Artensoft Photo Collage Maker Pro 2.0.135 Key: A Review</b></h1>
- <p>If you are looking for a software that can help you create stunning photo collages from your own photos, you might want to check out Artensoft Photo Collage Maker Pro 2.0.135 Key.</p>
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- <p>This is a software that allows you to create photo collages from any number of photos, using any photo as a source for the collage.</p>
- <p>You can create photo collages that look like portraits, landscapes, logos or anything else that you can imagine.</p>
- <p>In this article, we will review Artensoft Photo Collage Maker Pro 2.0.135 Key and see what it can do for you.</p>
- <h2><b>Features</b></h2>
- <p>Artensoft Photo Collage Maker Pro 2.0.135 Key has many features that make it one of the best photo collage makers on the market.</p>
- <p>Some of these features are:</p>
- <ul>
-<li><b>It can create photo collages from any number of photos.</b></li>
-<p>You can use as many photos as you want for your collage, whether it's dozens, hundreds or thousands.</p>
-<li><b>It can automatically adjust the size, orientation and color of the photos to create a seamless collage.</b></li>
-<p>You don't have to worry about cropping, rotating or resizing your photos manually.</p>
-<p>The software will do it for you and make sure that they fit perfectly into your collage.</p>
-<li><b>It can use any photo as a source for the collage, such as a portrait, a landscape or a logo.</b></li>
-<p>You can choose any photo that you like as the base for your collage.</p>
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-<p>The software will then use your other photos to recreate the source photo in a mosaic style.</p>
-<li><b>It can save the collage as a high-resolution image or print it directly from the software.</b></li>
-<p>You can save your collage as a JPEG, BMP, TIFF or PNG file with up to 300 dpi resolution.</p>
-<p>You can also print your collage directly from the software using any printer that supports Windows printing.</p>
-<li><b>It can edit the collage by adding, removing or moving photos, changing the background color or applying filters.</b></li>
-<p>You can fine-tune your collage by adding more photos, removing unwanted ones or moving them around.</p>
-<p>You can also change the background color of your collage or apply filters such as sepia, grayscale or negative.</p>
-</ul>
- <h2><b>Benefits</b></h2>
- <p>Besides having many features, Artensoft Photo Collage Maker Pro 2.0.135 Key also has many benefits that make it worth trying.</p>
- <p>Some of these benefits are:</p>
- <ul>
-<li><b>It can help you preserve your memories in a creative way.</b></li>
-<p>You can use your own photos to create beautiful collages that reflect your personality, style and emotions.</p>
-<p>You can also use photos from different occasions, such as birthdays, weddings or vacations, to create memorable collages that capture those moments forever.</p>
-<li><b>It can help you showcase your photos in a unique way.</b></li>
-<p>You can use your own photos to create stunning collages that look like artworks.</p>
-<p>You can also use photos from different sources, such as magazines, websites or social media, to create collages that express your interests, hobbies or passions.</p>
-<li><b>It can help you express your personality and style.</b></li>
-<p>You can use any photo that you like as the source for your collage and customize it according to your preferences.</p>
-<p>You can also choose from different themes, such as animals, flowers or abstract shapes, to create collages that suit your mood or taste.</p>
-<li><b>It can help you make personalized gifts for your friends and family.</b></li>
-<p>You can use photos of your loved ones to create beautiful collages that show how much you care about them.</p>
-<p>You can also use photos of their favorite things, such as celebrities, sports teams or movies, to create collages that match their personalities.</p>
-</ul>
- <h2><b>How to use</b></h2>
- you can use it:</p>
- <ol>
-<li><b>Visit the official website of Artensoft Photo Collage Maker Pro and click on the download button.</b></li>
-<p>You can download the software for free and try it for 30 days without any limitations.</p>
-<li><b>Run the installer and follow the instructions to complete the installation process.</b></li>
-<p>You can install the software on any Windows PC that meets the minimum system requirements.</p>
-<li><b>Launch the software and select the photos that you want to use for your collage.</b></li>
-<p>You can browse your computer or drag and drop your photos into the software.</p>
-<p>You can also use the built-in photo browser to find photos from your folders, albums or online sources.</p>
-<li><b>Choose a source photo for your collage and adjust the settings according to your preferences.</b></li>
-<p>You can choose any photo that you like as the base for your collage, such as a portrait, a landscape or a logo.</p>
-<p>You can also adjust the settings such as the number of photos, the size of the cells, the color correction and the rotation angle.</p>
-<li><b>Preview the collage and make any changes if needed.</b></li>
-<p>You can see how your collage looks like before saving or printing it.</p>
-<p>You can also edit the collage by adding, removing or moving photos, changing the background color or applying filters.</p>
-<li><b>Save or print your collage and enjoy your masterpiece.</b></li>
-<p>You can save your collage as a JPEG, BMP, TIFF or PNG file with up to 300 dpi resolution.</p>
-<p>You can also print your collage directly from the software using any printer that supports Windows printing.</p>
-</ol>
- <h2><b>Pros and cons</b></h2>
- <p>Like any software, Artensoft Photo Collage Maker Pro 2.0.135 Key has its pros and cons that you should consider before buying it.</p>
- <p>Here are some of them:</p>
- <h3><b>Pros</b></h3>
- <ul>
-<li><b>It is easy to use and has a user-friendly interface.</b></li>
-<p>You don't need any special skills or knowledge to use this software.</p>
-<p>The interface is simple and intuitive, with clear icons and buttons that guide you through the process.</p>
-<li><b>It has a lot of options and features to customize your collage.</b></li>
-<p>You can create photo collages from any number of photos, using any photo as a source for the collage.</p>
-<p>You can also adjust the settings such as the number of photos, the size of the cells, the color correction and the rotation angle.</p>
-<li><b>It can create high-quality and realistic collages from any photos.</b></li>
-<p>The software uses a smart algorithm that analyzes and matches the colors and shapes of your photos to create a seamless collage.</p>
-<p>The result is a high-resolution image that looks like a real photo mosaic.</p>
-<li><b>It is compatible with Windows XP, Vista, 7, 8 and 10.</b></li>
-<p>You can install and run this software on any Windows PC that meets the minimum system requirements.</p>
-<li><b>It has a free trial version that you can try before buying.</b></li>
-<p>You can download the software for free and try it for 30 days without any limitations.</p>
-</ul>
- <h3><b>Cons</b></h3>
- <ul>
-<li><b>It is not available for Mac or Linux users.</b></li>
-<p>This software is only designed for Windows users and does not support other operating systems.</p>
-<li><b>It requires a lot of disk space and memory to run smoothly.</b></li>
-<p>This software is quite heavy and may slow down your computer if you don't have enough disk space or memory available.</p>
-<li><b>It may take some time to process large numbers of photos or complex collages.</b></li>
-<p>This software may take longer to create collages if you use too many photos or choose a complicated source photo for your collage.</p>
-</ul>
- <h2><b>Conclusion</b></h2>
- <p>In conclusion, Artensoft Photo Collage Maker Pro 2.0.135 Key is a powerful and versatile software that allows you to create stunning photo collages from your own photos.</p>
- <p>It has many features and benefits that make it stand out from other photo collage makers. It is easy to use and has a free trial version that you can download from their website. </p>
- <p>If you want to unleash your creativity and turn your photos into amazing artworks, you should try Artensoft Photo Collage Maker Pro 2.0.135 Key today!</p>
- <h2><b>Frequently Asked Questions</b></h2>
- <ol>
-<li><b>How much does Artensoft Photo Collage Maker Pro 2.0.135 Key cost?</b></li>
-<p>The software costs $79.95 for a single-user license. You can also buy a family license for $149.95 or a business license for $299.95. You can pay with PayPal or credit card on their website. </p>
-<li><b>What are the minimum system requirements for Artensoft Photo Collage Maker Pro 2.0.135 Key?</b></li>
-<p>The minimum system requirements are: <br>- Windows XP/Vista/7/8/10 <br>- Pentium IV processor or higher <br>- 1 GB of RAM or more <br>- 100 MB of free disk space or more <br>- A monitor with at least 1024x768 resolution </p>
-<li><b>Can I use Artensoft Photo Collage Maker Pro 2.0.135 Key on multiple computers?</b></li>
-<p>If you buy a single-user license, you can only use it on one computer at a time. If you buy a family license, you can use it on up to five computers in your household. If you buy a business license, you can use it on up to ten computers in your company. </p>
-<li><b>Can I use Artensoft Photo Collage Maker Pro 2.0.135 Key offline?</b></li>
-<p>Yes, you can use it offline once you have downloaded and installed it on your computer. You don't need an internet connection to create collages with this software. </p>
-<li><b>Can I get technical support for Artensoft Photo Collage Maker Pro 2.0.135 Key?</b></li>
-<p>Yes, you can get technical support by contacting their customer service via email at [email protected]. They will reply within 24 hours on weekdays and within 48 hours on weekends. You can also visit their website for more information and tutorials on how to use their software. </p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Atlas Ti Coding ((TOP)).md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Vidstream Videos to Your Device with Example Downloader.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Fix 4ormulator DLL Missing or Not Found Error on Windows.md

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-<h1>How to Download and Install 4ormulator DLL for Windows</h1>
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-<p>To install 4ormulator DLL manually, you need to copy and paste it into the appropriate folder on your computer. The folder depends on the version of Windows you are using and the application that requires 4ormulator DLL. Here are some common folders where you can place 4ormulator DLL:</p>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Create Your Own Metropolis with SimCity BuildIt APK - Free Download from apkyukleme.com.md

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-<h1>SimCity BuildIt APK: How to Download and Play the Best City Building Game</h1>
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-<p>A: You can unlock new regions in SimCity BuildIt by reaching certain population milestones in your main city. You can choose from four regions: Green Valley (coast), Limestone Cliffs (mountain), Cactus Canyon (desert), or Frosty Fjords (forest).</p>
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spaces/1phancelerku/anime-remove-background/Dragon Ball Z Game APK - Enjoy the Ultimate Action Experience on Android.md

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- <p>If you are a fan of anime, manga, or video games, you have probably heard of Dragon Ball Z, one of the most popular and influential franchises in the world. Dragon Ball Z is a series that follows the adventures of Goku and his friends as they fight against various enemies who threaten the peace of the universe. Whether you grew up watching the anime, reading the manga, or playing the video games, you might be wondering how you can relive the epic battles and stories of Dragon Ball Z on your Android device. Well, wonder no more, because in this article, we will show you how to download game dragon ball z apk, a free and easy way to enjoy the anime action on your smartphone or tablet. Read on to find out more!</p>
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spaces/1phancelerku/anime-remove-background/Football League 2023 APK - The Best Soccer Game of the Year.md

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- <ul>
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- <h2>How to Play Football League 2023 Game on PC with BlueStacks Emulator</h2>
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- <ol>
-<li>Download and install BlueStacks on your PC from its official website.</li>
-<li>Launch BlueStacks and sign in with your Google account. If you don't have one, you can create one for free.</li>
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-</ol>
- <h2>Tips and Tricks for Football League 2023 Game</h2>
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- <ul>
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-</ul>
- <h2>Conclusion</h2>
-<p>Football League 2023 Game is a great soccer game that you can play on your Android device or PC with an emulator. It has realistic graphics and animations, various game modes and challenges, customizable teams and players, online multiplayer and leaderboards, offline mode and data saving, and more. It is easy to download and install, and it is free to play. If you love soccer games, you should definitely give Football League 2023 Game a try. You won't regret it!</p>
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spaces/AIConsultant/MusicGen/audiocraft/grids/musicgen/musicgen_base_32khz.py

@@ -1,43 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from ._explorers import LMExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@LMExplorer
-def explorer(launcher):
-    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
-    launcher.slurm_(gpus=32, partition=partitions)
-    launcher.bind_(solver='musicgen/musicgen_base_32khz')
-    # replace this by the desired music dataset
-    launcher.bind_(dset='internal/music_400k_32khz')
-
-    fsdp = {'autocast': False, 'fsdp.use': True}
-    medium = {'model/lm/model_scale': 'medium'}
-    large = {'model/lm/model_scale': 'large'}
-
-    cfg_low = {'classifier_free_guidance.training_dropout': 0.2}
-    wd_low = {'conditioners.description.t5.word_dropout': 0.2}
-
-    adam = {'optim.optimizer': 'adamw', 'optim.lr': 1e-4}
-
-    launcher.bind_(fsdp)
-
-    launcher.slurm_(gpus=32).bind_(label='32gpus')
-    with launcher.job_array():
-        sub = launcher.bind()
-        sub()
-
-    launcher.slurm_(gpus=64).bind_(label='64gpus')
-    with launcher.job_array():
-        sub = launcher.bind()
-        sub(medium, adam)
-
-    launcher.slurm_(gpus=96).bind_(label='96gpus')
-    with launcher.job_array():
-        sub = launcher.bind()
-        sub(large, cfg_low, wd_low, adam, {'optim.max_norm': 3})

spaces/AIConsultant/MusicGen/scripts/resample_dataset.py

@@ -1,207 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-"""Resampling script.
-"""
-import argparse
-from pathlib import Path
-import shutil
-import typing as tp
-
-import submitit
-import tqdm
-
-from audiocraft.data.audio import audio_read, audio_write
-from audiocraft.data.audio_dataset import load_audio_meta, find_audio_files
-from audiocraft.data.audio_utils import convert_audio
-from audiocraft.environment import AudioCraftEnvironment
-
-
-def read_txt_files(path: tp.Union[str, Path]):
-    with open(args.files_path) as f:
-        lines = [line.rstrip() for line in f]
-        print(f"Read {len(lines)} in .txt")
-        lines = [line for line in lines if Path(line).suffix not in ['.json', '.txt', '.csv']]
-        print(f"Filtered and keep {len(lines)} from .txt")
-        return lines
-
-
-def read_egs_files(path: tp.Union[str, Path]):
-    path = Path(path)
-    if path.is_dir():
-        if (path / 'data.jsonl').exists():
-            path = path / 'data.jsonl'
-        elif (path / 'data.jsonl.gz').exists():
-            path = path / 'data.jsonl.gz'
-        else:
-            raise ValueError("Don't know where to read metadata from in the dir. "
-                             "Expecting either a data.jsonl or data.jsonl.gz file but none found.")
-    meta = load_audio_meta(path)
-    return [m.path for m in meta]
-
-
-def process_dataset(args, n_shards: int, node_index: int, task_index: tp.Optional[int] = None):
-    if task_index is None:
-        env = submitit.JobEnvironment()
-        task_index = env.global_rank
-    shard_index = node_index * args.tasks_per_node + task_index
-
-    if args.files_path is None:
-        lines = [m.path for m in find_audio_files(args.root_path, resolve=False, progress=True, workers=8)]
-    else:
-        files_path = Path(args.files_path)
-        if files_path.suffix == '.txt':
-            print(f"Reading file list from .txt file: {args.files_path}")
-            lines = read_txt_files(args.files_path)
-        else:
-            print(f"Reading file list from egs: {args.files_path}")
-            lines = read_egs_files(args.files_path)
-
-    total_files = len(lines)
-    print(
-        f"Total of {total_files} processed with {n_shards} shards. " +
-        f"Current idx = {shard_index} -> {total_files // n_shards} files to process"
-    )
-    for idx, line in tqdm.tqdm(enumerate(lines)):
-
-        # skip if not part of this shard
-        if idx % n_shards != shard_index:
-            continue
-
-        path = str(AudioCraftEnvironment.apply_dataset_mappers(line))
-        root_path = str(args.root_path)
-        if not root_path.endswith('/'):
-            root_path += '/'
-        assert path.startswith(str(root_path)), \
-            f"Mismatch between path and provided root: {path} VS {root_path}"
-
-        try:
-            metadata_path = Path(path).with_suffix('.json')
-            out_path = args.out_path / path[len(root_path):]
-            out_metadata_path = out_path.with_suffix('.json')
-            out_done_token = out_path.with_suffix('.done')
-
-            # don't reprocess existing files
-            if out_done_token.exists():
-                continue
-
-            print(idx, out_path, path)
-            mix, sr = audio_read(path)
-            mix_channels = args.channels if args.channels is not None and args.channels > 0 else mix.size(0)
-            # enforce simple stereo
-            out_channels = mix_channels
-            if out_channels > 2:
-                print(f"Mix has more than two channels: {out_channels}, enforcing 2 channels")
-                out_channels = 2
-            out_sr = args.sample_rate if args.sample_rate is not None else sr
-            out_wav = convert_audio(mix, sr, out_sr, out_channels)
-            audio_write(out_path.with_suffix(''), out_wav, sample_rate=out_sr,
-                        format=args.format, normalize=False, strategy='clip')
-            if metadata_path.exists():
-                shutil.copy(metadata_path, out_metadata_path)
-            else:
-                print(f"No metadata found at {str(metadata_path)}")
-            out_done_token.touch()
-        except Exception as e:
-            print(f"Error processing file line: {line}, {e}")
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description="Resample dataset with SLURM.")
-    parser.add_argument(
-        "--log_root",
-        type=Path,
-        default=Path.home() / 'tmp' / 'resample_logs',
-    )
-    parser.add_argument(
-        "--files_path",
-        type=Path,
-        help="List of files to process, either .txt (one file per line) or a jsonl[.gz].",
-    )
-    parser.add_argument(
-        "--root_path",
-        type=Path,
-        required=True,
-        help="When rewriting paths, this will be the prefix to remove.",
-    )
-    parser.add_argument(
-        "--out_path",
-        type=Path,
-        required=True,
-        help="When rewriting paths, `root_path` will be replaced by this.",
-    )
-    parser.add_argument("--xp_name", type=str, default="shutterstock")
-    parser.add_argument(
-        "--nodes",
-        type=int,
-        default=4,
-    )
-    parser.add_argument(
-        "--tasks_per_node",
-        type=int,
-        default=20,
-    )
-    parser.add_argument(
-        "--cpus_per_task",
-        type=int,
-        default=4,
-    )
-    parser.add_argument(
-        "--memory_gb",
-        type=int,
-        help="Memory in GB."
-    )
-    parser.add_argument(
-        "--format",
-        type=str,
-        default="wav",
-    )
-    parser.add_argument(
-        "--sample_rate",
-        type=int,
-        default=32000,
-    )
-    parser.add_argument(
-        "--channels",
-        type=int,
-    )
-    parser.add_argument(
-        "--partition",
-        default='learnfair',
-    )
-    parser.add_argument("--qos")
-    parser.add_argument("--account")
-    parser.add_argument("--timeout", type=int, default=4320)
-    parser.add_argument('--debug', action='store_true', help='debug mode (local run)')
-    args = parser.parse_args()
-    n_shards = args.tasks_per_node * args.nodes
-    if args.files_path is None:
-        print("Warning: --files_path not provided, not recommended when processing more than 10k files.")
-    if args.debug:
-        print("Debugging mode")
-        process_dataset(args, n_shards=n_shards, node_index=0, task_index=0)
-    else:
-
-        log_folder = Path(args.log_root) / args.xp_name / '%j'
-        print(f"Logging to: {log_folder}")
-        log_folder.parent.mkdir(parents=True, exist_ok=True)
-        executor = submitit.AutoExecutor(folder=str(log_folder))
-        if args.qos:
-            executor.update_parameters(slurm_partition=args.partition, slurm_qos=args.qos, slurm_account=args.account)
-        else:
-            executor.update_parameters(slurm_partition=args.partition)
-        executor.update_parameters(
-            slurm_job_name=args.xp_name, timeout_min=args.timeout,
-            cpus_per_task=args.cpus_per_task, tasks_per_node=args.tasks_per_node, nodes=1)
-        if args.memory_gb:
-            executor.update_parameters(mem=f'{args.memory_gb}GB')
-        jobs = []
-        with executor.batch():
-            for node_index in range(args.nodes):
-                job = executor.submit(process_dataset, args, n_shards=n_shards, node_index=node_index)
-                jobs.append(job)
-        for job in jobs:
-            print(f"Waiting on job {job.job_id}")
-            job.results()

spaces/AIGC-Audio/AudioGPT/text_to_speech/data_gen/tts/base_preprocess.py

@@ -1,252 +0,0 @@
-import json
-import os
-import random
-import re
-import traceback
-from collections import Counter
-from functools import partial
-
-import librosa
-from tqdm import tqdm
-from text_to_speech.data_gen.tts.txt_processors.base_text_processor import get_txt_processor_cls
-from text_to_speech.data_gen.tts.wav_processors.base_processor import get_wav_processor_cls
-from text_to_speech.utils.commons.hparams import hparams
-from text_to_speech.utils.commons.multiprocess_utils import multiprocess_run_tqdm
-from text_to_speech.utils.os_utils import link_file, move_file, remove_file
-from text_to_speech.utils.text.text_encoder import is_sil_phoneme, build_token_encoder
-
-
-class BasePreprocessor:
-    def __init__(self):
-        self.preprocess_args = hparams['preprocess_args']
-        txt_processor = self.preprocess_args['txt_processor']
-        self.txt_processor = get_txt_processor_cls(txt_processor)
-        self.raw_data_dir = hparams['raw_data_dir']
-        self.processed_dir = hparams['processed_data_dir']
-        self.spk_map_fn = f"{self.processed_dir}/spk_map.json"
-
-    def meta_data(self):
-        """
-
-        :return: {'item_name': Str, 'wav_fn': Str, 'txt': Str, 'spk_name': Str, 'txt_loader': None or Func}
-        """
-        raise NotImplementedError
-
-    def process(self):
-        processed_dir = self.processed_dir
-        wav_processed_tmp_dir = f'{processed_dir}/processed_tmp'
-        remove_file(wav_processed_tmp_dir)
-        os.makedirs(wav_processed_tmp_dir, exist_ok=True)
-        wav_processed_dir = f'{processed_dir}/{self.wav_processed_dirname}'
-        remove_file(wav_processed_dir)
-        os.makedirs(wav_processed_dir, exist_ok=True)
-
-        meta_data = list(tqdm(self.meta_data(), desc='Load meta data'))
-        item_names = [d['item_name'] for d in meta_data]
-        assert len(item_names) == len(set(item_names)), 'Key `item_name` should be Unique.'
-
-        # preprocess data
-        phone_list = []
-        word_list = []
-        spk_names = set()
-        process_item = partial(self.preprocess_first_pass,
-                               txt_processor=self.txt_processor,
-                               wav_processed_dir=wav_processed_dir,
-                               wav_processed_tmp=wav_processed_tmp_dir,
-                               preprocess_args=self.preprocess_args)
-        items = []
-        args = [{
-            'item_name': item_raw['item_name'],
-            'txt_raw': item_raw['txt'],
-            'wav_fn': item_raw['wav_fn'],
-            'txt_loader': item_raw.get('txt_loader'),
-            'others': item_raw.get('others', None)
-        } for item_raw in meta_data]
-        for item_, (item_id, item) in zip(meta_data, multiprocess_run_tqdm(process_item, args, desc='Preprocess')):
-            if item is not None:
-                item_.update(item)
-                item = item_
-                if 'txt_loader' in item:
-                    del item['txt_loader']
-                item['id'] = item_id
-                item['spk_name'] = item.get('spk_name', '<SINGLE_SPK>')
-                item['others'] = item.get('others', None)
-                phone_list += item['ph'].split(" ")
-                word_list += item['word'].split(" ")
-                spk_names.add(item['spk_name'])
-                items.append(item)
-
-        # add encoded tokens
-        ph_encoder, word_encoder = self._phone_encoder(phone_list), self._word_encoder(word_list)
-        spk_map = self.build_spk_map(spk_names)
-        args = [{
-            'ph': item['ph'], 'word': item['word'], 'spk_name': item['spk_name'],
-            'word_encoder': word_encoder, 'ph_encoder': ph_encoder, 'spk_map': spk_map
-        } for item in items]
-        for idx, item_new_kv in multiprocess_run_tqdm(self.preprocess_second_pass, args, desc='Add encoded tokens'):
-            items[idx].update(item_new_kv)
-
-        # build mfa data
-        if self.preprocess_args['use_mfa']:
-            mfa_dict = set()
-            mfa_input_dir = f'{processed_dir}/mfa_inputs'
-            remove_file(mfa_input_dir)
-            # group MFA inputs for better parallelism
-            mfa_groups = [i // self.preprocess_args['nsample_per_mfa_group'] for i in range(len(items))]
-            if self.preprocess_args['mfa_group_shuffle']:
-                random.seed(hparams['seed'])
-                random.shuffle(mfa_groups)
-            args = [{
-                'item': item, 'mfa_input_dir': mfa_input_dir,
-                'mfa_group': mfa_group, 'wav_processed_tmp': wav_processed_tmp_dir,
-                'preprocess_args': self.preprocess_args
-            } for item, mfa_group in zip(items, mfa_groups)]
-            for i, (ph_gb_word_nosil, new_wav_align_fn) in multiprocess_run_tqdm(
-                    self.build_mfa_inputs, args, desc='Build MFA data'):
-                items[i]['wav_align_fn'] = new_wav_align_fn
-                for w in ph_gb_word_nosil.split(" "):
-                    mfa_dict.add(f"{w} {w.replace('_', ' ')}")
-            mfa_dict = sorted(mfa_dict)
-            with open(f'{processed_dir}/mfa_dict.txt', 'w') as f:
-                f.writelines([f'{l}\n' for l in mfa_dict])
-        with open(f"{processed_dir}/{self.meta_csv_filename}.json", 'w') as f:
-            f.write(re.sub(r'\n\s+([\d+\]])', r'\1', json.dumps(items, ensure_ascii=False, sort_keys=False, indent=1)))
-        remove_file(wav_processed_tmp_dir)
-
-    @classmethod
-    def preprocess_first_pass(cls, item_name, txt_raw, txt_processor,
-                              wav_fn, wav_processed_dir, wav_processed_tmp,
-                              preprocess_args, txt_loader=None, others=None):
-        try:
-            if txt_loader is not None:
-                txt_raw = txt_loader(txt_raw)
-            ph, txt, word, ph2word, ph_gb_word = cls.txt_to_ph(txt_processor, txt_raw, preprocess_args)
-            
-            wav_fn, wav_align_fn = cls.process_wav(
-                item_name, wav_fn,
-                hparams['processed_data_dir'],
-                wav_processed_tmp, preprocess_args)
-
-            # wav for binarization
-            ext = os.path.splitext(wav_fn)[1]
-            os.makedirs(wav_processed_dir, exist_ok=True)
-            new_wav_fn = f"{wav_processed_dir}/{item_name}{ext}"
-            move_link_func = move_file if os.path.dirname(wav_fn) == wav_processed_tmp else link_file
-            move_link_func(wav_fn, new_wav_fn)
-            return {
-                'txt': txt, 'txt_raw': txt_raw, 'ph': ph,
-                'word': word, 'ph2word': ph2word, 'ph_gb_word': ph_gb_word,
-                'wav_fn': new_wav_fn, 'wav_align_fn': wav_align_fn,
-                'others': others
-            }
-        except:
-            traceback.print_exc()
-            print(f"| Error is caught. item_name: {item_name}.")
-            return None
-
-    @staticmethod
-    def txt_to_ph(txt_processor, txt_raw, preprocess_args):
-        txt_struct, txt = txt_processor.process(txt_raw, preprocess_args)
-        ph = [p for w in txt_struct for p in w[1]]
-        ph_gb_word = ["_".join(w[1]) for w in txt_struct]
-        words = [w[0] for w in txt_struct]
-        # word_id=0 is reserved for padding
-        ph2word = [w_id + 1 for w_id, w in enumerate(txt_struct) for _ in range(len(w[1]))]
-        return " ".join(ph), txt, " ".join(words), ph2word, " ".join(ph_gb_word)
-
-    @staticmethod
-    def process_wav(item_name, wav_fn, processed_dir, wav_processed_tmp, preprocess_args):
-        processors = [get_wav_processor_cls(v) for v in preprocess_args['wav_processors']]
-        processors = [k() for k in processors if k is not None]
-        if len(processors) >= 1:
-            sr_file = librosa.core.get_samplerate(wav_fn)
-            output_fn_for_align = None
-            ext = os.path.splitext(wav_fn)[1]
-            input_fn = f"{wav_processed_tmp}/{item_name}{ext}"
-            link_file(wav_fn, input_fn)
-            for p in processors:
-                outputs = p.process(input_fn, sr_file, wav_processed_tmp, processed_dir, item_name, preprocess_args)
-                if len(outputs) == 3:
-                    input_fn, sr, output_fn_for_align = outputs
-                else:
-                    input_fn, sr = outputs
-            return input_fn, output_fn_for_align
-        else:
-            return wav_fn, wav_fn
-
-    def _phone_encoder(self, ph_set):
-        ph_set_fn = f"{self.processed_dir}/phone_set.json"
-        if self.preprocess_args['reset_phone_dict'] or not os.path.exists(ph_set_fn):
-            ph_set = sorted(set(ph_set))
-            json.dump(ph_set, open(ph_set_fn, 'w'), ensure_ascii=False)
-            print("| Build phone set: ", ph_set)
-        else:
-            ph_set = json.load(open(ph_set_fn, 'r'))
-            print("| Load phone set: ", ph_set)
-        return build_token_encoder(ph_set_fn)
-
-    def _word_encoder(self, word_set):
-        word_set_fn = f"{self.processed_dir}/word_set.json"
-        if self.preprocess_args['reset_word_dict']:
-            word_set = Counter(word_set)
-            total_words = sum(word_set.values())
-            word_set = word_set.most_common(hparams['word_dict_size'])
-            num_unk_words = total_words - sum([x[1] for x in word_set])
-            word_set = ['<BOS>', '<EOS>'] + [x[0] for x in word_set]
-            word_set = sorted(set(word_set))
-            json.dump(word_set, open(word_set_fn, 'w'), ensure_ascii=False)
-            print(f"| Build word set. Size: {len(word_set)}, #total words: {total_words},"
-                  f" #unk_words: {num_unk_words}, word_set[:10]:, {word_set[:10]}.")
-        else:
-            word_set = json.load(open(word_set_fn, 'r'))
-            print("| Load word set. Size: ", len(word_set), word_set[:10])
-        return build_token_encoder(word_set_fn)
-
-    @classmethod
-    def preprocess_second_pass(cls, word, ph, spk_name, word_encoder, ph_encoder, spk_map):
-        word_token = word_encoder.encode(word)
-        ph_token = ph_encoder.encode(ph)
-        spk_id = spk_map[spk_name]
-        return {'word_token': word_token, 'ph_token': ph_token, 'spk_id': spk_id}
-
-    def build_spk_map(self, spk_names):
-        spk_map = {x: i for i, x in enumerate(sorted(list(spk_names)))}
-        assert len(spk_map) == 0 or len(spk_map) <= hparams['num_spk'], len(spk_map)
-        print(f"| Number of spks: {len(spk_map)}, spk_map: {spk_map}")
-        json.dump(spk_map, open(self.spk_map_fn, 'w'), ensure_ascii=False)
-        return spk_map
-
-    @classmethod
-    def build_mfa_inputs(cls, item, mfa_input_dir, mfa_group, wav_processed_tmp, preprocess_args):
-        item_name = item['item_name']
-        wav_align_fn = item['wav_align_fn']
-        ph_gb_word = item['ph_gb_word']
-        ext = os.path.splitext(wav_align_fn)[1]
-        mfa_input_group_dir = f'{mfa_input_dir}/{mfa_group}'
-        os.makedirs(mfa_input_group_dir, exist_ok=True)
-        new_wav_align_fn = f"{mfa_input_group_dir}/{item_name}{ext}"
-        move_link_func = move_file if os.path.dirname(wav_align_fn) == wav_processed_tmp else link_file
-        move_link_func(wav_align_fn, new_wav_align_fn)
-        ph_gb_word_nosil = " ".join(["_".join([p for p in w.split("_") if not is_sil_phoneme(p)])
-                                     for w in ph_gb_word.split(" ") if not is_sil_phoneme(w)])
-        with open(f'{mfa_input_group_dir}/{item_name}.lab', 'w') as f_txt:
-            f_txt.write(ph_gb_word_nosil)
-        return ph_gb_word_nosil, new_wav_align_fn
-
-    def load_spk_map(self, base_dir):
-        spk_map_fn = f"{base_dir}/spk_map.json"
-        spk_map = json.load(open(spk_map_fn, 'r'))
-        return spk_map
-
-    def load_dict(self, base_dir):
-        ph_encoder = build_token_encoder(f'{base_dir}/phone_set.json')
-        word_encoder = build_token_encoder(f'{base_dir}/word_set.json')
-        return ph_encoder, word_encoder
-
-    @property
-    def meta_csv_filename(self):
-        return 'metadata'
-
-    @property
-    def wav_processed_dirname(self):
-        return 'wav_processed'

spaces/AIML-TUDA/does-clip-know-my-face/download_example_images.py

@@ -1,42 +0,0 @@
-import os
-import urllib.request
-from tqdm import tqdm
-from PIL import Image
-
-
-def read_actor_files(folder_path):
-    urls = {}
-    for file in os.listdir(folder_path):
-        if not file.endswith('.txt'):
-            continue
-
-        file_name_without_ext = os.path.splitext(file)[0]
-        with open(os.path.join(folder_path, file)) as text_file:
-            lines = text_file.readlines()
-            lines = [line.rstrip() for line in lines]
-
-        urls[file_name_without_ext] = lines
-
-    return urls
-
-
-def save_images_to_folder(folder_path, url_dict):
-    url_opener = urllib.request.URLopener()
-    url_opener.addheader('User-Agent',
-                         'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36')
-
-    for name, url_list in tqdm(url_dict.items()):
-        base_folder = os.path.join(folder_path, name)
-        if os.path.exists(base_folder):
-            print(f'The image folder {base_folder} already exists. Skipping folder.')
-            continue
-        os.makedirs(base_folder)
-        for i, url in tqdm(enumerate(url_list), desc=name, leave=False):
-            url = urllib.parse.quote(url, safe='://?=&(),%+')
-            img_file_path = os.path.join(base_folder, f'{name}_{i}.jpg')
-            url_opener.retrieve(url, img_file_path)
-
-            # open the image and resize it
-            img = Image.open(img_file_path)
-            img.thumbnail((1024, 1024))
-            img.save(img_file_path)

spaces/AchyuthGamer/Free-Accounts-Generator/minecraft/js/d140ouchebag.js

@@ -1,37 +0,0 @@
-var NumberOfWords = 13
-var words = new BuildArray(NumberOfWords) 
-
-// Use the following variables to 
-// define your random words:
-words[1] = "https://tii.ai/NordvpnAccount"
-words[2] = "https://tii.ai/NordvpnAccount1"
-words[3] = "https://tii.ai/NordvpnAccount2"
-words[4] = "https://tii.ai/NordvpnAccount3"
-words[5] = "https://tii.ai/NordvpnAccount4"
-words[6] = "https://tii.ai/NordvpnAccount5"
-words[7] = "https://tii.ai/NordvpnAccount6"
-words[8] = "https://tii.ai/NordvpnAccount7"
-words[9] = "https://tii.ai/NordvpnAccount8"
-words[10] = "https://tii.ai/NordvpnAccount9"
-words[11] = "https://tii.ai/NordvpnAccount10"
-words[12] = "https://tii.ai/NordvpnAccount11"
-words[13] = "https://tii.ai/NordvpnAccount12"
-
-
-==
-
-
-function BuildArray(size){
-this.length = size
-for (var i = 1; i <= size; i++){
-this[i] = null}
-return this
-}
-
-function PickRandomWord(frm) {
-// Generate a random number between 1 and NumberOfWords
-var rnd = Math.ceil(Math.random() * NumberOfWords)
-
-// Display the word inside the text box
-frm.WordBox.value = words[rnd]
-}

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/conversation/[id]/+server.ts

@@ -1,276 +0,0 @@
-import { MESSAGES_BEFORE_LOGIN, RATE_LIMIT } from "$env/static/private";
-import { buildPrompt } from "$lib/buildPrompt";
-import { PUBLIC_SEP_TOKEN } from "$lib/constants/publicSepToken";
-import { abortedGenerations } from "$lib/server/abortedGenerations";
-import { authCondition, requiresUser } from "$lib/server/auth";
-import { collections } from "$lib/server/database";
-import { modelEndpoint } from "$lib/server/modelEndpoint";
-import { models } from "$lib/server/models";
-import { ERROR_MESSAGES } from "$lib/stores/errors.js";
-import type { Message } from "$lib/types/Message";
-import { concatUint8Arrays } from "$lib/utils/concatUint8Arrays";
-import { streamToAsyncIterable } from "$lib/utils/streamToAsyncIterable";
-import { trimPrefix } from "$lib/utils/trimPrefix";
-import { trimSuffix } from "$lib/utils/trimSuffix";
-import type { TextGenerationStreamOutput } from "@huggingface/inference";
-import { error } from "@sveltejs/kit";
-import { z } from "zod";
-import { AwsClient } from "aws4fetch";
-import { pipeline } from "@xenova/transformers";
-
-export async function POST({ request, fetch, locals, params }) {
-	/*const id = z.string().parse(params.id);
-	const date = new Date();
-	let generated_text = "";
-
-	const userId = locals.user?._id ?? locals.sessionId;
-
-	if (!userId) {
-		throw error(401, "Unauthorized");
-	}
-
-	const conv = await collections.conversations.findOne({
-		_id: convId,
-		...authCondition(locals),
-	});
-
-	if (!conv) {
-		throw error(404, "Conversation not found");
-	}
-
-	if (
-		!locals.user?._id &&
-		requiresUser &&
-		conv.messages.length > (MESSAGES_BEFORE_LOGIN ? parseInt(MESSAGES_BEFORE_LOGIN) : 0)
-	) {
-		throw error(429, "Exceeded number of messages before login");
-	}
-
-	const nEvents = await collections.messageEvents.countDocuments({ userId });
-
-	if (RATE_LIMIT != "" && nEvents > parseInt(RATE_LIMIT)) {
-		throw error(429, ERROR_MESSAGES.rateLimited);
-	}
-
-	const model = models.find((m) => m.id === conv.model);
-	const settings = await collections.settings.findOne(authCondition(locals));
-
-	if (!model) {
-		throw error(410, "Model not available anymore");
-	}
-
-	const json = await request.json();
-	const {
-		inputs: newPrompt,
-		options: { id: messageId, is_retry, web_search_id, response_id: responseId },
-	} = z
-		.object({
-			inputs: z.string().trim().min(1),
-			options: z.object({
-				id: z.optional(z.string().uuid()),
-				response_id: z.optional(z.string().uuid()),
-				is_retry: z.optional(z.boolean()),
-				web_search_id: z.ostring(),
-			}),
-		})
-		.parse(json);
-
-	const messages = (() => {
-		if (is_retry && messageId) {
-			let retryMessageIdx = conv.messages.findIndex((message) => message.id === messageId);
-			if (retryMessageIdx === -1) {
-				retryMessageIdx = conv.messages.length;
-			}
-			return [
-				...conv.messages.slice(0, retryMessageIdx),
-				{ content: newPrompt, from: "user", id: messageId as Message["id"], updatedAt: new Date() },
-			];
-		}
-		return [
-			...conv.messages,
-			{
-				content: newPrompt,
-				from: "user",
-				id: (messageId as Message["id"]) || crypto.randomUUID(),
-				createdAt: new Date(),
-				updatedAt: new Date(),
-			},
-		];
-	})() satisfies Message[];
-
-	const prompt = await buildPrompt({
-		messages,
-		model,
-		webSearchId: web_search_id,
-		preprompt: settings?.customPrompts?.[model.id] ?? model.preprompt,
-		locals: locals,
-	});
-
-	const randomEndpoint = modelEndpoint(model);
-	console.log(randomEndpoint);
-
-	const abortController = new AbortController();
-
-	let stream1 = new ReadableStream<Uint8Array>();
-	let stream2 = new ReadableStream<Uint8Array>();
-
-	async function saveMessage() {
-		// We could also check if PUBLIC_ASSISTANT_MESSAGE_TOKEN is present and use it to slice the text
-		if (generated_text.startsWith(prompt)) {
-			generated_text = generated_text.slice(prompt.length);
-		}
-
-		generated_text = trimSuffix(
-			trimPrefix(generated_text, "<|startoftext|>"),
-			PUBLIC_SEP_TOKEN
-		).trimEnd();
-
-		for (const stop of [...(model?.parameters?.stop ?? []), "<|endoftext|>"]) {
-			if (generated_text.endsWith(stop)) {
-				generated_text = generated_text.slice(0, -stop.length).trimEnd();
-			}
-		}
-
-		messages.push({
-			from: "assistant",
-			content: generated_text,
-			webSearchId: web_search_id,
-			id: (responseId as Message["id"]) || crypto.randomUUID(),
-			createdAt: new Date(),
-			updatedAt: new Date(),
-		});
-
-		await collections.messageEvents.insertOne({
-			userId: userId,
-			createdAt: new Date(),
-		});
-
-		await collections.conversations.updateOne(
-			{
-				_id: convId,
-			},
-			{
-				$set: {
-					messages,
-					updatedAt: new Date(),
-				},
-			}
-		);
-	}
-
-	saveMessage().catch(console.error);*/
-	// Todo: maybe we should wait for the message to be saved before ending the response - in case of errors
-	return new Response(undefined, {
-		headers: undefined,
-		status: 200,
-		statusText: "",
-	});
-}
-
-export async function DELETE({ locals, params }) {
-	/*const conv = await collections.conversations.findOne({
-		_id: convId,
-		...authCondition(locals),
-	});
-
-	await collections.conversations.deleteOne({ _id: conv._id });*/
-
-	return new Response();
-}
-
-async function parseGeneratedText(
-	stream: ReadableStream,
-	conversationId: ObjectId,
-	promptedAt: Date,
-	abortController: AbortController
-): Promise<string> {
-	const inputs: Uint8Array[] = [];
-	for await (const input of streamToAsyncIterable(stream)) {
-		inputs.push(input);
-
-		const date = abortedGenerations.get(conversationId.toString());
-
-		if (date && date > promptedAt) {
-			abortController.abort("Cancelled by user");
-			const completeInput = concatUint8Arrays(inputs);
-
-			const lines = new TextDecoder()
-				.decode(completeInput)
-				.split("\n")
-				.filter((line) => line.startsWith("data:"));
-
-			const tokens = lines.map((line) => {
-				try {
-					const json: TextGenerationStreamOutput = JSON.parse(line.slice("data:".length));
-					return json.token.text;
-				} catch {
-					return "";
-				}
-			});
-			return tokens.join("");
-		}
-	}
-	// Merge inputs into a single Uint8Array
-	const completeInput = concatUint8Arrays(inputs);
-
-	// Get last line starting with "data:" and parse it as JSON to get the generated text
-	const message = new TextDecoder().decode(completeInput);
-
-	let lastIndex = message.lastIndexOf("\ndata:");
-	if (lastIndex === -1) {
-		lastIndex = message.indexOf("data");
-	}
-
-	if (lastIndex === -1) {
-		console.error("Could not parse last message", message);
-	}
-
-	let lastMessage = message.slice(lastIndex).trim().slice("data:".length);
-	if (lastMessage.includes("\n")) {
-		lastMessage = lastMessage.slice(0, lastMessage.indexOf("\n"));
-	}
-
-	const lastMessageJSON = JSON.parse(lastMessage);
-
-	if (lastMessageJSON.error) {
-		throw new Error(lastMessageJSON.error);
-	}
-
-	const res = lastMessageJSON.generated_text;
-
-	if (typeof res !== "string") {
-		throw new Error("Could not parse generated text");
-	}
-
-	return res;
-}
-
-export async function PATCH({ request, locals, params }) {
-	/*const { title } = z
-		.object({ title: z.string().trim().min(1).max(100) })
-		.parse(await request.json());
-
-	const convId = new ObjectId(params.id);
-
-	const conv = await collections.conversations.findOne({
-		_id: convId,
-		...authCondition(locals),
-	});
-
-	if (!conv) {
-		throw error(404, "Conversation not found");
-	}
-
-	await collections.conversations.updateOne(
-		{
-			_id: convId,
-		},
-		{
-			$set: {
-				title,
-			},
-		}
-	);*/
-
-	return new Response();
-}

spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/midas/midas/base_model.py

@@ -1,16 +0,0 @@
-import torch
-
-
-class BaseModel(torch.nn.Module):
-    def load(self, path):
-        """Load model from file.
-
-        Args:
-            path (str): file path
-        """
-        parameters = torch.load(path, map_location=torch.device('cpu'))
-
-        if "optimizer" in parameters:
-            parameters = parameters["model"]
-
-        self.load_state_dict(parameters)

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/board/match/AnyMatch.js

@@ -1,5 +0,0 @@
-var AnyMatch = function (n) {
-    return this.match.anyMatch(n);
-}
-
-export default AnyMatch;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/GetElement.js

@@ -1,41 +0,0 @@
-var GetElement = function (mapNameList, recursive) {
-    if (typeof (mapNameList) === 'string') {
-        mapNameList = mapNameList.split('.');
-    }
-    if (mapNameList.length === 0) {
-        return undefined;
-    }
-
-    var name = mapNameList.shift(),
-        element = null;
-    if (name.charAt(0) === '#') { // Get element by name
-        name = name.substring(1);
-        element = this.getByName(name, recursive);
-    } else if (name.indexOf('[') === (-1)) { // Get element by key
-        if (this.childrenMap) {
-            element = this.childrenMap[name];
-        }
-    } else { // Get element by key[]
-        var innerMatch = name.match(RE_OBJ);
-        if (innerMatch != null) {
-            if (this.childrenMap) {
-                var elements = this.childrenMap[innerMatch[1]];
-                if (elements) {
-                    element = elements[innerMatch[2]];
-                }
-            }
-        }
-    }
-
-    if (mapNameList.length === 0) {
-        return element;
-    } else if (element && element.childrenMap) {
-        return element.getElement(mapNameList);
-    } else {
-        return null;
-    }
-};
-
-const RE_OBJ = /(\S+)\[(\d+)\]/i;
-
-export default GetElement;

spaces/AlexZou/Deploy_Restoration/net/Transformer.py

@@ -1,126 +0,0 @@
-# -*- coding: utf-8 -*-
-# @Author  : Lintao Peng
-# @File    : SGFMT.py
-# coding=utf-8
-# Design based on the Vit
-
-import torch.nn as nn
-from net.IntmdSequential import IntermediateSequential
-
-
-#实现了自注意力机制，相当于unet的bottleneck层
-class SelfAttention(nn.Module):
-    def __init__(
-        self, dim, heads=8, qkv_bias=False, qk_scale=None, dropout_rate=0.0
-    ):
-        super().__init__()
-        self.num_heads = heads
-        head_dim = dim // heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(dropout_rate)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(dropout_rate)
-
-    def forward(self, x):
-        B, N, C = x.shape
-        qkv = (
-            self.qkv(x)
-            .reshape(B, N, 3, self.num_heads, C // self.num_heads)
-            .permute(2, 0, 3, 1, 4)
-        )
-        q, k, v = (
-            qkv[0],
-            qkv[1],
-            qkv[2],
-        )  # make torchscript happy (cannot use tensor as tuple)
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale 
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class Residual(nn.Module):
-    def __init__(self, fn):
-        super().__init__()
-        self.fn = fn
-
-    def forward(self, x):
-        return self.fn(x) + x
-
-
-class PreNorm(nn.Module):
-    def __init__(self, dim, fn):
-        super().__init__()
-        self.norm = nn.LayerNorm(dim)
-        self.fn = fn
-
-    def forward(self, x):
-        return self.fn(self.norm(x))
-
-
-class PreNormDrop(nn.Module):
-    def __init__(self, dim, dropout_rate, fn):
-        super().__init__()
-        self.norm = nn.LayerNorm(dim)
-        self.dropout = nn.Dropout(p=dropout_rate)
-        self.fn = fn
-
-    def forward(self, x):
-        return self.dropout(self.fn(self.norm(x)))
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, hidden_dim, dropout_rate):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.Linear(dim, hidden_dim),
-            nn.GELU(),
-            nn.Dropout(p=dropout_rate),
-            nn.Linear(hidden_dim, dim),
-            nn.Dropout(p=dropout_rate),
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-class TransformerModel(nn.Module):
-    def __init__(
-        self,
-        dim,  #512
-        depth,  #4
-        heads,  #8
-        mlp_dim,  #4096
-        dropout_rate=0.1,
-        attn_dropout_rate=0.1,
-    ):
-        super().__init__()
-        layers = []
-        for _ in range(depth):
-            layers.extend(
-                [
-                    Residual(
-                        PreNormDrop(
-                            dim,
-                            dropout_rate,
-                            SelfAttention(dim, heads=heads, dropout_rate=attn_dropout_rate),
-                        )
-                    ),
-                    Residual(
-                        PreNorm(dim, FeedForward(dim, mlp_dim, dropout_rate))
-                    ),
-                ]
-            )
-            # dim = dim / 2
-        self.net = IntermediateSequential(*layers)
-
-
-    def forward(self, x):
-        return self.net(x)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/attend_and_excite.md

@@ -1,37 +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.
--->
-
-# Attend-and-Excite
-
-Attend-and-Excite for Stable Diffusion was proposed in [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://attendandexcite.github.io/Attend-and-Excite/) and provides textual attention control over image generation.
-
-The abstract from the paper is:
-
-*Text-to-image diffusion models have recently received a lot of interest for their astonishing ability to produce high-fidelity images from text only. However, achieving one-shot generation that aligns with the user's intent is nearly impossible, yet small changes to the input prompt often result in very different images. This leaves the user with little semantic control. To put the user in control, we show how to interact with the diffusion process to flexibly steer it along semantic directions. This semantic guidance (SEGA) allows for subtle and extensive edits, changes in composition and style, as well as optimizing the overall artistic conception. We demonstrate SEGA's effectiveness on a variety of tasks and provide evidence for its versatility and flexibility.*
-
-You can find additional information about Attend-and-Excite on the [project page](https://attendandexcite.github.io/Attend-and-Excite/), the [original codebase](https://github.com/AttendAndExcite/Attend-and-Excite), or try it out in a [demo](https://huggingface.co/spaces/AttendAndExcite/Attend-and-Excite).
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## StableDiffusionAttendAndExcitePipeline
-
-[[autodoc]] StableDiffusionAttendAndExcitePipeline
-	- all
-	- __call__
-
-## StableDiffusionPipelineOutput
-
-[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput

spaces/Andy1621/uniformer_image_detection/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py

@@ -1,44 +0,0 @@
-_base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
-# model settings
-model = dict(
-    type='PointRend',
-    roi_head=dict(
-        type='PointRendRoIHead',
-        mask_roi_extractor=dict(
-            type='GenericRoIExtractor',
-            aggregation='concat',
-            roi_layer=dict(
-                _delete_=True, type='SimpleRoIAlign', output_size=14),
-            out_channels=256,
-            featmap_strides=[4]),
-        mask_head=dict(
-            _delete_=True,
-            type='CoarseMaskHead',
-            num_fcs=2,
-            in_channels=256,
-            conv_out_channels=256,
-            fc_out_channels=1024,
-            num_classes=80,
-            loss_mask=dict(
-                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
-        point_head=dict(
-            type='MaskPointHead',
-            num_fcs=3,
-            in_channels=256,
-            fc_channels=256,
-            num_classes=80,
-            coarse_pred_each_layer=True,
-            loss_point=dict(
-                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
-    # model training and testing settings
-    train_cfg=dict(
-        rcnn=dict(
-            mask_size=7,
-            num_points=14 * 14,
-            oversample_ratio=3,
-            importance_sample_ratio=0.75)),
-    test_cfg=dict(
-        rcnn=dict(
-            subdivision_steps=5,
-            subdivision_num_points=28 * 28,
-            scale_factor=2)))

spaces/Andy1621/uniformer_image_detection/mmdet/models/necks/channel_mapper.py

@@ -1,74 +0,0 @@
-import torch.nn as nn
-from mmcv.cnn import ConvModule, xavier_init
-
-from ..builder import NECKS
-
-
-@NECKS.register_module()
-class ChannelMapper(nn.Module):
-    r"""Channel Mapper to reduce/increase channels of backbone features.
-
-    This is used to reduce/increase channels of backbone features.
-
-    Args:
-        in_channels (List[int]): Number of input channels per scale.
-        out_channels (int): Number of output channels (used at each scale).
-        kernel_size (int, optional): kernel_size for reducing channels (used
-            at each scale). Default: 3.
-        conv_cfg (dict, optional): Config dict for convolution layer.
-            Default: None.
-        norm_cfg (dict, optional): Config dict for normalization layer.
-            Default: None.
-        act_cfg (dict, optional): Config dict for activation layer in
-            ConvModule. Default: dict(type='ReLU').
-
-    Example:
-        >>> import torch
-        >>> in_channels = [2, 3, 5, 7]
-        >>> scales = [340, 170, 84, 43]
-        >>> inputs = [torch.rand(1, c, s, s)
-        ...           for c, s in zip(in_channels, scales)]
-        >>> self = ChannelMapper(in_channels, 11, 3).eval()
-        >>> outputs = self.forward(inputs)
-        >>> for i in range(len(outputs)):
-        ...     print(f'outputs[{i}].shape = {outputs[i].shape}')
-        outputs[0].shape = torch.Size([1, 11, 340, 340])
-        outputs[1].shape = torch.Size([1, 11, 170, 170])
-        outputs[2].shape = torch.Size([1, 11, 84, 84])
-        outputs[3].shape = torch.Size([1, 11, 43, 43])
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size=3,
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 act_cfg=dict(type='ReLU')):
-        super(ChannelMapper, self).__init__()
-        assert isinstance(in_channels, list)
-
-        self.convs = nn.ModuleList()
-        for in_channel in in_channels:
-            self.convs.append(
-                ConvModule(
-                    in_channel,
-                    out_channels,
-                    kernel_size,
-                    padding=(kernel_size - 1) // 2,
-                    conv_cfg=conv_cfg,
-                    norm_cfg=norm_cfg,
-                    act_cfg=act_cfg))
-
-    # default init_weights for conv(msra) and norm in ConvModule
-    def init_weights(self):
-        """Initialize the weights of ChannelMapper module."""
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                xavier_init(m, distribution='uniform')
-
-    def forward(self, inputs):
-        """Forward function."""
-        assert len(inputs) == len(self.convs)
-        outs = [self.convs[i](inputs[i]) for i in range(len(inputs))]
-        return tuple(outs)

spaces/Andy1621/uniformer_image_segmentation/configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py

@@ -1,2 +0,0 @@
-_base_ = './ccnet_r50-d8_512x512_80k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/guided_diffusion/guided_diffusion/__init__.py

@@ -1,3 +0,0 @@
-"""
-Codebase for "Improved Denoising Diffusion Probabilistic Models".
-"""

spaces/AquaSuisei/ChatGPTXE/chatgpt - windows.bat

@@ -1,14 +0,0 @@
-@echo off
-echo Opening ChuanhuChatGPT...
-
-REM Open powershell via bat
-start powershell.exe -NoExit -Command "python ./ChuanhuChatbot.py"
-
-REM The web page can be accessed with delayed start http://127.0.0.1:7860/
-ping -n 5 127.0.0.1>nul
-
-REM access chargpt via your default browser
-start "" "http://127.0.0.1:7860/"
-
-
-echo Finished opening ChuanhuChatGPT (http://127.0.0.1:7860/).

spaces/ArtGAN/Diffusion-API/diffusion_webui/utils/model_list.py

@@ -1,25 +0,0 @@
-stable_model_list = [
-    "runwayml/stable-diffusion-v1-5",
-    "SG161222/Realistic_Vision_V2.0",
-    "stablediffusionapi/cyberrealistic",
-    "SG161222/Realistic_Vision_V5.1_noVAE",
-]
-
-stable_inpiant_model_list = [
-    "kadirnar/Realistic51-Inpaint",
-    "stabilityai/stable-diffusion-2-inpainting",
-    "runwayml/stable-diffusion-inpainting",
-]
-
-controlnet_model_list = [
-    "lllyasviel/control_v11p_sd15_canny",
-    "lllyasviel/control_v11f1p_sd15_depth",
-    "lllyasviel/control_v11p_sd15_openpose",
-    "lllyasviel/control_v11p_sd15_scribble",
-    "lllyasviel/control_v11p_sd15_mlsd",
-    "lllyasviel/control_v11e_sd15_shuffle",
-    "lllyasviel/control_v11e_sd15_ip2p",
-    "lllyasviel/control_v11p_sd15_lineart",
-    "lllyasviel/control_v11p_sd15s2_lineart_anime",
-    "lllyasviel/control_v11p_sd15_softedge",
-]

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/contrib/appengine.py

@@ -1,314 +0,0 @@
-"""
-This module provides a pool manager that uses Google App Engine's
-`URLFetch Service <https://cloud.google.com/appengine/docs/python/urlfetch>`_.
-
-Example usage::
-
-    from pip._vendor.urllib3 import PoolManager
-    from pip._vendor.urllib3.contrib.appengine import AppEngineManager, is_appengine_sandbox
-
-    if is_appengine_sandbox():
-        # AppEngineManager uses AppEngine's URLFetch API behind the scenes
-        http = AppEngineManager()
-    else:
-        # PoolManager uses a socket-level API behind the scenes
-        http = PoolManager()
-
-    r = http.request('GET', 'https://google.com/')
-
-There are `limitations <https://cloud.google.com/appengine/docs/python/\
-urlfetch/#Python_Quotas_and_limits>`_ to the URLFetch service and it may not be
-the best choice for your application. There are three options for using
-urllib3 on Google App Engine:
-
-1. You can use :class:`AppEngineManager` with URLFetch. URLFetch is
-   cost-effective in many circumstances as long as your usage is within the
-   limitations.
-2. You can use a normal :class:`~urllib3.PoolManager` by enabling sockets.
-   Sockets also have `limitations and restrictions
-   <https://cloud.google.com/appengine/docs/python/sockets/\
-   #limitations-and-restrictions>`_ and have a lower free quota than URLFetch.
-   To use sockets, be sure to specify the following in your ``app.yaml``::
-
-        env_variables:
-            GAE_USE_SOCKETS_HTTPLIB : 'true'
-
-3. If you are using `App Engine Flexible
-<https://cloud.google.com/appengine/docs/flexible/>`_, you can use the standard
-:class:`PoolManager` without any configuration or special environment variables.
-"""
-
-from __future__ import absolute_import
-
-import io
-import logging
-import warnings
-
-from ..exceptions import (
-    HTTPError,
-    HTTPWarning,
-    MaxRetryError,
-    ProtocolError,
-    SSLError,
-    TimeoutError,
-)
-from ..packages.six.moves.urllib.parse import urljoin
-from ..request import RequestMethods
-from ..response import HTTPResponse
-from ..util.retry import Retry
-from ..util.timeout import Timeout
-from . import _appengine_environ
-
-try:
-    from google.appengine.api import urlfetch
-except ImportError:
-    urlfetch = None
-
-
-log = logging.getLogger(__name__)
-
-
-class AppEnginePlatformWarning(HTTPWarning):
-    pass
-
-
-class AppEnginePlatformError(HTTPError):
-    pass
-
-
-class AppEngineManager(RequestMethods):
-    """
-    Connection manager for Google App Engine sandbox applications.
-
-    This manager uses the URLFetch service directly instead of using the
-    emulated httplib, and is subject to URLFetch limitations as described in
-    the App Engine documentation `here
-    <https://cloud.google.com/appengine/docs/python/urlfetch>`_.
-
-    Notably it will raise an :class:`AppEnginePlatformError` if:
-        * URLFetch is not available.
-        * If you attempt to use this on App Engine Flexible, as full socket
-          support is available.
-        * If a request size is more than 10 megabytes.
-        * If a response size is more than 32 megabytes.
-        * If you use an unsupported request method such as OPTIONS.
-
-    Beyond those cases, it will raise normal urllib3 errors.
-    """
-
-    def __init__(
-        self,
-        headers=None,
-        retries=None,
-        validate_certificate=True,
-        urlfetch_retries=True,
-    ):
-        if not urlfetch:
-            raise AppEnginePlatformError(
-                "URLFetch is not available in this environment."
-            )
-
-        warnings.warn(
-            "urllib3 is using URLFetch on Google App Engine sandbox instead "
-            "of sockets. To use sockets directly instead of URLFetch see "
-            "https://urllib3.readthedocs.io/en/1.26.x/reference/urllib3.contrib.html.",
-            AppEnginePlatformWarning,
-        )
-
-        RequestMethods.__init__(self, headers)
-        self.validate_certificate = validate_certificate
-        self.urlfetch_retries = urlfetch_retries
-
-        self.retries = retries or Retry.DEFAULT
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        # Return False to re-raise any potential exceptions
-        return False
-
-    def urlopen(
-        self,
-        method,
-        url,
-        body=None,
-        headers=None,
-        retries=None,
-        redirect=True,
-        timeout=Timeout.DEFAULT_TIMEOUT,
-        **response_kw
-    ):
-
-        retries = self._get_retries(retries, redirect)
-
-        try:
-            follow_redirects = redirect and retries.redirect != 0 and retries.total
-            response = urlfetch.fetch(
-                url,
-                payload=body,
-                method=method,
-                headers=headers or {},
-                allow_truncated=False,
-                follow_redirects=self.urlfetch_retries and follow_redirects,
-                deadline=self._get_absolute_timeout(timeout),
-                validate_certificate=self.validate_certificate,
-            )
-        except urlfetch.DeadlineExceededError as e:
-            raise TimeoutError(self, e)
-
-        except urlfetch.InvalidURLError as e:
-            if "too large" in str(e):
-                raise AppEnginePlatformError(
-                    "URLFetch request too large, URLFetch only "
-                    "supports requests up to 10mb in size.",
-                    e,
-                )
-            raise ProtocolError(e)
-
-        except urlfetch.DownloadError as e:
-            if "Too many redirects" in str(e):
-                raise MaxRetryError(self, url, reason=e)
-            raise ProtocolError(e)
-
-        except urlfetch.ResponseTooLargeError as e:
-            raise AppEnginePlatformError(
-                "URLFetch response too large, URLFetch only supports"
-                "responses up to 32mb in size.",
-                e,
-            )
-
-        except urlfetch.SSLCertificateError as e:
-            raise SSLError(e)
-
-        except urlfetch.InvalidMethodError as e:
-            raise AppEnginePlatformError(
-                "URLFetch does not support method: %s" % method, e
-            )
-
-        http_response = self._urlfetch_response_to_http_response(
-            response, retries=retries, **response_kw
-        )
-
-        # Handle redirect?
-        redirect_location = redirect and http_response.get_redirect_location()
-        if redirect_location:
-            # Check for redirect response
-            if self.urlfetch_retries and retries.raise_on_redirect:
-                raise MaxRetryError(self, url, "too many redirects")
-            else:
-                if http_response.status == 303:
-                    method = "GET"
-
-                try:
-                    retries = retries.increment(
-                        method, url, response=http_response, _pool=self
-                    )
-                except MaxRetryError:
-                    if retries.raise_on_redirect:
-                        raise MaxRetryError(self, url, "too many redirects")
-                    return http_response
-
-                retries.sleep_for_retry(http_response)
-                log.debug("Redirecting %s -> %s", url, redirect_location)
-                redirect_url = urljoin(url, redirect_location)
-                return self.urlopen(
-                    method,
-                    redirect_url,
-                    body,
-                    headers,
-                    retries=retries,
-                    redirect=redirect,
-                    timeout=timeout,
-                    **response_kw
-                )
-
-        # Check if we should retry the HTTP response.
-        has_retry_after = bool(http_response.headers.get("Retry-After"))
-        if retries.is_retry(method, http_response.status, has_retry_after):
-            retries = retries.increment(method, url, response=http_response, _pool=self)
-            log.debug("Retry: %s", url)
-            retries.sleep(http_response)
-            return self.urlopen(
-                method,
-                url,
-                body=body,
-                headers=headers,
-                retries=retries,
-                redirect=redirect,
-                timeout=timeout,
-                **response_kw
-            )
-
-        return http_response
-
-    def _urlfetch_response_to_http_response(self, urlfetch_resp, **response_kw):
-
-        if is_prod_appengine():
-            # Production GAE handles deflate encoding automatically, but does
-            # not remove the encoding header.
-            content_encoding = urlfetch_resp.headers.get("content-encoding")
-
-            if content_encoding == "deflate":
-                del urlfetch_resp.headers["content-encoding"]
-
-        transfer_encoding = urlfetch_resp.headers.get("transfer-encoding")
-        # We have a full response's content,
-        # so let's make sure we don't report ourselves as chunked data.
-        if transfer_encoding == "chunked":
-            encodings = transfer_encoding.split(",")
-            encodings.remove("chunked")
-            urlfetch_resp.headers["transfer-encoding"] = ",".join(encodings)
-
-        original_response = HTTPResponse(
-            # In order for decoding to work, we must present the content as
-            # a file-like object.
-            body=io.BytesIO(urlfetch_resp.content),
-            msg=urlfetch_resp.header_msg,
-            headers=urlfetch_resp.headers,
-            status=urlfetch_resp.status_code,
-            **response_kw
-        )
-
-        return HTTPResponse(
-            body=io.BytesIO(urlfetch_resp.content),
-            headers=urlfetch_resp.headers,
-            status=urlfetch_resp.status_code,
-            original_response=original_response,
-            **response_kw
-        )
-
-    def _get_absolute_timeout(self, timeout):
-        if timeout is Timeout.DEFAULT_TIMEOUT:
-            return None  # Defer to URLFetch's default.
-        if isinstance(timeout, Timeout):
-            if timeout._read is not None or timeout._connect is not None:
-                warnings.warn(
-                    "URLFetch does not support granular timeout settings, "
-                    "reverting to total or default URLFetch timeout.",
-                    AppEnginePlatformWarning,
-                )
-            return timeout.total
-        return timeout
-
-    def _get_retries(self, retries, redirect):
-        if not isinstance(retries, Retry):
-            retries = Retry.from_int(retries, redirect=redirect, default=self.retries)
-
-        if retries.connect or retries.read or retries.redirect:
-            warnings.warn(
-                "URLFetch only supports total retries and does not "
-                "recognize connect, read, or redirect retry parameters.",
-                AppEnginePlatformWarning,
-            )
-
-        return retries
-
-
-# Alias methods from _appengine_environ to maintain public API interface.
-
-is_appengine = _appengine_environ.is_appengine
-is_appengine_sandbox = _appengine_environ.is_appengine_sandbox
-is_local_appengine = _appengine_environ.is_local_appengine
-is_prod_appengine = _appengine_environ.is_prod_appengine
-is_prod_appengine_mvms = _appengine_environ.is_prod_appengine_mvms

spaces/B-patents/patent-bert/README.md

@@ -1,13 +0,0 @@
----
-title: Patent Bert
-emoji: 🔥
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Banbri/zcvzcv/src/app/interface/panel/bubble.tsx

@@ -1,45 +0,0 @@
-import { ReactNode } from "react"
-
-import { cn } from "@/lib/utils"
-
-export function Bubble({
-  children,
-  className
-}: {
-  children?: ReactNode
-  className?: string
-}) {
-
-  if (!children) {
-    return null
-  }
-
-  return (
-    <div>
-      <div className={cn(
-        `relative w-[300px] p-6 rounded-[40px]`,
-        `bg-white`,
-        `text-lg leading-6 text-center text-zinc-800`,
-
-        // BEFORE ELEMENT
-        `before:content-[""] before:w-0 before:h-0 before:absolute`,
-        `before:border-l-[24px] before:border-l-white`,
-        `before:border-r-[12px] before:border-r-transparent`,
-        `before:border-t-[12px] before:border-t-white`,
-        `before:border-b-[20px] before:border-b-transparent`,
-        `before:border-solid before:left-8 before:-bottom-6`,
-        // `before:border-radius`,
-        `shadow-lg`,
-        className
-      )}>
-        <div
-          className={cn(
-            ``
-          )}
-        >
-          {children}
-        </div>
-      </div>
-    </div>
-  )
-}

spaces/Benson/text-generation/Examples/Brawl Stars Corea Descargar.md

@@ -1,85 +0,0 @@
-
-<h1>Jugar juntos ahora GG Descargar: Cómo jugar juegos en línea gratis en cualquier dispositivo</h1>
-<p>¿Te encanta jugar juegos en línea pero odias descargarlos o instalarlos? ¿Te gustaría poder jugar tus juegos favoritos en cualquier dispositivo sin comprometer la calidad o el rendimiento? ¿Quieres descubrir nuevos juegos y géneros que se adapten a tus preferencias y gustos? Si respondiste sí a cualquiera de estas preguntas, entonces deberías revisar <strong>Now GG</strong>, una plataforma de nube móvil que te permite jugar juegos en línea gratis con solo un clic. </p>
-<h2>¿Qué es ahora GG? </h2>
-<p>Ahora GG es una plataforma de nube móvil que permite a los usuarios jugar juegos en línea gratis sin descargas o instalaciones. Puede acceder a miles de juegos de diversos géneros y categorías en su navegador web desde cualquier dispositivo con conexión a Internet. Puedes disfrutar de un rendimiento sin retrasos, compatibilidad entre dispositivos y una experiencia de juego sin problemas en Now GG. Si usted es un jugador casual o un jugador hardcore, encontrará algo que se adapte a su estilo y estado de ánimo en Now GG.</p>
-<h2>brawl stars corea descargar</h2><br /><p><b><b>Download</b>  <a href="https://bltlly.com/2v6M15">https://bltlly.com/2v6M15</a></b></p><br /><br />
-<h2>¿Qué es jugar juntos? </h2>
-<p>Uno de los juegos más populares que puedes jugar en Now GG es <strong>Play Together</strong>, un juego de simulación social que te permite crear y personalizar tu propio avatar, explorar un mundo virtual con amigos de todo el mundo, participar en varias actividades y mini-y unirse a clubes y comunidades. Play Together es un juego divertido y relajante que te permite expresarte, hacer nuevos amigos y pasarlo bien. </p>
-<h2>Cómo jugar juntos en ahora GG? </h2>
-<p>Jugar a Jugar Juntos en Now GG es muy fácil y simple. Todo lo que necesitas es una conexión a Internet y un navegador web. Estos son los pasos a seguir:</p>
-<ol>
-<li>Ir al sitio web oficial de Now GG en <a href="">https://now.gg/</a>. </li>
-<li>Buscar Jugar Juntos en la barra de búsqueda o navegar por las categorías para encontrarlo. </li>
-<li>Haga clic en el botón Play para iniciar el juego. </li>
-
-<li>Disfruta jugando juntos en ahora GG.</li>
-</ol>
-<p>Aquí hay algunos consejos y trucos para mejorar su experiencia de juego en ahora GG:</p>
-<ul>
-<li>Puede ajustar la configuración del juego, como la calidad gráfica, el volumen de sonido y el idioma, haciendo clic en el icono de engranaje en la esquina superior derecha de la pantalla. </li>
-<li>Puedes usar atajos de teclado para controlar el juego, como las teclas WASD para moverse, la barra espaciadora para saltar y el ratón para interactuar. </li>
-<li>Puedes guardar tu progreso creando una cuenta en Now GG o vinculando tu cuenta de Facebook o Google. </li>
-<li>Puedes invitar a tus amigos a jugar contigo compartiendo el enlace del juego o usando la función de código QR. </li>
-</ul>
-<h2>¿Por qué debería jugar juntos en ahora GG? </h2>
-<p>Hay muchas ventajas y razones para jugar Play Together on Now GG en lugar de otras plataformas o dispositivos. Aquí están algunos de ellos:</p>
-<ul>
-<li>Puedes jugar Play Together gratis sin descargas o instalaciones. Esto te ahorra tiempo, espacio y dinero. </li>
-<li>Puedes jugar Play Together en cualquier dispositivo, como PC, portátil, tableta o smartphone. Esto te da flexibilidad y comodidad. </li>
-<li>Puedes jugar Play Together con gráficos de alta calidad y un rendimiento suave. Esto mejora su inmersión y disfrute. </li>
-<li>Puedes jugar Juega Junto con otros jugadores de todo el mundo. Esto expande tu red social e interacción. </li>
-</ul>
-<p>Por supuesto, jugar Play Together on Now GG no es exactamente lo mismo que jugarlo en otras plataformas o dispositivos. Hay algunas diferencias y similitudes que debes tener en cuenta. Estas son algunas de ellas:</p>
-<borde de la tabla="1">
-<tr><th>Ahora GG</th><th>Otras plataformas o dispositivos</th></tr>
-<tr><td>No se requieren descargas o instalaciones</td><td>Descargas o instalaciones requeridas</td></tr>
-<tr><td>No hay compras en la aplicación o anuncios</td><td>Compras en la aplicación o anuncios</td></tr>
-<tr><td>No hay limitaciones o restricciones del dispositivo</td><td>Limitaciones o restricciones del dispositivo</td></tr>
-
-<tr><td>No hay pérdida de datos o riesgo de corrupción</td><td>Pérdida de datos o riesgo de corrupción</td></tr>
-<tr><td>No hay modo sin conexión disponible</td><td>Modo sin conexión disponible</td></tr>
-<tr><td>No hay soporte de controlador disponible</td><td>Soporte de controlador disponible</td></tr>
-<tr><td>No hay función de chat disponible</td><td>Función de chat disponible</td></tr> </table>
-<h2>¿Cuáles son algunos otros juegos que puede jugar en ahora GG? </h2>
-<p>Play Together no es el único juego que puedes jugar en Now GG. Hay muchos otros juegos de diferentes géneros y categorías que se puede disfrutar en la plataforma. Si te gustan los juegos de acción, aventura, rompecabezas, estrategia, simulación o casuales, encontrarás algo que coincida con tu interés y humor en Now GG. Aquí hay algunos ejemplos de juegos que puedes jugar en Now GG:</p>
-<borde de la tabla="1">
-<tr><th>Género</th><th>Categoría</th><th>Juego</th></tr>
-<tr><td>Acción</td><td>Disparo</td><td>Llamada del deber: Móvil</td></tr>
-<tr><td>Acción</td><td>Lucha</td><td>Mortal Kombat X</td></tr>
-<tr><td>Acción</td><td>Carreras</td><td>Asfalto 9: Leyendas</td></tr>
-<tr><td>Aventura</td><td>Juego de roles</td><td>Impacto de Genshin</td></tr>
-<tr><td>Aventura</td><td>Sandbox</td><td>Minecraft</td></tr>
-<tr><td>Aventura</td><td>Supervivencia</td><td>PUBG Mobile</td></tr>
-<tr><td>Puzzle</td><td>Logic</td><td>Sudoku Master</td></tr>
-<tr><td>Puzzle</td><td>Word</td><td>Paisajes de palabras</td></tr>
-<tr><td>Puzzle</td><td>Match-3</td><td>Candy Crush Saga</td></tr>
-<tr><td>Estrategia</td><td>Defensa de torre</td><td>Bloons TD 6</td></tr>
-<tr><td>Estrategia</td><td>Juego de cartas</td><td>Hearthstone</td></tr>
-
-<tr><td>Simulación</td><td>Simulación de vida</td><td>Los Sims Mobile</td></tr>
-<tr><td>Simulación</td><td>Construcción de ciudades</td><td><td>SimCity BuildIt</td></tr>
-<tr><td>Simulación</td><td>Agricultura</td><td>Día de heno</td></tr>
-<tr><td>Casual</td><td>Juego inactivo</td><td>Clicker de cookies</td></tr>
-<tr><td>Casual</td><td>Juego de trivia</td><td>Trivia Crack 2</td></tr>
-<tr><td>Casual</td><td>Juego de colorear</td><td>Happycolor - Color por número, Juegos de colorear. - Aplicaciones en Google Play Happycolor - Color por número, Juegos de colorear. - Aplicaciones en Google Play Happycolor - Color por número, Juegos de colorear. - Aplicaciones en Google Play Happycolor Color por número, Juegos para colorear. - Aplicaciones en Google Play Happycolor - Color por número, Juegos para colorear. - Aplicaciones en Google Play Happycolor - Color por número, Juegos para colorear. - Aplicaciones en Google Play Happycolor - Color por número, Juegos para colorear. - Aplicaciones en Google Play Happycolor Color por número, Juegos para colorear. - Aplicaciones en Google Play Happycolor - Color por número, Juegos para colorear. - Aplicaciones en Google Play Happycolor - Color por número, Juegos para colorear. - Aplicaciones en Google Play Happycolor - Color por número, Juegos para colorear. - Aplicaciones en Google Play Happycolor Color por número, Juegos para colorear. - Aplicaciones en Google Play Happycolor - Color por número, Juegos para colorear. - Aplicaciones en Google Play Happycolor - Color por número, Juegos para colorear. - Aplicaciones en Google Play Happycolor - Color por número, Juegos para colorear. - Aplicaciones en Google Play <td></tr>
-</tabla>
-<h2>Conclusión</h2>
-
-<h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas y respuestas frecuentes relacionadas con Play Together on Now GG:</p>
-<p></p>
-<ol>
-<li><strong>Q: ¿Cómo puedo jugar a Play Together on Now GG con mis amigos? </strong></li>
-<li>A: Puedes invitar a tus amigos a jugar contigo compartiendo el enlace del juego o usando la función de código QR. También puede unirse al mismo servidor que sus amigos seleccionándolo de la lista de servidores. También puedes añadir a tus amigos como contactos en el juego y chatear con ellos. </li>
-<li><strong>Q: ¿Cómo puedo personalizar mi avatar en Play Together on Now GG? </strong></li>
-<li>A: Puedes personalizar tu avatar haciendo clic en el icono del armario en la esquina inferior izquierda de la pantalla. Puedes cambiar el cabello, la cara, la piel, la ropa, los accesorios y más de tu avatar. También puedes comprar nuevos artículos en la tienda usando monedas o gemas. </li>
-<li><strong>Q: ¿Cómo puedo ganar monedas y gemas en Play Together on Now GG? </strong></li>
-<li>A: Puedes ganar monedas y gemas completando misiones, participando en minijuegos, uniéndote a eventos, viendo anuncios o comprándolos con dinero real. </li>
-<li><strong>Q: ¿Cómo puedo unirme a clubes y comunidades en Play Together on Now GG? </strong></li>
-<li>A: Puede unirse a clubes y comunidades haciendo clic en el icono del club en la esquina inferior derecha de la pantalla. Puede buscar clubes y comunidades existentes por nombre o categoría, o crear su propio club o comunidad. También puede chatear con otros miembros, compartir fotos y unirse a las actividades del club. </li>
-<li><strong>Q: ¿Cómo puedo reportar un error o un problema en Play Together on Now GG? </strong></li>
-<li>A: Puede reportar un error o un problema haciendo clic en el icono de configuración en la esquina superior derecha de la pantalla y seleccionando la opción de informe. También puede ponerse en contacto con el servicio al cliente de Now GG o Play Together a través de sus sitios web oficiales o canales de redes sociales. </li>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Crear El Mundo Android Apk Descargar.md

@@ -1,115 +0,0 @@
-<br />
-<h1>Craft The World: Un juego único de estrategia de caja de arena para dispositivos Android</h1>
-<p>Si estás buscando un juego divertido y desafiante que combine elementos de sandbox, estrategia, elaboración y géneros de simulación, entonces es posible que desees probar Craft the World. Este juego te permite controlar una tribu de enanos en un mundo generado al azar lleno de peligros y tesoros. Puede explorar, crear, construir y luchar a través de diferentes biomas y niveles, mientras desbloquea nuevas tecnologías y artículos. También puedes jugar con tus amigos y otros jugadores en línea en modos multijugador, o crear tus propios mundos personalizados y compartirlos con otros. En este artículo, te daremos una visión general de Craft the World, sus principales características, jugabilidad, multijugador, comparación con otros juegos similares, y algunos consejos y trucos para principiantes. </p>
- <h2>¿Qué es Craft The World? </h2>
-<p>Craft The World es un juego de estrategia sandbox único desarrollado por Dekovir Entertainment y publicado por Black Maple Games. Fue lanzado para PC en 2014, y posteriormente portado a dispositivos iOS y Android. El juego está inspirado en juegos como Dungeon Keeper, Terraria y Dwarf Fortress. Tiene un estilo artístico pixelado y una banda sonora alegre que crean un contraste con el oscuro y peligroso mundo en el que tienes que sobrevivir. </p>
-<h2>crear el mundo android apk descargar</h2><br /><p><b><b>Download</b> &bull;&bull;&bull; <a href="https://bltlly.com/2v6Laf">https://bltlly.com/2v6Laf</a></b></p><br /><br />
- <h3>¿Cuáles son las principales características de Craft The World? </h3>
-<p>Algunas de las características principales de Craft The World son:</p>
-<ul>
-<li>SIMULACIÓN DE DIOS: Controlas una tribu de enanos dándoles órdenes de cavar, atacar, construir y más. Tienes que proporcionarles comida, ropa y magia al luchar contra otras criaturas. Empiezas con un enano y ganas más a medida que subes de nivel. </li>
-<li>SANDBOX GAME: Cada nivel de juego tiene muchas capas de tierra para explorar, desde el cielo hasta la lava. El nivel se genera aleatoriamente como una isla con límites naturales. Los mundos difieren en tamaño, humedad, temperatura, terreno, flora y fauna. También hay salones ocultos y habitaciones con tesoro. </li>
-
-<li>RTS: Tienes que defender tu base de oleadas de enemigos que atacan por la noche o durante eventos especiales. Puedes usar trampas, torretas, paredes, puertas, hechizos y las habilidades de tus enanos para defenderlos. También puedes asaltar bases enemigas para obtener botín. </li>
-</ul>
- <h3>¿Cómo descargar e instalar Craft The World en dispositivos Android? </h3>
-<p>Para descargar e instalar Craft The World en tu dispositivo Android, debes seguir estos pasos:</p>
-<ol>
-<li>Ir a [1](https://apkpure.com/craft-the-world/com.dekovir.CraftTheWorld) o [2](https://play.google.com/store/apps/details?id=com.dekovir.CraftTheWorld) en el navegador de su dispositivo. </li>
-<li>Toque en "Descargar APK" o "Instalar" botón. </li>
-<li>Espera a que termine la descarga. </li>
-<li>Abra el archivo descargado o vaya a la configuración de su dispositivo > - Seguridad > Fuentes desconocidas > Permitir la instalación de aplicaciones de fuentes desconocidas. </li>
-<li>Toque en "Instalar" y espere a que la instalación termine. </li>
-<li>Iniciar el juego y disfrutar! </li>
-</ol>
- <h2>¿Cómo se juega Craft The World? </h2>
-<p>Craft The World es un juego que combina diferentes géneros y mecánicas, por lo que puede tardar algún tiempo en acostumbrarse. Aquí hay algunos consejos básicos sobre cómo jugar el juego:</p>
- <h3>¿Cómo controlar una tribu de enanos en un mundo de caja de arena? </h3>
-<p>Puedes controlar a tus enanos tocando sobre ellos y seleccionando una acción del menú, como mover, cavar, construir, atacar, etc. También puedes arrastrar y soltar elementos de tu inventario a tus enanos o al entorno. También puedes usar los botones en la parte inferior de la pantalla para seleccionar todos los enanos, pausar el juego, acelerar el juego o acceder al menú. Puedes acercar y alejar la pantalla, y girar la cámara deslizando la pantalla. </p>
- <h3>¿Cómo explorar, crear, construir y luchar? </h3>
-
- <h3>¿Cómo avanzar a través del árbol de tecnología y desbloquear nuevos elementos? </h3>
-<p>Puedes progresar a través del árbol de tecnología creando elementos relacionados con una tecnología. Por ejemplo, si quieres desbloquear la tecnología agrícola, necesitas crear una azada de madera, un cubo de madera, una valla de madera, etc. Cada tecnología tiene una barra de progreso que te muestra cuánto has creado. Cuando llenas la barra, desbloqueas nuevas recetas y artículos. También puedes encontrar libros en cofres o tiendas que te dan acceso instantáneo a una tecnología. </p>
-<p></p>
- <h2>¿Cómo jugar multijugador en Craft The World? </h2>
-<p>Craft The World también tiene un modo multijugador que te permite jugar con tus amigos y otros jugadores online. Aquí hay algunas cosas que necesitas saber sobre el modo multijugador:</p>
- <h3>¿Cómo jugar con amigos y otros jugadores online? </h3>
-<p>Puedes jugar con amigos y otros jugadores online usando el menú multijugador en la pantalla principal. Puedes elegir entre el modo de supervivencia o el modo creativo. En el modo de supervivencia, tienes que sobrevivir contra los enemigos y el hambre con recursos limitados. En el modo creativo, tienes recursos ilimitados y no tienes enemigos. También puedes elegir entre el modo cooperativo o el modo competitivo. En el modo cooperativo, trabajarás junto con otros jugadores para lograr un objetivo común. En el modo competitivo, compites contra otros jugadores por recursos y territorio. </p>
- <h3>¿Cuáles son las diferencias entre la supervivencia y los modos creativos? </h3>
-<p>Las diferencias entre los modos de supervivencia y creativo son:</p>
-<tabla>
-<tr><th>Modo de supervivencia</th><th>Modo creativo</th></tr>
-<tr><td>Tiene recursos y espacio de inventario limitados. </td><td>Tiene recursos y espacio de inventario ilimitados. </td></tr>
-<tr><td>Tienes que comer y beber agua para sobrevivir. </td><td>No tienes que comer ni beber agua. </td></tr>
-<tr><td>Tienes que lidiar con enemigos y peligros ambientales. </td><td>No tienes enemigos ni peligros ambientales. </td></tr>
-
-<tr><td>Tienes un sistema de niveles que determina tu número de enanos y hechizos. </td><td>No tienes sistema de niveles y puedes generar tantos enanos y hechizos como quieras. </td></tr>
-</tabla>
- <h3>¿Cómo personalizar tus propios mundos y compartirlos con otros? </h3>
-<p>Puedes personalizar tus propios mundos y compartirlos con otros usando el modo editor de mundo. Puedes acceder a este modo pulsando el botón "Crear mundo" en el menú multijugador. Puedes elegir el tamaño, bioma, terreno, flora, fauna, recursos, estructuras, enemigos, eventos y escenarios de tu mundo. También puedes colocar bloques, objetos, criaturas, trampas, portales, etc. donde quieras. Puede guardar su mundo como un archivo y compartirlo con otros a través de correo electrónico o redes sociales. También puede descargar mundos de otros jugadores de [3](https://craft -the-world.com/worlds) o [4](https://steamcommunity.com/app/248390/workshop/) y reproducirlos en su dispositivo. </p>
- <h2>¿Cómo se compara Craft The World con otros juegos similares? </h2>
-<p>Craft The World es un juego que tiene muchas similitudes con otros juegos en el sandbox, estrategia, elaboración y géneros de simulación. Sin embargo, también tiene algunas características y aspectos únicos que lo hacen destacar del resto. Aquí hay algunas comparaciones entre Craft The World y otros juegos similares:</p>
- <h3>¿Cómo se compara Craft The World con Terraria? </h3>
-<p>Ambos juegos son juegos 2D sandbox que te permiten explorar, crear, construir y luchar en un mundo generado al azar. Sin embargo, hay algunas diferencias entre ellos:</p>
-<ul>
-<li>Terraria se centra más en el combate y la exploración, mientras que Craft The World se centra más en la estrategia y la simulación. </li>
-<li>Terraria tiene más variedad y profundidad en términos de objetos, enemigos, biomas, jefes, eventos, etc., mientras que Craft The World tiene más simplicidad y accesibilidad en términos de jugabilidad e interfaz. </li>
-<li>Terraria tiene un mundo más dinámico e interactivo, mientras que Craft The World tiene un mundo más estático y basado en la red. </li>
-
-</ul>
- <h3>¿Cómo se compara Craft The World con Minecraft? </h3>
-<p>Ambos juegos son juegos 3D sandbox que te permiten crear y modificar el mundo con bloques. Sin embargo, hay algunas diferencias entre ellos:</p>
-<ul>
-<li>Minecraft es más abierto y creativo, mientras que Craft The World es más estructurado y orientado a objetivos. </li>
-<li>Minecraft tiene más libertad y flexibilidad en términos de construcción y elaboración, mientras que Craft The World tiene más limitaciones y restricciones en términos de recursos y recetas. </li>
-<li>Minecraft tiene un estilo gráfico más realista y minimalista, mientras que Craft The World tiene un estilo gráfico más caricaturesco y detallado. </li>
-<li>Minecraft tiene una perspectiva más inmersiva y en primera persona, mientras que Craft The World tiene una perspectiva más separada y en tercera persona. </li>
-</ul>
- <h3>¿Cómo se compara Craft The World con Dwarf Fortress? </h3>
-<p>Ambos juegos son juegos de simulación complejos y desafiantes que te permiten gestionar una colonia de enanos en un mundo generado por procedimientos. Sin embargo, hay algunas diferencias entre ellos:</p>
-<ul>
-<li>Dwarf Fortress es más hardcore y realista, mientras que Craft The World es más casual y basado en la fantasía. </li>
-<li>Dwarf Fortress tiene más profundidad y detalle en términos de mecánica, sistemas, características, etc., mientras que Craft The World tiene más simplicidad y claridad en términos de jugabilidad e interfaz. </li>
-<li>Dwarf Fortress tiene un estilo gráfico más abstracto y basado en ASCII, mientras que Craft The World tiene un estilo gráfico más concreto y basado en píxeles. </li>
-<li>Dwarf Fortress tiene una jugabilidad más emergente e impredecible, mientras que Craft The World tiene una jugabilidad más predecible. </li>
-</ul>
- <h2>Conclusión</h2>
-
- <p>En mi opinión, Craft The World es un juego divertido y desafiante que ofrece mucho valor de repetición y variedad. Me gusta la mezcla de géneros y mecánicas que hacen que el juego sea interesante y atractivo. También me gusta el hecho de que el juego se actualiza constantemente con nuevos contenidos y características. Creo que cualquiera que le guste el sandbox, la estrategia, la elaboración o los juegos de simulación disfrutaría jugando Craft The World.</p>
- <p>Si estás interesado en jugar Craft The World en tu dispositivo Android, aquí hay algunos consejos y trucos para principiantes:</p>
-<ul>
-<li>Comience con el modo tutorial para aprender los fundamentos del juego. </li>
-<li> Utilice el botón de ayuda en la parte superior derecha de la pantalla para acceder a la wiki, el foro y la guía. </li>
-<li>Planifica con anticipación y prioriza tus tareas y objetivos. </li>
-<li>Mantén a tus enanos felices y saludables proporcionándoles comida, agua, camas, luz, etc.</li>
-<li>Utilice los botones de pausa y avance rápido para administrar su tiempo y recursos. </li>
-<li>Guarda tu juego con frecuencia y usa múltiples ranuras. </li>
-<li>Experimenta con diferentes elementos, bloques, hechizos y estrategias. </li>
-¡Diviértete y sé creativo! </li>
-</ul>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Craft The World:</p>
- <h3>¿Cuáles son los requisitos del sistema para Craft The World en dispositivos Android? </h3>
-<p>Los requisitos del sistema para Craft The World en dispositivos Android son:</p>
-<ul>
-<li>Android 4.4 o superior</li>
-<li>1 GB de RAM o más</li>
-<li>300 MB de espacio de almacenamiento libre o más</li>
-<li>Una conexión a Internet estable para el modo multijugador</li>
-</ul>
- <h3>¿Cuánto cuesta Craft The World en dispositivos Android? </h3>
-<p>Arte El Mundo cuesta $4.99 en dispositivos Android. También puedes comprar contenido y funciones adicionales como compras en la aplicación, como DLC, skins, monedas, etc.</p>
- <h3>¿Craft The World es un juego gratuito? </h3>
-
- <h3>¿Craft The World se actualiza regularmente con nuevos contenidos y características? </h3>
-<p>Sí, Craft The World se actualiza regularmente con nuevos contenidos y características. Los desarrolladores trabajan constantemente para mejorar el juego y añadir nuevos biomas, objetos, enemigos, modos, etc. Puedes consultar el historial de actualizaciones y la hoja de ruta en [5](https://steamcommunity.com/app/248390/announcements/) o [6](https:/craft-the-world.com/news). </p>
- <h3>¿Dónde puedo encontrar más información y guías sobre Craft The World? </h3>
-<p>Puedes encontrar más información y guías sobre Craft The World en estos sitios web:</p>
-<ul>
-<li>[7](https://crafttheworld.gamepedia.com/Craft_The_World_Wiki) - La wiki oficial del juego. </li>
-<li>[8](https://steamcommunity.com/app/248390/guides/) - Las guías de la comunidad de Steam del juego. </li>
-<li>[9](https://www.youtube.com/results?search_query=craftǐthe,) - Los vídeos de YouTube del juego. </li>
-</ul>
- <p>Espero que hayas disfrutado este artículo sobre Craft The World. Si tienes alguna pregunta o comentario, por favor deja un comentario a continuación. ¡Gracias por leer! </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/docs/action.py

@@ -1,197 +0,0 @@
-# Copyright 2015 Amazon.com, Inc. or its affiliates. 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. A copy of
-# the License is located at
-#
-# https://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-import os
-
-from botocore import xform_name
-from botocore.docs.bcdoc.restdoc import DocumentStructure
-from botocore.docs.method import (
-    document_custom_method,
-    document_model_driven_method,
-)
-from botocore.model import OperationModel
-from botocore.utils import get_service_module_name
-
-from boto3.docs.base import NestedDocumenter
-from boto3.docs.method import document_model_driven_resource_method
-from boto3.docs.utils import (
-    add_resource_type_overview,
-    get_resource_ignore_params,
-    get_resource_public_actions,
-)
-
-
-class ActionDocumenter(NestedDocumenter):
-    def document_actions(self, section):
-        modeled_actions_list = self._resource_model.actions
-        modeled_actions = {}
-        for modeled_action in modeled_actions_list:
-            modeled_actions[modeled_action.name] = modeled_action
-        resource_actions = get_resource_public_actions(
-            self._resource.__class__
-        )
-        self.member_map['actions'] = sorted(resource_actions)
-        add_resource_type_overview(
-            section=section,
-            resource_type='Actions',
-            description=(
-                'Actions call operations on resources.  They may '
-                'automatically handle the passing in of arguments set '
-                'from identifiers and some attributes.'
-            ),
-            intro_link='actions_intro',
-        )
-
-        for action_name in sorted(resource_actions):
-            # Create a new DocumentStructure for each action and add contents.
-            action_doc = DocumentStructure(action_name, target='html')
-            breadcrumb_section = action_doc.add_new_section('breadcrumb')
-            breadcrumb_section.style.ref(self._resource_class_name, 'index')
-            breadcrumb_section.write(f' / Action / {action_name}')
-            action_doc.add_title_section(action_name)
-            action_section = action_doc.add_new_section(
-                action_name,
-                context={'qualifier': f'{self.class_name}.'},
-            )
-            if action_name in ['load', 'reload'] and self._resource_model.load:
-                document_load_reload_action(
-                    section=action_section,
-                    action_name=action_name,
-                    resource_name=self._resource_name,
-                    event_emitter=self._resource.meta.client.meta.events,
-                    load_model=self._resource_model.load,
-                    service_model=self._service_model,
-                )
-            elif action_name in modeled_actions:
-                document_action(
-                    section=action_section,
-                    resource_name=self._resource_name,
-                    event_emitter=self._resource.meta.client.meta.events,
-                    action_model=modeled_actions[action_name],
-                    service_model=self._service_model,
-                )
-            else:
-                document_custom_method(
-                    action_section, action_name, resource_actions[action_name]
-                )
-            # Write actions in individual/nested files.
-            # Path: <root>/reference/services/<service>/<resource_name>/<action_name>.rst
-            actions_dir_path = os.path.join(
-                self._root_docs_path,
-                f'{self._service_name}',
-                f'{self._resource_sub_path}',
-            )
-            action_doc.write_to_file(actions_dir_path, action_name)
-
-
-def document_action(
-    section,
-    resource_name,
-    event_emitter,
-    action_model,
-    service_model,
-    include_signature=True,
-):
-    """Documents a resource action
-
-    :param section: The section to write to
-
-    :param resource_name: The name of the resource
-
-    :param event_emitter: The event emitter to use to emit events
-
-    :param action_model: The model of the action
-
-    :param service_model: The model of the service
-
-    :param include_signature: Whether or not to include the signature.
-        It is useful for generating docstrings.
-    """
-    operation_model = service_model.operation_model(
-        action_model.request.operation
-    )
-    ignore_params = get_resource_ignore_params(action_model.request.params)
-
-    example_return_value = 'response'
-    if action_model.resource:
-        example_return_value = xform_name(action_model.resource.type)
-    example_resource_name = xform_name(resource_name)
-    if service_model.service_name == resource_name:
-        example_resource_name = resource_name
-    example_prefix = '{} = {}.{}'.format(
-        example_return_value, example_resource_name, action_model.name
-    )
-    full_action_name = (
-        f"{section.context.get('qualifier', '')}{action_model.name}"
-    )
-    document_model_driven_resource_method(
-        section=section,
-        method_name=full_action_name,
-        operation_model=operation_model,
-        event_emitter=event_emitter,
-        method_description=operation_model.documentation,
-        example_prefix=example_prefix,
-        exclude_input=ignore_params,
-        resource_action_model=action_model,
-        include_signature=include_signature,
-    )
-
-
-def document_load_reload_action(
-    section,
-    action_name,
-    resource_name,
-    event_emitter,
-    load_model,
-    service_model,
-    include_signature=True,
-):
-    """Documents the resource load action
-
-    :param section: The section to write to
-
-    :param action_name: The name of the loading action should be load or reload
-
-    :param resource_name: The name of the resource
-
-    :param event_emitter: The event emitter to use to emit events
-
-    :param load_model: The model of the load action
-
-    :param service_model: The model of the service
-
-    :param include_signature: Whether or not to include the signature.
-        It is useful for generating docstrings.
-    """
-    description = (
-        'Calls :py:meth:`{}.Client.{}` to update the attributes of the '
-        '{} resource. Note that the load and reload methods are '
-        'the same method and can be used interchangeably.'.format(
-            get_service_module_name(service_model),
-            xform_name(load_model.request.operation),
-            resource_name,
-        )
-    )
-    example_resource_name = xform_name(resource_name)
-    if service_model.service_name == resource_name:
-        example_resource_name = resource_name
-    example_prefix = f'{example_resource_name}.{action_name}'
-    full_action_name = f"{section.context.get('qualifier', '')}{action_name}"
-    document_model_driven_method(
-        section=section,
-        method_name=full_action_name,
-        operation_model=OperationModel({}, service_model),
-        event_emitter=event_emitter,
-        method_description=description,
-        example_prefix=example_prefix,
-        include_signature=include_signature,
-    )

spaces/Bravefe/Artist_Classification/app.py

@@ -1,17 +0,0 @@
-import gradio as gr
-import pickle
-from fastai.learner import load_learner
-
-learn = load_learner('/home/user/app/ai_builder1.1.pkl')
-learn1 = load_learner('/home/user/app/export.pkl')
-
-def greet(image):
-    pred, pred_idx, probs = learn.predict(image)
-    pred2, pred_idx2, probs2 = learn1.predict(image)
-    float = probs[pred_idx]*100
-    float2 = probs2[pred_idx2]*100
-    txt = f'({pred2} {float2:.02f}%) Artist: {pred} Probability: {float:.02f}%'
-    return txt
-
-iface = gr.Interface(fn=greet, inputs="image", outputs="label")
-iface.launch()

spaces/CALM/Dashboard/streamlit_observable/frontend/build/static/js/runtime-main.11ec9aca.js

@@ -1,2 +0,0 @@
-!function(e){function t(t){for(var n,l,a=t[0],p=t[1],i=t[2],c=0,s=[];c<a.length;c++)l=a[c],Object.prototype.hasOwnProperty.call(o,l)&&o[l]&&s.push(o[l][0]),o[l]=0;for(n in p)Object.prototype.hasOwnProperty.call(p,n)&&(e[n]=p[n]);for(f&&f(t);s.length;)s.shift()();return u.push.apply(u,i||[]),r()}function r(){for(var e,t=0;t<u.length;t++){for(var r=u[t],n=!0,a=1;a<r.length;a++){var p=r[a];0!==o[p]&&(n=!1)}n&&(u.splice(t--,1),e=l(l.s=r[0]))}return e}var n={},o={1:0},u=[];function l(t){if(n[t])return n[t].exports;var r=n[t]={i:t,l:!1,exports:{}};return e[t].call(r.exports,r,r.exports,l),r.l=!0,r.exports}l.m=e,l.c=n,l.d=function(e,t,r){l.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:r})},l.r=function(e){"undefined"!==typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},l.t=function(e,t){if(1&t&&(e=l(e)),8&t)return e;if(4&t&&"object"===typeof e&&e&&e.__esModule)return e;var r=Object.create(null);if(l.r(r),Object.defineProperty(r,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var n in e)l.d(r,n,function(t){return e[t]}.bind(null,n));return r},l.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return l.d(t,"a",t),t},l.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},l.p="./";var a=this.webpackJsonpstreamlit_component_template=this.webpackJsonpstreamlit_component_template||[],p=a.push.bind(a);a.push=t,a=a.slice();for(var i=0;i<a.length;i++)t(a[i]);var f=p;r()}([]);
-//# sourceMappingURL=runtime-main.11ec9aca.js.map

spaces/CForGETaass/vits-uma-genshin-honkai/Docker/Dockerfile

@@ -1,12 +0,0 @@
-FROM python:3.9-bullseye
-VOLUME ["/app"]
-WORKDIR /app
-# Set apt to Chinese mirror
-RUN sed -i 's/deb.debian.org/mirrors.ustc.edu.cn/g' /etc/apt/sources.list
-RUN apt-get update && apt-get -y install cmake git
-RUN git clone https://huggingface.co/spaces/ikechan8370/vits-uma-genshin-honkai
-WORKDIR /app/vits-uma-genshin-honkai
-RUN sed -i "s/\.launch()/\.launch(server_name=\"0.0.0.0\")/" /app/vits-uma-genshin-honkai/app.py
-ADD vits.sh /app/vits.sh
-EXPOSE 7860
-ENTRYPOINT [ "/app/vits.sh" ]

spaces/CVPR/LIVE/pybind11/include/pybind11/stl_bind.h

@@ -1,661 +0,0 @@
-/*
-    pybind11/std_bind.h: Binding generators for STL data types
-
-    Copyright (c) 2016 Sergey Lyskov and Wenzel Jakob
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-#pragma once
-
-#include "detail/common.h"
-#include "operators.h"
-
-#include <algorithm>
-#include <sstream>
-
-PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
-PYBIND11_NAMESPACE_BEGIN(detail)
-
-/* SFINAE helper class used by 'is_comparable */
-template <typename T>  struct container_traits {
-    template <typename T2> static std::true_type test_comparable(decltype(std::declval<const T2 &>() == std::declval<const T2 &>())*);
-    template <typename T2> static std::false_type test_comparable(...);
-    template <typename T2> static std::true_type test_value(typename T2::value_type *);
-    template <typename T2> static std::false_type test_value(...);
-    template <typename T2> static std::true_type test_pair(typename T2::first_type *, typename T2::second_type *);
-    template <typename T2> static std::false_type test_pair(...);
-
-    static constexpr const bool is_comparable = std::is_same<std::true_type, decltype(test_comparable<T>(nullptr))>::value;
-    static constexpr const bool is_pair = std::is_same<std::true_type, decltype(test_pair<T>(nullptr, nullptr))>::value;
-    static constexpr const bool is_vector = std::is_same<std::true_type, decltype(test_value<T>(nullptr))>::value;
-    static constexpr const bool is_element = !is_pair && !is_vector;
-};
-
-/* Default: is_comparable -> std::false_type */
-template <typename T, typename SFINAE = void>
-struct is_comparable : std::false_type { };
-
-/* For non-map data structures, check whether operator== can be instantiated */
-template <typename T>
-struct is_comparable<
-    T, enable_if_t<container_traits<T>::is_element &&
-                   container_traits<T>::is_comparable>>
-    : std::true_type { };
-
-/* For a vector/map data structure, recursively check the value type (which is std::pair for maps) */
-template <typename T>
-struct is_comparable<T, enable_if_t<container_traits<T>::is_vector>> {
-    static constexpr const bool value =
-        is_comparable<typename T::value_type>::value;
-};
-
-/* For pairs, recursively check the two data types */
-template <typename T>
-struct is_comparable<T, enable_if_t<container_traits<T>::is_pair>> {
-    static constexpr const bool value =
-        is_comparable<typename T::first_type>::value &&
-        is_comparable<typename T::second_type>::value;
-};
-
-/* Fallback functions */
-template <typename, typename, typename... Args> void vector_if_copy_constructible(const Args &...) { }
-template <typename, typename, typename... Args> void vector_if_equal_operator(const Args &...) { }
-template <typename, typename, typename... Args> void vector_if_insertion_operator(const Args &...) { }
-template <typename, typename, typename... Args> void vector_modifiers(const Args &...) { }
-
-template<typename Vector, typename Class_>
-void vector_if_copy_constructible(enable_if_t<is_copy_constructible<Vector>::value, Class_> &cl) {
-    cl.def(init<const Vector &>(), "Copy constructor");
-}
-
-template<typename Vector, typename Class_>
-void vector_if_equal_operator(enable_if_t<is_comparable<Vector>::value, Class_> &cl) {
-    using T = typename Vector::value_type;
-
-    cl.def(self == self);
-    cl.def(self != self);
-
-    cl.def("count",
-        [](const Vector &v, const T &x) {
-            return std::count(v.begin(), v.end(), x);
-        },
-        arg("x"),
-        "Return the number of times ``x`` appears in the list"
-    );
-
-    cl.def("remove", [](Vector &v, const T &x) {
-            auto p = std::find(v.begin(), v.end(), x);
-            if (p != v.end())
-                v.erase(p);
-            else
-                throw value_error();
-        },
-        arg("x"),
-        "Remove the first item from the list whose value is x. "
-        "It is an error if there is no such item."
-    );
-
-    cl.def("__contains__",
-        [](const Vector &v, const T &x) {
-            return std::find(v.begin(), v.end(), x) != v.end();
-        },
-        arg("x"),
-        "Return true the container contains ``x``"
-    );
-}
-
-// Vector modifiers -- requires a copyable vector_type:
-// (Technically, some of these (pop and __delitem__) don't actually require copyability, but it seems
-// silly to allow deletion but not insertion, so include them here too.)
-template <typename Vector, typename Class_>
-void vector_modifiers(enable_if_t<is_copy_constructible<typename Vector::value_type>::value, Class_> &cl) {
-    using T = typename Vector::value_type;
-    using SizeType = typename Vector::size_type;
-    using DiffType = typename Vector::difference_type;
-
-    auto wrap_i = [](DiffType i, SizeType n) {
-        if (i < 0)
-            i += n;
-        if (i < 0 || (SizeType)i >= n)
-            throw index_error();
-        return i;
-    };
-
-    cl.def("append",
-           [](Vector &v, const T &value) { v.push_back(value); },
-           arg("x"),
-           "Add an item to the end of the list");
-
-    cl.def(init([](iterable it) {
-        auto v = std::unique_ptr<Vector>(new Vector());
-        v->reserve(len_hint(it));
-        for (handle h : it)
-           v->push_back(h.cast<T>());
-        return v.release();
-    }));
-
-    cl.def("clear",
-        [](Vector &v) {
-            v.clear();
-        },
-        "Clear the contents"
-    );
-
-    cl.def("extend",
-       [](Vector &v, const Vector &src) {
-           v.insert(v.end(), src.begin(), src.end());
-       },
-       arg("L"),
-       "Extend the list by appending all the items in the given list"
-    );
-
-    cl.def("extend",
-       [](Vector &v, iterable it) {
-           const size_t old_size = v.size();
-           v.reserve(old_size + len_hint(it));
-           try {
-               for (handle h : it) {
-                   v.push_back(h.cast<T>());
-               }
-           } catch (const cast_error &) {
-               v.erase(v.begin() + static_cast<typename Vector::difference_type>(old_size), v.end());
-               try {
-                   v.shrink_to_fit();
-               } catch (const std::exception &) {
-                   // Do nothing
-               }
-               throw;
-           }
-       },
-       arg("L"),
-       "Extend the list by appending all the items in the given list"
-    );
-
-    cl.def("insert",
-        [](Vector &v, DiffType i, const T &x) {
-            // Can't use wrap_i; i == v.size() is OK
-            if (i < 0)
-                i += v.size();
-            if (i < 0 || (SizeType)i > v.size())
-                throw index_error();
-            v.insert(v.begin() + i, x);
-        },
-        arg("i") , arg("x"),
-        "Insert an item at a given position."
-    );
-
-    cl.def("pop",
-        [](Vector &v) {
-            if (v.empty())
-                throw index_error();
-            T t = v.back();
-            v.pop_back();
-            return t;
-        },
-        "Remove and return the last item"
-    );
-
-    cl.def("pop",
-        [wrap_i](Vector &v, DiffType i) {
-            i = wrap_i(i, v.size());
-            T t = v[(SizeType) i];
-            v.erase(v.begin() + i);
-            return t;
-        },
-        arg("i"),
-        "Remove and return the item at index ``i``"
-    );
-
-    cl.def("__setitem__",
-        [wrap_i](Vector &v, DiffType i, const T &t) {
-            i = wrap_i(i, v.size());
-            v[(SizeType)i] = t;
-        }
-    );
-
-    /// Slicing protocol
-    cl.def("__getitem__",
-        [](const Vector &v, slice slice) -> Vector * {
-            size_t start, stop, step, slicelength;
-
-            if (!slice.compute(v.size(), &start, &stop, &step, &slicelength))
-                throw error_already_set();
-
-            Vector *seq = new Vector();
-            seq->reserve((size_t) slicelength);
-
-            for (size_t i=0; i<slicelength; ++i) {
-                seq->push_back(v[start]);
-                start += step;
-            }
-            return seq;
-        },
-        arg("s"),
-        "Retrieve list elements using a slice object"
-    );
-
-    cl.def("__setitem__",
-        [](Vector &v, slice slice,  const Vector &value) {
-            size_t start, stop, step, slicelength;
-            if (!slice.compute(v.size(), &start, &stop, &step, &slicelength))
-                throw error_already_set();
-
-            if (slicelength != value.size())
-                throw std::runtime_error("Left and right hand size of slice assignment have different sizes!");
-
-            for (size_t i=0; i<slicelength; ++i) {
-                v[start] = value[i];
-                start += step;
-            }
-        },
-        "Assign list elements using a slice object"
-    );
-
-    cl.def("__delitem__",
-        [wrap_i](Vector &v, DiffType i) {
-            i = wrap_i(i, v.size());
-            v.erase(v.begin() + i);
-        },
-        "Delete the list elements at index ``i``"
-    );
-
-    cl.def("__delitem__",
-        [](Vector &v, slice slice) {
-            size_t start, stop, step, slicelength;
-
-            if (!slice.compute(v.size(), &start, &stop, &step, &slicelength))
-                throw error_already_set();
-
-            if (step == 1 && false) {
-                v.erase(v.begin() + (DiffType) start, v.begin() + DiffType(start + slicelength));
-            } else {
-                for (size_t i = 0; i < slicelength; ++i) {
-                    v.erase(v.begin() + DiffType(start));
-                    start += step - 1;
-                }
-            }
-        },
-        "Delete list elements using a slice object"
-    );
-
-}
-
-// If the type has an operator[] that doesn't return a reference (most notably std::vector<bool>),
-// we have to access by copying; otherwise we return by reference.
-template <typename Vector> using vector_needs_copy = negation<
-    std::is_same<decltype(std::declval<Vector>()[typename Vector::size_type()]), typename Vector::value_type &>>;
-
-// The usual case: access and iterate by reference
-template <typename Vector, typename Class_>
-void vector_accessor(enable_if_t<!vector_needs_copy<Vector>::value, Class_> &cl) {
-    using T = typename Vector::value_type;
-    using SizeType = typename Vector::size_type;
-    using DiffType = typename Vector::difference_type;
-    using ItType   = typename Vector::iterator;
-
-    auto wrap_i = [](DiffType i, SizeType n) {
-        if (i < 0)
-            i += n;
-        if (i < 0 || (SizeType)i >= n)
-            throw index_error();
-        return i;
-    };
-
-    cl.def("__getitem__",
-        [wrap_i](Vector &v, DiffType i) -> T & {
-            i = wrap_i(i, v.size());
-            return v[(SizeType)i];
-        },
-        return_value_policy::reference_internal // ref + keepalive
-    );
-
-    cl.def("__iter__",
-           [](Vector &v) {
-               return make_iterator<
-                   return_value_policy::reference_internal, ItType, ItType, T&>(
-                   v.begin(), v.end());
-           },
-           keep_alive<0, 1>() /* Essential: keep list alive while iterator exists */
-    );
-}
-
-// The case for special objects, like std::vector<bool>, that have to be returned-by-copy:
-template <typename Vector, typename Class_>
-void vector_accessor(enable_if_t<vector_needs_copy<Vector>::value, Class_> &cl) {
-    using T = typename Vector::value_type;
-    using SizeType = typename Vector::size_type;
-    using DiffType = typename Vector::difference_type;
-    using ItType   = typename Vector::iterator;
-    cl.def("__getitem__",
-        [](const Vector &v, DiffType i) -> T {
-            if (i < 0 && (i += v.size()) < 0)
-                throw index_error();
-            if ((SizeType)i >= v.size())
-                throw index_error();
-            return v[(SizeType)i];
-        }
-    );
-
-    cl.def("__iter__",
-           [](Vector &v) {
-               return make_iterator<
-                   return_value_policy::copy, ItType, ItType, T>(
-                   v.begin(), v.end());
-           },
-           keep_alive<0, 1>() /* Essential: keep list alive while iterator exists */
-    );
-}
-
-template <typename Vector, typename Class_> auto vector_if_insertion_operator(Class_ &cl, std::string const &name)
-    -> decltype(std::declval<std::ostream&>() << std::declval<typename Vector::value_type>(), void()) {
-    using size_type = typename Vector::size_type;
-
-    cl.def("__repr__",
-           [name](Vector &v) {
-            std::ostringstream s;
-            s << name << '[';
-            for (size_type i=0; i < v.size(); ++i) {
-                s << v[i];
-                if (i != v.size() - 1)
-                    s << ", ";
-            }
-            s << ']';
-            return s.str();
-        },
-        "Return the canonical string representation of this list."
-    );
-}
-
-// Provide the buffer interface for vectors if we have data() and we have a format for it
-// GCC seems to have "void std::vector<bool>::data()" - doing SFINAE on the existence of data() is insufficient, we need to check it returns an appropriate pointer
-template <typename Vector, typename = void>
-struct vector_has_data_and_format : std::false_type {};
-template <typename Vector>
-struct vector_has_data_and_format<Vector, enable_if_t<std::is_same<decltype(format_descriptor<typename Vector::value_type>::format(), std::declval<Vector>().data()), typename Vector::value_type*>::value>> : std::true_type {};
-
-// Add the buffer interface to a vector
-template <typename Vector, typename Class_, typename... Args>
-enable_if_t<detail::any_of<std::is_same<Args, buffer_protocol>...>::value>
-vector_buffer(Class_& cl) {
-    using T = typename Vector::value_type;
-
-    static_assert(vector_has_data_and_format<Vector>::value, "There is not an appropriate format descriptor for this vector");
-
-    // numpy.h declares this for arbitrary types, but it may raise an exception and crash hard at runtime if PYBIND11_NUMPY_DTYPE hasn't been called, so check here
-    format_descriptor<T>::format();
-
-    cl.def_buffer([](Vector& v) -> buffer_info {
-        return buffer_info(v.data(), static_cast<ssize_t>(sizeof(T)), format_descriptor<T>::format(), 1, {v.size()}, {sizeof(T)});
-    });
-
-    cl.def(init([](buffer buf) {
-        auto info = buf.request();
-        if (info.ndim != 1 || info.strides[0] % static_cast<ssize_t>(sizeof(T)))
-            throw type_error("Only valid 1D buffers can be copied to a vector");
-        if (!detail::compare_buffer_info<T>::compare(info) || (ssize_t) sizeof(T) != info.itemsize)
-            throw type_error("Format mismatch (Python: " + info.format + " C++: " + format_descriptor<T>::format() + ")");
-
-        T *p = static_cast<T*>(info.ptr);
-        ssize_t step = info.strides[0] / static_cast<ssize_t>(sizeof(T));
-        T *end = p + info.shape[0] * step;
-        if (step == 1) {
-            return Vector(p, end);
-        }
-        else {
-            Vector vec;
-            vec.reserve((size_t) info.shape[0]);
-            for (; p != end; p += step)
-                vec.push_back(*p);
-            return vec;
-        }
-    }));
-
-    return;
-}
-
-template <typename Vector, typename Class_, typename... Args>
-enable_if_t<!detail::any_of<std::is_same<Args, buffer_protocol>...>::value> vector_buffer(Class_&) {}
-
-PYBIND11_NAMESPACE_END(detail)
-
-//
-// std::vector
-//
-template <typename Vector, typename holder_type = std::unique_ptr<Vector>, typename... Args>
-class_<Vector, holder_type> bind_vector(handle scope, std::string const &name, Args&&... args) {
-    using Class_ = class_<Vector, holder_type>;
-
-    // If the value_type is unregistered (e.g. a converting type) or is itself registered
-    // module-local then make the vector binding module-local as well:
-    using vtype = typename Vector::value_type;
-    auto vtype_info = detail::get_type_info(typeid(vtype));
-    bool local = !vtype_info || vtype_info->module_local;
-
-    Class_ cl(scope, name.c_str(), pybind11::module_local(local), std::forward<Args>(args)...);
-
-    // Declare the buffer interface if a buffer_protocol() is passed in
-    detail::vector_buffer<Vector, Class_, Args...>(cl);
-
-    cl.def(init<>());
-
-    // Register copy constructor (if possible)
-    detail::vector_if_copy_constructible<Vector, Class_>(cl);
-
-    // Register comparison-related operators and functions (if possible)
-    detail::vector_if_equal_operator<Vector, Class_>(cl);
-
-    // Register stream insertion operator (if possible)
-    detail::vector_if_insertion_operator<Vector, Class_>(cl, name);
-
-    // Modifiers require copyable vector value type
-    detail::vector_modifiers<Vector, Class_>(cl);
-
-    // Accessor and iterator; return by value if copyable, otherwise we return by ref + keep-alive
-    detail::vector_accessor<Vector, Class_>(cl);
-
-    cl.def("__bool__",
-        [](const Vector &v) -> bool {
-            return !v.empty();
-        },
-        "Check whether the list is nonempty"
-    );
-
-    cl.def("__len__", &Vector::size);
-
-
-
-
-#if 0
-    // C++ style functions deprecated, leaving it here as an example
-    cl.def(init<size_type>());
-
-    cl.def("resize",
-         (void (Vector::*) (size_type count)) & Vector::resize,
-         "changes the number of elements stored");
-
-    cl.def("erase",
-        [](Vector &v, SizeType i) {
-        if (i >= v.size())
-            throw index_error();
-        v.erase(v.begin() + i);
-    }, "erases element at index ``i``");
-
-    cl.def("empty",         &Vector::empty,         "checks whether the container is empty");
-    cl.def("size",          &Vector::size,          "returns the number of elements");
-    cl.def("push_back", (void (Vector::*)(const T&)) &Vector::push_back, "adds an element to the end");
-    cl.def("pop_back",                               &Vector::pop_back, "removes the last element");
-
-    cl.def("max_size",      &Vector::max_size,      "returns the maximum possible number of elements");
-    cl.def("reserve",       &Vector::reserve,       "reserves storage");
-    cl.def("capacity",      &Vector::capacity,      "returns the number of elements that can be held in currently allocated storage");
-    cl.def("shrink_to_fit", &Vector::shrink_to_fit, "reduces memory usage by freeing unused memory");
-
-    cl.def("clear", &Vector::clear, "clears the contents");
-    cl.def("swap",   &Vector::swap, "swaps the contents");
-
-    cl.def("front", [](Vector &v) {
-        if (v.size()) return v.front();
-        else throw index_error();
-    }, "access the first element");
-
-    cl.def("back", [](Vector &v) {
-        if (v.size()) return v.back();
-        else throw index_error();
-    }, "access the last element ");
-
-#endif
-
-    return cl;
-}
-
-
-
-//
-// std::map, std::unordered_map
-//
-
-PYBIND11_NAMESPACE_BEGIN(detail)
-
-/* Fallback functions */
-template <typename, typename, typename... Args> void map_if_insertion_operator(const Args &...) { }
-template <typename, typename, typename... Args> void map_assignment(const Args &...) { }
-
-// Map assignment when copy-assignable: just copy the value
-template <typename Map, typename Class_>
-void map_assignment(enable_if_t<is_copy_assignable<typename Map::mapped_type>::value, Class_> &cl) {
-    using KeyType = typename Map::key_type;
-    using MappedType = typename Map::mapped_type;
-
-    cl.def("__setitem__",
-           [](Map &m, const KeyType &k, const MappedType &v) {
-               auto it = m.find(k);
-               if (it != m.end()) it->second = v;
-               else m.emplace(k, v);
-           }
-    );
-}
-
-// Not copy-assignable, but still copy-constructible: we can update the value by erasing and reinserting
-template<typename Map, typename Class_>
-void map_assignment(enable_if_t<
-        !is_copy_assignable<typename Map::mapped_type>::value &&
-        is_copy_constructible<typename Map::mapped_type>::value,
-        Class_> &cl) {
-    using KeyType = typename Map::key_type;
-    using MappedType = typename Map::mapped_type;
-
-    cl.def("__setitem__",
-           [](Map &m, const KeyType &k, const MappedType &v) {
-               // We can't use m[k] = v; because value type might not be default constructable
-               auto r = m.emplace(k, v);
-               if (!r.second) {
-                   // value type is not copy assignable so the only way to insert it is to erase it first...
-                   m.erase(r.first);
-                   m.emplace(k, v);
-               }
-           }
-    );
-}
-
-
-template <typename Map, typename Class_> auto map_if_insertion_operator(Class_ &cl, std::string const &name)
--> decltype(std::declval<std::ostream&>() << std::declval<typename Map::key_type>() << std::declval<typename Map::mapped_type>(), void()) {
-
-    cl.def("__repr__",
-           [name](Map &m) {
-            std::ostringstream s;
-            s << name << '{';
-            bool f = false;
-            for (auto const &kv : m) {
-                if (f)
-                    s << ", ";
-                s << kv.first << ": " << kv.second;
-                f = true;
-            }
-            s << '}';
-            return s.str();
-        },
-        "Return the canonical string representation of this map."
-    );
-}
-
-
-PYBIND11_NAMESPACE_END(detail)
-
-template <typename Map, typename holder_type = std::unique_ptr<Map>, typename... Args>
-class_<Map, holder_type> bind_map(handle scope, const std::string &name, Args&&... args) {
-    using KeyType = typename Map::key_type;
-    using MappedType = typename Map::mapped_type;
-    using Class_ = class_<Map, holder_type>;
-
-    // If either type is a non-module-local bound type then make the map binding non-local as well;
-    // otherwise (e.g. both types are either module-local or converting) the map will be
-    // module-local.
-    auto tinfo = detail::get_type_info(typeid(MappedType));
-    bool local = !tinfo || tinfo->module_local;
-    if (local) {
-        tinfo = detail::get_type_info(typeid(KeyType));
-        local = !tinfo || tinfo->module_local;
-    }
-
-    Class_ cl(scope, name.c_str(), pybind11::module_local(local), std::forward<Args>(args)...);
-
-    cl.def(init<>());
-
-    // Register stream insertion operator (if possible)
-    detail::map_if_insertion_operator<Map, Class_>(cl, name);
-
-    cl.def("__bool__",
-        [](const Map &m) -> bool { return !m.empty(); },
-        "Check whether the map is nonempty"
-    );
-
-    cl.def("__iter__",
-           [](Map &m) { return make_key_iterator(m.begin(), m.end()); },
-           keep_alive<0, 1>() /* Essential: keep list alive while iterator exists */
-    );
-
-    cl.def("items",
-           [](Map &m) { return make_iterator(m.begin(), m.end()); },
-           keep_alive<0, 1>() /* Essential: keep list alive while iterator exists */
-    );
-
-    cl.def("__getitem__",
-        [](Map &m, const KeyType &k) -> MappedType & {
-            auto it = m.find(k);
-            if (it == m.end())
-              throw key_error();
-           return it->second;
-        },
-        return_value_policy::reference_internal // ref + keepalive
-    );
-
-    cl.def("__contains__",
-        [](Map &m, const KeyType &k) -> bool {
-            auto it = m.find(k);
-            if (it == m.end())
-              return false;
-           return true;
-        }
-    );
-
-    // Assignment provided only if the type is copyable
-    detail::map_assignment<Map, Class_>(cl);
-
-    cl.def("__delitem__",
-           [](Map &m, const KeyType &k) {
-               auto it = m.find(k);
-               if (it == m.end())
-                   throw key_error();
-               m.erase(it);
-           }
-    );
-
-    cl.def("__len__", &Map::size);
-
-    return cl;
-}
-
-PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)

spaces/CVPR/LIVE/pydiffvg/save_svg.py

@@ -1,167 +0,0 @@
-import torch
-import pydiffvg
-import xml.etree.ElementTree as etree
-from xml.dom import minidom
-def prettify(elem):
-    """Return a pretty-printed XML string for the Element.
-    """
-    rough_string = etree.tostring(elem, 'utf-8')
-    reparsed = minidom.parseString(rough_string)
-    return reparsed.toprettyxml(indent="  ")
-def save_svg(filename, width, height, shapes, shape_groups, use_gamma = False, background=None):
-    root = etree.Element('svg')
-    root.set('version', '1.1')
-    root.set('xmlns', 'http://www.w3.org/2000/svg')
-    root.set('width', str(width))
-    root.set('height', str(height))
-    if background is not None:
-        print(f"setting background to {background}")
-        root.set('style', str(background))
-    defs = etree.SubElement(root, 'defs')
-    g = etree.SubElement(root, 'g')
-    if use_gamma:
-        f = etree.SubElement(defs, 'filter')
-        f.set('id', 'gamma')
-        f.set('x', '0')
-        f.set('y', '0')
-        f.set('width', '100%')
-        f.set('height', '100%')
-        gamma = etree.SubElement(f, 'feComponentTransfer')
-        gamma.set('color-interpolation-filters', 'sRGB')
-        feFuncR = etree.SubElement(gamma, 'feFuncR')
-        feFuncR.set('type', 'gamma')
-        feFuncR.set('amplitude', str(1))
-        feFuncR.set('exponent', str(1/2.2))
-        feFuncG = etree.SubElement(gamma, 'feFuncG')
-        feFuncG.set('type', 'gamma')
-        feFuncG.set('amplitude', str(1))
-        feFuncG.set('exponent', str(1/2.2))
-        feFuncB = etree.SubElement(gamma, 'feFuncB')
-        feFuncB.set('type', 'gamma')
-        feFuncB.set('amplitude', str(1))
-        feFuncB.set('exponent', str(1/2.2))
-        feFuncA = etree.SubElement(gamma, 'feFuncA')
-        feFuncA.set('type', 'gamma')
-        feFuncA.set('amplitude', str(1))
-        feFuncA.set('exponent', str(1/2.2))
-        g.set('style', 'filter:url(#gamma)')
-    # Store color
-    for i, shape_group in enumerate(shape_groups):
-        def add_color(shape_color, name):
-            if isinstance(shape_color, pydiffvg.LinearGradient):
-                lg = shape_color
-                color = etree.SubElement(defs, 'linearGradient')
-                color.set('id', name)
-                color.set('x1', str(lg.begin[0].item()/width))
-                color.set('y1', str(lg.begin[1].item()/height))
-                color.set('x2', str(lg.end[0].item()/width))
-                color.set('y2', str(lg.end[1].item()/height))
-                offsets = lg.offsets.data.cpu().numpy()
-                stop_colors = lg.stop_colors.data.cpu().numpy()
-                for j in range(offsets.shape[0]):
-                    stop = etree.SubElement(color, 'stop')
-                    stop.set('offset', str(offsets[j]))
-                    c = lg.stop_colors[j, :]
-                    stop.set('stop-color', 'rgb({}, {}, {})'.format(\
-                        int(255 * c[0]), int(255 * c[1]), int(255 * c[2])))
-                    stop.set('stop-opacity', '{}'.format(c[3]))
-            if isinstance(shape_color, pydiffvg.RadialGradient):
-                lg = shape_color
-                color = etree.SubElement(defs, 'radialGradient')
-                color.set('id', name)
-                color.set('cx', str(lg.center[0].item()/width))
-                color.set('cy', str(lg.center[1].item()/height))
-                # this only support width=height
-                color.set('r', str(lg.radius[0].item()/width))
-                offsets = lg.offsets.data.cpu().numpy()
-                stop_colors = lg.stop_colors.data.cpu().numpy()
-                for j in range(offsets.shape[0]):
-                    stop = etree.SubElement(color, 'stop')
-                    stop.set('offset', str(offsets[j]))
-                    c = lg.stop_colors[j, :]
-                    stop.set('stop-color', 'rgb({}, {}, {})'.format(\
-                        int(255 * c[0]), int(255 * c[1]), int(255 * c[2])))
-                    stop.set('stop-opacity', '{}'.format(c[3]))
-        if shape_group.fill_color is not None:
-            add_color(shape_group.fill_color, 'shape_{}_fill'.format(i))
-        if shape_group.stroke_color is not None:
-            add_color(shape_group.stroke_color, 'shape_{}_stroke'.format(i))
-    for i, shape_group in enumerate(shape_groups):
-        shape = shapes[shape_group.shape_ids[0]]
-        if isinstance(shape, pydiffvg.Circle):
-            shape_node = etree.SubElement(g, 'circle')
-            shape_node.set('r', str(shape.radius.item()))
-            shape_node.set('cx', str(shape.center[0].item()))
-            shape_node.set('cy', str(shape.center[1].item()))
-        elif isinstance(shape, pydiffvg.Polygon):
-            shape_node = etree.SubElement(g, 'polygon')
-            points = shape.points.data.cpu().numpy()
-            path_str = ''
-            for j in range(0, shape.points.shape[0]):
-                path_str += '{} {}'.format(points[j, 0], points[j, 1])
-                if j != shape.points.shape[0] - 1:
-                    path_str +=  ' '
-            shape_node.set('points', path_str)
-        elif isinstance(shape, pydiffvg.Path):
-            shape_node = etree.SubElement(g, 'path')
-            num_segments = shape.num_control_points.shape[0]
-            num_control_points = shape.num_control_points.data.cpu().numpy()
-            points = shape.points.data.cpu().numpy()
-            num_points = shape.points.shape[0]
-            path_str = 'M {} {}'.format(points[0, 0], points[0, 1])
-            point_id = 1
-            for j in range(0, num_segments):
-                if num_control_points[j] == 0:
-                    p = point_id % num_points
-                    path_str += ' L {} {}'.format(\
-                            points[p, 0], points[p, 1])
-                    point_id += 1
-                elif num_control_points[j] == 1:
-                    p1 = (point_id + 1) % num_points
-                    path_str += ' Q {} {} {} {}'.format(\
-                            points[point_id, 0], points[point_id, 1],
-                            points[p1, 0], points[p1, 1])
-                    point_id += 2
-                elif num_control_points[j] == 2:
-                    p2 = (point_id + 2) % num_points
-                    path_str += ' C {} {} {} {} {} {}'.format(\
-                            points[point_id, 0], points[point_id, 1],
-                            points[point_id + 1, 0], points[point_id + 1, 1],
-                            points[p2, 0], points[p2, 1])
-                    point_id += 3
-            shape_node.set('d', path_str)
-        elif isinstance(shape, pydiffvg.Rect):
-            shape_node = etree.SubElement(g, 'rect')
-            shape_node.set('x', str(shape.p_min[0].item()))
-            shape_node.set('y', str(shape.p_min[1].item()))
-            shape_node.set('width', str(shape.p_max[0].item() - shape.p_min[0].item()))
-            shape_node.set('height', str(shape.p_max[1].item() - shape.p_min[1].item()))
-        else:
-            assert(False)
-        shape_node.set('stroke-width', str(2 * shape.stroke_width.data.cpu().item()))
-        if shape_group.fill_color is not None:
-            if isinstance(shape_group.fill_color, pydiffvg.LinearGradient):
-                shape_node.set('fill', 'url(#shape_{}_fill)'.format(i))
-            elif isinstance(shape_group.fill_color, pydiffvg.RadialGradient):
-                shape_node.set('fill', 'url(#shape_{}_fill)'.format(i))
-            else:
-                c = shape_group.fill_color.data.cpu().numpy()
-                shape_node.set('fill', 'rgb({}, {}, {})'.format(\
-                    int(255 * c[0]), int(255 * c[1]), int(255 * c[2])))
-                shape_node.set('opacity', str(c[3]))
-        else:
-            shape_node.set('fill', 'none')
-        if shape_group.stroke_color is not None:
-            if isinstance(shape_group.stroke_color, pydiffvg.LinearGradient):
-                shape_node.set('stroke', 'url(#shape_{}_stroke)'.format(i))
-            elif isinstance(shape_group.stroke_color, pydiffvg.LinearGradient):
-                shape_node.set('stroke', 'url(#shape_{}_stroke)'.format(i))
-            else:
-                c = shape_group.stroke_color.data.cpu().numpy()
-                shape_node.set('stroke', 'rgb({}, {}, {})'.format(\
-                    int(255 * c[0]), int(255 * c[1]), int(255 * c[2])))
-                shape_node.set('stroke-opacity', str(c[3]))
-            shape_node.set('stroke-linecap', 'round')
-            shape_node.set('stroke-linejoin', 'round')
-    with open(filename, "w") as f:
-        f.write(prettify(root))

spaces/CVPR/WALT/mmdet/models/roi_heads/grid_roi_head.py

@@ -1,176 +0,0 @@
-import torch
-
-from mmdet.core import bbox2result, bbox2roi
-from ..builder import HEADS, build_head, build_roi_extractor
-from .standard_roi_head import StandardRoIHead
-
-
-@HEADS.register_module()
-class GridRoIHead(StandardRoIHead):
-    """Grid roi head for Grid R-CNN.
-
-    https://arxiv.org/abs/1811.12030
-    """
-
-    def __init__(self, grid_roi_extractor, grid_head, **kwargs):
-        assert grid_head is not None
-        super(GridRoIHead, self).__init__(**kwargs)
-        if grid_roi_extractor is not None:
-            self.grid_roi_extractor = build_roi_extractor(grid_roi_extractor)
-            self.share_roi_extractor = False
-        else:
-            self.share_roi_extractor = True
-            self.grid_roi_extractor = self.bbox_roi_extractor
-        self.grid_head = build_head(grid_head)
-
-    def init_weights(self, pretrained):
-        """Initialize the weights in head.
-
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-        super(GridRoIHead, self).init_weights(pretrained)
-        self.grid_head.init_weights()
-        if not self.share_roi_extractor:
-            self.grid_roi_extractor.init_weights()
-
-    def _random_jitter(self, sampling_results, img_metas, amplitude=0.15):
-        """Ramdom jitter positive proposals for training."""
-        for sampling_result, img_meta in zip(sampling_results, img_metas):
-            bboxes = sampling_result.pos_bboxes
-            random_offsets = bboxes.new_empty(bboxes.shape[0], 4).uniform_(
-                -amplitude, amplitude)
-            # before jittering
-            cxcy = (bboxes[:, 2:4] + bboxes[:, :2]) / 2
-            wh = (bboxes[:, 2:4] - bboxes[:, :2]).abs()
-            # after jittering
-            new_cxcy = cxcy + wh * random_offsets[:, :2]
-            new_wh = wh * (1 + random_offsets[:, 2:])
-            # xywh to xyxy
-            new_x1y1 = (new_cxcy - new_wh / 2)
-            new_x2y2 = (new_cxcy + new_wh / 2)
-            new_bboxes = torch.cat([new_x1y1, new_x2y2], dim=1)
-            # clip bboxes
-            max_shape = img_meta['img_shape']
-            if max_shape is not None:
-                new_bboxes[:, 0::2].clamp_(min=0, max=max_shape[1] - 1)
-                new_bboxes[:, 1::2].clamp_(min=0, max=max_shape[0] - 1)
-
-            sampling_result.pos_bboxes = new_bboxes
-        return sampling_results
-
-    def forward_dummy(self, x, proposals):
-        """Dummy forward function."""
-        # bbox head
-        outs = ()
-        rois = bbox2roi([proposals])
-        if self.with_bbox:
-            bbox_results = self._bbox_forward(x, rois)
-            outs = outs + (bbox_results['cls_score'],
-                           bbox_results['bbox_pred'])
-
-        # grid head
-        grid_rois = rois[:100]
-        grid_feats = self.grid_roi_extractor(
-            x[:self.grid_roi_extractor.num_inputs], grid_rois)
-        if self.with_shared_head:
-            grid_feats = self.shared_head(grid_feats)
-        grid_pred = self.grid_head(grid_feats)
-        outs = outs + (grid_pred, )
-
-        # mask head
-        if self.with_mask:
-            mask_rois = rois[:100]
-            mask_results = self._mask_forward(x, mask_rois)
-            outs = outs + (mask_results['mask_pred'], )
-        return outs
-
-    def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
-                            img_metas):
-        """Run forward function and calculate loss for box head in training."""
-        bbox_results = super(GridRoIHead,
-                             self)._bbox_forward_train(x, sampling_results,
-                                                       gt_bboxes, gt_labels,
-                                                       img_metas)
-
-        # Grid head forward and loss
-        sampling_results = self._random_jitter(sampling_results, img_metas)
-        pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
-
-        # GN in head does not support zero shape input
-        if pos_rois.shape[0] == 0:
-            return bbox_results
-
-        grid_feats = self.grid_roi_extractor(
-            x[:self.grid_roi_extractor.num_inputs], pos_rois)
-        if self.with_shared_head:
-            grid_feats = self.shared_head(grid_feats)
-        # Accelerate training
-        max_sample_num_grid = self.train_cfg.get('max_num_grid', 192)
-        sample_idx = torch.randperm(
-            grid_feats.shape[0])[:min(grid_feats.shape[0], max_sample_num_grid
-                                      )]
-        grid_feats = grid_feats[sample_idx]
-
-        grid_pred = self.grid_head(grid_feats)
-
-        grid_targets = self.grid_head.get_targets(sampling_results,
-                                                  self.train_cfg)
-        grid_targets = grid_targets[sample_idx]
-
-        loss_grid = self.grid_head.loss(grid_pred, grid_targets)
-
-        bbox_results['loss_bbox'].update(loss_grid)
-        return bbox_results
-
-    def simple_test(self,
-                    x,
-                    proposal_list,
-                    img_metas,
-                    proposals=None,
-                    rescale=False):
-        """Test without augmentation."""
-        assert self.with_bbox, 'Bbox head must be implemented.'
-
-        det_bboxes, det_labels = self.simple_test_bboxes(
-            x, img_metas, proposal_list, self.test_cfg, rescale=False)
-        # pack rois into bboxes
-        grid_rois = bbox2roi([det_bbox[:, :4] for det_bbox in det_bboxes])
-        if grid_rois.shape[0] != 0:
-            grid_feats = self.grid_roi_extractor(
-                x[:len(self.grid_roi_extractor.featmap_strides)], grid_rois)
-            self.grid_head.test_mode = True
-            grid_pred = self.grid_head(grid_feats)
-            # split batch grid head prediction back to each image
-            num_roi_per_img = tuple(len(det_bbox) for det_bbox in det_bboxes)
-            grid_pred = {
-                k: v.split(num_roi_per_img, 0)
-                for k, v in grid_pred.items()
-            }
-
-            # apply bbox post-processing to each image individually
-            bbox_results = []
-            num_imgs = len(det_bboxes)
-            for i in range(num_imgs):
-                if det_bboxes[i].shape[0] == 0:
-                    bbox_results.append(grid_rois.new_tensor([]))
-                else:
-                    det_bbox = self.grid_head.get_bboxes(
-                        det_bboxes[i], grid_pred['fused'][i], [img_metas[i]])
-                    if rescale:
-                        det_bbox[:, :4] /= img_metas[i]['scale_factor']
-                    bbox_results.append(
-                        bbox2result(det_bbox, det_labels[i],
-                                    self.bbox_head.num_classes))
-        else:
-            bbox_results = [
-                grid_rois.new_tensor([]) for _ in range(len(det_bboxes))
-            ]
-
-        if not self.with_mask:
-            return bbox_results
-        else:
-            segm_results = self.simple_test_mask(
-                x, img_metas, det_bboxes, det_labels, rescale=rescale)
-            return list(zip(bbox_results, segm_results))

spaces/CVPR/ml-talking-face/docs/article.md

@@ -1,23 +0,0 @@
-
-## Why learn a new language, when your model can learn it for you?
-
-<div style="max-width: 720px;max-height: 405px;margin: auto;">
-    <div style="float: none;clear: both;position: relative;padding-bottom: 56.25%;height: 0;width: 100%">
-        <iframe width="720" height="405" src="https://www.youtube.com/embed/toqdD1F_ZsU" title="YouTube video player" style="position: absolute;top: 0;left: 0;width: 100%;height: 100%;" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen>
-        </iframe>
-    </div>
-</div>
-
-### Abstract
-
-Recent studies in talking face generation have focused on building a train-once-use-everywhere model i.e. a model that will generalize from any source speech to any target identity. A number of works have already claimed this functionality and have added that their models will also generalize to any language. However, we show, using languages from different language families, that these models do not translate well when the training language and the testing language are sufficiently different. We reduce the scope of the problem to building a language-robust talking face generation system on seen identities i.e. the target identity is the same as the training identity. In this work, we introduce a talking face generation system that will generalize to different languages. We evaluate the efficacy of our system using a multilingual text-to-speech system. We also discuss the usage of joint text-to-speech system and the talking face generation system as a neural dubber system.
-
-[CVPR Open Access](https://openaccess.thecvf.com/content/CVPR2022/html/Song_Talking_Face_Generation_With_Multilingual_TTS_CVPR_2022_paper.html) [arXiv](https://arxiv.org/abs/2205.06421)
-
-### News
-
-(2022.08.18.) We got the CVPR Hugging Face prize! Thank you all and special thanks to AK([@akhaliq](https://huggingface.co/akhaliq)).
-
-<center>
-<img alt="we-got-huggingface-prize" src="https://github.com/deepkyu/ml-talking-face/blob/main/docs/we-got-huggingface-prize.jpeg?raw=true" width="50%" />
-</center>

spaces/CVPR/unicl-zero-shot-img-recog/model/text_encoder/transformer.py

@@ -1,194 +0,0 @@
-from collections import OrderedDict
-from typing import Tuple, Union
-import logging
-import os
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from timm.models.layers import DropPath, trunc_normal_
-
-from .registry import register_lang_encoder
-
-logger = logging.getLogger(__name__)
-
-class LayerNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-12):
-        """Construct a layernorm module in the TF style (epsilon inside the square root).
-        """
-        super(LayerNorm, self).__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.bias = nn.Parameter(torch.zeros(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, x):
-        pdtype = x.dtype
-        x = x.float()
-        u = x.mean(-1, keepdim=True)
-        s = (x - u).pow(2).mean(-1, keepdim=True)
-        x = (x - u) / torch.sqrt(s + self.variance_epsilon)
-        return self.weight * x.to(pdtype) + self.bias
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x: torch.Tensor):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(self,
-                 d_model: int,
-                 n_head: int,
-                 attn_mask: torch.Tensor = None,
-                 drop_path: float = 0.0):
-        super().__init__()
-
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ln_1 = LayerNorm(d_model)
-        self.mlp = nn.Sequential(OrderedDict([
-            ("c_fc", nn.Linear(d_model, d_model * 4)),
-            ("gelu", QuickGELU()),
-            ("c_proj", nn.Linear(d_model * 4, d_model))
-        ]))
-        self.ln_2 = LayerNorm(d_model)
-        self.attn_mask = attn_mask
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-
-    def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor = None):
-        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) \
-            if self.attn_mask is not None else None
-
-
-        return self.attn(
-            x, x, x,
-            key_padding_mask=key_padding_mask,
-            need_weights=False,
-            attn_mask=self.attn_mask
-        )[0]
-
-    def forward(self, x: torch.Tensor, key_padding_mask: torch.Tensor = None):
-        x = x + self.drop_path(self.attention(self.ln_1(x), key_padding_mask=key_padding_mask))
-        x = x + self.drop_path(self.mlp(self.ln_2(x)))
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(self,
-                 context_length: int,
-                 vocab_size: int,
-                 width: int,
-                 layers: int,
-                 heads: int,
-                 drop_path: float = 0.0,
-                 autogressive: bool =True):
-        super().__init__()
-
-        self.token_embedding = nn.Embedding(vocab_size, width)
-
-        self.context_length = context_length
-        self.positional_embedding = nn.Parameter(
-            torch.empty(self.context_length, width)
-        )
-
-        self.width = width
-        self.layers = layers
-        self.autogressive = autogressive
-        attn_mask = self.build_attention_mask() if autogressive else None
-        dpr = [x.item() for x in torch.linspace(0, drop_path, layers)]  # stochastic depth decay rule
-        self.resblocks = nn.ModuleList(
-            [
-                ResidualAttentionBlock(width, heads, attn_mask, dpr[i])
-                for i in range(layers)
-            ]
-        )
-
-        self.ln_final = LayerNorm(width)
-
-        trunc_normal_(self.positional_embedding, std=.02)
-        # nn.init.normal_(self.token_embedding, std=.02)
-        trunc_normal_(self.token_embedding.weight, std=.02)
-        self.apply(self._init_weights)
-
-    @property
-    def dim_out(self):
-        return self.width
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the vision tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.context_length, self.context_length)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    def _init_weights(self, m):
-        if isinstance(m, (nn.Linear, nn.Conv2d)):
-            logger.info('=> init weight of Linear/Conv2d from trunc norm')
-            trunc_normal_(m.weight, std=0.02)
-            if m.bias is not None:
-                logger.info('=> init bias of Linear/Conv2d to zeros')
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)):
-            nn.init.constant_(m.bias, 0)
-
-    def load_pretrained(self, pretrained='', pretrained_layers=[], verbose=True):
-        if os.path.isfile(pretrained):
-            pretrained_dict = torch.load(pretrained, map_location='cpu')
-            logging.info(f'=> loading pretrained model {pretrained}')
-            model_dict = self.state_dict()
-            pretrained_dict = {
-                k: v for k, v in pretrained_dict.items()
-                if k in model_dict.keys()
-            }
-            need_init_state_dict = {}
-            for k, v in pretrained_dict.items():
-                need_init = (
-                    k.split('.')[0] in pretrained_layers
-                    or pretrained_layers[0] == '*'
-                )
-                if need_init:
-                    if verbose:
-                        logging.info(f'=> init {k} from {pretrained}')
-
-                    need_init_state_dict[k] = v
-            self.load_state_dict(need_init_state_dict, strict=False)
-
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {
-            'positional_embedding',
-            'token_embedding',
-        }
-
-    def forward(self, input_ids, attention_mask=None):
-        key_padding_mask = (input_ids == 0) if not self.autogressive else None
-        x = self.token_embedding(input_ids)  # [batch_size, n_ctx, d_model]
-        x = x + self.positional_embedding
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        for block in self.resblocks:
-            x = block(x, key_padding_mask)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-
-        x = self.ln_final(x)
-
-        return {'last_hidden_state': x}
-
-
-@register_lang_encoder
-def lang_encoder(config_encoder, tokenizer, verbose, **kwargs):
-    transformer = Transformer(
-        context_length=config_encoder['CONTEXT_LENGTH'],
-        vocab_size=tokenizer.vocab_size,
-        width=config_encoder['WIDTH'],
-        layers=config_encoder['LAYERS'],
-        heads=config_encoder['HEADS'],
-        autogressive=config_encoder.get('AUTOGRESSIVE', True)
-    )
-
-    if config_encoder['LOAD_PRETRAINED']:
-        transformer.load_pretrained()
-
-    return transformer

spaces/CarlDennis/HYTTS/text/english.py

@@ -1,191 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-'''
-
-
-# Regular expression matching whitespace:
-
-
-import re
-import inflect
-from unidecode import unidecode
-import eng_to_ipa as ipa
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
-_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
-_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
-_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
-_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
-_number_re = re.compile(r'[0-9]+')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('mrs', 'misess'),
-    ('mr', 'mister'),
-    ('dr', 'doctor'),
-    ('st', 'saint'),
-    ('co', 'company'),
-    ('jr', 'junior'),
-    ('maj', 'major'),
-    ('gen', 'general'),
-    ('drs', 'doctors'),
-    ('rev', 'reverend'),
-    ('lt', 'lieutenant'),
-    ('hon', 'honorable'),
-    ('sgt', 'sergeant'),
-    ('capt', 'captain'),
-    ('esq', 'esquire'),
-    ('ltd', 'limited'),
-    ('col', 'colonel'),
-    ('ft', 'fort'),
-]]
-
-
-# List of (ipa, lazy ipa) pairs:
-_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('r', 'ɹ'),
-    ('æ', 'e'),
-    ('ɑ', 'a'),
-    ('ɔ', 'o'),
-    ('ð', 'z'),
-    ('θ', 's'),
-    ('ɛ', 'e'),
-    ('ɪ', 'i'),
-    ('ʊ', 'u'),
-    ('ʒ', 'ʥ'),
-    ('ʤ', 'ʥ'),
-    ('ˈ', '↓'),
-]]
-
-# List of (ipa, lazy ipa2) pairs:
-_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('r', 'ɹ'),
-    ('ð', 'z'),
-    ('θ', 's'),
-    ('ʒ', 'ʑ'),
-    ('ʤ', 'dʑ'),
-    ('ˈ', '↓'),
-    ('ɑ', 'a'),
-]]
-
-# List of (ipa, ipa2) pairs
-_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('r', 'ɹ'),
-    ('ʤ', 'dʒ'),
-    ('ʧ', 'tʃ'),
-    ('ɑ', 'a'),
-]]
-
-
-def expand_abbreviations(text):
-    for regex, replacement in _abbreviations:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def collapse_whitespace(text):
-    return re.sub(r'\s+', ' ', text)
-
-
-def _remove_commas(m):
-    return m.group(1).replace(',', '')
-
-
-def _expand_decimal_point(m):
-    return m.group(1).replace('.', ' point ')
-
-
-def _expand_dollars(m):
-    match = m.group(1)
-    parts = match.split('.')
-    if len(parts) > 2:
-        return match + ' dollars'  # Unexpected format
-    dollars = int(parts[0]) if parts[0] else 0
-    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
-    if dollars and cents:
-        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-        cent_unit = 'cent' if cents == 1 else 'cents'
-        return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
-    elif dollars:
-        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-        return '%s %s' % (dollars, dollar_unit)
-    elif cents:
-        cent_unit = 'cent' if cents == 1 else 'cents'
-        return '%s %s' % (cents, cent_unit)
-    else:
-        return 'zero dollars'
-
-
-def _expand_ordinal(m):
-    return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
-    num = int(m.group(0))
-    if num > 1000 and num < 3000:
-        if num == 2000:
-            return 'two thousand'
-        elif num > 2000 and num < 2010:
-            return 'two thousand ' + _inflect.number_to_words(num % 100)
-        elif num % 100 == 0:
-            return _inflect.number_to_words(num // 100) + ' hundred'
-        else:
-            return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
-    else:
-        return _inflect.number_to_words(num, andword='')
-
-
-def normalize_numbers(text):
-    text = re.sub(_comma_number_re, _remove_commas, text)
-    text = re.sub(_pounds_re, r'\1 pounds', text)
-    text = re.sub(_dollars_re, _expand_dollars, text)
-    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
-    text = re.sub(_ordinal_re, _expand_ordinal, text)
-    text = re.sub(_number_re, _expand_number, text)
-    return text
-
-
-def mark_dark_l(text):
-    return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
-
-
-def english_to_ipa(text):
-    text = unidecode(text).lower()
-    text = expand_abbreviations(text)
-    text = normalize_numbers(text)
-    phonemes = ipa.convert(text)
-    phonemes = collapse_whitespace(phonemes)
-    return phonemes
-
-
-def english_to_lazy_ipa(text):
-    text = english_to_ipa(text)
-    for regex, replacement in _lazy_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def english_to_ipa2(text):
-    text = english_to_ipa(text)
-    text = mark_dark_l(text)
-    for regex, replacement in _ipa_to_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text.replace('...', '…')
-
-
-def english_to_lazy_ipa2(text):
-    text = english_to_ipa(text)
-    for regex, replacement in _lazy_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text
-

spaces/ChallengeHub/Chinese-LangChain/corpus/zh_wikipedia/chinese_t2s.py

@@ -1,82 +0,0 @@
-#!/usr/bin/env python
-# -*- coding:utf-8 _*-
-"""
-@author:quincy qiang
-@license: Apache Licence
-@file: chinese_t2s.py.py
-@time: 2023/04/19
-@contact: [email protected]
-@software: PyCharm
-@description: coding..
-"""
-import sys
-import os
-import opencc
-from optparse import OptionParser
-
-
-class T2S(object):
-    def __init__(self, infile, outfile):
-        self.infile = infile
-        self.outfile = outfile
-        self.cc = opencc.OpenCC('t2s')
-        self.t_corpus = []
-        self.s_corpus = []
-        self.read(self.infile)
-        self.t2s()
-        self.write(self.s_corpus, self.outfile)
-
-    def read(self, path):
-        print(path)
-        if os.path.isfile(path) is False:
-            print("path is not a file")
-            exit()
-        now_line = 0
-        with open(path, encoding="UTF-8") as f:
-            for line in f:
-                now_line += 1
-                line = line.replace("\n", "").replace("\t", "")
-                self.t_corpus.append(line)
-        print("read finished")
-
-    def t2s(self):
-        now_line = 0
-        all_line = len(self.t_corpus)
-        for line in self.t_corpus:
-            now_line += 1
-            if now_line % 1000 == 0:
-                sys.stdout.write("\rhandling with the {} line, all {} lines.".format(now_line, all_line))
-            self.s_corpus.append(self.cc.convert(line))
-        sys.stdout.write("\rhandling with the {} line, all {} lines.".format(now_line, all_line))
-        print("\nhandling finished")
-
-    def write(self, list, path):
-        print("writing now......")
-        if os.path.exists(path):
-            os.remove(path)
-        file = open(path, encoding="UTF-8", mode="w")
-        for line in list:
-            file.writelines(line + "\n")
-        file.close()
-        print("writing finished.")
-
-
-if __name__ == "__main__":
-    print("Traditional Chinese to Simplified Chinese")
-    # input = "./wiki_zh_10.txt"
-    # output = "wiki_zh_10_sim.txt"
-    # T2S(infile=input, outfile=output)
-
-    parser = OptionParser()
-    parser.add_option("--input", dest="input", default="", help="traditional file")
-    parser.add_option("--output", dest="output", default="", help="simplified file")
-    (options, args) = parser.parse_args()
-
-    input = options.input
-    output = options.output
-
-    try:
-        T2S(infile=input, outfile=output)
-        print("All Finished.")
-    except Exception as err:
-        print(err)

spaces/ChatGPT-GAIA/GAIA-GPT/backupapp.py

@@ -1,209 +0,0 @@
-import gradio as gr
-import os 
-import json 
-import requests
-
-#Streaming endpoint 
-API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
-OPENAI_API_KEY= os.environ["HF_TOKEN"] # Add a token to this space .  Then copy it to the repository secret in this spaces settings panel.  os.environ reads from there.
-# Keys for Open AI ChatGPT API usage are created from here: https://platform.openai.com/account/api-keys
-
-def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):  #repetition_penalty, top_k
-
-    # 1. Set up a payload
-    payload = {
-    "model": "gpt-3.5-turbo",
-    "messages": [{"role": "user", "content": f"{inputs}"}],
-    "temperature" : 1.0,
-    "top_p":1.0,
-    "n" : 1,
-    "stream": True,
-    "presence_penalty":0,
-    "frequency_penalty":0,
-    }
-
-    # 2. Define your headers and add a key from https://platform.openai.com/account/api-keys
-    headers = {
-    "Content-Type": "application/json",
-    "Authorization": f"Bearer {OPENAI_API_KEY}"
-    }
-
-    # 3. Create a chat counter loop that feeds [Predict next best anything based on last input and attention with memory defined by introspective attention over time]
-    print(f"chat_counter - {chat_counter}")
-    if chat_counter != 0 :
-        messages=[]
-        for data in chatbot:
-          temp1 = {}
-          temp1["role"] = "user" 
-          temp1["content"] = data[0] 
-          temp2 = {}
-          temp2["role"] = "assistant" 
-          temp2["content"] = data[1]
-          messages.append(temp1)
-          messages.append(temp2)
-        temp3 = {}
-        temp3["role"] = "user" 
-        temp3["content"] = inputs
-        messages.append(temp3)
-        payload = {
-        "model": "gpt-3.5-turbo",
-        "messages": messages, #[{"role": "user", "content": f"{inputs}"}],
-        "temperature" : temperature, #1.0,
-        "top_p": top_p, #1.0,
-        "n" : 1,
-        "stream": True,
-        "presence_penalty":0,
-        "frequency_penalty":0,
-        }
-    chat_counter+=1
-
-    # 4. POST it to OPENAI API
-    history.append(inputs)
-    print(f"payload is - {payload}")
-    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
-    token_counter = 0 
-    partial_words = "" 
-
-    # 5. Iterate through response lines and structure readable response
-    counter=0
-    for chunk in response.iter_lines():
-        if counter == 0:
-          counter+=1
-          continue
-        if chunk.decode() :
-          chunk = chunk.decode()
-          if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
-              partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
-              if token_counter == 0:
-                history.append(" " + partial_words)
-              else:
-                history[-1] = partial_words
-              chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
-              token_counter+=1
-              yield chat, history, chat_counter 
-                   
-
-def reset_textbox():
-    return gr.update(value='')
-
-
-    
-
-# Episodic and Semantic IO
-def list_files(file_path):
-    import os
-    icon_csv = "📄 "
-    icon_txt = "📑 "
-    current_directory = os.getcwd()
-    file_list = []
-    for filename in os.listdir(current_directory):
-        if filename.endswith(".csv"):
-            file_list.append(icon_csv + filename)
-        elif filename.endswith(".txt"):
-            file_list.append(icon_txt + filename)
-    if file_list:
-        return "\n".join(file_list)
-    else:
-        return "No .csv or .txt files found in the current directory."
-
-# Function to read a file
-def read_file(file_path):
-    try:
-        with open(file_path, "r") as file:
-            contents = file.read()
-            return f"{contents}"
-            #return f"Contents of {file_path}:\n{contents}"
-    except FileNotFoundError:
-        return "File not found."
-
-# Function to delete a file
-def delete_file(file_path):
-    try:
-        import os
-        os.remove(file_path)
-        return f"{file_path} has been deleted."
-    except FileNotFoundError:
-        return "File not found."
-
-# Function to write to a file
-def write_file(file_path, content):
-    try:
-        with open(file_path, "w") as file:
-            file.write(content)
-        return f"Successfully written to {file_path}."
-    except:
-        return "Error occurred while writing to file."
-
-# Function to append to a file
-def append_file(file_path, content):
-    try:
-        with open(file_path, "a") as file:
-            file.write(content)
-        return f"Successfully appended to {file_path}."
-    except:
-        return "Error occurred while appending to file."
-
-
-title = """<h1 align="center">Memory Chat Story Generator ChatGPT</h1>"""
-description = """
-## ChatGPT Datasets 📚
-- WebText
-- Common Crawl
-- BooksCorpus
-- English Wikipedia
-- Toronto Books Corpus
-- OpenWebText
-## ChatGPT Datasets - Details 📚
-- **WebText:** A dataset of web pages crawled from domains on the Alexa top 5,000 list. This dataset was used to pretrain GPT-2.
-  - [WebText: A Large-Scale Unsupervised Text Corpus by Radford et al.](https://paperswithcode.com/dataset/webtext)
-- **Common Crawl:** A dataset of web pages from a variety of domains, which is updated regularly. This dataset was used to pretrain GPT-3.
-  - [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/common-crawl) by Brown et al.
-- **BooksCorpus:** A dataset of over 11,000 books from a variety of genres.
-  - [Scalable Methods for 8 Billion Token Language Modeling](https://paperswithcode.com/dataset/bookcorpus) by Zhu et al.
-- **English Wikipedia:** A dump of the English-language Wikipedia as of 2018, with articles from 2001-2017.
-  - [Improving Language Understanding by Generative Pre-Training](https://huggingface.co/spaces/awacke1/WikipediaUltimateAISearch?logs=build) Space for Wikipedia Search
-- **Toronto Books Corpus:** A dataset of over 7,000 books from a variety of genres, collected by the University of Toronto.
-  - [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://paperswithcode.com/dataset/bookcorpus) by Schwenk and Douze.
-- **OpenWebText:** A dataset of web pages that were filtered to remove content that was likely to be low-quality or spammy. This dataset was used to pretrain GPT-3.
-  - [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/openwebtext) by Brown et al.  
-  """
-
-# 6. Use Gradio to pull it all together
-with gr.Blocks(css = """#col_container {width: 1400px; margin-left: auto; margin-right: auto;} #chatbot {height: 600px; overflow: auto;}""") as demo:
-    gr.HTML(title)
-    with gr.Column(elem_id = "col_container"):
-        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter")
-        chatbot = gr.Chatbot(elem_id='chatbot')
-        state = gr.State([])
-        b1 = gr.Button()
-        with gr.Accordion("Parameters", open=False):
-            top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
-            temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
-            chat_counter = gr.Number(value=0, visible=True, precision=0)
-
-            
-    # Episodic/Semantic IO
-    fileName = gr.Textbox(label="Filename")
-    fileContent = gr.TextArea(label="File Content")
-    completedMessage = gr.Textbox(label="Completed")
-    label = gr.Label()
-    with gr.Row():
-        listFiles = gr.Button("📄 List File(s)")
-        readFile = gr.Button("📖 Read File")
-        saveFile = gr.Button("💾 Save File")
-        deleteFile = gr.Button("🗑️ Delete File")
-        appendFile = gr.Button("➕ Append File")
-    listFiles.click(list_files, inputs=fileName, outputs=fileContent)
-    readFile.click(read_file, inputs=fileName, outputs=fileContent)
-    saveFile.click(write_file, inputs=[fileName, fileContent], outputs=completedMessage)
-    deleteFile.click(delete_file, inputs=fileName, outputs=completedMessage)
-    appendFile.click(append_file, inputs=[fileName, fileContent], outputs=completedMessage )
-
-    
-    inputs.submit(predict, [inputs, top_p, temperature,chat_counter, chatbot, state], [chatbot, state, chat_counter])
-    b1.click(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter])
-    b1.click(reset_textbox, [], [inputs])
-    inputs.submit(reset_textbox, [], [inputs])
-    gr.Markdown(description)
-    
-    demo.queue().launch(debug=True)

spaces/CikeyQI/meme-api/meme_generator/memes/5000choyen/__init__.py

@@ -1,198 +0,0 @@
-from typing import List, Tuple
-
-from PIL.Image import Image as IMG
-from PIL.Image import Resampling, Transform
-from pil_utils import BuildImage, Text2Image
-from pil_utils.gradient import ColorStop, LinearGradient
-
-from meme_generator import add_meme
-
-
-def fivethousand_choyen(images, texts: List[str], args):
-    fontsize = 200
-    fontname = "Noto Sans SC"
-    text = texts[0]
-    pos_x = 40
-    pos_y = 220
-    imgs: List[Tuple[IMG, Tuple[int, int]]] = []
-
-    def transform(img: IMG) -> IMG:
-        skew = 0.45
-        dw = round(img.height * skew)
-        return img.transform(
-            (img.width + dw, img.height),
-            Transform.AFFINE,
-            (1, skew, -dw, 0, 1, 0),
-            Resampling.BILINEAR,
-        )
-
-    def shift(t2m: Text2Image) -> Tuple[int, int]:
-        return (
-            pos_x
-            - t2m.lines[0].chars[0].stroke_width
-            - max(char.stroke_width for char in t2m.lines[0].chars),
-            pos_y - t2m.lines[0].ascent,
-        )
-
-    def add_color_text(stroke_width: int, fill: str, pos: Tuple[int, int]):
-        t2m = Text2Image.from_text(
-            text, fontsize, fontname=fontname, stroke_width=stroke_width, fill=fill
-        )
-        dx, dy = shift(t2m)
-        imgs.append((transform(t2m.to_image()), (dx + pos[0], dy + pos[1])))
-
-    def add_gradient_text(
-        stroke_width: int,
-        dir: Tuple[int, int, int, int],
-        color_stops: List[Tuple[float, Tuple[int, int, int]]],
-        pos: Tuple[int, int],
-    ):
-        t2m = Text2Image.from_text(
-            text, fontsize, fontname=fontname, stroke_width=stroke_width, fill="white"
-        )
-        mask = transform(t2m.to_image()).convert("L")
-        dx, dy = shift(t2m)
-        gradient = LinearGradient(
-            (dir[0] - dx, dir[1] - dy, dir[2] - dx, dir[3] - dy),
-            [ColorStop(*color_stop) for color_stop in color_stops],
-        )
-        bg = gradient.create_image(mask.size)
-        bg.putalpha(mask)
-        imgs.append((bg, (dx + pos[0], dy + pos[1])))
-
-    # 黑
-    add_color_text(22, "black", (8, 8))
-    # 银
-    add_gradient_text(
-        20,
-        (0, 38, 0, 234),
-        [
-            (0.0, (0, 15, 36)),
-            (0.1, (255, 255, 255)),
-            (0.18, (55, 58, 59)),
-            (0.25, (55, 58, 59)),
-            (0.5, (200, 200, 200)),
-            (0.75, (55, 58, 59)),
-            (0.85, (25, 20, 31)),
-            (0.91, (240, 240, 240)),
-            (0.95, (166, 175, 194)),
-            (1, (50, 50, 50)),
-        ],
-        (8, 8),
-    )
-    # 黑
-    add_color_text(16, "black", (0, 0))
-    # 金
-    add_gradient_text(
-        10,
-        (0, 40, 0, 200),
-        [
-            (0, (253, 241, 0)),
-            (0.25, (245, 253, 187)),
-            (0.4, (255, 255, 255)),
-            (0.75, (253, 219, 9)),
-            (0.9, (127, 53, 0)),
-            (1, (243, 196, 11)),
-        ],
-        (0, 0),
-    )
-    # 黑
-    add_color_text(6, "black", (4, -6))
-    # 白
-    add_color_text(6, "white", (0, -6))
-    # 红
-    add_gradient_text(
-        4,
-        (0, 50, 0, 200),
-        [
-            (0, (255, 100, 0)),
-            (0.5, (123, 0, 0)),
-            (0.51, (240, 0, 0)),
-            (1, (5, 0, 0)),
-        ],
-        (0, -6),
-    )
-    # 红
-    add_gradient_text(
-        0,
-        (0, 50, 0, 200),
-        [
-            (0, (230, 0, 0)),
-            (0.5, (123, 0, 0)),
-            (0.51, (240, 0, 0)),
-            (1, (5, 0, 0)),
-        ],
-        (0, -6),
-    )
-
-    text = texts[1]
-    fontname = "Noto Serif SC"
-    pos_x = 300
-    pos_y = 480
-    # 黑
-    add_color_text(22, "black", (10, 4))
-    # 银
-    add_gradient_text(
-        19,
-        (0, 320, 0, 506),
-        [
-            (0, (0, 15, 36)),
-            (0.25, (250, 250, 250)),
-            (0.5, (150, 150, 150)),
-            (0.75, (55, 58, 59)),
-            (0.85, (25, 20, 31)),
-            (0.91, (240, 240, 240)),
-            (0.95, (166, 175, 194)),
-            (1, (50, 50, 50)),
-        ],
-        (10, 4),
-    )
-    # 黑
-    add_color_text(17, "#10193A", (0, 0))
-    # 白
-    add_color_text(8, "#D0D0D0", (0, 0))
-    # 绀
-    add_gradient_text(
-        7,
-        (0, 320, 0, 480),
-        [
-            (0, (16, 25, 58)),
-            (0.03, (255, 255, 255)),
-            (0.08, (16, 25, 58)),
-            (0.2, (16, 25, 58)),
-            (1, (16, 25, 58)),
-        ],
-        (0, 0),
-    )
-    # 银
-    add_gradient_text(
-        0,
-        (0, 320, 0, 480),
-        [
-            (0, (245, 246, 248)),
-            (0.15, (255, 255, 255)),
-            (0.35, (195, 213, 220)),
-            (0.5, (160, 190, 201)),
-            (0.51, (160, 190, 201)),
-            (0.52, (196, 215, 222)),
-            (1.0, (255, 255, 255)),
-        ],
-        (0, -6),
-    )
-
-    img_h = 580
-    img_w = max([img.width + pos[0] for img, pos in imgs])
-    frame = BuildImage.new("RGBA", (img_w, img_h), "white")
-    for img, pos in imgs:
-        frame.paste(img, pos, alpha=True)
-    return frame.save_jpg()
-
-
-add_meme(
-    "5000choyen",
-    fivethousand_choyen,
-    min_texts=2,
-    max_texts=2,
-    default_texts=["我去", "洛天依"],
-    keywords=["5000兆"],
-)

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

@@ -1,32 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.utils import FrameAlignPolicy, Maker, make_gif_or_combined_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def confuse(images: List[BuildImage], texts, args):
-    img_w = min(images[0].width, 500)
-
-    def maker(i: int) -> Maker:
-        def make(img: BuildImage) -> BuildImage:
-            img = img.convert("RGBA").resize_width(img_w)
-            frame = BuildImage.open(img_dir / f"{i}.png").resize(
-                img.size, keep_ratio=True
-            )
-            bg = BuildImage.new("RGB", img.size, "white")
-            bg.paste(img, alpha=True).paste(frame, alpha=True)
-            return bg
-
-        return make
-
-    return make_gif_or_combined_gif(
-        images[0], maker, 100, 0.02, FrameAlignPolicy.extend_loop, input_based=True
-    )
-
-
-add_meme("confuse", confuse, min_images=1, max_images=1, keywords=["迷惑"])

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/charset_normalizer/api.py

@@ -1,626 +0,0 @@
-import logging
-from os import PathLike
-from typing import BinaryIO, List, Optional, Set, Union
-
-from .cd import (
-    coherence_ratio,
-    encoding_languages,
-    mb_encoding_languages,
-    merge_coherence_ratios,
-)
-from .constant import IANA_SUPPORTED, TOO_BIG_SEQUENCE, TOO_SMALL_SEQUENCE, TRACE
-from .md import mess_ratio
-from .models import CharsetMatch, CharsetMatches
-from .utils import (
-    any_specified_encoding,
-    cut_sequence_chunks,
-    iana_name,
-    identify_sig_or_bom,
-    is_cp_similar,
-    is_multi_byte_encoding,
-    should_strip_sig_or_bom,
-)
-
-# Will most likely be controversial
-# logging.addLevelName(TRACE, "TRACE")
-logger = logging.getLogger("charset_normalizer")
-explain_handler = logging.StreamHandler()
-explain_handler.setFormatter(
-    logging.Formatter("%(asctime)s | %(levelname)s | %(message)s")
-)
-
-
-def from_bytes(
-    sequences: Union[bytes, bytearray],
-    steps: int = 5,
-    chunk_size: int = 512,
-    threshold: float = 0.2,
-    cp_isolation: Optional[List[str]] = None,
-    cp_exclusion: Optional[List[str]] = None,
-    preemptive_behaviour: bool = True,
-    explain: bool = False,
-    language_threshold: float = 0.1,
-    enable_fallback: bool = True,
-) -> CharsetMatches:
-    """
-    Given a raw bytes sequence, return the best possibles charset usable to render str objects.
-    If there is no results, it is a strong indicator that the source is binary/not text.
-    By default, the process will extract 5 blocks of 512o each to assess the mess and coherence of a given sequence.
-    And will give up a particular code page after 20% of measured mess. Those criteria are customizable at will.
-
-    The preemptive behavior DOES NOT replace the traditional detection workflow, it prioritize a particular code page
-    but never take it for granted. Can improve the performance.
-
-    You may want to focus your attention to some code page or/and not others, use cp_isolation and cp_exclusion for that
-    purpose.
-
-    This function will strip the SIG in the payload/sequence every time except on UTF-16, UTF-32.
-    By default the library does not setup any handler other than the NullHandler, if you choose to set the 'explain'
-    toggle to True it will alter the logger configuration to add a StreamHandler that is suitable for debugging.
-    Custom logging format and handler can be set manually.
-    """
-
-    if not isinstance(sequences, (bytearray, bytes)):
-        raise TypeError(
-            "Expected object of type bytes or bytearray, got: {0}".format(
-                type(sequences)
-            )
-        )
-
-    if explain:
-        previous_logger_level: int = logger.level
-        logger.addHandler(explain_handler)
-        logger.setLevel(TRACE)
-
-    length: int = len(sequences)
-
-    if length == 0:
-        logger.debug("Encoding detection on empty bytes, assuming utf_8 intention.")
-        if explain:
-            logger.removeHandler(explain_handler)
-            logger.setLevel(previous_logger_level or logging.WARNING)
-        return CharsetMatches([CharsetMatch(sequences, "utf_8", 0.0, False, [], "")])
-
-    if cp_isolation is not None:
-        logger.log(
-            TRACE,
-            "cp_isolation is set. use this flag for debugging purpose. "
-            "limited list of encoding allowed : %s.",
-            ", ".join(cp_isolation),
-        )
-        cp_isolation = [iana_name(cp, False) for cp in cp_isolation]
-    else:
-        cp_isolation = []
-
-    if cp_exclusion is not None:
-        logger.log(
-            TRACE,
-            "cp_exclusion is set. use this flag for debugging purpose. "
-            "limited list of encoding excluded : %s.",
-            ", ".join(cp_exclusion),
-        )
-        cp_exclusion = [iana_name(cp, False) for cp in cp_exclusion]
-    else:
-        cp_exclusion = []
-
-    if length <= (chunk_size * steps):
-        logger.log(
-            TRACE,
-            "override steps (%i) and chunk_size (%i) as content does not fit (%i byte(s) given) parameters.",
-            steps,
-            chunk_size,
-            length,
-        )
-        steps = 1
-        chunk_size = length
-
-    if steps > 1 and length / steps < chunk_size:
-        chunk_size = int(length / steps)
-
-    is_too_small_sequence: bool = len(sequences) < TOO_SMALL_SEQUENCE
-    is_too_large_sequence: bool = len(sequences) >= TOO_BIG_SEQUENCE
-
-    if is_too_small_sequence:
-        logger.log(
-            TRACE,
-            "Trying to detect encoding from a tiny portion of ({}) byte(s).".format(
-                length
-            ),
-        )
-    elif is_too_large_sequence:
-        logger.log(
-            TRACE,
-            "Using lazy str decoding because the payload is quite large, ({}) byte(s).".format(
-                length
-            ),
-        )
-
-    prioritized_encodings: List[str] = []
-
-    specified_encoding: Optional[str] = (
-        any_specified_encoding(sequences) if preemptive_behaviour else None
-    )
-
-    if specified_encoding is not None:
-        prioritized_encodings.append(specified_encoding)
-        logger.log(
-            TRACE,
-            "Detected declarative mark in sequence. Priority +1 given for %s.",
-            specified_encoding,
-        )
-
-    tested: Set[str] = set()
-    tested_but_hard_failure: List[str] = []
-    tested_but_soft_failure: List[str] = []
-
-    fallback_ascii: Optional[CharsetMatch] = None
-    fallback_u8: Optional[CharsetMatch] = None
-    fallback_specified: Optional[CharsetMatch] = None
-
-    results: CharsetMatches = CharsetMatches()
-
-    sig_encoding, sig_payload = identify_sig_or_bom(sequences)
-
-    if sig_encoding is not None:
-        prioritized_encodings.append(sig_encoding)
-        logger.log(
-            TRACE,
-            "Detected a SIG or BOM mark on first %i byte(s). Priority +1 given for %s.",
-            len(sig_payload),
-            sig_encoding,
-        )
-
-    prioritized_encodings.append("ascii")
-
-    if "utf_8" not in prioritized_encodings:
-        prioritized_encodings.append("utf_8")
-
-    for encoding_iana in prioritized_encodings + IANA_SUPPORTED:
-        if cp_isolation and encoding_iana not in cp_isolation:
-            continue
-
-        if cp_exclusion and encoding_iana in cp_exclusion:
-            continue
-
-        if encoding_iana in tested:
-            continue
-
-        tested.add(encoding_iana)
-
-        decoded_payload: Optional[str] = None
-        bom_or_sig_available: bool = sig_encoding == encoding_iana
-        strip_sig_or_bom: bool = bom_or_sig_available and should_strip_sig_or_bom(
-            encoding_iana
-        )
-
-        if encoding_iana in {"utf_16", "utf_32"} and not bom_or_sig_available:
-            logger.log(
-                TRACE,
-                "Encoding %s won't be tested as-is because it require a BOM. Will try some sub-encoder LE/BE.",
-                encoding_iana,
-            )
-            continue
-        if encoding_iana in {"utf_7"} and not bom_or_sig_available:
-            logger.log(
-                TRACE,
-                "Encoding %s won't be tested as-is because detection is unreliable without BOM/SIG.",
-                encoding_iana,
-            )
-            continue
-
-        try:
-            is_multi_byte_decoder: bool = is_multi_byte_encoding(encoding_iana)
-        except (ModuleNotFoundError, ImportError):
-            logger.log(
-                TRACE,
-                "Encoding %s does not provide an IncrementalDecoder",
-                encoding_iana,
-            )
-            continue
-
-        try:
-            if is_too_large_sequence and is_multi_byte_decoder is False:
-                str(
-                    sequences[: int(50e4)]
-                    if strip_sig_or_bom is False
-                    else sequences[len(sig_payload) : int(50e4)],
-                    encoding=encoding_iana,
-                )
-            else:
-                decoded_payload = str(
-                    sequences
-                    if strip_sig_or_bom is False
-                    else sequences[len(sig_payload) :],
-                    encoding=encoding_iana,
-                )
-        except (UnicodeDecodeError, LookupError) as e:
-            if not isinstance(e, LookupError):
-                logger.log(
-                    TRACE,
-                    "Code page %s does not fit given bytes sequence at ALL. %s",
-                    encoding_iana,
-                    str(e),
-                )
-            tested_but_hard_failure.append(encoding_iana)
-            continue
-
-        similar_soft_failure_test: bool = False
-
-        for encoding_soft_failed in tested_but_soft_failure:
-            if is_cp_similar(encoding_iana, encoding_soft_failed):
-                similar_soft_failure_test = True
-                break
-
-        if similar_soft_failure_test:
-            logger.log(
-                TRACE,
-                "%s is deemed too similar to code page %s and was consider unsuited already. Continuing!",
-                encoding_iana,
-                encoding_soft_failed,
-            )
-            continue
-
-        r_ = range(
-            0 if not bom_or_sig_available else len(sig_payload),
-            length,
-            int(length / steps),
-        )
-
-        multi_byte_bonus: bool = (
-            is_multi_byte_decoder
-            and decoded_payload is not None
-            and len(decoded_payload) < length
-        )
-
-        if multi_byte_bonus:
-            logger.log(
-                TRACE,
-                "Code page %s is a multi byte encoding table and it appear that at least one character "
-                "was encoded using n-bytes.",
-                encoding_iana,
-            )
-
-        max_chunk_gave_up: int = int(len(r_) / 4)
-
-        max_chunk_gave_up = max(max_chunk_gave_up, 2)
-        early_stop_count: int = 0
-        lazy_str_hard_failure = False
-
-        md_chunks: List[str] = []
-        md_ratios = []
-
-        try:
-            for chunk in cut_sequence_chunks(
-                sequences,
-                encoding_iana,
-                r_,
-                chunk_size,
-                bom_or_sig_available,
-                strip_sig_or_bom,
-                sig_payload,
-                is_multi_byte_decoder,
-                decoded_payload,
-            ):
-                md_chunks.append(chunk)
-
-                md_ratios.append(
-                    mess_ratio(
-                        chunk,
-                        threshold,
-                        explain is True and 1 <= len(cp_isolation) <= 2,
-                    )
-                )
-
-                if md_ratios[-1] >= threshold:
-                    early_stop_count += 1
-
-                if (early_stop_count >= max_chunk_gave_up) or (
-                    bom_or_sig_available and strip_sig_or_bom is False
-                ):
-                    break
-        except (
-            UnicodeDecodeError
-        ) as e:  # Lazy str loading may have missed something there
-            logger.log(
-                TRACE,
-                "LazyStr Loading: After MD chunk decode, code page %s does not fit given bytes sequence at ALL. %s",
-                encoding_iana,
-                str(e),
-            )
-            early_stop_count = max_chunk_gave_up
-            lazy_str_hard_failure = True
-
-        # We might want to check the sequence again with the whole content
-        # Only if initial MD tests passes
-        if (
-            not lazy_str_hard_failure
-            and is_too_large_sequence
-            and not is_multi_byte_decoder
-        ):
-            try:
-                sequences[int(50e3) :].decode(encoding_iana, errors="strict")
-            except UnicodeDecodeError as e:
-                logger.log(
-                    TRACE,
-                    "LazyStr Loading: After final lookup, code page %s does not fit given bytes sequence at ALL. %s",
-                    encoding_iana,
-                    str(e),
-                )
-                tested_but_hard_failure.append(encoding_iana)
-                continue
-
-        mean_mess_ratio: float = sum(md_ratios) / len(md_ratios) if md_ratios else 0.0
-        if mean_mess_ratio >= threshold or early_stop_count >= max_chunk_gave_up:
-            tested_but_soft_failure.append(encoding_iana)
-            logger.log(
-                TRACE,
-                "%s was excluded because of initial chaos probing. Gave up %i time(s). "
-                "Computed mean chaos is %f %%.",
-                encoding_iana,
-                early_stop_count,
-                round(mean_mess_ratio * 100, ndigits=3),
-            )
-            # Preparing those fallbacks in case we got nothing.
-            if (
-                enable_fallback
-                and encoding_iana in ["ascii", "utf_8", specified_encoding]
-                and not lazy_str_hard_failure
-            ):
-                fallback_entry = CharsetMatch(
-                    sequences, encoding_iana, threshold, False, [], decoded_payload
-                )
-                if encoding_iana == specified_encoding:
-                    fallback_specified = fallback_entry
-                elif encoding_iana == "ascii":
-                    fallback_ascii = fallback_entry
-                else:
-                    fallback_u8 = fallback_entry
-            continue
-
-        logger.log(
-            TRACE,
-            "%s passed initial chaos probing. Mean measured chaos is %f %%",
-            encoding_iana,
-            round(mean_mess_ratio * 100, ndigits=3),
-        )
-
-        if not is_multi_byte_decoder:
-            target_languages: List[str] = encoding_languages(encoding_iana)
-        else:
-            target_languages = mb_encoding_languages(encoding_iana)
-
-        if target_languages:
-            logger.log(
-                TRACE,
-                "{} should target any language(s) of {}".format(
-                    encoding_iana, str(target_languages)
-                ),
-            )
-
-        cd_ratios = []
-
-        # We shall skip the CD when its about ASCII
-        # Most of the time its not relevant to run "language-detection" on it.
-        if encoding_iana != "ascii":
-            for chunk in md_chunks:
-                chunk_languages = coherence_ratio(
-                    chunk,
-                    language_threshold,
-                    ",".join(target_languages) if target_languages else None,
-                )
-
-                cd_ratios.append(chunk_languages)
-
-        cd_ratios_merged = merge_coherence_ratios(cd_ratios)
-
-        if cd_ratios_merged:
-            logger.log(
-                TRACE,
-                "We detected language {} using {}".format(
-                    cd_ratios_merged, encoding_iana
-                ),
-            )
-
-        results.append(
-            CharsetMatch(
-                sequences,
-                encoding_iana,
-                mean_mess_ratio,
-                bom_or_sig_available,
-                cd_ratios_merged,
-                decoded_payload,
-            )
-        )
-
-        if (
-            encoding_iana in [specified_encoding, "ascii", "utf_8"]
-            and mean_mess_ratio < 0.1
-        ):
-            logger.debug(
-                "Encoding detection: %s is most likely the one.", encoding_iana
-            )
-            if explain:
-                logger.removeHandler(explain_handler)
-                logger.setLevel(previous_logger_level)
-            return CharsetMatches([results[encoding_iana]])
-
-        if encoding_iana == sig_encoding:
-            logger.debug(
-                "Encoding detection: %s is most likely the one as we detected a BOM or SIG within "
-                "the beginning of the sequence.",
-                encoding_iana,
-            )
-            if explain:
-                logger.removeHandler(explain_handler)
-                logger.setLevel(previous_logger_level)
-            return CharsetMatches([results[encoding_iana]])
-
-    if len(results) == 0:
-        if fallback_u8 or fallback_ascii or fallback_specified:
-            logger.log(
-                TRACE,
-                "Nothing got out of the detection process. Using ASCII/UTF-8/Specified fallback.",
-            )
-
-        if fallback_specified:
-            logger.debug(
-                "Encoding detection: %s will be used as a fallback match",
-                fallback_specified.encoding,
-            )
-            results.append(fallback_specified)
-        elif (
-            (fallback_u8 and fallback_ascii is None)
-            or (
-                fallback_u8
-                and fallback_ascii
-                and fallback_u8.fingerprint != fallback_ascii.fingerprint
-            )
-            or (fallback_u8 is not None)
-        ):
-            logger.debug("Encoding detection: utf_8 will be used as a fallback match")
-            results.append(fallback_u8)
-        elif fallback_ascii:
-            logger.debug("Encoding detection: ascii will be used as a fallback match")
-            results.append(fallback_ascii)
-
-    if results:
-        logger.debug(
-            "Encoding detection: Found %s as plausible (best-candidate) for content. With %i alternatives.",
-            results.best().encoding,  # type: ignore
-            len(results) - 1,
-        )
-    else:
-        logger.debug("Encoding detection: Unable to determine any suitable charset.")
-
-    if explain:
-        logger.removeHandler(explain_handler)
-        logger.setLevel(previous_logger_level)
-
-    return results
-
-
-def from_fp(
-    fp: BinaryIO,
-    steps: int = 5,
-    chunk_size: int = 512,
-    threshold: float = 0.20,
-    cp_isolation: Optional[List[str]] = None,
-    cp_exclusion: Optional[List[str]] = None,
-    preemptive_behaviour: bool = True,
-    explain: bool = False,
-    language_threshold: float = 0.1,
-    enable_fallback: bool = True,
-) -> CharsetMatches:
-    """
-    Same thing than the function from_bytes but using a file pointer that is already ready.
-    Will not close the file pointer.
-    """
-    return from_bytes(
-        fp.read(),
-        steps,
-        chunk_size,
-        threshold,
-        cp_isolation,
-        cp_exclusion,
-        preemptive_behaviour,
-        explain,
-        language_threshold,
-        enable_fallback,
-    )
-
-
-def from_path(
-    path: Union[str, bytes, PathLike],  # type: ignore[type-arg]
-    steps: int = 5,
-    chunk_size: int = 512,
-    threshold: float = 0.20,
-    cp_isolation: Optional[List[str]] = None,
-    cp_exclusion: Optional[List[str]] = None,
-    preemptive_behaviour: bool = True,
-    explain: bool = False,
-    language_threshold: float = 0.1,
-    enable_fallback: bool = True,
-) -> CharsetMatches:
-    """
-    Same thing than the function from_bytes but with one extra step. Opening and reading given file path in binary mode.
-    Can raise IOError.
-    """
-    with open(path, "rb") as fp:
-        return from_fp(
-            fp,
-            steps,
-            chunk_size,
-            threshold,
-            cp_isolation,
-            cp_exclusion,
-            preemptive_behaviour,
-            explain,
-            language_threshold,
-            enable_fallback,
-        )
-
-
-def is_binary(
-    fp_or_path_or_payload: Union[PathLike, str, BinaryIO, bytes],  # type: ignore[type-arg]
-    steps: int = 5,
-    chunk_size: int = 512,
-    threshold: float = 0.20,
-    cp_isolation: Optional[List[str]] = None,
-    cp_exclusion: Optional[List[str]] = None,
-    preemptive_behaviour: bool = True,
-    explain: bool = False,
-    language_threshold: float = 0.1,
-    enable_fallback: bool = False,
-) -> bool:
-    """
-    Detect if the given input (file, bytes, or path) points to a binary file. aka. not a string.
-    Based on the same main heuristic algorithms and default kwargs at the sole exception that fallbacks match
-    are disabled to be stricter around ASCII-compatible but unlikely to be a string.
-    """
-    if isinstance(fp_or_path_or_payload, (str, PathLike)):
-        guesses = from_path(
-            fp_or_path_or_payload,
-            steps=steps,
-            chunk_size=chunk_size,
-            threshold=threshold,
-            cp_isolation=cp_isolation,
-            cp_exclusion=cp_exclusion,
-            preemptive_behaviour=preemptive_behaviour,
-            explain=explain,
-            language_threshold=language_threshold,
-            enable_fallback=enable_fallback,
-        )
-    elif isinstance(
-        fp_or_path_or_payload,
-        (
-            bytes,
-            bytearray,
-        ),
-    ):
-        guesses = from_bytes(
-            fp_or_path_or_payload,
-            steps=steps,
-            chunk_size=chunk_size,
-            threshold=threshold,
-            cp_isolation=cp_isolation,
-            cp_exclusion=cp_exclusion,
-            preemptive_behaviour=preemptive_behaviour,
-            explain=explain,
-            language_threshold=language_threshold,
-            enable_fallback=enable_fallback,
-        )
-    else:
-        guesses = from_fp(
-            fp_or_path_or_payload,
-            steps=steps,
-            chunk_size=chunk_size,
-            threshold=threshold,
-            cp_isolation=cp_isolation,
-            cp_exclusion=cp_exclusion,
-            preemptive_behaviour=preemptive_behaviour,
-            explain=explain,
-            language_threshold=language_threshold,
-            enable_fallback=enable_fallback,
-        )
-
-    return not guesses

spaces/Datasculptor/DescriptionGPT/tools/create_imagenetlvis_json.py

@@ -1,52 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import argparse
-import json
-import os
-import cv2
-from nltk.corpus import wordnet
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--imagenet_path', default='datasets/imagenet/ImageNet-LVIS')
-    parser.add_argument('--lvis_meta_path', default='datasets/lvis/lvis_v1_val.json')
-    parser.add_argument('--out_path', default='datasets/imagenet/annotations/imagenet_lvis_image_info.json')
-    args = parser.parse_args()
-
-    print('Loading LVIS meta')
-    data = json.load(open(args.lvis_meta_path, 'r'))
-    print('Done')
-    synset2cat = {x['synset']: x for x in data['categories']}
-    count = 0
-    images = []
-    image_counts = {}
-    folders = sorted(os.listdir(args.imagenet_path))
-    for i, folder in enumerate(folders):
-        class_path = args.imagenet_path + folder
-        files = sorted(os.listdir(class_path))
-        synset = wordnet.synset_from_pos_and_offset('n', int(folder[1:])).name()
-        cat = synset2cat[synset]
-        cat_id = cat['id']
-        cat_name = cat['name']
-        cat_images = []
-        for file in files:
-            count = count + 1
-            file_name = '{}/{}'.format(folder, file)
-            img = cv2.imread('{}/{}'.format(args.imagenet_path, file_name))
-            h, w = img.shape[:2]
-            image = {
-                'id': count,
-                'file_name': file_name,
-                'pos_category_ids': [cat_id],
-                'width': w,
-                'height': h
-            }
-            cat_images.append(image)
-        images.extend(cat_images)
-        image_counts[cat_id] = len(cat_images)
-        print(i, cat_name, len(cat_images))
-    print('# Images', len(images))
-    for x in data['categories']:
-        x['image_count'] = image_counts[x['id']] if x['id'] in image_counts else 0
-    out = {'categories': data['categories'], 'images': images, 'annotations': []}
-    print('Writing to', args.out_path)
-    json.dump(out, open(args.out_path, 'w'))

spaces/Detomo/AI-Galary/app.py

@@ -1,28 +0,0 @@
-import gradio as gr
-import pandas as pd
-
-
-def make_clickable_model(model_name, link=None):
-    name = model_name.replace("https://huggingface.co/spaces/","")
-    return f'<a target="_blank" href="{model_name}">{name.split("/")[-1].replace("_", " ")}</a>'
-
-def read_df():
-    df = pd.read_excel("demo_df.xlsx")
-    links = []
-    for i in range(df.shape[0]):
-        links.append(make_clickable_model(df.iloc[i, 2]))
-    df.drop(columns="Link", inplace=True)
-    df.insert(2, "Link", links)
-    df.insert(0, "ID", list(range(1, len(df) + 1)))
-    return df
-
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-        """# Detomo AI Galary  🧙‍♀️ 🧛‍♀️ 🤖  """
-    )
-    galary = gr.Dataframe(
-                    type="pandas", datatype=["number", "markdown", "markdown", "markdown"]
-                )
-    demo.load(read_df, inputs=None, outputs=galary)
-    
-demo.launch()

spaces/DrewKarn/CarperAI-stable-vicuna-13b-delta/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/CarperAI/stable-vicuna-13b-delta").launch()

spaces/EronSamez/RVC_HFmeu/Applio-RVC-Fork/utils/dependency.py

@@ -1,170 +0,0 @@
-import os
-import csv
-import shutil
-import tarfile
-import subprocess
-from pathlib import Path
-from datetime import datetime
-
-def install_packages_but_jank_af():
-    packages = ['build-essential', 'python3-dev', 'ffmpeg', 'aria2']
-    pip_packages = ['pip', 'setuptools', 'wheel', 'httpx==0.23.0', 'faiss-gpu', 'fairseq', 'gradio==3.34.0',
-                    'ffmpeg', 'ffmpeg-python', 'praat-parselmouth', 'pyworld', 'numpy==1.23.5',
-                    'numba==0.56.4', 'librosa==0.9.2', 'mega.py', 'gdown', 'onnxruntime', 'pyngrok==4.1.12',
-                    'gTTS', 'elevenlabs', 'wget', 'tensorboardX', 'unidecode', 'huggingface-hub', 'stftpitchshift==1.5.1', 
-                    'yt-dlp', 'pedalboard', 'pathvalidate', 'nltk', 'edge-tts', 'git+https://github.com/suno-ai/bark.git', 'python-dotenv' , 'av']
-
-    print("Updating and installing system packages...")
-    for package in packages:
-        print(f"Installing {package}...")
-        subprocess.check_call(['apt-get', 'install', '-qq', '-y', package])
-
-    print("Updating and installing pip packages...")
-    subprocess.check_call(['pip', 'install', '--upgrade'] + pip_packages)
-
-    print('Packages up to date.')
-
-
-def setup_environment(ForceUpdateDependencies, ForceTemporaryStorage):
-    # Mounting Google Drive
-    if not ForceTemporaryStorage:
-        from google.colab import drive
-
-        if not os.path.exists('/content/drive'):
-            drive.mount('/content/drive')
-        else:
-            print('Drive is already mounted. Proceeding...')
-
-    # Function to install dependencies with progress
-    def install_packages():
-        packages = ['build-essential', 'python3-dev', 'ffmpeg', 'aria2']
-        pip_packages = ['pip', 'setuptools', 'wheel', 'httpx==0.23.0', 'faiss-gpu', 'fairseq', 'gradio==3.34.0',
-                        'ffmpeg', 'ffmpeg-python', 'praat-parselmouth', 'pyworld', 'numpy==1.23.5',
-                        'numba==0.56.4', 'librosa==0.9.2', 'mega.py', 'gdown', 'onnxruntime', 'pyngrok==4.1.12',
-                        'gTTS', 'elevenlabs', 'wget', 'tensorboardX', 'unidecode', 'huggingface-hub', 'stftpitchshift==1.5.1', 
-                        'yt-dlp', 'pedalboard', 'pathvalidate', 'nltk', 'edge-tts', 'git+https://github.com/suno-ai/bark.git', 'python-dotenv' , 'av']
-
-        print("Updating and installing system packages...")
-        for package in packages:
-            print(f"Installing {package}...")
-            subprocess.check_call(['apt-get', 'install', '-qq', '-y', package])
-
-        print("Updating and installing pip packages...")
-        subprocess.check_call(['pip', 'install', '--upgrade'] + pip_packages)
-
-
-        print('Packages up to date.')
-
-    # Function to scan a directory and writes filenames and timestamps
-    def scan_and_write(base_path, output_file):
-        with open(output_file, 'w', newline='') as f:
-            writer = csv.writer(f)
-            for dirpath, dirs, files in os.walk(base_path):
-                for filename in files:
-                    fname = os.path.join(dirpath, filename)
-                    try:
-                        mtime = os.path.getmtime(fname)
-                        writer.writerow([fname, mtime])
-                    except Exception as e:
-                        print(f'Skipping irrelevant nonexistent file {fname}: {str(e)}')
-        print(f'Finished recording filesystem timestamps to {output_file}.')
-
-    # Function to compare files
-    def compare_files(old_file, new_file):
-        old_files = {}
-        new_files = {}
-
-        with open(old_file, 'r') as f:
-            reader = csv.reader(f)
-            old_files = {rows[0]:rows[1] for rows in reader}
-
-        with open(new_file, 'r') as f:
-            reader = csv.reader(f)
-            new_files = {rows[0]:rows[1] for rows in reader}
-
-        removed_files = old_files.keys() - new_files.keys()
-        added_files = new_files.keys() - old_files.keys()
-        unchanged_files = old_files.keys() & new_files.keys()
-
-        changed_files = {f for f in unchanged_files if old_files[f] != new_files[f]}
-
-        for file in removed_files:
-            print(f'File has been removed: {file}')
-
-        for file in changed_files:
-            print(f'File has been updated: {file}')
-
-        return list(added_files) + list(changed_files)
-
-    # Check if CachedRVC.tar.gz exists
-    if ForceTemporaryStorage:
-        file_path = '/content/CachedRVC.tar.gz'
-    else:
-        file_path = '/content/drive/MyDrive/RVC_Cached/CachedRVC.tar.gz'
-
-    content_file_path = '/content/CachedRVC.tar.gz'
-    extract_path = '/'
-
-    if not os.path.exists(file_path):
-        folder_path = os.path.dirname(file_path)
-        os.makedirs(folder_path, exist_ok=True)
-        print('No cached dependency install found. Attempting to download GitHub backup..')
-
-        try:
-            download_url = "https://github.com/kalomaze/QuickMangioFixes/releases/download/release3/CachedRVC.tar.gz"
-            subprocess.run(["wget", "-O", file_path, download_url])
-            print('Download completed successfully!')
-        except Exception as e:
-            print('Download failed:', str(e))
-
-            # Delete the failed download file
-            if os.path.exists(file_path):
-                os.remove(file_path)
-            print('Failed download file deleted. Continuing manual backup..')
-
-    if Path(file_path).exists():
-        if ForceTemporaryStorage:
-            print('Finished downloading CachedRVC.tar.gz.')
-        else:
-            print('CachedRVC.tar.gz found on Google Drive. Proceeding to copy and extract...')
-
-        # Check if ForceTemporaryStorage is True and skip copying if it is
-        if ForceTemporaryStorage:
-            pass
-        else:
-            shutil.copy(file_path, content_file_path)
-
-        print('Beginning backup copy operation...')
-
-        with tarfile.open(content_file_path, 'r:gz') as tar:
-            for member in tar.getmembers():
-                target_path = os.path.join(extract_path, member.name)
-                try:
-                    tar.extract(member, extract_path)
-                except Exception as e:
-                    print('Failed to extract a file (this isn\'t normal)... forcing an update to compensate')
-                    ForceUpdateDependencies = True
-            print(f'Extraction of {content_file_path} to {extract_path} completed.')
-
-        if ForceUpdateDependencies:
-            install_packages()
-            ForceUpdateDependencies = False
-    else:
-        print('CachedRVC.tar.gz not found. Proceeding to create an index of all current files...')
-        scan_and_write('/usr/', '/content/usr_files.csv')
-
-        install_packages()
-
-        scan_and_write('/usr/', '/content/usr_files_new.csv')
-        changed_files = compare_files('/content/usr_files.csv', '/content/usr_files_new.csv')
-
-        with tarfile.open('/content/CachedRVC.tar.gz', 'w:gz') as new_tar:
-            for file in changed_files:
-                new_tar.add(file)
-                print(f'Added to tar: {file}')
-
-        os.makedirs('/content/drive/MyDrive/RVC_Cached', exist_ok=True)
-        shutil.copy('/content/CachedRVC.tar.gz', '/content/drive/MyDrive/RVC_Cached/CachedRVC.tar.gz')
-        print('Updated CachedRVC.tar.gz copied to Google Drive.')
-        print('Dependencies fully up to date; future runs should be faster.')
-