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Method 2: Using a browser extension or plugin
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Conclusion
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Gentility mp3 is a song by Trendybeatz that is a remix of a Nigerian folk song. It has become popular on TikTok, where many users have used it to create funny and creative videos.
-You can download gentility mp3 from TikTok by using a free online TikTok mp3 downloader, a browser extension or plugin, or a desktop or mobile app. You can also stream, buy, or rent the song from legal sources, or create your own remixes.
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-The disadvantage of downloading from Crunchyroll is that you need to pay for a premium account or use a free trial that expires after 14 days. You also need to have enough storage space on your device to store the downloaded episodes. You also need to have an internet connection to start the download process.
Download from Other Anime Streaming Sites
-If you don't want to pay for a premium account or use a free trial, you can also download Hataraku Maou-sama S1 from other anime streaming sites. However, as we warned you before, these sites are not legal, safe, or reliable. They may contain malware, viruses, pop-up ads, or other annoying or harmful elements.
-If you decide to download from these sites, you should be careful and use a VPN and antivirus software to protect your device and identity. You should also avoid clicking on any suspicious links or downloading any files from these sites.
-There are different tools or methods to download from these sites, such as video downloader extensions, online converters, screen recorders, etc. However, they may not work for all sites or videos. They may also have some limitations or drawbacks, such as low quality, slow speed, watermarks, etc.
-Here are some examples of tools or methods to download from these sites:
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-
- Video downloader extensions: These are browser add-ons that allow you to download videos from various websites. Some examples are Video DownloadHelper, Video Downloader Professional, etc. To use them, you need to install them on your browser and then visit the site that has the video you want to download. You will see a download icon on the toolbar or on the video player. Click on it and choose the format and quality you want. Then wait for the download to finish. -
- Online converters: These are websites that allow you to convert and download videos from various websites. Some examples are OnlineVideoConverter, SaveFrom.net, etc. To use them, you need to copy the URL of the video you want to download and paste it on the website. Then choose the format and quality you want and click on the download button. Then wait for the conversion and download to finish. -
- Screen recorders: These are software or apps that allow you to record your screen and save it as a video file. Some examples are OBS Studio, Camtasia, etc. To use them, you need to install them on your device and then open the site that has the video you want to download. Then start the screen recorder and adjust the settings and area you want to capture. Then play the video and record it. Then stop the recording and save it as a video file. -
Here are some screenshots or images to illustrate the tools or methods:
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The advantage of downloading from other anime streaming sites is that you can do it for free without any subscription or trial. You can also choose from different sites and sources that have Hataraku Maou-sama S1.
-The disadvantage of downloading from other anime streaming sites is that you may face some risks or challenges, such as malware, viruses, legal issues, etc. You may also get low-quality videos or subtitles that are not synchronized or accurate. You may also have to deal with ads or interruptions during the download process.
-Conclusion
-Hataraku Maou-sama S1 is a comedy-fantasy anime that is worth watching or rewatching if you enjoy hilarious and heartwarming stories with a twist. It has a unique premise, witty humor, charming characters, and an engaging plot.
-If you want to download Hataraku Maou-sama S1 for offline viewing, you have several options to choose from. However, not all of them are legal, safe, or reliable. The best option is to download it from Crunchyroll using a premium account or a free trial. This way, you can enjoy high-quality videos and subtitles without any ads or interruptions. You can also support the official release and the creators of the anime by paying for a subscription.
-If you don't want to pay for a premium account or use a free trial, you can also download it from other anime streaming sites using different tools or methods. However, you should be careful and use a VPN and antivirus software to protect your device and identity. You should also avoid clicking on any suspicious links or downloading any files from these sites. You may also face some risks or challenges, such as malware, viruses, legal issues, etc. You may also get low-quality videos or subtitles that are not synchronized or accurate. You may also have to deal with ads or interruptions during the download process.
-We hope this article has helped you learn how to download Hataraku Maou-sama S1 from different sources and what are the advantages and disadvantages of each option. We also hope you enjoy watching this anime and have a good time.
-FAQs
--
-
- Q: Is there a season 2 of Hataraku Maou-sama? -
- A: Unfortunately, there is no official confirmation or announcement of a season 2 of Hataraku Maou-sama as of now. However, there are rumors and speculations that a season 2 might be in the works or planned for the future. The anime is based on a light novel series that has 21 volumes and is still ongoing. The anime only adapted the first two volumes, so there is plenty of material for a season 2. The anime also has a loyal fan base and a high demand for a sequel. Therefore, there is still hope that a season 2 might happen someday. -
- Q: Who is the main character of Hataraku Maou-sama? -
- A: The main character of Hataraku Maou-sama is Sadao Maou, the demon lord of Ente Isla who ends up in modern-day Tokyo and works at a fast-food restaurant. He is voiced by Ryota Ohsaka in Japanese and Josh Grelle in English. He is a charismatic, intelligent, and ambitious leader who wants to conquer the world. However, he also has a kind, generous, and hardworking side that he shows on Earth. He develops feelings for Emilia, the hero who defeated him in Ente Isla. -
- Q: What is the genre of Hataraku Maou-sama? -
- A: Hataraku Maou-sama is a comedy-fantasy anime that blends elements of action, romance, drama, and parody. It has a unique premise, witty humor, charming characters, and an engaging plot. It is suitable for viewers who enjoy laugh-out-loud comedy and fantasy scenarios. -
- Q: How many episodes are there in Hataraku Maou-sama S1? -
- A: Hataraku Maou-sama S1 has 13 episodes that aired from April to June 2013. Each episode has a duration of about 24 minutes. There is also an OVA episode that was released in December 2013 as a bonus for the DVD and Blu-ray release. The OVA episode has a duration of about 27 minutes. -
- Q: Where can I read the light novel series of Hataraku Maou-sama? -
- A: The light novel series of Hataraku Maou-sama is written by Satoshi Wagahara and illustrated by Oniku. It has 21 volumes and is still ongoing as of now. You can read the light novel series online or buy the physical copies from various sources. Some examples are: -
- Yen Press: This is the official English publisher of the light novel series. You can buy the digital or print versions from their website or other online retailers. -
- Baka-Tsuki: This is a fan translation website that has translated some of the light novel volumes into English and other languages. You can read them online for free. -
- Novel Updates: This is a directory website that lists various sources and links to read the light novel series online. -
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Dynamons World Game Mod APK The Best Role Playing Game of 2023.md b/spaces/1phancelerku/anime-remove-background/Dynamons World Game Mod APK The Best Role Playing Game of 2023.md deleted file mode 100644 index 541ad4d20f7271681b1562826eb73c193e3bdabd..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Dynamons World Game Mod APK The Best Role Playing Game of 2023.md +++ /dev/null @@ -1,128 +0,0 @@ - -Dynamons World Game Mod Apk: A Review
-If you are a fan of RPG games, you might have heard of Dynamons World, a popular game that lets you catch and train dozens of unique monsters and battle them in online multiplayer matches. But did you know that there is a modded version of this game that gives you unlimited money and other advantages? In this article, we will review Dynamons World Game Mod Apk, a modified version of the original game that enhances your gaming experience. We will also show you how to download and install it on your device, and share some tips and tricks for playing it.
-What is Dynamons World?
-Dynamons World is a role-playing game developed by Azerion Casual, a company that specializes in casual games for web and mobile platforms. The game was released in 2017 and has been downloaded over 10 million times on Google Play Store. It is also available on App Store and BlueStacks, an emulator that allows you to play Android games on PC.
-dynamons world game mod apk
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-A fun and addictive RPG game
-Dynamons World is a game that features:
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- An exciting campaign with multiple challenges and a cool storyline -
- Online matches with other players -
- Dozens of unique Dynamons with varied powers and abilities -
- Useful items and boosters to enhance your battles -
- A tactical turn-based battle system with strategic elements -
- Multiple areas on the maps to explore -
- Tons of new updates with interesting content -
- Free to play on web browser, Android, and iOS platforms -
Features of Dynamons World
-Some of the features that make Dynamons World stand out are:
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-Feature Description Online Battle Arena You can battle your friends and players worldwide in online PvP multiplayer battles. You can also join tournaments and leagues to compete for prizes and glory. Catch and train Dynamons You can catch and train dozens of unique Dynamons, each with their own strengths and weaknesses. You can also evolve them into more powerful forms and customize them with skill cards. Unleash powerful skills You can unleash powerful skills and brilliant tactics to defeat even the strongest rivals in Klaude's kingdom. You can also use items and boosters to gain an edge in battle. Travel across the world You can travel all the way from Dynamons Camp to the Temple Ruins in an addictive and immersive RPG story game. You can also explore different areas on the maps, such as forests, deserts, caves, and more. New updates Dynamons World is being updated all the time with even more new Dynamons, quests, battles, and more. You can also expect new features, such as new online PvP battle arena, new maps, new Dynamon types, new skill cards, new rare dragon Dynamons, and more. Free to play Dynamons World is free to play on web browser, Android, and iOS platforms. You can also play it offline without internet connection. However, some in-game items may require real money purchases. What is Dynamons World Mod Apk?
-Dynamons World Mod Apk is a modified version of the original game that gives you unlimited money and other benefits. With this mod apk, you can enjoy the game without any limitations or restrictions.
A modified version of the original game -Dynamons World Mod Apk is a modified version of the original game that comes with exciting features and benefits. You can enjoy unlimited resources, including coins, gems, and energy, to help you progress faster and unlock more Dynamons. The modded version also offers access to exclusive Dynamons that are not available in the original game.
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-role playing games best gamesBenefits of Dynamons World Mod Apk
-Some of the benefits that you can get from Dynamons World Mod Apk are:
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- Unlimited money: You can get unlimited money to buy items, boosters, and skill cards. You can also upgrade your Dynamons and evolve them without any cost. -
- Unlocked content: You can access all the content in the game, such as maps, quests, battles, and Dynamons. You can also catch any Dynamon you want without any difficulty. -
- Removed ads: You can play the game without any annoying ads that interrupt your gameplay. You can also save your data and battery life. -
- Enhanced graphics: You can enjoy the game with enhanced graphics and sound quality. You can also adjust the settings to suit your device's performance. -
- No root required: You can install and play the game without rooting your device. You can also update the game without any problem. -
How to download and install Dynamons World Mod Apk?
-If you want to download and install Dynamons World Mod Apk on your device, you need to follow these steps:
-Steps to download and install
--
-
- Click on this link to download the Dynamons World Mod Apk file on your device. -
- Go to your device's settings and enable the installation of unknown sources. This will allow you to install apps from sources other than Google Play Store. -
- Locate the downloaded file in your file manager and tap on it to start the installation process. -
- Follow the instructions on the screen and wait for the installation to complete. -
- Launch the game and enjoy playing Dynamons World Mod Apk with unlimited money and other benefits. -
Tips and tricks for playing Dynamons World Mod Apk
-Here are some tips and tricks that can help you play Dynamons World Mod Apk better:
--
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- Choose your starter Dynamon wisely. Each Dynamon has a different type, such as fire, water, plant, electric, etc. Each type has its own strengths and weaknesses against other types. For example, fire is strong against plant but weak against water. You can check the type chart in the game to see which type is effective or ineffective against another type. -
- Catch and train as many Dynamons as you can. You can catch Dynamons by using capture balls that you can buy or find in the game. You can also train your Dynamons by battling other Dynamons or using skill cards that you can buy or find in the game. Training your Dynamons will increase their level, stats, and skills. -
- Use items and boosters wisely. Items and boosters are useful tools that can help you in battles. Items can heal your Dynamons, revive them, or cure them from status effects. Boosters can increase your Dynamons' attack, defense, speed, or accuracy. However, items and boosters are limited in number, so use them only when necessary. -
- Plan your strategy carefully. Battles in Dynamons World are turn-based, which means you have to choose your actions wisely. You have to consider your Dynamons' type, skills, stats, and status effects when choosing which Dynamon to use and which skill to unleash. You also have to anticipate your opponent's moves and counter them accordingly. -
- Have fun and explore the world. Dynamons World is a game that offers a lot of fun and adventure. You can explore different areas on the maps, such as forests, deserts, caves, and more. You can also meet new characters, complete quests, join tournaments, and discover secrets along the way. -
Conclusion
-Dynamons World is a fun and addictive RPG game that lets you catch and train dozens of unique monsters and battle them in online multiplayer matches. However, if you want to enjoy the game without any limitations or restrictions, you should try Dynamons World Mod Apk, a modified version of the original game that gives you unlimited money and other benefits. With this mod apk, you can access all the content in the game, catch any Dynamon you want, use items and boosters freely, play without ads, and more. All you have to do is download and install it on your device, and follow the steps and tips we have provided in this article. We hope you have fun playing Dynamons World Mod Apk and become the best Dynamon master in the world.
-FAQs
-Here are some frequently asked questions about Dynamons World Mod Apk:
--
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- Is Dynamons World Mod Apk safe to use? -
- Is Dynamons World Mod Apk legal to use? -
- Can I play Dynamons World Mod Apk online? -
- Can I update Dynamons World Mod Apk? -
- Can I uninstall Dynamons World Mod Apk? -
Yes, Dynamons World Mod Apk is safe to use as long as you download it from a trusted source, such as the link we have provided in this article. However, you should always be careful when downloading and installing any modded apps, as they may contain viruses or malware that can harm your device. You should also backup your data before installing any modded apps, as they may overwrite or delete your original game data.
-Dynamons World Mod Apk is not legal to use, as it violates the terms and conditions of the original game. By using this mod apk, you are breaking the rules of the game and risking your account being banned or suspended. You are also depriving the developers of their rightful income from the game. Therefore, we do not encourage or endorse the use of Dynamons World Mod Apk, and we are not responsible for any consequences that may arise from using it.
-Yes, you can play Dynamons World Mod Apk online with other players. However, you may face some issues or errors when playing online, as the modded version may not be compatible with the latest version of the original game. You may also encounter players who are using the original game or other modded versions, which may cause unfairness or imbalance in the gameplay. Therefore, we recommend playing Dynamons World Mod Apk offline or with your friends who are using the same modded version.
-No, you cannot update Dynamons World Mod Apk, as it is a modified version of the original game. If you try to update it from Google Play Store or App Store, you will lose all the benefits and features of the modded version. You will also lose all your progress and data in the modded version. Therefore, you should avoid updating Dynamons World Mod Apk, and stick to the version that you have downloaded and installed.
-Yes, you can uninstall Dynamons World Mod Apk anytime you want. You can simply go to your device's settings and uninstall it like any other app. However, you should note that uninstalling Dynamons World Mod Apk will delete all your progress and data in the modded version. You will also lose all the benefits and features of the modded version. Therefore, you should backup your data before uninstalling Dynamons World Mod Apk, or keep a copy of the original game on your device.
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\ No newline at end of file diff --git a/spaces/AIatUIUC/CodeLATS/generators/generator_types.py b/spaces/AIatUIUC/CodeLATS/generators/generator_types.py deleted file mode 100644 index 83ab027484b8205a9bde227dab4d9a40d903950f..0000000000000000000000000000000000000000 --- a/spaces/AIatUIUC/CodeLATS/generators/generator_types.py +++ /dev/null @@ -1,33 +0,0 @@ -from typing import List, Optional, Union -from abc import abstractmethod, ABC - -from generators.model import ModelBase - - -class Generator: - @abstractmethod - def self_reflection(self, func: str, feedback: str, model: ModelBase) -> str: - ... - - @abstractmethod - def func_impl( - self, - func_sig: str, - model: ModelBase, - strategy: str, - prev_func_impl: Optional[str] = None, - feedback: Optional[str] = None, - self_reflection: Optional[str] = None, - num_comps: int = 1, - temperature: float = 0.0, - ) -> Union[str, List[str]]: - ... - - @abstractmethod - def internal_tests( - self, - func_sig: str, - model: ModelBase, - max_num_tests: int = 5 - ) -> List[str]: - ... diff --git a/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/data/audio.py b/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/data/audio.py deleted file mode 100644 index 05fa53ae8ad1b40ab8b9c5dd134227a2a58c55fe..0000000000000000000000000000000000000000 --- a/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/data/audio.py +++ /dev/null @@ -1,217 +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. - -""" -Audio IO methods are defined in this module (info, read, write), -We rely on av library for faster read when possible, otherwise on torchaudio. -""" - -from dataclasses import dataclass -from pathlib import Path -import logging -import typing as tp - -import numpy as np -import soundfile -import torch -from torch.nn import functional as F -import torchaudio as ta - -import av - -from .audio_utils import f32_pcm, i16_pcm, normalize_audio, convert_audio - - -_av_initialized = False - - -def _init_av(): - global _av_initialized - if _av_initialized: - return - logger = logging.getLogger('libav.mp3') - logger.setLevel(logging.ERROR) - _av_initialized = True - - -@dataclass(frozen=True) -class AudioFileInfo: - sample_rate: int - duration: float - channels: int - - -def _av_info(filepath: tp.Union[str, Path]) -> AudioFileInfo: - _init_av() - with av.open(str(filepath)) as af: - stream = af.streams.audio[0] - sample_rate = stream.codec_context.sample_rate - duration = float(stream.duration * stream.time_base) - channels = stream.channels - return AudioFileInfo(sample_rate, duration, channels) - - -def _soundfile_info(filepath: tp.Union[str, Path]) -> AudioFileInfo: - info = soundfile.info(filepath) - return AudioFileInfo(info.samplerate, info.duration, info.channels) - - -def audio_info(filepath: tp.Union[str, Path]) -> AudioFileInfo: - # torchaudio no longer returns useful duration informations for some formats like mp3s. - filepath = Path(filepath) - if filepath.suffix in ['.flac', '.ogg']: # TODO: Validate .ogg can be safely read with av_info - # ffmpeg has some weird issue with flac. - return _soundfile_info(filepath) - else: - return _av_info(filepath) - - -def _av_read(filepath: tp.Union[str, Path], seek_time: float = 0, duration: float = -1.) -> tp.Tuple[torch.Tensor, int]: - """FFMPEG-based audio file reading using PyAV bindings. - Soundfile cannot read mp3 and av_read is more efficient than torchaudio. - - Args: - filepath (str or Path): Path to audio file to read. - seek_time (float): Time at which to start reading in the file. - duration (float): Duration to read from the file. If set to -1, the whole file is read. - Returns: - Tuple[torch.Tensor, int]: Tuple containing audio data and sample rate - """ - _init_av() - with av.open(str(filepath)) as af: - stream = af.streams.audio[0] - sr = stream.codec_context.sample_rate - num_frames = int(sr * duration) if duration >= 0 else -1 - frame_offset = int(sr * seek_time) - # we need a small negative offset otherwise we get some edge artifact - # from the mp3 decoder. - af.seek(int(max(0, (seek_time - 0.1)) / stream.time_base), stream=stream) - frames = [] - length = 0 - for frame in af.decode(streams=stream.index): - current_offset = int(frame.rate * frame.pts * frame.time_base) - strip = max(0, frame_offset - current_offset) - buf = torch.from_numpy(frame.to_ndarray()) - if buf.shape[0] != stream.channels: - buf = buf.view(-1, stream.channels).t() - buf = buf[:, strip:] - frames.append(buf) - length += buf.shape[1] - if num_frames > 0 and length >= num_frames: - break - assert frames - # If the above assert fails, it is likely because we seeked past the end of file point, - # in which case ffmpeg returns a single frame with only zeros, and a weird timestamp. - # This will need proper debugging, in due time. - wav = torch.cat(frames, dim=1) - assert wav.shape[0] == stream.channels - if num_frames > 0: - wav = wav[:, :num_frames] - return f32_pcm(wav), sr - - -def audio_read(filepath: tp.Union[str, Path], seek_time: float = 0., - duration: float = -1., pad: bool = False) -> tp.Tuple[torch.Tensor, int]: - """Read audio by picking the most appropriate backend tool based on the audio format. - - Args: - filepath (str or Path): Path to audio file to read. - seek_time (float): Time at which to start reading in the file. - duration (float): Duration to read from the file. If set to -1, the whole file is read. - pad (bool): Pad output audio if not reaching expected duration. - Returns: - Tuple[torch.Tensor, int]: Tuple containing audio data and sample rate. - """ - fp = Path(filepath) - if fp.suffix in ['.flac', '.ogg']: # TODO: check if we can safely use av_read for .ogg - # There is some bug with ffmpeg and reading flac - info = _soundfile_info(filepath) - frames = -1 if duration <= 0 else int(duration * info.sample_rate) - frame_offset = int(seek_time * info.sample_rate) - wav, sr = soundfile.read(filepath, start=frame_offset, frames=frames, dtype=np.float32) - assert info.sample_rate == sr, f"Mismatch of sample rates {info.sample_rate} {sr}" - wav = torch.from_numpy(wav).t().contiguous() - if len(wav.shape) == 1: - wav = torch.unsqueeze(wav, 0) - elif ( - fp.suffix in ['.wav', '.mp3'] and fp.suffix[1:] in ta.utils.sox_utils.list_read_formats() - and duration <= 0 and seek_time == 0 - ): - # Torchaudio is faster if we load an entire file at once. - wav, sr = ta.load(fp) - else: - wav, sr = _av_read(filepath, seek_time, duration) - if pad and duration > 0: - expected_frames = int(duration * sr) - wav = F.pad(wav, (0, expected_frames - wav.shape[-1])) - return wav, sr - - -def audio_write(stem_name: tp.Union[str, Path], - wav: torch.Tensor, sample_rate: int, - format: str = 'wav', mp3_rate: int = 320, normalize: bool = True, - strategy: str = 'peak', peak_clip_headroom_db: float = 1, - rms_headroom_db: float = 18, loudness_headroom_db: float = 14, - loudness_compressor: bool = False, - log_clipping: bool = True, make_parent_dir: bool = True, - add_suffix: bool = True, channels:int = 1) -> Path: - """Convenience function for saving audio to disk. Returns the filename the audio was written to. - - Args: - stem_name (str or Path): Filename without extension which will be added automatically. - format (str): Either "wav" or "mp3". - mp3_rate (int): kbps when using mp3s. - normalize (bool): if `True` (default), normalizes according to the prescribed - strategy (see after). If `False`, the strategy is only used in case clipping - would happen. - strategy (str): Can be either 'clip', 'peak', or 'rms'. Default is 'peak', - i.e. audio is normalized by its largest value. RMS normalizes by root-mean-square - with extra headroom to avoid clipping. 'clip' just clips. - peak_clip_headroom_db (float): Headroom in dB when doing 'peak' or 'clip' strategy. - rms_headroom_db (float): Headroom in dB when doing 'rms' strategy. This must be much larger - than the `peak_clip` one to avoid further clipping. - loudness_headroom_db (float): Target loudness for loudness normalization. - loudness_compressor (bool): Uses tanh for soft clipping when strategy is 'loudness'. - when strategy is 'loudness'log_clipping (bool): If True, basic logging on stderr when clipping still - occurs despite strategy (only for 'rms'). - make_parent_dir (bool): Make parent directory if it doesn't exist. - Returns: - Path: Path of the saved audio. - """ - assert wav.dtype.is_floating_point, "wav is not floating point" - if wav.dim() == 1: - wav = wav[None] - elif wav.dim() > 2: - raise ValueError("Input wav should be at most 2 dimension.") - assert wav.isfinite().all() - wav = normalize_audio(wav, normalize, strategy, peak_clip_headroom_db, - rms_headroom_db, loudness_headroom_db, log_clipping=log_clipping, - sample_rate=sample_rate, stem_name=str(stem_name)) - if channels > 1: - wav = convert_audio(wav,sample_rate, sample_rate, channels) - kwargs: dict = {} - if format == 'mp3': - suffix = '.mp3' - kwargs.update({"compression": mp3_rate}) - elif format == 'wav': - wav = i16_pcm(wav) - suffix = '.wav' - kwargs.update({"encoding": "PCM_S", "bits_per_sample": 16}) - else: - raise RuntimeError(f"Invalid format {format}. Only wav or mp3 are supported.") - if not add_suffix: - suffix = '' - path = Path(str(stem_name) + suffix) - if make_parent_dir: - path.parent.mkdir(exist_ok=True, parents=True) - try: - ta.save(path, wav, sample_rate, **kwargs) - except Exception: - if path.exists(): - # we do not want to leave half written files around. - path.unlink() - raise - return path diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/shareConversation.ts b/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/shareConversation.ts deleted file mode 100644 index 4768b604a42258d5d97231dd0e44f9198ef1864c..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/shareConversation.ts +++ /dev/null @@ -1,27 +0,0 @@ -import { base } from "$app/paths"; -import { ERROR_MESSAGES, error } from "$lib/stores/errors"; -import { share } from "./utils/share"; - -export async function shareConversation(id: string, title: string) { - try { - const res = await fetch(`${base}/conversation/${id}/share`, { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - }); - - if (!res.ok) { - error.set("Error while sharing conversation, try again."); - console.error("Error while sharing conversation: " + (await res.text())); - return; - } - - const { url } = await res.json(); - - share(url, title); - } catch (err) { - error.set(ERROR_MESSAGES.default); - console.error(err); - } -} diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/Factory.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/Factory.js deleted file mode 100644 index 5be2f18eb71a0e6967c56a0160f811096080f23d..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/Factory.js +++ /dev/null @@ -1,11 +0,0 @@ -import Maker from './Maker.js'; -import ObjectFactory from '../ObjectFactory.js'; -import SetValue from '../../../plugins/utils/object/SetValue.js'; - -ObjectFactory.register('maker', function (styles, customBuilders) { - return new Maker(this.scene, styles, customBuilders); -}); - -SetValue(window, 'RexPlugins.UI.Maker', Maker); - -export default Maker; \ No newline at end of file diff --git a/spaces/AlexWortega/Kandinsky2.0/README.md b/spaces/AlexWortega/Kandinsky2.0/README.md deleted file mode 100644 index 770b71977951ad5c67dc13902d7136fc9a6a07ac..0000000000000000000000000000000000000000 --- a/spaces/AlexWortega/Kandinsky2.0/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Kandinsky2.0 -emoji: 📉 -colorFrom: indigo -colorTo: green -sdk: gradio -sdk_version: 3.11.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/utils/visualizer.py b/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/utils/visualizer.py deleted file mode 100644 index 8c4a1fba06bf6bc680aa59bf645f796283f6f1c6..0000000000000000000000000000000000000000 --- a/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/utils/visualizer.py +++ /dev/null @@ -1,605 +0,0 @@ -# python 3.7 -"""Utility functions for visualizing results on html page.""" - -import base64 -import os.path -import cv2 -import numpy as np - -__all__ = [ - 'get_grid_shape', 'get_blank_image', 'load_image', 'save_image', - 'resize_image', 'add_text_to_image', 'fuse_images', 'HtmlPageVisualizer', - 'VideoReader', 'VideoWriter', 'adjust_pixel_range' -] - - -def adjust_pixel_range(images, min_val=-1.0, max_val=1.0, channel_order='NCHW'): - """Adjusts the pixel range of the input images. - - This function assumes the input array (image batch) is with shape [batch_size, - channel, height, width] if `channel_order = NCHW`, or with shape [batch_size, - height, width] if `channel_order = NHWC`. The returned images are with shape - [batch_size, height, width, channel] and pixel range [0, 255]. - - NOTE: The channel order of output images will remain the same as the input. - - Args: - images: Input images to adjust pixel range. - min_val: Min value of the input images. (default: -1.0) - max_val: Max value of the input images. (default: 1.0) - channel_order: Channel order of the input array. (default: NCHW) - - Returns: - The postprocessed images with dtype `numpy.uint8` and range [0, 255]. - - Raises: - ValueError: If the input `images` are not with type `numpy.ndarray` or the - shape is invalid according to `channel_order`. - """ - if not isinstance(images, np.ndarray): - raise ValueError(f'Images should be with type `numpy.ndarray`!') - - channel_order = channel_order.upper() - if channel_order not in ['NCHW', 'NHWC']: - raise ValueError(f'Invalid channel order `{channel_order}`!') - - if images.ndim != 4: - raise ValueError(f'Input images are expected to be with shape `NCHW` or ' - f'`NHWC`, but `{images.shape}` is received!') - if channel_order == 'NCHW' and images.shape[1] not in [1, 3]: - raise ValueError(f'Input images should have 1 or 3 channels under `NCHW` ' - f'channel order!') - if channel_order == 'NHWC' and images.shape[3] not in [1, 3]: - raise ValueError(f'Input images should have 1 or 3 channels under `NHWC` ' - f'channel order!') - - images = images.astype(np.float32) - images = (images - min_val) * 255 / (max_val - min_val) - images = np.clip(images + 0.5, 0, 255).astype(np.uint8) - if channel_order == 'NCHW': - images = images.transpose(0, 2, 3, 1) - - return images - - -def get_grid_shape(size, row=0, col=0, is_portrait=False): - """Gets the shape of a grid based on the size. - - This function makes greatest effort on making the output grid square if - neither `row` nor `col` is set. If `is_portrait` is set as `False`, the height - will always be equal to or smaller than the width. For example, if input - `size = 16`, output shape will be `(4, 4)`; if input `size = 15`, output shape - will be (3, 5). Otherwise, the height will always be equal to or larger than - the width. - - Args: - size: Size (height * width) of the target grid. - is_portrait: Whether to return a portrait size of a landscape size. - (default: False) - - Returns: - A two-element tuple, representing height and width respectively. - """ - assert isinstance(size, int) - assert isinstance(row, int) - assert isinstance(col, int) - if size == 0: - return (0, 0) - - if row > 0 and col > 0 and row * col != size: - row = 0 - col = 0 - - if row > 0 and size % row == 0: - return (row, size // row) - if col > 0 and size % col == 0: - return (size // col, col) - - row = int(np.sqrt(size)) - while row > 0: - if size % row == 0: - col = size // row - break - row = row - 1 - - return (col, row) if is_portrait else (row, col) - - -def get_blank_image(height, width, channels=3, is_black=True): - """Gets a blank image, either white of black. - - NOTE: This function will always return an image with `RGB` channel order for - color image and pixel range [0, 255]. - - Args: - height: Height of the returned image. - width: Width of the returned image. - channels: Number of channels. (default: 3) - is_black: Whether to return a black image or white image. (default: True) - """ - shape = (height, width, channels) - if is_black: - return np.zeros(shape, dtype=np.uint8) - return np.ones(shape, dtype=np.uint8) * 255 - - -def load_image(path): - """Loads an image from disk. - - NOTE: This function will always return an image with `RGB` channel order for - color image and pixel range [0, 255]. - - Args: - path: Path to load the image from. - - Returns: - An image with dtype `np.ndarray` or `None` if input `path` does not exist. - """ - if not os.path.isfile(path): - return None - - image = cv2.imread(path) - return image[:, :, ::-1] - - -def save_image(path, image): - """Saves an image to disk. - - NOTE: The input image (if colorful) is assumed to be with `RGB` channel order - and pixel range [0, 255]. - - Args: - path: Path to save the image to. - image: Image to save. - """ - if image is None: - return - - assert len(image.shape) == 3 and image.shape[2] in [1, 3] - cv2.imwrite(path, image[:, :, ::-1]) - - -def resize_image(image, *args, **kwargs): - """Resizes image. - - This is a wrap of `cv2.resize()`. - - NOTE: THe channel order of the input image will not be changed. - - Args: - image: Image to resize. - """ - if image is None: - return None - - assert image.ndim == 3 and image.shape[2] in [1, 3] - image = cv2.resize(image, *args, **kwargs) - if image.ndim == 2: - return image[:, :, np.newaxis] - return image - - -def add_text_to_image(image, - text='', - position=None, - font=cv2.FONT_HERSHEY_TRIPLEX, - font_size=1.0, - line_type=cv2.LINE_8, - line_width=1, - color=(255, 255, 255)): - """Overlays text on given image. - - NOTE: The input image is assumed to be with `RGB` channel order. - - Args: - image: The image to overlay text on. - text: Text content to overlay on the image. (default: '') - position: Target position (bottom-left corner) to add text. If not set, - center of the image will be used by default. (default: None) - font: Font of the text added. (default: cv2.FONT_HERSHEY_TRIPLEX) - font_size: Font size of the text added. (default: 1.0) - line_type: Line type used to depict the text. (default: cv2.LINE_8) - line_width: Line width used to depict the text. (default: 1) - color: Color of the text added in `RGB` channel order. (default: - (255, 255, 255)) - - Returns: - An image with target text overlayed on. - """ - if image is None or not text: - return image - - cv2.putText(img=image, - text=text, - org=position, - fontFace=font, - fontScale=font_size, - color=color, - thickness=line_width, - lineType=line_type, - bottomLeftOrigin=False) - - return image - - -def fuse_images(images, - image_size=None, - row=0, - col=0, - is_row_major=True, - is_portrait=False, - row_spacing=0, - col_spacing=0, - border_left=0, - border_right=0, - border_top=0, - border_bottom=0, - black_background=True): - """Fuses a collection of images into an entire image. - - Args: - images: A collection of images to fuse. Should be with shape [num, height, - width, channels]. - image_size: Int or two-element tuple. This field is used to resize the image - before fusing. `None` disables resizing. (default: None) - row: Number of rows used for image fusion. If not set, this field will be - automatically assigned based on `col` and total number of images. - (default: None) - col: Number of columns used for image fusion. If not set, this field will be - automatically assigned based on `row` and total number of images. - (default: None) - is_row_major: Whether the input images should be arranged row-major or - column-major. (default: True) - is_portrait: Only active when both `row` and `col` should be assigned - automatically. (default: False) - row_spacing: Space between rows. (default: 0) - col_spacing: Space between columns. (default: 0) - border_left: Width of left border. (default: 0) - border_right: Width of right border. (default: 0) - border_top: Width of top border. (default: 0) - border_bottom: Width of bottom border. (default: 0) - - Returns: - The fused image. - - Raises: - ValueError: If the input `images` is not with shape [num, height, width, - width]. - """ - if images is None: - return images - - if not images.ndim == 4: - raise ValueError(f'Input `images` should be with shape [num, height, ' - f'width, channels], but {images.shape} is received!') - - num, image_height, image_width, channels = images.shape - if image_size is not None: - if isinstance(image_size, int): - image_size = (image_size, image_size) - assert isinstance(image_size, (list, tuple)) and len(image_size) == 2 - width, height = image_size - else: - height, width = image_height, image_width - row, col = get_grid_shape(num, row=row, col=col, is_portrait=is_portrait) - fused_height = ( - height * row + row_spacing * (row - 1) + border_top + border_bottom) - fused_width = ( - width * col + col_spacing * (col - 1) + border_left + border_right) - fused_image = get_blank_image( - fused_height, fused_width, channels=channels, is_black=black_background) - images = images.reshape(row, col, image_height, image_width, channels) - if not is_row_major: - images = images.transpose(1, 0, 2, 3, 4) - - for i in range(row): - y = border_top + i * (height + row_spacing) - for j in range(col): - x = border_left + j * (width + col_spacing) - if image_size is not None: - image = cv2.resize(images[i, j], image_size) - else: - image = images[i, j] - fused_image[y:y + height, x:x + width] = image - - return fused_image - - -def get_sortable_html_header(column_name_list, sort_by_ascending=False): - """Gets header for sortable html page. - - Basically, the html page contains a sortable table, where user can sort the - rows by a particular column by clicking the column head. - - Example: - - column_name_list = [name_1, name_2, name_3] - header = get_sortable_html_header(column_name_list) - footer = get_sortable_html_footer() - sortable_table = ... - html_page = header + sortable_table + footer - - Args: - column_name_list: List of column header names. - sort_by_ascending: Default sorting order. If set as `True`, the html page - will be sorted by ascending order when the header is clicked for the first - time. - - Returns: - A string, which represents for the header for a sortable html page. - """ - header = '\n'.join([ - '', - '', - '', - '', - '', - '', - '', - '', - '', - '', - '', - '', - '
\n\n\n\n' - - -def encode_image_to_html_str(image, image_size=None): - """Encodes an image to html language. - - Args: - image: The input image to encode. Should be with `RGB` channel order. - image_size: Int or two-element tuple. This field is used to resize the image - before encoding. `None` disables resizing. (default: None) - - Returns: - A string which represents the encoded image. - """ - if image is None: - return '' - - assert len(image.shape) == 3 and image.shape[2] in [1, 3] - - # Change channel order to `BGR`, which is opencv-friendly. - image = image[:, :, ::-1] - - # Resize the image if needed. - if image_size is not None: - if isinstance(image_size, int): - image_size = (image_size, image_size) - assert isinstance(image_size, (list, tuple)) and len(image_size) == 2 - image = cv2.resize(image, image_size) - - # Encode the image to html-format string. - encoded_image = cv2.imencode(".jpg", image)[1].tostring() - encoded_image_base64 = base64.b64encode(encoded_image).decode('utf-8') - html_str = f'', - '']) - for idx, column_name in enumerate(column_name_list): - header += f' \n' - header += '\n' - header += '\n' - - return header - - -def get_sortable_html_footer(): - """Gets footer for sortable html page. - - Check function `get_sortable_html_header()` for more details. - """ - return '\n{column_name} \n' - header += '
'
-
- return html_str
-
-
-class HtmlPageVisualizer(object):
- """Defines the html page visualizer.
-
- This class can be used to visualize image results as html page. Basically, it
- is based on an html-format sorted table with helper functions
- `get_sortable_html_header()`, `get_sortable_html_footer()`, and
- `encode_image_to_html_str()`. To simplify the usage, specifying the following
- fields is enough to create a visualization page:
-
- (1) num_rows: Number of rows of the table (header-row exclusive).
- (2) num_cols: Number of columns of the table.
- (3) header contents (optional): Title of each column.
-
- NOTE: `grid_size` can be used to assign `num_rows` and `num_cols`
- automatically.
-
- Example:
-
- html = HtmlPageVisualizer(num_rows, num_cols)
- html.set_headers([...])
- for i in range(num_rows):
- for j in range(num_cols):
- html.set_cell(i, j, text=..., image=...)
- html.save('visualize.html')
- """
-
- def __init__(self,
- num_rows=0,
- num_cols=0,
- grid_size=0,
- is_portrait=False,
- viz_size=None):
- if grid_size > 0:
- num_rows, num_cols = get_grid_shape(
- grid_size, row=num_rows, col=num_cols, is_portrait=is_portrait)
- assert num_rows > 0 and num_cols > 0
-
- self.num_rows = num_rows
- self.num_cols = num_cols
- self.viz_size = viz_size
- self.headers = ['' for _ in range(self.num_cols)]
- self.cells = [[{
- 'text': '',
- 'image': '',
- } for _ in range(self.num_cols)] for _ in range(self.num_rows)]
-
- def set_header(self, column_idx, content):
- """Sets the content of a particular header by column index."""
- self.headers[column_idx] = content
-
- def set_headers(self, contents):
- """Sets the contents of all headers."""
- if isinstance(contents, str):
- contents = [contents]
- assert isinstance(contents, (list, tuple))
- assert len(contents) == self.num_cols
- for column_idx, content in enumerate(contents):
- self.set_header(column_idx, content)
-
- def set_cell(self, row_idx, column_idx, text='', image=None):
- """Sets the content of a particular cell.
-
- Basically, a cell contains some text as well as an image. Both text and
- image can be empty.
-
- Args:
- row_idx: Row index of the cell to edit.
- column_idx: Column index of the cell to edit.
- text: Text to add into the target cell.
- image: Image to show in the target cell. Should be with `RGB` channel
- order.
- """
- self.cells[row_idx][column_idx]['text'] = text
- self.cells[row_idx][column_idx]['image'] = encode_image_to_html_str(
- image, self.viz_size)
-
- def save(self, save_path):
- """Saves the html page."""
- html = ''
- for i in range(self.num_rows):
- html += f'\n' - for j in range(self.num_cols): - text = self.cells[i][j]['text'] - image = self.cells[i][j]['image'] - if text: - html += f' \n' - - header = get_sortable_html_header(self.headers) - footer = get_sortable_html_footer() - - with open(save_path, 'w') as f: - f.write(header + html + footer) - - -class VideoReader(object): - """Defines the video reader. - - This class can be used to read frames from a given video. - """ - - def __init__(self, path): - """Initializes the video reader by loading the video from disk.""" - if not os.path.isfile(path): - raise ValueError(f'Video `{path}` does not exist!') - - self.path = path - self.video = cv2.VideoCapture(path) - assert self.video.isOpened() - self.position = 0 - - self.length = int(self.video.get(cv2.CAP_PROP_FRAME_COUNT)) - self.frame_height = int(self.video.get(cv2.CAP_PROP_FRAME_HEIGHT)) - self.frame_width = int(self.video.get(cv2.CAP_PROP_FRAME_WIDTH)) - self.fps = self.video.get(cv2.CAP_PROP_FPS) - - def __del__(self): - """Releases the opened video.""" - self.video.release() - - def read(self, position=None): - """Reads a certain frame. - - NOTE: The returned frame is assumed to be with `RGB` channel order. - - Args: - position: Optional. If set, the reader will read frames from the exact - position. Otherwise, the reader will read next frames. (default: None) - """ - if position is not None and position < self.length: - self.video.set(cv2.CAP_PROP_POS_FRAMES, position) - self.position = position - - success, frame = self.video.read() - self.position = self.position + 1 - - return frame[:, :, ::-1] if success else None - - -class VideoWriter(object): - """Defines the video writer. - - This class can be used to create a video. - - NOTE: `.avi` and `DIVX` is the most recommended codec format since it does not - rely on other dependencies. - """ - - def __init__(self, path, frame_height, frame_width, fps=24, codec='DIVX'): - """Creates the video writer.""" - self.path = path - self.frame_height = frame_height - self.frame_width = frame_width - self.fps = fps - self.codec = codec - - self.video = cv2.VideoWriter(filename=path, - fourcc=cv2.VideoWriter_fourcc(*codec), - fps=fps, - frameSize=(frame_width, frame_height)) - - def __del__(self): - """Releases the opened video.""" - self.video.release() - - def write(self, frame): - """Writes a target frame. - - NOTE: The input frame is assumed to be with `RGB` channel order. - """ - self.video.write(frame[:, :, ::-1]) diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/models/stylegan2/op/__init__.py b/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/models/stylegan2/op/__init__.py deleted file mode 100644 index d0918d92285955855be89f00096b888ee5597ce3..0000000000000000000000000000000000000000 --- a/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/models/stylegan2/op/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .fused_act import FusedLeakyReLU, fused_leaky_relu -from .upfirdn2d import upfirdn2d diff --git a/spaces/AnandSoni2001/StockMarketPrediction/app.py b/spaces/AnandSoni2001/StockMarketPrediction/app.py deleted file mode 100644 index 1b4e9945212325586c48533affbe2b76e038b799..0000000000000000000000000000000000000000 --- a/spaces/AnandSoni2001/StockMarketPrediction/app.py +++ /dev/null @@ -1,393 +0,0 @@ -#Import Libraries -import streamlit as st -import plotly.graph_objects as go -import pandas as pd -import plotly.express as px -from yahoo_fin import stock_info -from yahoo_fin.stock_info import * -import math -import numpy as np -from sklearn.preprocessing import MinMaxScaler -import joblib - -#Heading -st.title('Research Project on Stock Market Analysis and Prediction') -st.write("#") - -#TCS Data Taken -tcsdaily = stock_info.get_data("TCS.NS", interval="1d") -tcsmonthly= stock_info.get_data("TCS.NS", interval="1mo") -tcsyearly = pd.read_csv('data/tcs-yearly.csv') - -#Reliance Data Taken -reldaily = stock_info.get_data("RELIANCE.NS", interval="1d") -relmonthly= stock_info.get_data("RELIANCE.NS", interval="1mo") -relyearly = pd.read_csv('data/relianceind-yearly.csv') - -#Infosys Data Taken -infdaily = stock_info.get_data("INFY.NS", interval="1d") -infmonthly= stock_info.get_data("INFY.NS", interval="1mo") -infyearly = pd.read_csv('data/infosys-yearly.csv') - -#Select Box -comp = st.selectbox('Select a Company from the below options :', ('Tata Consultancy Services - TCS', 'Reliance Industries - RELIANCE', 'Infosys - INFY')) - -if comp == 'Tata Consultancy Services - TCS': - col1, col2, col3, col4 = st.columns(4) - x = round(stock_info.get_live_price("TCS.NS"),2) - y = round(tcsdaily['close'].iloc[-2],2) - tcs = get_stats('TCS.NS')['Value'] - col1.metric(label="Market Price", value=x, delta = round(x-y,2)) - col2.metric(label="52 Week High", value=tcs[3]) - col3.metric(label="52 Week Low", value=tcs[4]) - col4.metric(label="Return on Equity", value=tcs[34]) - - col1, col2, col3, col4 = st.columns(4) - col1.metric(label='Previous Close', value=y) - col2.metric(label="Book Value Per Share", value=tcs[48]) - col3.metric(label='Earning Per Share', value=tcs[41]) - col4.metric(label="Dividend Yield", value=tcs[22]) - - -if comp == 'Reliance Industries - RELIANCE': - col1, col2, col3, col4 = st.columns(4) - x = round(stock_info.get_live_price("RELIANCE.NS"),2) - y = round(reldaily['close'].iloc[-2],2) - rel = get_stats('RELIANCE.NS')['Value'] - col1.metric(label="Market Price", value=x, delta = round(x-y,2)) - col2.metric(label="52 Week High", value=rel[3]) - col3.metric(label="52 Week Low", value=rel[4]) - col4.metric(label="Return on Equity", value='8.21%') - - col1, col2, col3, col4 = st.columns(4) - col1.metric(label='Previous Close', value=y) - col2.metric(label="Book Value Per Share", value=1202.45) - col3.metric(label='Earning Per Share', value=93.96) - col4.metric(label="Dividend Yield", value='0.36%') - -if comp == 'Infosys - INFY': - col1, col2, col3, col4 = st.columns(4) - x = round(stock_info.get_live_price("INFY.NS"),2) - y = round(infdaily['close'].iloc[-2],2) - inf = get_stats('INFY.NS')['Value'] - col1.metric(label="Market Price", value=x, delta = round(x-y,2)) - col2.metric(label="52 Week High", value=inf[3]) - col3.metric(label="52 Week Low", value=inf[4]) - col4.metric(label="Return on Equity", value=inf[34]) - - col1, col2, col3, col4 = st.columns(4) - col1.metric(label='Previous Close', value=y) - col2.metric(label="Book Value Per Share", value=inf[48]) - col3.metric(label='Earning Per Share', value=inf[41]) - col4.metric(label="Dividend Yield", value=inf[22]) - -#Tab for Hist Data -st.write("#") -st.subheader('Historic data : ') -option1, option2, option3 = st.tabs(["Daily", "Monthly", "Yearly"]) - -cl1, cl2, cl3, cl4 = st.columns(4) -with cl1: - ag1 = st.checkbox('Close', value='True') -with cl2: - ag2 = st.checkbox('Open', value='True') -with cl3: - ag3 = st.checkbox('High', value='True') -with cl4: - ag4 = st.checkbox('Low', value='True') - -with option1: - opt = st.radio("Select timelength :", ('All Time', '1 Week', '1 Month', '1 Year')) - st.write('', unsafe_allow_html=True) - - if comp == 'Tata Consultancy Services - TCS': - if opt=='All Time' : - fig = px.line(tcsdaily, y='close',markers=False, title='Tata Consultancy Services daily data of all time') - if opt=='1 Week' : - fig = px.line(tcsdaily.tail(5), y='close',markers=False, title='Tata Consultancy Services daily data of 1 week') - if opt=='1 Month' : - fig = px.line(tcsdaily.tail(20), y='close',markers=False, title='Tata Consultancy Services daily data of 1 month') - if opt=='1 Year' : - fig = px.line(tcsdaily.tail(251), y='close',markers=False, title='Tata Consultancy Services daily data of 1 year') - st.plotly_chart(fig, use_container_width=True) - - fig = go.Figure() - if(ag1): - fig.add_trace(go.Scatter(x=tcsdaily.index,y=tcsdaily['close'], name='Closing')) - if(ag2): - fig.add_trace(go.Scatter(x=tcsdaily.index,y=tcsdaily['open'], name = 'Opening', line=dict(color='yellow'))) - if(ag3): - fig.add_trace(go.Scatter(x=tcsdaily.index,y=tcsdaily['high'], name = 'High', line=dict(color='green'))) - if(ag4): - fig.add_trace(go.Scatter(x=tcsdaily.index,y=tcsdaily['low'], name = 'Low', line=dict(color='red'))) - fig.update_layout(xaxis_title='Date', yaxis_title='Price', title='Comparing other relevant parameters along close') - st.plotly_chart(fig, use_container_width=True, title='Comparing other relevant parameters') - - if comp == 'Infosys - INFY': - if opt=='All Time' : - fig = px.line(infdaily, y='close',markers=False, title='Infosys daily data of all time') - if opt=='1 Week' : - fig = px.line(infdaily.tail(5), y='close',markers=False, title='Infosys daily data of 1 week') - if opt=='1 Month' : - fig = px.line(infdaily.tail(20), y='close',markers=False, title='Infosys daily data of 1 month') - if opt=='1 Year' : - fig = px.line(infdaily.tail(251), y='close',markers=False, title='Infosys daily data of 1 year') - st.plotly_chart(fig, use_container_width=True) - - fig = go.Figure() - if(ag1): - fig.add_trace(go.Scatter(x=infdaily.index, y=infdaily['close'], name='Closing', line=dict(color='blue'))) - if(ag2): - fig.add_trace(go.Scatter(x=infdaily.index,y=infdaily['open'], name = 'Opening', line=dict(color='yellow'))) - if(ag3): - fig.add_trace(go.Scatter(x=infdaily.index,y=infdaily['high'], name = 'High', line=dict(color='green'))) - if(ag4): - fig.add_trace(go.Scatter(x=infdaily.index,y=infdaily['low'], name = 'Low', line=dict(color='red'))) - fig.update_layout(xaxis_title='Date', yaxis_title='Price', title='Comparing other relevant parameters') - st.plotly_chart(fig, use_container_width=True) - - if comp == 'Reliance Industries - RELIANCE': - if opt=='All Time' : - fig = px.line(reldaily, y='close',markers=False, title='Reliance Industries daily data of all time') - if opt=='1 Week' : - fig = px.line(reldaily.tail(5), y='close',markers=False, title='Reliance Industries daily data of 1 week') - if opt=='1 Month' : - fig = px.line(reldaily.tail(20), y='close',markers=False, title='Reliance Industries daily data of 1 month') - if opt=='1 Year' : - fig = px.line(reldaily.tail(251), y='close',markers=False, title='Reliance Industries daily data of 1 year') - st.plotly_chart(fig, use_container_width=True) - - fig = go.Figure() - if(ag1): - fig.add_trace(go.Scatter(x=reldaily.index, y=reldaily['close'], name='Closing', line=dict(color='blue'))) - if(ag2): - fig.add_trace(go.Scatter(x=reldaily.index,y=reldaily['open'], name = 'Opening', line=dict(color='yellow'))) - if(ag3): - fig.add_trace(go.Scatter(x=reldaily.index,y=reldaily['high'], name = 'High', line=dict(color='green'))) - if(ag4): - fig.add_trace(go.Scatter(x=reldaily.index,y=reldaily['low'], name = 'Low', line=dict(color='red'))) - fig.update_layout(xaxis_title='Date', yaxis_title='Price', title='Comparing other relevant parameters along close') - st.plotly_chart(fig, use_container_width=True) - -with option2: - if comp == 'Tata Consultancy Services - TCS': - fig = px.line(tcsmonthly,y='close', markers=False, title='Tata Consultancy Services monthly data') - st.plotly_chart(fig, use_container_width=True) - - fig = go.Figure() - if(ag1): - fig.add_trace(go.Scatter(x=tcsmonthly.index,y=tcsmonthly['close'], name='Closing', line=dict(color='blue'))) - if(ag2): - fig.add_trace(go.Scatter(x=tcsmonthly.index,y=tcsmonthly['open'], name = 'Opening', line=dict(color='yellow'))) - if(ag3): - fig.add_trace(go.Scatter(x=tcsmonthly.index,y=tcsmonthly['high'], name = 'High', line=dict(color='green'))) - if(ag4): - fig.add_trace(go.Scatter(x=tcsmonthly.index,y=tcsmonthly['low'], name = 'Low', line=dict(color='red'))) - fig.update_layout(xaxis_title='Month', yaxis_title='Price', title='Comparing other relevant parameters') - st.plotly_chart(fig, use_container_width=True) - - if comp == 'Infosys - INFY': - fig = px.line(infmonthly, y='close',markers=False, title='Infosys monthly data') - st.plotly_chart(fig, use_container_width=True) - - fig = go.Figure() - if(ag1): - fig.add_trace(go.Scatter(x=infmonthly.index, y=infmonthly['close'], name='Closing', line=dict(color='blue'))) - if(ag2): - fig.add_trace(go.Scatter(x=infmonthly.index,y=infmonthly['open'], name = 'Opening', line=dict(color='yellow'))) - if(ag3): - fig.add_trace(go.Scatter(x=infmonthly.index,y=infmonthly['high'], name = 'High', line=dict(color='green'))) - if(ag4): - fig.add_trace(go.Scatter(y=infmonthly['low'], name = 'Low', line=dict(color='red'))) - fig.update_layout(xaxis_title='Month', yaxis_title='Price', title='Comparing other relevant parameters') - st.plotly_chart(fig, use_container_width=True) - - if comp == 'Reliance Industries - RELIANCE': - fig = px.line(relmonthly, y='close',markers=False, title='Reliance Industries monthly data') - st.plotly_chart(fig, use_container_width=True) - - fig = go.Figure() - if(ag1): - fig.add_trace(go.Scatter(x=relmonthly.index,y=relmonthly['close'], name='Closing', line=dict(color='blue'))) - if(ag2): - fig.add_trace(go.Scatter(x=relmonthly.index,y=relmonthly['open'], name = 'Opening', line=dict(color='yellow'))) - if(ag3): - fig.add_trace(go.Scatter(x=relmonthly.index,y=relmonthly['high'], name = 'High', line=dict(color='green'))) - if(ag4): - fig.add_trace(go.Scatter(x=relmonthly.index,y=relmonthly['low'], name = 'Low', line=dict(color='red'))) - fig.update_layout(xaxis_title='Month', yaxis_title='Price', title='Comparing other relevant parameters') - st.plotly_chart(fig, use_container_width=True) - -with option3: - if comp == 'Tata Consultancy Services - TCS': - fig = px.line(tcsyearly, x='Year', y='Close Price',markers=True, title='Tata Consultancy Services Yearly Data from 2004') - st.plotly_chart(fig, use_container_width=True) - - fig = go.Figure() - if(ag1): - fig.add_trace(go.Scatter(x=tcsyearly['Year'], y=tcsyearly['Close Price'], name='Closing', line=dict(color='blue'))) - if(ag2): - fig.add_trace(go.Scatter(x=tcsyearly['Year'], y=tcsyearly['Open Price'], name = 'Opening', line=dict(color='yellow'))) - if(ag3): - fig.add_trace(go.Scatter(x=tcsyearly['Year'], y=tcsyearly['High Price'], name = 'High', line=dict(color='green'))) - if(ag4): - fig.add_trace(go.Scatter(x=tcsyearly['Year'], y=tcsyearly['Low Price'], name = 'Low', line=dict(color='red'))) - fig.update_layout(xaxis_title='Year', yaxis_title='Price', title='Comparing other relevant parameters along close price') - st.plotly_chart(fig, use_container_width=True, title='Comparing other relevant parameters') - - if comp == 'Infosys - INFY': - fig = px.line(infyearly, x='Year', y='Close Price',markers=True, title='Infosys Yearly Data from 2004') - st.plotly_chart(fig, use_container_width=True) - - fig = go.Figure() - if(ag1): - fig.add_trace(go.Scatter(x=infyearly['Year'], y=infyearly['Close Price'], name='Closing', line=dict(color='blue'))) - if(ag2): - fig.add_trace(go.Scatter(x=infyearly['Year'], y=infyearly['Open Price'], name = 'Opening', line=dict(color='yellow'))) - if(ag3): - fig.add_trace(go.Scatter(x=infyearly['Year'], y=infyearly['High Price'], name = 'High', line=dict(color='green'))) - if(ag4): - fig.add_trace(go.Scatter(x=infyearly['Year'], y=infyearly['Low Price'], name = 'Low', line=dict(color='red'))) - fig.update_layout(xaxis_title='Year', yaxis_title='Price', title='Comparing other relevant parameters') - st.plotly_chart(fig, use_container_width=True) - - if comp == 'Reliance Industries - RELIANCE': - fig = px.line(relyearly, x='Year', y='Close Price',markers=True, title='Reliance Industries Yearly Data from 2004') - st.plotly_chart(fig, use_container_width=True) - - fig = go.Figure() - if(ag1): - fig.add_trace(go.Scatter(x=relyearly['Year'], y=relyearly['Close Price'], name='Closing', line=dict(color='blue'))) - if(ag2): - fig.add_trace(go.Scatter(x=relyearly['Year'], y=relyearly['Open Price'], name = 'Opening', line=dict(color='yellow'))) - if(ag3): - fig.add_trace(go.Scatter(x=relyearly['Year'], y=relyearly['High Price'], name = 'High', line=dict(color='green'))) - if(ag4): - fig.add_trace(go.Scatter(x=relyearly['Year'], y=relyearly['Low Price'], name = 'Low', line=dict(color='red'))) - fig.update_layout(xaxis_title='Year', yaxis_title='Price', title='Comparing other relevant parameters') - st.plotly_chart(fig, use_container_width=True) - -#Predictions -st.write("#") -st.subheader('Predict : ') - -if st.button('Click Here'): - if comp == 'Tata Consultancy Services - TCS': - x = round(stock_info.get_live_price("TCS.NS"),2) - tcsweekly = stock_info.get_data("TCS.NS", interval="1d") - tcsweekly=tcsweekly.dropna() - values = tcsweekly['close'].values - data_len = math.ceil(len(values)*0.8) - scaler = MinMaxScaler(feature_range=(0,1)) - scaled_data = scaler.fit_transform(values.reshape(-1,1)) - test_data = scaled_data[data_len-60: , : ] - x_test = [] - for i in range(60, len(test_data)): - x_test.append(test_data[i-60:i, 0]) - x_test = np.array(x_test) - x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1)) - new = joblib.load('tcsdail_1.pkl') - ans = new.predict(x_test) - ans1 = scaler.inverse_transform(ans) - val = np.around(ans1[-1][0], decimals=2) - st.metric(label="Prediction", value=val, delta = round(val-x,2)) - - if comp == 'Reliance Industries - RELIANCE': - x = round(stock_info.get_live_price("RELIANCE.NS"),2) - relweekly = stock_info.get_data("RELIANCE.NS", interval="1d") - relweekly=relweekly.dropna() - values = relweekly['close'].values - data_len = math.ceil(len(values)*0.8) - scaler = MinMaxScaler(feature_range=(0,1)) - scaled_data = scaler.fit_transform(values.reshape(-1,1)) - test_data = scaled_data[data_len-60: , : ] - x_test = [] - for i in range(60, len(test_data)): - x_test.append(test_data[i-60:i, 0]) - x_test = np.array(x_test) - x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1)) - new = joblib.load('reldail_1.pkl') - ans = new.predict(x_test) - ans1 = scaler.inverse_transform(ans) - val = np.around(ans1[-1][0], decimals=2) - st.metric(label="Prediction", value=val, delta = round(val-x,2)) - - if comp == 'Infosys - INFY': - x = round(stock_info.get_live_price("INFY.NS"),2) - infweekly = stock_info.get_data("INFY.NS", interval="1d") - infweekly=infweekly.dropna() - values = infweekly['close'].values - data_len = math.ceil(len(values)*0.8) - scaler = MinMaxScaler(feature_range=(0,1)) - scaled_data = scaler.fit_transform(values.reshape(-1,1)) - test_data = scaled_data[data_len-60: , : ] - x_test = [] - for i in range(60, len(test_data)): - x_test.append(test_data[i-60:i, 0]) - x_test = np.array(x_test) - x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1)) - new = joblib.load('infdail_1.pkl') - ans = new.predict(x_test) - ans1 = scaler.inverse_transform(ans) - val = np.around(ans1[-1][0], decimals=2) - st.metric(label="Prediction", value=val, delta = round(val-x,2)) - - -#Tab for Hist Data -st.write("#") -st.subheader('Financial data : ') -a1, a2, a3 = st.tabs(["Revenue & Profit", "Net Worth", "Shareholding Pattern"]) - -tier=['Promoters', 'Mutual Funds', 'Retail', 'Foreign Institutions','Others'] -y=['2018', '2019', '2020', '2021', '2022'] - -with a1: - st.caption('All values in Crs') - if comp == 'Infosys - INFY': - chart_data = pd.DataFrame([[70522,16029], [82675,15404], [90791,16594], [100472,19351], [121641,22110]], - index=y, columns=["Revenue", "Profit"]) - st.bar_chart(chart_data, height=350) - - if comp == 'Tata Consultancy Services - TCS': - chart_data = pd.DataFrame([[123104,25826], [146463,31472], [156949,32430], [164177,32430], [191754,38327]], - index=y, columns=["Revenue", "Profit"]) - st.bar_chart(chart_data, height=350) - - if comp == 'Reliance Industries - RELIANCE': - chart_data = pd.DataFrame([[408265,36075], [583094,39588], [611645,39354], [486326,49128], [721634,60705]], - index=y, columns=["Revenue", "Profit"]) - st.bar_chart(chart_data, height=350) - - -with a2: - st.caption('All values in Crs') - if comp == 'Infosys - INFY': - chart_data = pd.DataFrame([64923, 64948, 65450, 76351, 75350], index=y, columns=['Net Worth']) - st.bar_chart(chart_data, height=350) - - if comp == 'Tata Consultancy Services - TCS': - chart_data = pd.DataFrame([85128, 89446, 84126, 86433, 89139], index=y, columns=['Net Worth']) - st.bar_chart(chart_data, height=350) - - if comp == 'Reliance Industries - RELIANCE': - chart_data = pd.DataFrame([293506, 387112, 453331, 700172, 779485], index=y, columns=['Net Worth']) - st.bar_chart(chart_data, height=350) - -with a3: - st.caption('As of March, 2023') - if comp == 'Infosys - INFY': - x = [15.11, 17.71, 18.22, 36.28, 12.68] - fig = px.pie(values=x, names=tier) - st.plotly_chart(fig, use_container_width=True, height=350) - - if comp == 'Tata Consultancy Services - TCS': - x = [72.30, 3.31, 5.96, 12.94, 5.49] - fig = px.pie(values=x, names=tier) - st.plotly_chart(fig, use_container_width=True, height=350) - - if comp == 'Reliance Industries - RELIANCE': - x = [50.49, 5.81, 11.64, 23.43, 8.63] - fig = px.pie(values=x, names=tier) - st.plotly_chart(fig, use_container_width=True, height=350) - -st.caption('The Web Application was made by Anand Soni and Deepak Rathore.') diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/latent_diffusion/test_latent_diffusion_superresolution.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/latent_diffusion/test_latent_diffusion_superresolution.py deleted file mode 100644 index d21ead543af8bbbf60cb16177b76deb5bf33595e..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/latent_diffusion/test_latent_diffusion_superresolution.py +++ /dev/null @@ -1,131 +0,0 @@ -# coding=utf-8 -# Copyright 2023 HuggingFace Inc. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import random -import unittest - -import numpy as np -import torch - -from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNet2DModel, VQModel -from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device -from diffusers.utils.testing_utils import enable_full_determinism, require_torch - - -enable_full_determinism() - - -class LDMSuperResolutionPipelineFastTests(unittest.TestCase): - @property - def dummy_image(self): - batch_size = 1 - num_channels = 3 - sizes = (32, 32) - - image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device) - return image - - @property - def dummy_uncond_unet(self): - torch.manual_seed(0) - model = UNet2DModel( - block_out_channels=(32, 64), - layers_per_block=2, - sample_size=32, - in_channels=6, - out_channels=3, - down_block_types=("DownBlock2D", "AttnDownBlock2D"), - up_block_types=("AttnUpBlock2D", "UpBlock2D"), - ) - return model - - @property - def dummy_vq_model(self): - torch.manual_seed(0) - model = VQModel( - block_out_channels=[32, 64], - in_channels=3, - out_channels=3, - down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], - up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], - latent_channels=3, - ) - return model - - def test_inference_superresolution(self): - device = "cpu" - unet = self.dummy_uncond_unet - scheduler = DDIMScheduler() - vqvae = self.dummy_vq_model - - ldm = LDMSuperResolutionPipeline(unet=unet, vqvae=vqvae, scheduler=scheduler) - ldm.to(device) - ldm.set_progress_bar_config(disable=None) - - init_image = self.dummy_image.to(device) - - generator = torch.Generator(device=device).manual_seed(0) - image = ldm(image=init_image, generator=generator, num_inference_steps=2, output_type="numpy").images - - image_slice = image[0, -3:, -3:, -1] - - assert image.shape == (1, 64, 64, 3) - expected_slice = np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150]) - - assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 - - @unittest.skipIf(torch_device != "cuda", "This test requires a GPU") - def test_inference_superresolution_fp16(self): - unet = self.dummy_uncond_unet - scheduler = DDIMScheduler() - vqvae = self.dummy_vq_model - - # put models in fp16 - unet = unet.half() - vqvae = vqvae.half() - - ldm = LDMSuperResolutionPipeline(unet=unet, vqvae=vqvae, scheduler=scheduler) - ldm.to(torch_device) - ldm.set_progress_bar_config(disable=None) - - init_image = self.dummy_image.to(torch_device) - - image = ldm(init_image, num_inference_steps=2, output_type="numpy").images - - assert image.shape == (1, 64, 64, 3) - - -@slow -@require_torch -class LDMSuperResolutionPipelineIntegrationTests(unittest.TestCase): - def test_inference_superresolution(self): - init_image = load_image( - "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" - "/vq_diffusion/teddy_bear_pool.png" - ) - init_image = init_image.resize((64, 64), resample=PIL_INTERPOLATION["lanczos"]) - - ldm = LDMSuperResolutionPipeline.from_pretrained("duongna/ldm-super-resolution", device_map="auto") - ldm.set_progress_bar_config(disable=None) - - generator = torch.manual_seed(0) - image = ldm(image=init_image, generator=generator, num_inference_steps=20, output_type="numpy").images - - image_slice = image[0, -3:, -3:, -1] - - assert image.shape == (1, 256, 256, 3) - expected_slice = np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907]) - - assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 diff --git a/spaces/Andy1621/uniformer_image_detection/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py deleted file mode 100644 index d2f080e9d3b1ddade22341aa38c6258eaee78a50..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py +++ /dev/null @@ -1,52 +0,0 @@ -_base_ = [ - '../_base_/models/fast_rcnn_r50_fpn.py', - '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' -] -dataset_type = 'CocoDataset' -data_root = 'data/coco/' -img_norm_cfg = dict( - mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadProposals', num_max_proposals=2000), - dict(type='LoadAnnotations', with_bbox=True), - dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), - dict(type='RandomFlip', flip_ratio=0.5), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='DefaultFormatBundle'), - dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']), -] -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadProposals', num_max_proposals=None), - dict( - type='MultiScaleFlipAug', - img_scale=(1333, 800), - flip=False, - transforms=[ - dict(type='Resize', keep_ratio=True), - dict(type='RandomFlip'), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='ImageToTensor', keys=['img']), - dict(type='ToTensor', keys=['proposals']), - dict( - type='ToDataContainer', - fields=[dict(key='proposals', stack=False)]), - dict(type='Collect', keys=['img', 'proposals']), - ]) -] -data = dict( - samples_per_gpu=2, - workers_per_gpu=2, - train=dict( - proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_train2017.pkl', - pipeline=train_pipeline), - val=dict( - proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl', - pipeline=test_pipeline), - test=dict( - proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl', - pipeline=test_pipeline)) diff --git a/spaces/Andy1621/uniformer_image_detection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/spaces/Andy1621/uniformer_image_detection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py deleted file mode 100644 index 2d2816c2dee68b60376e67e78e9fba277da826c0..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/spaces/Andy1621/uniformer_image_detection/configs/swin/cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/swin/cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py deleted file mode 100644 index e01a9eff6197fb80e3a541910c9b94c00510323e..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/configs/swin/cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py +++ /dev/null @@ -1,140 +0,0 @@ -_base_ = [ - '../_base_/models/cascade_mask_rcnn_swin_fpn.py', - '../_base_/datasets/coco_instance.py', - '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' -] - -model = dict( - backbone=dict( - embed_dim=96, - depths=[2, 2, 6, 2], - num_heads=[3, 6, 12, 24], - window_size=7, - ape=False, - drop_path_rate=0.2, - patch_norm=True, - use_checkpoint=False - ), - neck=dict(in_channels=[96, 192, 384, 768]), - roi_head=dict( - bbox_head=[ - dict( - type='ConvFCBBoxHead', - num_shared_convs=4, - num_shared_fcs=1, - in_channels=256, - conv_out_channels=256, - fc_out_channels=1024, - roi_feat_size=7, - num_classes=80, - bbox_coder=dict( - type='DeltaXYWHBBoxCoder', - target_means=[0., 0., 0., 0.], - target_stds=[0.1, 0.1, 0.2, 0.2]), - reg_class_agnostic=False, - reg_decoded_bbox=True, - norm_cfg=dict(type='SyncBN', requires_grad=True), - loss_cls=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), - loss_bbox=dict(type='GIoULoss', loss_weight=10.0)), - dict( - type='ConvFCBBoxHead', - num_shared_convs=4, - num_shared_fcs=1, - in_channels=256, - conv_out_channels=256, - fc_out_channels=1024, - roi_feat_size=7, - num_classes=80, - bbox_coder=dict( - type='DeltaXYWHBBoxCoder', - target_means=[0., 0., 0., 0.], - target_stds=[0.05, 0.05, 0.1, 0.1]), - reg_class_agnostic=False, - reg_decoded_bbox=True, - norm_cfg=dict(type='SyncBN', requires_grad=True), - loss_cls=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), - loss_bbox=dict(type='GIoULoss', loss_weight=10.0)), - dict( - type='ConvFCBBoxHead', - num_shared_convs=4, - num_shared_fcs=1, - in_channels=256, - conv_out_channels=256, - fc_out_channels=1024, - roi_feat_size=7, - num_classes=80, - bbox_coder=dict( - type='DeltaXYWHBBoxCoder', - target_means=[0., 0., 0., 0.], - target_stds=[0.033, 0.033, 0.067, 0.067]), - reg_class_agnostic=False, - reg_decoded_bbox=True, - norm_cfg=dict(type='SyncBN', requires_grad=True), - loss_cls=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), - loss_bbox=dict(type='GIoULoss', loss_weight=10.0)) - ])) - -img_norm_cfg = dict( - mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) - -# augmentation strategy originates from DETR / Sparse RCNN -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations', with_bbox=True, with_mask=True), - dict(type='RandomFlip', flip_ratio=0.5), - dict(type='AutoAugment', - policies=[ - [ - dict(type='Resize', - img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), - (608, 1333), (640, 1333), (672, 1333), (704, 1333), - (736, 1333), (768, 1333), (800, 1333)], - multiscale_mode='value', - keep_ratio=True) - ], - [ - dict(type='Resize', - img_scale=[(400, 1333), (500, 1333), (600, 1333)], - multiscale_mode='value', - keep_ratio=True), - dict(type='RandomCrop', - crop_type='absolute_range', - crop_size=(384, 600), - allow_negative_crop=True), - dict(type='Resize', - img_scale=[(480, 1333), (512, 1333), (544, 1333), - (576, 1333), (608, 1333), (640, 1333), - (672, 1333), (704, 1333), (736, 1333), - (768, 1333), (800, 1333)], - multiscale_mode='value', - override=True, - keep_ratio=True) - ] - ]), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='DefaultFormatBundle'), - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), -] -data = dict(train=dict(pipeline=train_pipeline)) - -optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05, - paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.), - 'relative_position_bias_table': dict(decay_mult=0.), - 'norm': dict(decay_mult=0.)})) -lr_config = dict(step=[27, 33]) -runner = dict(type='EpochBasedRunnerAmp', max_epochs=36) - -# do not use mmdet version fp16 -fp16 = None -optimizer_config = dict( - type="DistOptimizerHook", - update_interval=1, - grad_clip=None, - coalesce=True, - bucket_size_mb=-1, - use_fp16=True, -) diff --git a/spaces/Ariharasudhan/YoloV5/utils/torch_utils.py b/spaces/Ariharasudhan/YoloV5/utils/torch_utils.py deleted file mode 100644 index 04a3873854ee03f3eef260d8139dafc46fc69988..0000000000000000000000000000000000000000 --- a/spaces/Ariharasudhan/YoloV5/utils/torch_utils.py +++ /dev/null @@ -1,431 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -PyTorch utils -""" - -import math -import os -import platform -import subprocess -import time -import warnings -from contextlib import contextmanager -from copy import deepcopy -from pathlib import Path - -import torch -import torch.distributed as dist -import torch.nn as nn -import torch.nn.functional as F -from torch.nn.parallel import DistributedDataParallel as DDP - -from utils.general import LOGGER, check_version, colorstr, file_date, git_describe - -LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html -RANK = int(os.getenv('RANK', -1)) -WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) - -try: - import thop # for FLOPs computation -except ImportError: - thop = None - -# Suppress PyTorch warnings -warnings.filterwarnings('ignore', message='User provided device_type of \'cuda\', but CUDA is not available. Disabling') - - -def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')): - # Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator - def decorate(fn): - return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn) - - return decorate - - -def smartCrossEntropyLoss(label_smoothing=0.0): - # Returns nn.CrossEntropyLoss with label smoothing enabled for torch>=1.10.0 - if check_version(torch.__version__, '1.10.0'): - return nn.CrossEntropyLoss(label_smoothing=label_smoothing) - if label_smoothing > 0: - LOGGER.warning(f'WARNING ⚠️ label smoothing {label_smoothing} requires torch>=1.10.0') - return nn.CrossEntropyLoss() - - -def smart_DDP(model): - # Model DDP creation with checks - assert not check_version(torch.__version__, '1.12.0', pinned=True), \ - 'torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. ' \ - 'Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395' - if check_version(torch.__version__, '1.11.0'): - return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True) - else: - return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK) - - -def reshape_classifier_output(model, n=1000): - # Update a TorchVision classification model to class count 'n' if required - from models.common import Classify - name, m = list((model.model if hasattr(model, 'model') else model).named_children())[-1] # last module - if isinstance(m, Classify): # YOLOv5 Classify() head - if m.linear.out_features != n: - m.linear = nn.Linear(m.linear.in_features, n) - elif isinstance(m, nn.Linear): # ResNet, EfficientNet - if m.out_features != n: - setattr(model, name, nn.Linear(m.in_features, n)) - elif isinstance(m, nn.Sequential): - types = [type(x) for x in m] - if nn.Linear in types: - i = types.index(nn.Linear) # nn.Linear index - if m[i].out_features != n: - m[i] = nn.Linear(m[i].in_features, n) - elif nn.Conv2d in types: - i = types.index(nn.Conv2d) # nn.Conv2d index - if m[i].out_channels != n: - m[i] = nn.Conv2d(m[i].in_channels, n, m[i].kernel_size, m[i].stride, bias=m[i].bias) - - -@contextmanager -def torch_distributed_zero_first(local_rank: int): - # Decorator to make all processes in distributed training wait for each local_master to do something - if local_rank not in [-1, 0]: - dist.barrier(device_ids=[local_rank]) - yield - if local_rank == 0: - dist.barrier(device_ids=[0]) - - -def device_count(): - # Returns number of CUDA devices available. Safe version of torch.cuda.device_count(). Supports Linux and Windows - assert platform.system() in ('Linux', 'Windows'), 'device_count() only supported on Linux or Windows' - try: - cmd = 'nvidia-smi -L | wc -l' if platform.system() == 'Linux' else 'nvidia-smi -L | find /c /v ""' # Windows - return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]) - except Exception: - return 0 - - -def select_device(device='', batch_size=0, newline=True): - # device = None or 'cpu' or 0 or '0' or '0,1,2,3' - s = f'YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} ' - device = str(device).strip().lower().replace('cuda:', '').replace('none', '') # to string, 'cuda:0' to '0' - cpu = device == 'cpu' - mps = device == 'mps' # Apple Metal Performance Shaders (MPS) - if cpu or mps: - os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False - elif device: # non-cpu device requested - os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - must be before assert is_available() - assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \ - f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)" - - if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available - devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7 - n = len(devices) # device count - if n > 1 and batch_size > 0: # check batch_size is divisible by device_count - assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}' - space = ' ' * (len(s) + 1) - for i, d in enumerate(devices): - p = torch.cuda.get_device_properties(i) - s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB - arg = 'cuda:0' - elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available(): # prefer MPS if available - s += 'MPS\n' - arg = 'mps' - else: # revert to CPU - s += 'CPU\n' - arg = 'cpu' - - if not newline: - s = s.rstrip() - LOGGER.info(s) - return torch.device(arg) - - -def time_sync(): - # PyTorch-accurate time - if torch.cuda.is_available(): - torch.cuda.synchronize() - return time.time() - - -def profile(input, ops, n=10, device=None): - """ YOLOv5 speed/memory/FLOPs profiler - Usage: - input = torch.randn(16, 3, 640, 640) - m1 = lambda x: x * torch.sigmoid(x) - m2 = nn.SiLU() - profile(input, [m1, m2], n=100) # profile over 100 iterations - """ - results = [] - if not isinstance(device, torch.device): - device = select_device(device) - print(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}" - f"{'input':>24s}{'output':>24s}") - - for x in input if isinstance(input, list) else [input]: - x = x.to(device) - x.requires_grad = True - for m in ops if isinstance(ops, list) else [ops]: - m = m.to(device) if hasattr(m, 'to') else m # device - m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m - tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward - try: - flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPs - except Exception: - flops = 0 - - try: - for _ in range(n): - t[0] = time_sync() - y = m(x) - t[1] = time_sync() - try: - _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward() - t[2] = time_sync() - except Exception: # no backward method - # print(e) # for debug - t[2] = float('nan') - tf += (t[1] - t[0]) * 1000 / n # ms per op forward - tb += (t[2] - t[1]) * 1000 / n # ms per op backward - mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0 # (GB) - s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y)) # shapes - p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 # parameters - print(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}') - results.append([p, flops, mem, tf, tb, s_in, s_out]) - except Exception as e: - print(e) - results.append(None) - torch.cuda.empty_cache() - return results - - -def is_parallel(model): - # Returns True if model is of type DP or DDP - return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) - - -def de_parallel(model): - # De-parallelize a model: returns single-GPU model if model is of type DP or DDP - return model.module if is_parallel(model) else model - - -def initialize_weights(model): - for m in model.modules(): - t = type(m) - if t is nn.Conv2d: - pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - elif t is nn.BatchNorm2d: - m.eps = 1e-3 - m.momentum = 0.03 - elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]: - m.inplace = True - - -def find_modules(model, mclass=nn.Conv2d): - # Finds layer indices matching module class 'mclass' - return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] - - -def sparsity(model): - # Return global model sparsity - a, b = 0, 0 - for p in model.parameters(): - a += p.numel() - b += (p == 0).sum() - return b / a - - -def prune(model, amount=0.3): - # Prune model to requested global sparsity - import torch.nn.utils.prune as prune - for name, m in model.named_modules(): - if isinstance(m, nn.Conv2d): - prune.l1_unstructured(m, name='weight', amount=amount) # prune - prune.remove(m, 'weight') # make permanent - LOGGER.info(f'Model pruned to {sparsity(model):.3g} global sparsity') - - -def fuse_conv_and_bn(conv, bn): - # Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/ - fusedconv = nn.Conv2d(conv.in_channels, - conv.out_channels, - kernel_size=conv.kernel_size, - stride=conv.stride, - padding=conv.padding, - dilation=conv.dilation, - groups=conv.groups, - bias=True).requires_grad_(False).to(conv.weight.device) - - # Prepare filters - w_conv = conv.weight.clone().view(conv.out_channels, -1) - w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) - fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape)) - - # Prepare spatial bias - b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias - b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps)) - fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn) - - return fusedconv - - -def model_info(model, verbose=False, imgsz=640): - # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320] - n_p = sum(x.numel() for x in model.parameters()) # number parameters - n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients - if verbose: - print(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}") - for i, (name, p) in enumerate(model.named_parameters()): - name = name.replace('module_list.', '') - print('%5g %40s %9s %12g %20s %10.3g %10.3g' % - (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) - - try: # FLOPs - p = next(model.parameters()) - stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 # max stride - im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format - flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2 # stride GFLOPs - imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float - fs = f', {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs' # 640x640 GFLOPs - except Exception: - fs = '' - - name = Path(model.yaml_file).stem.replace('yolov5', 'YOLOv5') if hasattr(model, 'yaml_file') else 'Model' - LOGGER.info(f"{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}") - - -def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) - # Scales img(bs,3,y,x) by ratio constrained to gs-multiple - if ratio == 1.0: - return img - h, w = img.shape[2:] - s = (int(h * ratio), int(w * ratio)) # new size - img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize - if not same_shape: # pad/crop img - h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w)) - return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean - - -def copy_attr(a, b, include=(), exclude=()): - # Copy attributes from b to a, options to only include [...] and to exclude [...] - for k, v in b.__dict__.items(): - if (len(include) and k not in include) or k.startswith('_') or k in exclude: - continue - else: - setattr(a, k, v) - - -def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5): - # YOLOv5 3-param group optimizer: 0) weights with decay, 1) weights no decay, 2) biases no decay - g = [], [], [] # optimizer parameter groups - bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d() - for v in model.modules(): - for p_name, p in v.named_parameters(recurse=0): - if p_name == 'bias': # bias (no decay) - g[2].append(p) - elif p_name == 'weight' and isinstance(v, bn): # weight (no decay) - g[1].append(p) - else: - g[0].append(p) # weight (with decay) - - if name == 'Adam': - optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999)) # adjust beta1 to momentum - elif name == 'AdamW': - optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0) - elif name == 'RMSProp': - optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum) - elif name == 'SGD': - optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True) - else: - raise NotImplementedError(f'Optimizer {name} not implemented.') - - optimizer.add_param_group({'params': g[0], 'weight_decay': decay}) # add g0 with weight_decay - optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0}) # add g1 (BatchNorm2d weights) - LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups " - f"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias") - return optimizer - - -def smart_hub_load(repo='ultralytics/yolov5', model='yolov5s', **kwargs): - # YOLOv5 torch.hub.load() wrapper with smart error/issue handling - if check_version(torch.__version__, '1.9.1'): - kwargs['skip_validation'] = True # validation causes GitHub API rate limit errors - if check_version(torch.__version__, '1.12.0'): - kwargs['trust_repo'] = True # argument required starting in torch 0.12 - try: - return torch.hub.load(repo, model, **kwargs) - except Exception: - return torch.hub.load(repo, model, force_reload=True, **kwargs) - - -def smart_resume(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs=300, resume=True): - # Resume training from a partially trained checkpoint - best_fitness = 0.0 - start_epoch = ckpt['epoch'] + 1 - if ckpt['optimizer'] is not None: - optimizer.load_state_dict(ckpt['optimizer']) # optimizer - best_fitness = ckpt['best_fitness'] - if ema and ckpt.get('ema'): - ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) # EMA - ema.updates = ckpt['updates'] - if resume: - assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.\n' \ - f"Start a new training without --resume, i.e. 'python train.py --weights {weights}'" - LOGGER.info(f'Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs') - if epochs < start_epoch: - LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.") - epochs += ckpt['epoch'] # finetune additional epochs - return best_fitness, start_epoch, epochs - - -class EarlyStopping: - # YOLOv5 simple early stopper - def __init__(self, patience=30): - self.best_fitness = 0.0 # i.e. mAP - self.best_epoch = 0 - self.patience = patience or float('inf') # epochs to wait after fitness stops improving to stop - self.possible_stop = False # possible stop may occur next epoch - - def __call__(self, epoch, fitness): - if fitness >= self.best_fitness: # >= 0 to allow for early zero-fitness stage of training - self.best_epoch = epoch - self.best_fitness = fitness - delta = epoch - self.best_epoch # epochs without improvement - self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch - stop = delta >= self.patience # stop training if patience exceeded - if stop: - LOGGER.info(f'Stopping training early as no improvement observed in last {self.patience} epochs. ' - f'Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n' - f'To update EarlyStopping(patience={self.patience}) pass a new patience value, ' - f'i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping.') - return stop - - -class ModelEMA: - """ Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models - Keeps a moving average of everything in the model state_dict (parameters and buffers) - For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage - """ - - def __init__(self, model, decay=0.9999, tau=2000, updates=0): - # Create EMA - self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA - self.updates = updates # number of EMA updates - self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs) - for p in self.ema.parameters(): - p.requires_grad_(False) - - def update(self, model): - # Update EMA parameters - self.updates += 1 - d = self.decay(self.updates) - - msd = de_parallel(model).state_dict() # model state_dict - for k, v in self.ema.state_dict().items(): - if v.dtype.is_floating_point: # true for FP16 and FP32 - v *= d - v += (1 - d) * msd[k].detach() - # assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype} and model {msd[k].dtype} must be FP32' - - def update_attr(self, model, include=(), exclude=('process_group', 'reducer')): - # Update EMA attributes - copy_attr(self.ema, model, include, exclude) diff --git a/spaces/Arikkod/FoodVisionMini/model.py b/spaces/Arikkod/FoodVisionMini/model.py deleted file mode 100644 index e3fa8e60f4f473ca4835708795c6ae77e7e5f143..0000000000000000000000000000000000000000 --- a/spaces/Arikkod/FoodVisionMini/model.py +++ /dev/null @@ -1,19 +0,0 @@ -import torch -import torchvision -import torch.nn as nn - -def create_effnetb2_model(num_classes:int=3, seed:int=3): - weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT - transforms = weights.transforms() - model = torchvision.models.efficientnet_b2(weights=weights) - - # Freeze the base layers in the model (this will stop all layers from training) - for param in model.parameters(): - param.requires_grad = False - - torch.manual_seed(seed) - model.classifier = nn.Sequential( - nn.Dropout(p=0.3, inplace=True), - nn.Linear(in_features=1408, out_features=num_classes, bias=True)) - - return model, transforms diff --git a/spaces/Artrajz/vits-simple-api/vits/text/cantonese.py b/spaces/Artrajz/vits-simple-api/vits/text/cantonese.py deleted file mode 100644 index 656319181e2e1674e61f63b0a24a84895e4bf82b..0000000000000000000000000000000000000000 --- a/spaces/Artrajz/vits-simple-api/vits/text/cantonese.py +++ /dev/null @@ -1,75 +0,0 @@ -import os.path -import re -import cn2an -import opencc -import config -from utils.download import download_and_verify - -URLS = [ - "https://github.com/CjangCjengh/chinese-dialect-lexicons/releases/download/v1.0.3/chinese_dialects.7z", - "https://ghproxy.com/https://github.com/CjangCjengh/chinese-dialect-lexicons/releases/download/v1.0.3/chinese_dialects.7z", -] -TARGET_PATH = os.path.join(config.ABS_PATH, "vits/text/chinese_dialects.7z") -EXTRACT_DESTINATION = os.path.join(config.ABS_PATH, "vits/text/chinese_dialect_lexicons/") -EXPECTED_MD5 = None -OPENCC_FILE_PATH = os.path.join(config.ABS_PATH, "vits/text/chinese_dialect_lexicons/jyutjyu.json") - -if not os.path.exists(OPENCC_FILE_PATH): - success, message = download_and_verify(URLS, TARGET_PATH, EXPECTED_MD5, EXTRACT_DESTINATION) - -converter = opencc.OpenCC(OPENCC_FILE_PATH) - -# List of (Latin alphabet, ipa) pairs: -_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('A', 'ei˥'), - ('B', 'biː˥'), - ('C', 'siː˥'), - ('D', 'tiː˥'), - ('E', 'iː˥'), - ('F', 'e˥fuː˨˩'), - ('G', 'tsiː˥'), - ('H', 'ɪk̚˥tsʰyː˨˩'), - ('I', 'ɐi˥'), - ('J', 'tsei˥'), - ('K', 'kʰei˥'), - ('L', 'e˥llou˨˩'), - ('M', 'ɛːm˥'), - ('N', 'ɛːn˥'), - ('O', 'ou˥'), - ('P', 'pʰiː˥'), - ('Q', 'kʰiːu˥'), - ('R', 'aː˥lou˨˩'), - ('S', 'ɛː˥siː˨˩'), - ('T', 'tʰiː˥'), - ('U', 'juː˥'), - ('V', 'wiː˥'), - ('W', 'tʊk̚˥piː˥juː˥'), - ('X', 'ɪk̚˥siː˨˩'), - ('Y', 'waːi˥'), - ('Z', 'iː˨sɛːt̚˥') -]] - - -def number_to_cantonese(text): - return re.sub(r'\d+(?:\.?\d+)?', lambda x: cn2an.an2cn(x.group()), text) - - -def latin_to_ipa(text): - for regex, replacement in _latin_to_ipa: - text = re.sub(regex, replacement, text) - return text - - -def cantonese_to_ipa(text): - from vits.text.mandarin import symbols_to_chinese - text = symbols_to_chinese(text) - text = number_to_cantonese(text.upper()) - text = converter.convert(text).replace('-', '').replace('$', ' ') - text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group()) + ' ', text) - text = re.sub(r'[、；：]', '，', text) - text = re.sub(r'\s*，\s*', ', ', text) - text = re.sub(r'\s*。\s*', '. ', text) - text = re.sub(r'\s*？\s*', '? ', text) - text = re.sub(r'\s*！\s*', '! ', text) - text = re.sub(r'\s*$', '', text) - return text diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/formatters/svg.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/formatters/svg.py deleted file mode 100644 index 075150a4b586d668c1666513fbf90463cdbb11ab..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/formatters/svg.py +++ /dev/null @@ -1,188 +0,0 @@ -""" - pygments.formatters.svg - ~~~~~~~~~~~~~~~~~~~~~~~ - - Formatter for SVG output. - - :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS. - :license: BSD, see LICENSE for details. -""" - -from pip._vendor.pygments.formatter import Formatter -from pip._vendor.pygments.token import Comment -from pip._vendor.pygments.util import get_bool_opt, get_int_opt - -__all__ = ['SvgFormatter'] - - -def escape_html(text): - """Escape &, <, > as well as single and double quotes for HTML.""" - return text.replace('&', '&'). \ - replace('<', '<'). \ - replace('>', '>'). \ - replace('"', '"'). \ - replace("'", ''') - - -class2style = {} - -class SvgFormatter(Formatter): - """ - Format tokens as an SVG graphics file. This formatter is still experimental. - Each line of code is a ``{text} \n' - else: - html += f'
{image}{image} \n' - html += f'`` element with explicit ``x`` and ``y`` - coordinates containing `` \n') - if self.linenos and counter % counter_step == 0: - outfile.write('`` elements with the individual token styles. - - By default, this formatter outputs a full SVG document including doctype - declaration and the `` ' or '' - value = escape_html(value) - if self.spacehack: - value = value.expandtabs().replace(' ', ' ') - parts = value.split('\n') - for part in parts[:-1]: - outfile.write(tspan + part + tspanend) - y += self.ystep - outfile.write('%s ' % - (x+self.linenowidth,y,counter_style,counter)) - - counter += 1 - outfile.write('' % (line_x,y)) - outfile.write(tspan + parts[-1] + tspanend) - outfile.write(' ') - - if not self.nowrap: - outfile.write('\n') - - def _get_style(self, tokentype): - if tokentype in self._stylecache: - return self._stylecache[tokentype] - otokentype = tokentype - while not self.style.styles_token(tokentype): - tokentype = tokentype.parent - value = self.style.style_for_token(tokentype) - result = '' - if value['color']: - result = ' fill="#' + value['color'] + '"' - if value['bold']: - result += ' font-weight="bold"' - if value['italic']: - result += ' font-style="italic"' - self._stylecache[otokentype] = result - return result diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/logging.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/logging.py deleted file mode 100644 index 91368dda78aad590837aa12023dee67e224709ba..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/logging.py +++ /dev/null @@ -1,289 +0,0 @@ -import logging -from datetime import datetime -from logging import Handler, LogRecord -from pathlib import Path -from types import ModuleType -from typing import ClassVar, Iterable, List, Optional, Type, Union - -from pip._vendor.rich._null_file import NullFile - -from . import get_console -from ._log_render import FormatTimeCallable, LogRender -from .console import Console, ConsoleRenderable -from .highlighter import Highlighter, ReprHighlighter -from .text import Text -from .traceback import Traceback - - -class RichHandler(Handler): - """A logging handler that renders output with Rich. The time / level / message and file are displayed in columns. - The level is color coded, and the message is syntax highlighted. - - Note: - Be careful when enabling console markup in log messages if you have configured logging for libraries not - under your control. If a dependency writes messages containing square brackets, it may not produce the intended output. - - Args: - level (Union[int, str], optional): Log level. Defaults to logging.NOTSET. - console (:class:`~rich.console.Console`, optional): Optional console instance to write logs. - Default will use a global console instance writing to stdout. - show_time (bool, optional): Show a column for the time. Defaults to True. - omit_repeated_times (bool, optional): Omit repetition of the same time. Defaults to True. - show_level (bool, optional): Show a column for the level. Defaults to True. - show_path (bool, optional): Show the path to the original log call. Defaults to True. - enable_link_path (bool, optional): Enable terminal link of path column to file. Defaults to True. - highlighter (Highlighter, optional): Highlighter to style log messages, or None to use ReprHighlighter. Defaults to None. - markup (bool, optional): Enable console markup in log messages. Defaults to False. - rich_tracebacks (bool, optional): Enable rich tracebacks with syntax highlighting and formatting. Defaults to False. - tracebacks_width (Optional[int], optional): Number of characters used to render tracebacks, or None for full width. Defaults to None. - tracebacks_extra_lines (int, optional): Additional lines of code to render tracebacks, or None for full width. Defaults to None. - tracebacks_theme (str, optional): Override pygments theme used in traceback. - tracebacks_word_wrap (bool, optional): Enable word wrapping of long tracebacks lines. Defaults to True. - tracebacks_show_locals (bool, optional): Enable display of locals in tracebacks. Defaults to False. - tracebacks_suppress (Sequence[Union[str, ModuleType]]): Optional sequence of modules or paths to exclude from traceback. - locals_max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation. - Defaults to 10. - locals_max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to 80. - log_time_format (Union[str, TimeFormatterCallable], optional): If ``log_time`` is enabled, either string for strftime or callable that formats the time. Defaults to "[%x %X] ". - keywords (List[str], optional): List of words to highlight instead of ``RichHandler.KEYWORDS``. - """ - - KEYWORDS: ClassVar[Optional[List[str]]] = [ - "GET", - "POST", - "HEAD", - "PUT", - "DELETE", - "OPTIONS", - "TRACE", - "PATCH", - ] - HIGHLIGHTER_CLASS: ClassVar[Type[Highlighter]] = ReprHighlighter - - def __init__( - self, - level: Union[int, str] = logging.NOTSET, - console: Optional[Console] = None, - *, - show_time: bool = True, - omit_repeated_times: bool = True, - show_level: bool = True, - show_path: bool = True, - enable_link_path: bool = True, - highlighter: Optional[Highlighter] = None, - markup: bool = False, - rich_tracebacks: bool = False, - tracebacks_width: Optional[int] = None, - tracebacks_extra_lines: int = 3, - tracebacks_theme: Optional[str] = None, - tracebacks_word_wrap: bool = True, - tracebacks_show_locals: bool = False, - tracebacks_suppress: Iterable[Union[str, ModuleType]] = (), - locals_max_length: int = 10, - locals_max_string: int = 80, - log_time_format: Union[str, FormatTimeCallable] = "[%x %X]", - keywords: Optional[List[str]] = None, - ) -> None: - super().__init__(level=level) - self.console = console or get_console() - self.highlighter = highlighter or self.HIGHLIGHTER_CLASS() - self._log_render = LogRender( - show_time=show_time, - show_level=show_level, - show_path=show_path, - time_format=log_time_format, - omit_repeated_times=omit_repeated_times, - level_width=None, - ) - self.enable_link_path = enable_link_path - self.markup = markup - self.rich_tracebacks = rich_tracebacks - self.tracebacks_width = tracebacks_width - self.tracebacks_extra_lines = tracebacks_extra_lines - self.tracebacks_theme = tracebacks_theme - self.tracebacks_word_wrap = tracebacks_word_wrap - self.tracebacks_show_locals = tracebacks_show_locals - self.tracebacks_suppress = tracebacks_suppress - self.locals_max_length = locals_max_length - self.locals_max_string = locals_max_string - self.keywords = keywords - - def get_level_text(self, record: LogRecord) -> Text: - """Get the level name from the record. - - Args: - record (LogRecord): LogRecord instance. - - Returns: - Text: A tuple of the style and level name. - """ - level_name = record.levelname - level_text = Text.styled( - level_name.ljust(8), f"logging.level.{level_name.lower()}" - ) - return level_text - - def emit(self, record: LogRecord) -> None: - """Invoked by logging.""" - message = self.format(record) - traceback = None - if ( - self.rich_tracebacks - and record.exc_info - and record.exc_info != (None, None, None) - ): - exc_type, exc_value, exc_traceback = record.exc_info - assert exc_type is not None - assert exc_value is not None - traceback = Traceback.from_exception( - exc_type, - exc_value, - exc_traceback, - width=self.tracebacks_width, - extra_lines=self.tracebacks_extra_lines, - theme=self.tracebacks_theme, - word_wrap=self.tracebacks_word_wrap, - show_locals=self.tracebacks_show_locals, - locals_max_length=self.locals_max_length, - locals_max_string=self.locals_max_string, - suppress=self.tracebacks_suppress, - ) - message = record.getMessage() - if self.formatter: - record.message = record.getMessage() - formatter = self.formatter - if hasattr(formatter, "usesTime") and formatter.usesTime(): - record.asctime = formatter.formatTime(record, formatter.datefmt) - message = formatter.formatMessage(record) - - message_renderable = self.render_message(record, message) - log_renderable = self.render( - record=record, traceback=traceback, message_renderable=message_renderable - ) - if isinstance(self.console.file, NullFile): - # Handles pythonw, where stdout/stderr are null, and we return NullFile - # instance from Console.file. In this case, we still want to make a log record - # even though we won't be writing anything to a file. - self.handleError(record) - else: - try: - self.console.print(log_renderable) - except Exception: - self.handleError(record) - - def render_message(self, record: LogRecord, message: str) -> "ConsoleRenderable": - """Render message text in to Text. - - Args: - record (LogRecord): logging Record. - message (str): String containing log message. - - Returns: - ConsoleRenderable: Renderable to display log message. - """ - use_markup = getattr(record, "markup", self.markup) - message_text = Text.from_markup(message) if use_markup else Text(message) - - highlighter = getattr(record, "highlighter", self.highlighter) - if highlighter: - message_text = highlighter(message_text) - - if self.keywords is None: - self.keywords = self.KEYWORDS - - if self.keywords: - message_text.highlight_words(self.keywords, "logging.keyword") - - return message_text - - def render( - self, - *, - record: LogRecord, - traceback: Optional[Traceback], - message_renderable: "ConsoleRenderable", - ) -> "ConsoleRenderable": - """Render log for display. - - Args: - record (LogRecord): logging Record. - traceback (Optional[Traceback]): Traceback instance or None for no Traceback. - message_renderable (ConsoleRenderable): Renderable (typically Text) containing log message contents. - - Returns: - ConsoleRenderable: Renderable to display log. - """ - path = Path(record.pathname).name - level = self.get_level_text(record) - time_format = None if self.formatter is None else self.formatter.datefmt - log_time = datetime.fromtimestamp(record.created) - - log_renderable = self._log_render( - self.console, - [message_renderable] if not traceback else [message_renderable, traceback], - log_time=log_time, - time_format=time_format, - level=level, - path=path, - line_no=record.lineno, - link_path=record.pathname if self.enable_link_path else None, - ) - return log_renderable - - -if __name__ == "__main__": # pragma: no cover - from time import sleep - - FORMAT = "%(message)s" - # FORMAT = "%(asctime)-15s - %(levelname)s - %(message)s" - logging.basicConfig( - level="NOTSET", - format=FORMAT, - datefmt="[%X]", - handlers=[RichHandler(rich_tracebacks=True, tracebacks_show_locals=True)], - ) - log = logging.getLogger("rich") - - log.info("Server starting...") - log.info("Listening on http://127.0.0.1:8080") - sleep(1) - - log.info("GET /index.html 200 1298") - log.info("GET /imgs/backgrounds/back1.jpg 200 54386") - log.info("GET /css/styles.css 200 54386") - log.warning("GET /favicon.ico 404 242") - sleep(1) - - log.debug( - "JSONRPC request\n--> %r\n<-- %r", - { - "version": "1.1", - "method": "confirmFruitPurchase", - "params": [["apple", "orange", "mangoes", "pomelo"], 1.123], - "id": "194521489", - }, - {"version": "1.1", "result": True, "error": None, "id": "194521489"}, - ) - log.debug( - "Loading configuration file /adasd/asdasd/qeqwe/qwrqwrqwr/sdgsdgsdg/werwerwer/dfgerert/ertertert/ertetert/werwerwer" - ) - log.error("Unable to find 'pomelo' in database!") - log.info("POST /jsonrpc/ 200 65532") - log.info("POST /admin/ 401 42234") - log.warning("password was rejected for admin site.") - - def divide() -> None: - number = 1 - divisor = 0 - foos = ["foo"] * 100 - log.debug("in divide") - try: - number / divisor - except: - log.exception("An error of some kind occurred!") - - divide() - sleep(1) - log.critical("Out of memory!") - log.info("Server exited with code=-1") - log.info("[bold]EXITING...[/bold]", extra=dict(markup=True)) diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/pyparsing/core.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/pyparsing/core.py deleted file mode 100644 index 9acba3f3e984b404f52702964805732f03965048..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/pyparsing/core.py +++ /dev/null @@ -1,5814 +0,0 @@ -# -# core.py -# -import os -import typing -from typing import ( - NamedTuple, - Union, - Callable, - Any, - Generator, - Tuple, - List, - TextIO, - Set, - Sequence, -) -from abc import ABC, abstractmethod -from enum import Enum -import string -import copy -import warnings -import re -import sys -from collections.abc import Iterable -import traceback -import types -from operator import itemgetter -from functools import wraps -from threading import RLock -from pathlib import Path - -from .util import ( - _FifoCache, - _UnboundedCache, - __config_flags, - _collapse_string_to_ranges, - _escape_regex_range_chars, - _bslash, - _flatten, - LRUMemo as _LRUMemo, - UnboundedMemo as _UnboundedMemo, -) -from .exceptions import * -from .actions import * -from .results import ParseResults, _ParseResultsWithOffset -from .unicode import pyparsing_unicode - -_MAX_INT = sys.maxsize -str_type: Tuple[type, ...] = (str, bytes) - -# -# Copyright (c) 2003-2022 Paul T. McGuire -# -# Permission is hereby granted, free of charge, to any person obtaining -# a copy of this software and associated documentation files (the -# "Software"), to deal in the Software without restriction, including -# without limitation the rights to use, copy, modify, merge, publish, -# distribute, sublicense, and/or sell copies of the Software, and to -# permit persons to whom the Software is furnished to do so, subject to -# the following conditions: -# -# The above copyright notice and this permission notice shall be -# included in all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, -# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF -# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. -# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY -# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, -# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE -# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. -# - - -if sys.version_info >= (3, 8): - from functools import cached_property -else: - - class cached_property: - def __init__(self, func): - self._func = func - - def __get__(self, instance, owner=None): - ret = instance.__dict__[self._func.__name__] = self._func(instance) - return ret - - -class __compat__(__config_flags): - """ - A cross-version compatibility configuration for pyparsing features that will be - released in a future version. By setting values in this configuration to True, - those features can be enabled in prior versions for compatibility development - and testing. - - - ``collect_all_And_tokens`` - flag to enable fix for Issue #63 that fixes erroneous grouping - of results names when an :class:`And` expression is nested within an :class:`Or` or :class:`MatchFirst`; - maintained for compatibility, but setting to ``False`` no longer restores pre-2.3.1 - behavior - """ - - _type_desc = "compatibility" - - collect_all_And_tokens = True - - _all_names = [__ for __ in locals() if not __.startswith("_")] - _fixed_names = """ - collect_all_And_tokens - """.split() - - -class __diag__(__config_flags): - _type_desc = "diagnostic" - - warn_multiple_tokens_in_named_alternation = False - warn_ungrouped_named_tokens_in_collection = False - warn_name_set_on_empty_Forward = False - warn_on_parse_using_empty_Forward = False - warn_on_assignment_to_Forward = False - warn_on_multiple_string_args_to_oneof = False - warn_on_match_first_with_lshift_operator = False - enable_debug_on_named_expressions = False - - _all_names = [__ for __ in locals() if not __.startswith("_")] - _warning_names = [name for name in _all_names if name.startswith("warn")] - _debug_names = [name for name in _all_names if name.startswith("enable_debug")] - - @classmethod - def enable_all_warnings(cls) -> None: - for name in cls._warning_names: - cls.enable(name) - - -class Diagnostics(Enum): - """ - Diagnostic configuration (all default to disabled) - - ``warn_multiple_tokens_in_named_alternation`` - flag to enable warnings when a results - name is defined on a :class:`MatchFirst` or :class:`Or` expression with one or more :class:`And` subexpressions - - ``warn_ungrouped_named_tokens_in_collection`` - flag to enable warnings when a results - name is defined on a containing expression with ungrouped subexpressions that also - have results names - - ``warn_name_set_on_empty_Forward`` - flag to enable warnings when a :class:`Forward` is defined - with a results name, but has no contents defined - - ``warn_on_parse_using_empty_Forward`` - flag to enable warnings when a :class:`Forward` is - defined in a grammar but has never had an expression attached to it - - ``warn_on_assignment_to_Forward`` - flag to enable warnings when a :class:`Forward` is defined - but is overwritten by assigning using ``'='`` instead of ``'<<='`` or ``'<<'`` - - ``warn_on_multiple_string_args_to_oneof`` - flag to enable warnings when :class:`one_of` is - incorrectly called with multiple str arguments - - ``enable_debug_on_named_expressions`` - flag to auto-enable debug on all subsequent - calls to :class:`ParserElement.set_name` - - Diagnostics are enabled/disabled by calling :class:`enable_diag` and :class:`disable_diag`. - All warnings can be enabled by calling :class:`enable_all_warnings`. - """ - - warn_multiple_tokens_in_named_alternation = 0 - warn_ungrouped_named_tokens_in_collection = 1 - warn_name_set_on_empty_Forward = 2 - warn_on_parse_using_empty_Forward = 3 - warn_on_assignment_to_Forward = 4 - warn_on_multiple_string_args_to_oneof = 5 - warn_on_match_first_with_lshift_operator = 6 - enable_debug_on_named_expressions = 7 - - -def enable_diag(diag_enum: Diagnostics) -> None: - """ - Enable a global pyparsing diagnostic flag (see :class:`Diagnostics`). - """ - __diag__.enable(diag_enum.name) - - -def disable_diag(diag_enum: Diagnostics) -> None: - """ - Disable a global pyparsing diagnostic flag (see :class:`Diagnostics`). - """ - __diag__.disable(diag_enum.name) - - -def enable_all_warnings() -> None: - """ - Enable all global pyparsing diagnostic warnings (see :class:`Diagnostics`). - """ - __diag__.enable_all_warnings() - - -# hide abstract class -del __config_flags - - -def _should_enable_warnings( - cmd_line_warn_options: typing.Iterable[str], warn_env_var: typing.Optional[str] -) -> bool: - enable = bool(warn_env_var) - for warn_opt in cmd_line_warn_options: - w_action, w_message, w_category, w_module, w_line = (warn_opt + "::::").split( - ":" - )[:5] - if not w_action.lower().startswith("i") and ( - not (w_message or w_category or w_module) or w_module == "pyparsing" - ): - enable = True - elif w_action.lower().startswith("i") and w_module in ("pyparsing", ""): - enable = False - return enable - - -if _should_enable_warnings( - sys.warnoptions, os.environ.get("PYPARSINGENABLEALLWARNINGS") -): - enable_all_warnings() - - -# build list of single arg builtins, that can be used as parse actions -_single_arg_builtins = { - sum, - len, - sorted, - reversed, - list, - tuple, - set, - any, - all, - min, - max, -} - -_generatorType = types.GeneratorType -ParseAction = Union[ - Callable[[], Any], - Callable[[ParseResults], Any], - Callable[[int, ParseResults], Any], - Callable[[str, int, ParseResults], Any], -] -ParseCondition = Union[ - Callable[[], bool], - Callable[[ParseResults], bool], - Callable[[int, ParseResults], bool], - Callable[[str, int, ParseResults], bool], -] -ParseFailAction = Callable[[str, int, "ParserElement", Exception], None] -DebugStartAction = Callable[[str, int, "ParserElement", bool], None] -DebugSuccessAction = Callable[ - [str, int, int, "ParserElement", ParseResults, bool], None -] -DebugExceptionAction = Callable[[str, int, "ParserElement", Exception, bool], None] - - -alphas = string.ascii_uppercase + string.ascii_lowercase -identchars = pyparsing_unicode.Latin1.identchars -identbodychars = pyparsing_unicode.Latin1.identbodychars -nums = "0123456789" -hexnums = nums + "ABCDEFabcdef" -alphanums = alphas + nums -printables = "".join([c for c in string.printable if c not in string.whitespace]) - -_trim_arity_call_line: traceback.StackSummary = None - - -def _trim_arity(func, max_limit=3): - """decorator to trim function calls to match the arity of the target""" - global _trim_arity_call_line - - if func in _single_arg_builtins: - return lambda s, l, t: func(t) - - limit = 0 - found_arity = False - - def extract_tb(tb, limit=0): - frames = traceback.extract_tb(tb, limit=limit) - frame_summary = frames[-1] - return [frame_summary[:2]] - - # synthesize what would be returned by traceback.extract_stack at the call to - # user's parse action 'func', so that we don't incur call penalty at parse time - - # fmt: off - LINE_DIFF = 7 - # IF ANY CODE CHANGES, EVEN JUST COMMENTS OR BLANK LINES, BETWEEN THE NEXT LINE AND - # THE CALL TO FUNC INSIDE WRAPPER, LINE_DIFF MUST BE MODIFIED!!!! - _trim_arity_call_line = (_trim_arity_call_line or traceback.extract_stack(limit=2)[-1]) - pa_call_line_synth = (_trim_arity_call_line[0], _trim_arity_call_line[1] + LINE_DIFF) - - def wrapper(*args): - nonlocal found_arity, limit - while 1: - try: - ret = func(*args[limit:]) - found_arity = True - return ret - except TypeError as te: - # re-raise TypeErrors if they did not come from our arity testing - if found_arity: - raise - else: - tb = te.__traceback__ - trim_arity_type_error = ( - extract_tb(tb, limit=2)[-1][:2] == pa_call_line_synth - ) - del tb - - if trim_arity_type_error: - if limit < max_limit: - limit += 1 - continue - - raise - # fmt: on - - # copy func name to wrapper for sensible debug output - # (can't use functools.wraps, since that messes with function signature) - func_name = getattr(func, "__name__", getattr(func, "__class__").__name__) - wrapper.__name__ = func_name - wrapper.__doc__ = func.__doc__ - - return wrapper - - -def condition_as_parse_action( - fn: ParseCondition, message: str = None, fatal: bool = False -) -> ParseAction: - """ - Function to convert a simple predicate function that returns ``True`` or ``False`` - into a parse action. Can be used in places when a parse action is required - and :class:`ParserElement.add_condition` cannot be used (such as when adding a condition - to an operator level in :class:`infix_notation`). - - Optional keyword arguments: - - - ``message`` - define a custom message to be used in the raised exception - - ``fatal`` - if True, will raise :class:`ParseFatalException` to stop parsing immediately; - otherwise will raise :class:`ParseException` - - """ - msg = message if message is not None else "failed user-defined condition" - exc_type = ParseFatalException if fatal else ParseException - fn = _trim_arity(fn) - - @wraps(fn) - def pa(s, l, t): - if not bool(fn(s, l, t)): - raise exc_type(s, l, msg) - - return pa - - -def _default_start_debug_action( - instring: str, loc: int, expr: "ParserElement", cache_hit: bool = False -): - cache_hit_str = "*" if cache_hit else "" - print( - ( - "{}Match {} at loc {}({},{})\n {}\n {}^".format( - cache_hit_str, - expr, - loc, - lineno(loc, instring), - col(loc, instring), - line(loc, instring), - " " * (col(loc, instring) - 1), - ) - ) - ) - - -def _default_success_debug_action( - instring: str, - startloc: int, - endloc: int, - expr: "ParserElement", - toks: ParseResults, - cache_hit: bool = False, -): - cache_hit_str = "*" if cache_hit else "" - print("{}Matched {} -> {}".format(cache_hit_str, expr, toks.as_list())) - - -def _default_exception_debug_action( - instring: str, - loc: int, - expr: "ParserElement", - exc: Exception, - cache_hit: bool = False, -): - cache_hit_str = "*" if cache_hit else "" - print( - "{}Match {} failed, {} raised: {}".format( - cache_hit_str, expr, type(exc).__name__, exc - ) - ) - - -def null_debug_action(*args): - """'Do-nothing' debug action, to suppress debugging output during parsing.""" - - -class ParserElement(ABC): - """Abstract base level parser element class.""" - - DEFAULT_WHITE_CHARS: str = " \n\t\r" - verbose_stacktrace: bool = False - _literalStringClass: typing.Optional[type] = None - - @staticmethod - def set_default_whitespace_chars(chars: str) -> None: - r""" - Overrides the default whitespace chars - - Example:: - - # default whitespace chars are space,and newline - Word(alphas)[1, ...].parse_string("abc def\nghi jkl") # -> ['abc', 'def', 'ghi', 'jkl'] - - # change to just treat newline as significant - ParserElement.set_default_whitespace_chars(" \t") - Word(alphas)[1, ...].parse_string("abc def\nghi jkl") # -> ['abc', 'def'] - """ - ParserElement.DEFAULT_WHITE_CHARS = chars - - # update whitespace all parse expressions defined in this module - for expr in _builtin_exprs: - if expr.copyDefaultWhiteChars: - expr.whiteChars = set(chars) - - @staticmethod - def inline_literals_using(cls: type) -> None: - """ - Set class to be used for inclusion of string literals into a parser. - - Example:: - - # default literal class used is Literal - integer = Word(nums) - date_str = integer("year") + '/' + integer("month") + '/' + integer("day") - - date_str.parse_string("1999/12/31") # -> ['1999', '/', '12', '/', '31'] - - - # change to Suppress - ParserElement.inline_literals_using(Suppress) - date_str = integer("year") + '/' + integer("month") + '/' + integer("day") - - date_str.parse_string("1999/12/31") # -> ['1999', '12', '31'] - """ - ParserElement._literalStringClass = cls - - class DebugActions(NamedTuple): - debug_try: typing.Optional[DebugStartAction] - debug_match: typing.Optional[DebugSuccessAction] - debug_fail: typing.Optional[DebugExceptionAction] - - def __init__(self, savelist: bool = False): - self.parseAction: List[ParseAction] = list() - self.failAction: typing.Optional[ParseFailAction] = None - self.customName = None - self._defaultName = None - self.resultsName = None - self.saveAsList = savelist - self.skipWhitespace = True - self.whiteChars = set(ParserElement.DEFAULT_WHITE_CHARS) - self.copyDefaultWhiteChars = True - # used when checking for left-recursion - self.mayReturnEmpty = False - self.keepTabs = False - self.ignoreExprs: List["ParserElement"] = list() - self.debug = False - self.streamlined = False - # optimize exception handling for subclasses that don't advance parse index - self.mayIndexError = True - self.errmsg = "" - # mark results names as modal (report only last) or cumulative (list all) - self.modalResults = True - # custom debug actions - self.debugActions = self.DebugActions(None, None, None) - # avoid redundant calls to preParse - self.callPreparse = True - self.callDuringTry = False - self.suppress_warnings_: List[Diagnostics] = [] - - def suppress_warning(self, warning_type: Diagnostics) -> "ParserElement": - """ - Suppress warnings emitted for a particular diagnostic on this expression. - - Example:: - - base = pp.Forward() - base.suppress_warning(Diagnostics.warn_on_parse_using_empty_Forward) - - # statement would normally raise a warning, but is now suppressed - print(base.parseString("x")) - - """ - self.suppress_warnings_.append(warning_type) - return self - - def copy(self) -> "ParserElement": - """ - Make a copy of this :class:`ParserElement`. Useful for defining - different parse actions for the same parsing pattern, using copies of - the original parse element. - - Example:: - - integer = Word(nums).set_parse_action(lambda toks: int(toks[0])) - integerK = integer.copy().add_parse_action(lambda toks: toks[0] * 1024) + Suppress("K") - integerM = integer.copy().add_parse_action(lambda toks: toks[0] * 1024 * 1024) + Suppress("M") - - print((integerK | integerM | integer)[1, ...].parse_string("5K 100 640K 256M")) - - prints:: - - [5120, 100, 655360, 268435456] - - Equivalent form of ``expr.copy()`` is just ``expr()``:: - - integerM = integer().add_parse_action(lambda toks: toks[0] * 1024 * 1024) + Suppress("M") - """ - cpy = copy.copy(self) - cpy.parseAction = self.parseAction[:] - cpy.ignoreExprs = self.ignoreExprs[:] - if self.copyDefaultWhiteChars: - cpy.whiteChars = set(ParserElement.DEFAULT_WHITE_CHARS) - return cpy - - def set_results_name( - self, name: str, list_all_matches: bool = False, *, listAllMatches: bool = False - ) -> "ParserElement": - """ - Define name for referencing matching tokens as a nested attribute - of the returned parse results. - - Normally, results names are assigned as you would assign keys in a dict: - any existing value is overwritten by later values. If it is necessary to - keep all values captured for a particular results name, call ``set_results_name`` - with ``list_all_matches`` = True. - - NOTE: ``set_results_name`` returns a *copy* of the original :class:`ParserElement` object; - this is so that the client can define a basic element, such as an - integer, and reference it in multiple places with different names. - - You can also set results names using the abbreviated syntax, - ``expr("name")`` in place of ``expr.set_results_name("name")`` - - see :class:`__call__`. If ``list_all_matches`` is required, use - ``expr("name*")``. - - Example:: - - date_str = (integer.set_results_name("year") + '/' - + integer.set_results_name("month") + '/' - + integer.set_results_name("day")) - - # equivalent form: - date_str = integer("year") + '/' + integer("month") + '/' + integer("day") - """ - listAllMatches = listAllMatches or list_all_matches - return self._setResultsName(name, listAllMatches) - - def _setResultsName(self, name, listAllMatches=False): - if name is None: - return self - newself = self.copy() - if name.endswith("*"): - name = name[:-1] - listAllMatches = True - newself.resultsName = name - newself.modalResults = not listAllMatches - return newself - - def set_break(self, break_flag: bool = True) -> "ParserElement": - """ - Method to invoke the Python pdb debugger when this element is - about to be parsed. Set ``break_flag`` to ``True`` to enable, ``False`` to - disable. - """ - if break_flag: - _parseMethod = self._parse - - def breaker(instring, loc, doActions=True, callPreParse=True): - import pdb - - # this call to pdb.set_trace() is intentional, not a checkin error - pdb.set_trace() - return _parseMethod(instring, loc, doActions, callPreParse) - - breaker._originalParseMethod = _parseMethod - self._parse = breaker - else: - if hasattr(self._parse, "_originalParseMethod"): - self._parse = self._parse._originalParseMethod - return self - - def set_parse_action(self, *fns: ParseAction, **kwargs) -> "ParserElement": - """ - Define one or more actions to perform when successfully matching parse element definition. - - Parse actions can be called to perform data conversions, do extra validation, - update external data structures, or enhance or replace the parsed tokens. - Each parse action ``fn`` is a callable method with 0-3 arguments, called as - ``fn(s, loc, toks)`` , ``fn(loc, toks)`` , ``fn(toks)`` , or just ``fn()`` , where: - - - s = the original string being parsed (see note below) - - loc = the location of the matching substring - - toks = a list of the matched tokens, packaged as a :class:`ParseResults` object - - The parsed tokens are passed to the parse action as ParseResults. They can be - modified in place using list-style append, extend, and pop operations to update - the parsed list elements; and with dictionary-style item set and del operations - to add, update, or remove any named results. If the tokens are modified in place, - it is not necessary to return them with a return statement. - - Parse actions can also completely replace the given tokens, with another ``ParseResults`` - object, or with some entirely different object (common for parse actions that perform data - conversions). A convenient way to build a new parse result is to define the values - using a dict, and then create the return value using :class:`ParseResults.from_dict`. - - If None is passed as the ``fn`` parse action, all previously added parse actions for this - expression are cleared. - - Optional keyword arguments: - - - call_during_try = (default= ``False``) indicate if parse action should be run during - lookaheads and alternate testing. For parse actions that have side effects, it is - important to only call the parse action once it is determined that it is being - called as part of a successful parse. For parse actions that perform additional - validation, then call_during_try should be passed as True, so that the validation - code is included in the preliminary "try" parses. - - Note: the default parsing behavior is to expand tabs in the input string - before starting the parsing process. See :class:`parse_string` for more - information on parsing strings containing `` `` s, and suggested - methods to maintain a consistent view of the parsed string, the parse - location, and line and column positions within the parsed string. - - Example:: - - # parse dates in the form YYYY/MM/DD - - # use parse action to convert toks from str to int at parse time - def convert_to_int(toks): - return int(toks[0]) - - # use a parse action to verify that the date is a valid date - def is_valid_date(instring, loc, toks): - from datetime import date - year, month, day = toks[::2] - try: - date(year, month, day) - except ValueError: - raise ParseException(instring, loc, "invalid date given") - - integer = Word(nums) - date_str = integer + '/' + integer + '/' + integer - - # add parse actions - integer.set_parse_action(convert_to_int) - date_str.set_parse_action(is_valid_date) - - # note that integer fields are now ints, not strings - date_str.run_tests(''' - # successful parse - note that integer fields were converted to ints - 1999/12/31 - - # fail - invalid date - 1999/13/31 - ''') - """ - if list(fns) == [None]: - self.parseAction = [] - else: - if not all(callable(fn) for fn in fns): - raise TypeError("parse actions must be callable") - self.parseAction = [_trim_arity(fn) for fn in fns] - self.callDuringTry = kwargs.get( - "call_during_try", kwargs.get("callDuringTry", False) - ) - return self - - def add_parse_action(self, *fns: ParseAction, **kwargs) -> "ParserElement": - """ - Add one or more parse actions to expression's list of parse actions. See :class:`set_parse_action`. - - See examples in :class:`copy`. - """ - self.parseAction += [_trim_arity(fn) for fn in fns] - self.callDuringTry = self.callDuringTry or kwargs.get( - "call_during_try", kwargs.get("callDuringTry", False) - ) - return self - - def add_condition(self, *fns: ParseCondition, **kwargs) -> "ParserElement": - """Add a boolean predicate function to expression's list of parse actions. See - :class:`set_parse_action` for function call signatures. Unlike ``set_parse_action``, - functions passed to ``add_condition`` need to return boolean success/fail of the condition. - - Optional keyword arguments: - - - message = define a custom message to be used in the raised exception - - fatal = if True, will raise ParseFatalException to stop parsing immediately; otherwise will raise - ParseException - - call_during_try = boolean to indicate if this method should be called during internal tryParse calls, - default=False - - Example:: - - integer = Word(nums).set_parse_action(lambda toks: int(toks[0])) - year_int = integer.copy() - year_int.add_condition(lambda toks: toks[0] >= 2000, message="Only support years 2000 and later") - date_str = year_int + '/' + integer + '/' + integer - - result = date_str.parse_string("1999/12/31") # -> Exception: Only support years 2000 and later (at char 0), - (line:1, col:1) - """ - for fn in fns: - self.parseAction.append( - condition_as_parse_action( - fn, message=kwargs.get("message"), fatal=kwargs.get("fatal", False) - ) - ) - - self.callDuringTry = self.callDuringTry or kwargs.get( - "call_during_try", kwargs.get("callDuringTry", False) - ) - return self - - def set_fail_action(self, fn: ParseFailAction) -> "ParserElement": - """ - Define action to perform if parsing fails at this expression. - Fail acton fn is a callable function that takes the arguments - ``fn(s, loc, expr, err)`` where: - - - s = string being parsed - - loc = location where expression match was attempted and failed - - expr = the parse expression that failed - - err = the exception thrown - - The function returns no value. It may throw :class:`ParseFatalException` - if it is desired to stop parsing immediately.""" - self.failAction = fn - return self - - def _skipIgnorables(self, instring, loc): - exprsFound = True - while exprsFound: - exprsFound = False - for e in self.ignoreExprs: - try: - while 1: - loc, dummy = e._parse(instring, loc) - exprsFound = True - except ParseException: - pass - return loc - - def preParse(self, instring, loc): - if self.ignoreExprs: - loc = self._skipIgnorables(instring, loc) - - if self.skipWhitespace: - instrlen = len(instring) - white_chars = self.whiteChars - while loc < instrlen and instring[loc] in white_chars: - loc += 1 - - return loc - - def parseImpl(self, instring, loc, doActions=True): - return loc, [] - - def postParse(self, instring, loc, tokenlist): - return tokenlist - - # @profile - def _parseNoCache( - self, instring, loc, doActions=True, callPreParse=True - ) -> Tuple[int, ParseResults]: - TRY, MATCH, FAIL = 0, 1, 2 - debugging = self.debug # and doActions) - len_instring = len(instring) - - if debugging or self.failAction: - # print("Match {} at loc {}({}, {})".format(self, loc, lineno(loc, instring), col(loc, instring))) - try: - if callPreParse and self.callPreparse: - pre_loc = self.preParse(instring, loc) - else: - pre_loc = loc - tokens_start = pre_loc - if self.debugActions.debug_try: - self.debugActions.debug_try(instring, tokens_start, self, False) - if self.mayIndexError or pre_loc >= len_instring: - try: - loc, tokens = self.parseImpl(instring, pre_loc, doActions) - except IndexError: - raise ParseException(instring, len_instring, self.errmsg, self) - else: - loc, tokens = self.parseImpl(instring, pre_loc, doActions) - except Exception as err: - # print("Exception raised:", err) - if self.debugActions.debug_fail: - self.debugActions.debug_fail( - instring, tokens_start, self, err, False - ) - if self.failAction: - self.failAction(instring, tokens_start, self, err) - raise - else: - if callPreParse and self.callPreparse: - pre_loc = self.preParse(instring, loc) - else: - pre_loc = loc - tokens_start = pre_loc - if self.mayIndexError or pre_loc >= len_instring: - try: - loc, tokens = self.parseImpl(instring, pre_loc, doActions) - except IndexError: - raise ParseException(instring, len_instring, self.errmsg, self) - else: - loc, tokens = self.parseImpl(instring, pre_loc, doActions) - - tokens = self.postParse(instring, loc, tokens) - - ret_tokens = ParseResults( - tokens, self.resultsName, asList=self.saveAsList, modal=self.modalResults - ) - if self.parseAction and (doActions or self.callDuringTry): - if debugging: - try: - for fn in self.parseAction: - try: - tokens = fn(instring, tokens_start, ret_tokens) - except IndexError as parse_action_exc: - exc = ParseException("exception raised in parse action") - raise exc from parse_action_exc - - if tokens is not None and tokens is not ret_tokens: - ret_tokens = ParseResults( - tokens, - self.resultsName, - asList=self.saveAsList - and isinstance(tokens, (ParseResults, list)), - modal=self.modalResults, - ) - except Exception as err: - # print "Exception raised in user parse action:", err - if self.debugActions.debug_fail: - self.debugActions.debug_fail( - instring, tokens_start, self, err, False - ) - raise - else: - for fn in self.parseAction: - try: - tokens = fn(instring, tokens_start, ret_tokens) - except IndexError as parse_action_exc: - exc = ParseException("exception raised in parse action") - raise exc from parse_action_exc - - if tokens is not None and tokens is not ret_tokens: - ret_tokens = ParseResults( - tokens, - self.resultsName, - asList=self.saveAsList - and isinstance(tokens, (ParseResults, list)), - modal=self.modalResults, - ) - if debugging: - # print("Matched", self, "->", ret_tokens.as_list()) - if self.debugActions.debug_match: - self.debugActions.debug_match( - instring, tokens_start, loc, self, ret_tokens, False - ) - - return loc, ret_tokens - - def try_parse(self, instring: str, loc: int, raise_fatal: bool = False) -> int: - try: - return self._parse(instring, loc, doActions=False)[0] - except ParseFatalException: - if raise_fatal: - raise - raise ParseException(instring, loc, self.errmsg, self) - - def can_parse_next(self, instring: str, loc: int) -> bool: - try: - self.try_parse(instring, loc) - except (ParseException, IndexError): - return False - else: - return True - - # cache for left-recursion in Forward references - recursion_lock = RLock() - recursion_memos: typing.Dict[ - Tuple[int, "Forward", bool], Tuple[int, Union[ParseResults, Exception]] - ] = {} - - # argument cache for optimizing repeated calls when backtracking through recursive expressions - packrat_cache = ( - {} - ) # this is set later by enabled_packrat(); this is here so that reset_cache() doesn't fail - packrat_cache_lock = RLock() - packrat_cache_stats = [0, 0] - - # this method gets repeatedly called during backtracking with the same arguments - - # we can cache these arguments and save ourselves the trouble of re-parsing the contained expression - def _parseCache( - self, instring, loc, doActions=True, callPreParse=True - ) -> Tuple[int, ParseResults]: - HIT, MISS = 0, 1 - TRY, MATCH, FAIL = 0, 1, 2 - lookup = (self, instring, loc, callPreParse, doActions) - with ParserElement.packrat_cache_lock: - cache = ParserElement.packrat_cache - value = cache.get(lookup) - if value is cache.not_in_cache: - ParserElement.packrat_cache_stats[MISS] += 1 - try: - value = self._parseNoCache(instring, loc, doActions, callPreParse) - except ParseBaseException as pe: - # cache a copy of the exception, without the traceback - cache.set(lookup, pe.__class__(*pe.args)) - raise - else: - cache.set(lookup, (value[0], value[1].copy(), loc)) - return value - else: - ParserElement.packrat_cache_stats[HIT] += 1 - if self.debug and self.debugActions.debug_try: - try: - self.debugActions.debug_try(instring, loc, self, cache_hit=True) - except TypeError: - pass - if isinstance(value, Exception): - if self.debug and self.debugActions.debug_fail: - try: - self.debugActions.debug_fail( - instring, loc, self, value, cache_hit=True - ) - except TypeError: - pass - raise value - - loc_, result, endloc = value[0], value[1].copy(), value[2] - if self.debug and self.debugActions.debug_match: - try: - self.debugActions.debug_match( - instring, loc_, endloc, self, result, cache_hit=True - ) - except TypeError: - pass - - return loc_, result - - _parse = _parseNoCache - - @staticmethod - def reset_cache() -> None: - ParserElement.packrat_cache.clear() - ParserElement.packrat_cache_stats[:] = [0] * len( - ParserElement.packrat_cache_stats - ) - ParserElement.recursion_memos.clear() - - _packratEnabled = False - _left_recursion_enabled = False - - @staticmethod - def disable_memoization() -> None: - """ - Disables active Packrat or Left Recursion parsing and their memoization - - This method also works if neither Packrat nor Left Recursion are enabled. - This makes it safe to call before activating Packrat nor Left Recursion - to clear any previous settings. - """ - ParserElement.reset_cache() - ParserElement._left_recursion_enabled = False - ParserElement._packratEnabled = False - ParserElement._parse = ParserElement._parseNoCache - - @staticmethod - def enable_left_recursion( - cache_size_limit: typing.Optional[int] = None, *, force=False - ) -> None: - """ - Enables "bounded recursion" parsing, which allows for both direct and indirect - left-recursion. During parsing, left-recursive :class:`Forward` elements are - repeatedly matched with a fixed recursion depth that is gradually increased - until finding the longest match. - - Example:: - - import pyparsing as pp - pp.ParserElement.enable_left_recursion() - - E = pp.Forward("E") - num = pp.Word(pp.nums) - # match `num`, or `num '+' num`, or `num '+' num '+' num`, ... - E <<= E + '+' - num | num - - print(E.parse_string("1+2+3")) - - Recursion search naturally memoizes matches of ``Forward`` elements and may - thus skip reevaluation of parse actions during backtracking. This may break - programs with parse actions which rely on strict ordering of side-effects. - - Parameters: - - - cache_size_limit - (default=``None``) - memoize at most this many - ``Forward`` elements during matching; if ``None`` (the default), - memoize all ``Forward`` elements. - - Bounded Recursion parsing works similar but not identical to Packrat parsing, - thus the two cannot be used together. Use ``force=True`` to disable any - previous, conflicting settings. - """ - if force: - ParserElement.disable_memoization() - elif ParserElement._packratEnabled: - raise RuntimeError("Packrat and Bounded Recursion are not compatible") - if cache_size_limit is None: - ParserElement.recursion_memos = _UnboundedMemo() - elif cache_size_limit > 0: - ParserElement.recursion_memos = _LRUMemo(capacity=cache_size_limit) - else: - raise NotImplementedError("Memo size of %s" % cache_size_limit) - ParserElement._left_recursion_enabled = True - - @staticmethod - def enable_packrat(cache_size_limit: int = 128, *, force: bool = False) -> None: - """ - Enables "packrat" parsing, which adds memoizing to the parsing logic. - Repeated parse attempts at the same string location (which happens - often in many complex grammars) can immediately return a cached value, - instead of re-executing parsing/validating code. Memoizing is done of - both valid results and parsing exceptions. - - Parameters: - - - cache_size_limit - (default= ``128``) - if an integer value is provided - will limit the size of the packrat cache; if None is passed, then - the cache size will be unbounded; if 0 is passed, the cache will - be effectively disabled. - - This speedup may break existing programs that use parse actions that - have side-effects. For this reason, packrat parsing is disabled when - you first import pyparsing. To activate the packrat feature, your - program must call the class method :class:`ParserElement.enable_packrat`. - For best results, call ``enable_packrat()`` immediately after - importing pyparsing. - - Example:: - - import pyparsing - pyparsing.ParserElement.enable_packrat() - - Packrat parsing works similar but not identical to Bounded Recursion parsing, - thus the two cannot be used together. Use ``force=True`` to disable any - previous, conflicting settings. - """ - if force: - ParserElement.disable_memoization() - elif ParserElement._left_recursion_enabled: - raise RuntimeError("Packrat and Bounded Recursion are not compatible") - if not ParserElement._packratEnabled: - ParserElement._packratEnabled = True - if cache_size_limit is None: - ParserElement.packrat_cache = _UnboundedCache() - else: - ParserElement.packrat_cache = _FifoCache(cache_size_limit) - ParserElement._parse = ParserElement._parseCache - - def parse_string( - self, instring: str, parse_all: bool = False, *, parseAll: bool = False - ) -> ParseResults: - """ - Parse a string with respect to the parser definition. This function is intended as the primary interface to the - client code. - - :param instring: The input string to be parsed. - :param parse_all: If set, the entire input string must match the grammar. - :param parseAll: retained for pre-PEP8 compatibility, will be removed in a future release. - :raises ParseException: Raised if ``parse_all`` is set and the input string does not match the whole grammar. - :returns: the parsed data as a :class:`ParseResults` object, which may be accessed as a `list`, a `dict`, or - an object with attributes if the given parser includes results names. - - If the input string is required to match the entire grammar, ``parse_all`` flag must be set to ``True``. This - is also equivalent to ending the grammar with :class:`StringEnd`(). - - To report proper column numbers, ``parse_string`` operates on a copy of the input string where all tabs are - converted to spaces (8 spaces per tab, as per the default in ``string.expandtabs``). If the input string - contains tabs and the grammar uses parse actions that use the ``loc`` argument to index into the string - being parsed, one can ensure a consistent view of the input string by doing one of the following: - - - calling ``parse_with_tabs`` on your grammar before calling ``parse_string`` (see :class:`parse_with_tabs`), - - define your parse action using the full ``(s,loc,toks)`` signature, and reference the input string using the - parse action's ``s`` argument, or - - explicitly expand the tabs in your input string before calling ``parse_string``. - - Examples: - - By default, partial matches are OK. - - >>> res = Word('a').parse_string('aaaaabaaa') - >>> print(res) - ['aaaaa'] - - The parsing behavior varies by the inheriting class of this abstract class. Please refer to the children - directly to see more examples. - - It raises an exception if parse_all flag is set and instring does not match the whole grammar. - - >>> res = Word('a').parse_string('aaaaabaaa', parse_all=True) - Traceback (most recent call last): - ... - pyparsing.ParseException: Expected end of text, found 'b' (at char 5), (line:1, col:6) - """ - parseAll = parse_all or parseAll - - ParserElement.reset_cache() - if not self.streamlined: - self.streamline() - for e in self.ignoreExprs: - e.streamline() - if not self.keepTabs: - instring = instring.expandtabs() - try: - loc, tokens = self._parse(instring, 0) - if parseAll: - loc = self.preParse(instring, loc) - se = Empty() + StringEnd() - se._parse(instring, loc) - except ParseBaseException as exc: - if ParserElement.verbose_stacktrace: - raise - else: - # catch and re-raise exception from here, clearing out pyparsing internal stack trace - raise exc.with_traceback(None) - else: - return tokens - - def scan_string( - self, - instring: str, - max_matches: int = _MAX_INT, - overlap: bool = False, - *, - debug: bool = False, - maxMatches: int = _MAX_INT, - ) -> Generator[Tuple[ParseResults, int, int], None, None]: - """ - Scan the input string for expression matches. Each match will return the - matching tokens, start location, and end location. May be called with optional - ``max_matches`` argument, to clip scanning after 'n' matches are found. If - ``overlap`` is specified, then overlapping matches will be reported. - - Note that the start and end locations are reported relative to the string - being parsed. See :class:`parse_string` for more information on parsing - strings with embedded tabs. - - Example:: - - source = "sldjf123lsdjjkf345sldkjf879lkjsfd987" - print(source) - for tokens, start, end in Word(alphas).scan_string(source): - print(' '*start + '^'*(end-start)) - print(' '*start + tokens[0]) - - prints:: - - sldjf123lsdjjkf345sldkjf879lkjsfd987 - ^^^^^ - sldjf - ^^^^^^^ - lsdjjkf - ^^^^^^ - sldkjf - ^^^^^^ - lkjsfd - """ - maxMatches = min(maxMatches, max_matches) - if not self.streamlined: - self.streamline() - for e in self.ignoreExprs: - e.streamline() - - if not self.keepTabs: - instring = str(instring).expandtabs() - instrlen = len(instring) - loc = 0 - preparseFn = self.preParse - parseFn = self._parse - ParserElement.resetCache() - matches = 0 - try: - while loc <= instrlen and matches < maxMatches: - try: - preloc = preparseFn(instring, loc) - nextLoc, tokens = parseFn(instring, preloc, callPreParse=False) - except ParseException: - loc = preloc + 1 - else: - if nextLoc > loc: - matches += 1 - if debug: - print( - { - "tokens": tokens.asList(), - "start": preloc, - "end": nextLoc, - } - ) - yield tokens, preloc, nextLoc - if overlap: - nextloc = preparseFn(instring, loc) - if nextloc > loc: - loc = nextLoc - else: - loc += 1 - else: - loc = nextLoc - else: - loc = preloc + 1 - except ParseBaseException as exc: - if ParserElement.verbose_stacktrace: - raise - else: - # catch and re-raise exception from here, clears out pyparsing internal stack trace - raise exc.with_traceback(None) - - def transform_string(self, instring: str, *, debug: bool = False) -> str: - """ - Extension to :class:`scan_string`, to modify matching text with modified tokens that may - be returned from a parse action. To use ``transform_string``, define a grammar and - attach a parse action to it that modifies the returned token list. - Invoking ``transform_string()`` on a target string will then scan for matches, - and replace the matched text patterns according to the logic in the parse - action. ``transform_string()`` returns the resulting transformed string. - - Example:: - - wd = Word(alphas) - wd.set_parse_action(lambda toks: toks[0].title()) - - print(wd.transform_string("now is the winter of our discontent made glorious summer by this sun of york.")) - - prints:: - - Now Is The Winter Of Our Discontent Made Glorious Summer By This Sun Of York. - """ - out: List[str] = [] - lastE = 0 - # force preservation of s, to minimize unwanted transformation of string, and to - # keep string locs straight between transform_string and scan_string - self.keepTabs = True - try: - for t, s, e in self.scan_string(instring, debug=debug): - out.append(instring[lastE:s]) - if t: - if isinstance(t, ParseResults): - out += t.as_list() - elif isinstance(t, Iterable) and not isinstance(t, str_type): - out.extend(t) - else: - out.append(t) - lastE = e - out.append(instring[lastE:]) - out = [o for o in out if o] - return "".join([str(s) for s in _flatten(out)]) - except ParseBaseException as exc: - if ParserElement.verbose_stacktrace: - raise - else: - # catch and re-raise exception from here, clears out pyparsing internal stack trace - raise exc.with_traceback(None) - - def search_string( - self, - instring: str, - max_matches: int = _MAX_INT, - *, - debug: bool = False, - maxMatches: int = _MAX_INT, - ) -> ParseResults: - """ - Another extension to :class:`scan_string`, simplifying the access to the tokens found - to match the given parse expression. May be called with optional - ``max_matches`` argument, to clip searching after 'n' matches are found. - - Example:: - - # a capitalized word starts with an uppercase letter, followed by zero or more lowercase letters - cap_word = Word(alphas.upper(), alphas.lower()) - - print(cap_word.search_string("More than Iron, more than Lead, more than Gold I need Electricity")) - - # the sum() builtin can be used to merge results into a single ParseResults object - print(sum(cap_word.search_string("More than Iron, more than Lead, more than Gold I need Electricity"))) - - prints:: - - [['More'], ['Iron'], ['Lead'], ['Gold'], ['I'], ['Electricity']] - ['More', 'Iron', 'Lead', 'Gold', 'I', 'Electricity'] - """ - maxMatches = min(maxMatches, max_matches) - try: - return ParseResults( - [t for t, s, e in self.scan_string(instring, maxMatches, debug=debug)] - ) - except ParseBaseException as exc: - if ParserElement.verbose_stacktrace: - raise - else: - # catch and re-raise exception from here, clears out pyparsing internal stack trace - raise exc.with_traceback(None) - - def split( - self, - instring: str, - maxsplit: int = _MAX_INT, - include_separators: bool = False, - *, - includeSeparators=False, - ) -> Generator[str, None, None]: - """ - Generator method to split a string using the given expression as a separator. - May be called with optional ``maxsplit`` argument, to limit the number of splits; - and the optional ``include_separators`` argument (default= ``False``), if the separating - matching text should be included in the split results. - - Example:: - - punc = one_of(list(".,;:/-!?")) - print(list(punc.split("This, this?, this sentence, is badly punctuated!"))) - - prints:: - - ['This', ' this', '', ' this sentence', ' is badly punctuated', ''] - """ - includeSeparators = includeSeparators or include_separators - last = 0 - for t, s, e in self.scan_string(instring, max_matches=maxsplit): - yield instring[last:s] - if includeSeparators: - yield t[0] - last = e - yield instring[last:] - - def __add__(self, other) -> "ParserElement": - """ - Implementation of ``+`` operator - returns :class:`And`. Adding strings to a :class:`ParserElement` - converts them to :class:`Literal`s by default. - - Example:: - - greet = Word(alphas) + "," + Word(alphas) + "!" - hello = "Hello, World!" - print(hello, "->", greet.parse_string(hello)) - - prints:: - - Hello, World! -> ['Hello', ',', 'World', '!'] - - ``...`` may be used as a parse expression as a short form of :class:`SkipTo`. - - Literal('start') + ... + Literal('end') - - is equivalent to: - - Literal('start') + SkipTo('end')("_skipped*") + Literal('end') - - Note that the skipped text is returned with '_skipped' as a results name, - and to support having multiple skips in the same parser, the value returned is - a list of all skipped text. - """ - if other is Ellipsis: - return _PendingSkip(self) - - if isinstance(other, str_type): - other = self._literalStringClass(other) - if not isinstance(other, ParserElement): - raise TypeError( - "Cannot combine element of type {} with ParserElement".format( - type(other).__name__ - ) - ) - return And([self, other]) - - def __radd__(self, other) -> "ParserElement": - """ - Implementation of ``+`` operator when left operand is not a :class:`ParserElement` - """ - if other is Ellipsis: - return SkipTo(self)("_skipped*") + self - - if isinstance(other, str_type): - other = self._literalStringClass(other) - if not isinstance(other, ParserElement): - raise TypeError( - "Cannot combine element of type {} with ParserElement".format( - type(other).__name__ - ) - ) - return other + self - - def __sub__(self, other) -> "ParserElement": - """ - Implementation of ``-`` operator, returns :class:`And` with error stop - """ - if isinstance(other, str_type): - other = self._literalStringClass(other) - if not isinstance(other, ParserElement): - raise TypeError( - "Cannot combine element of type {} with ParserElement".format( - type(other).__name__ - ) - ) - return self + And._ErrorStop() + other - - def __rsub__(self, other) -> "ParserElement": - """ - Implementation of ``-`` operator when left operand is not a :class:`ParserElement` - """ - if isinstance(other, str_type): - other = self._literalStringClass(other) - if not isinstance(other, ParserElement): - raise TypeError( - "Cannot combine element of type {} with ParserElement".format( - type(other).__name__ - ) - ) - return other - self - - def __mul__(self, other) -> "ParserElement": - """ - Implementation of ``*`` operator, allows use of ``expr * 3`` in place of - ``expr + expr + expr``. Expressions may also be multiplied by a 2-integer - tuple, similar to ``{min, max}`` multipliers in regular expressions. Tuples - may also include ``None`` as in: - - ``expr*(n, None)`` or ``expr*(n, )`` is equivalent - to ``expr*n + ZeroOrMore(expr)`` - (read as "at least n instances of ``expr``") - - ``expr*(None, n)`` is equivalent to ``expr*(0, n)`` - (read as "0 to n instances of ``expr``") - - ``expr*(None, None)`` is equivalent to ``ZeroOrMore(expr)`` - - ``expr*(1, None)`` is equivalent to ``OneOrMore(expr)`` - - Note that ``expr*(None, n)`` does not raise an exception if - more than n exprs exist in the input stream; that is, - ``expr*(None, n)`` does not enforce a maximum number of expr - occurrences. If this behavior is desired, then write - ``expr*(None, n) + ~expr`` - """ - if other is Ellipsis: - other = (0, None) - elif isinstance(other, tuple) and other[:1] == (Ellipsis,): - other = ((0,) + other[1:] + (None,))[:2] - - if isinstance(other, int): - minElements, optElements = other, 0 - elif isinstance(other, tuple): - other = tuple(o if o is not Ellipsis else None for o in other) - other = (other + (None, None))[:2] - if other[0] is None: - other = (0, other[1]) - if isinstance(other[0], int) and other[1] is None: - if other[0] == 0: - return ZeroOrMore(self) - if other[0] == 1: - return OneOrMore(self) - else: - return self * other[0] + ZeroOrMore(self) - elif isinstance(other[0], int) and isinstance(other[1], int): - minElements, optElements = other - optElements -= minElements - else: - raise TypeError( - "cannot multiply ParserElement and ({}) objects".format( - ",".join(type(item).__name__ for item in other) - ) - ) - else: - raise TypeError( - "cannot multiply ParserElement and {} objects".format( - type(other).__name__ - ) - ) - - if minElements < 0: - raise ValueError("cannot multiply ParserElement by negative value") - if optElements < 0: - raise ValueError( - "second tuple value must be greater or equal to first tuple value" - ) - if minElements == optElements == 0: - return And([]) - - if optElements: - - def makeOptionalList(n): - if n > 1: - return Opt(self + makeOptionalList(n - 1)) - else: - return Opt(self) - - if minElements: - if minElements == 1: - ret = self + makeOptionalList(optElements) - else: - ret = And([self] * minElements) + makeOptionalList(optElements) - else: - ret = makeOptionalList(optElements) - else: - if minElements == 1: - ret = self - else: - ret = And([self] * minElements) - return ret - - def __rmul__(self, other) -> "ParserElement": - return self.__mul__(other) - - def __or__(self, other) -> "ParserElement": - """ - Implementation of ``|`` operator - returns :class:`MatchFirst` - """ - if other is Ellipsis: - return _PendingSkip(self, must_skip=True) - - if isinstance(other, str_type): - other = self._literalStringClass(other) - if not isinstance(other, ParserElement): - raise TypeError( - "Cannot combine element of type {} with ParserElement".format( - type(other).__name__ - ) - ) - return MatchFirst([self, other]) - - def __ror__(self, other) -> "ParserElement": - """ - Implementation of ``|`` operator when left operand is not a :class:`ParserElement` - """ - if isinstance(other, str_type): - other = self._literalStringClass(other) - if not isinstance(other, ParserElement): - raise TypeError( - "Cannot combine element of type {} with ParserElement".format( - type(other).__name__ - ) - ) - return other | self - - def __xor__(self, other) -> "ParserElement": - """ - Implementation of ``^`` operator - returns :class:`Or` - """ - if isinstance(other, str_type): - other = self._literalStringClass(other) - if not isinstance(other, ParserElement): - raise TypeError( - "Cannot combine element of type {} with ParserElement".format( - type(other).__name__ - ) - ) - return Or([self, other]) - - def __rxor__(self, other) -> "ParserElement": - """ - Implementation of ``^`` operator when left operand is not a :class:`ParserElement` - """ - if isinstance(other, str_type): - other = self._literalStringClass(other) - if not isinstance(other, ParserElement): - raise TypeError( - "Cannot combine element of type {} with ParserElement".format( - type(other).__name__ - ) - ) - return other ^ self - - def __and__(self, other) -> "ParserElement": - """ - Implementation of ``&`` operator - returns :class:`Each` - """ - if isinstance(other, str_type): - other = self._literalStringClass(other) - if not isinstance(other, ParserElement): - raise TypeError( - "Cannot combine element of type {} with ParserElement".format( - type(other).__name__ - ) - ) - return Each([self, other]) - - def __rand__(self, other) -> "ParserElement": - """ - Implementation of ``&`` operator when left operand is not a :class:`ParserElement` - """ - if isinstance(other, str_type): - other = self._literalStringClass(other) - if not isinstance(other, ParserElement): - raise TypeError( - "Cannot combine element of type {} with ParserElement".format( - type(other).__name__ - ) - ) - return other & self - - def __invert__(self) -> "ParserElement": - """ - Implementation of ``~`` operator - returns :class:`NotAny` - """ - return NotAny(self) - - # disable __iter__ to override legacy use of sequential access to __getitem__ to - # iterate over a sequence - __iter__ = None - - def __getitem__(self, key): - """ - use ``[]`` indexing notation as a short form for expression repetition: - - - ``expr[n]`` is equivalent to ``expr*n`` - - ``expr[m, n]`` is equivalent to ``expr*(m, n)`` - - ``expr[n, ...]`` or ``expr[n,]`` is equivalent - to ``expr*n + ZeroOrMore(expr)`` - (read as "at least n instances of ``expr``") - - ``expr[..., n]`` is equivalent to ``expr*(0, n)`` - (read as "0 to n instances of ``expr``") - - ``expr[...]`` and ``expr[0, ...]`` are equivalent to ``ZeroOrMore(expr)`` - - ``expr[1, ...]`` is equivalent to ``OneOrMore(expr)`` - - ``None`` may be used in place of ``...``. - - Note that ``expr[..., n]`` and ``expr[m, n]``do not raise an exception - if more than ``n`` ``expr``s exist in the input stream. If this behavior is - desired, then write ``expr[..., n] + ~expr``. - """ - - # convert single arg keys to tuples - try: - if isinstance(key, str_type): - key = (key,) - iter(key) - except TypeError: - key = (key, key) - - if len(key) > 2: - raise TypeError( - "only 1 or 2 index arguments supported ({}{})".format( - key[:5], "... [{}]".format(len(key)) if len(key) > 5 else "" - ) - ) - - # clip to 2 elements - ret = self * tuple(key[:2]) - return ret - - def __call__(self, name: str = None) -> "ParserElement": - """ - Shortcut for :class:`set_results_name`, with ``list_all_matches=False``. - - If ``name`` is given with a trailing ``'*'`` character, then ``list_all_matches`` will be - passed as ``True``. - - If ``name` is omitted, same as calling :class:`copy`. - - Example:: - - # these are equivalent - userdata = Word(alphas).set_results_name("name") + Word(nums + "-").set_results_name("socsecno") - userdata = Word(alphas)("name") + Word(nums + "-")("socsecno") - """ - if name is not None: - return self._setResultsName(name) - else: - return self.copy() - - def suppress(self) -> "ParserElement": - """ - Suppresses the output of this :class:`ParserElement`; useful to keep punctuation from - cluttering up returned output. - """ - return Suppress(self) - - def ignore_whitespace(self, recursive: bool = True) -> "ParserElement": - """ - Enables the skipping of whitespace before matching the characters in the - :class:`ParserElement`'s defined pattern. - - :param recursive: If ``True`` (the default), also enable whitespace skipping in child elements (if any) - """ - self.skipWhitespace = True - return self - - def leave_whitespace(self, recursive: bool = True) -> "ParserElement": - """ - Disables the skipping of whitespace before matching the characters in the - :class:`ParserElement`'s defined pattern. This is normally only used internally by - the pyparsing module, but may be needed in some whitespace-sensitive grammars. - - :param recursive: If true (the default), also disable whitespace skipping in child elements (if any) - """ - self.skipWhitespace = False - return self - - def set_whitespace_chars( - self, chars: Union[Set[str], str], copy_defaults: bool = False - ) -> "ParserElement": - """ - Overrides the default whitespace chars - """ - self.skipWhitespace = True - self.whiteChars = set(chars) - self.copyDefaultWhiteChars = copy_defaults - return self - - def parse_with_tabs(self) -> "ParserElement": - """ - Overrides default behavior to expand `` `` s to spaces before parsing the input string. - Must be called before ``parse_string`` when the input grammar contains elements that - match `` `` characters. - """ - self.keepTabs = True - return self - - def ignore(self, other: "ParserElement") -> "ParserElement": - """ - Define expression to be ignored (e.g., comments) while doing pattern - matching; may be called repeatedly, to define multiple comment or other - ignorable patterns. - - Example:: - - patt = Word(alphas)[1, ...] - patt.parse_string('ablaj /* comment */ lskjd') - # -> ['ablaj'] - - patt.ignore(c_style_comment) - patt.parse_string('ablaj /* comment */ lskjd') - # -> ['ablaj', 'lskjd'] - """ - import typing - - if isinstance(other, str_type): - other = Suppress(other) - - if isinstance(other, Suppress): - if other not in self.ignoreExprs: - self.ignoreExprs.append(other) - else: - self.ignoreExprs.append(Suppress(other.copy())) - return self - - def set_debug_actions( - self, - start_action: DebugStartAction, - success_action: DebugSuccessAction, - exception_action: DebugExceptionAction, - ) -> "ParserElement": - """ - Customize display of debugging messages while doing pattern matching: - - - ``start_action`` - method to be called when an expression is about to be parsed; - should have the signature ``fn(input_string: str, location: int, expression: ParserElement, cache_hit: bool)`` - - - ``success_action`` - method to be called when an expression has successfully parsed; - should have the signature ``fn(input_string: str, start_location: int, end_location: int, expression: ParserELement, parsed_tokens: ParseResults, cache_hit: bool)`` - - - ``exception_action`` - method to be called when expression fails to parse; - should have the signature ``fn(input_string: str, location: int, expression: ParserElement, exception: Exception, cache_hit: bool)`` - """ - self.debugActions = self.DebugActions( - start_action or _default_start_debug_action, - success_action or _default_success_debug_action, - exception_action or _default_exception_debug_action, - ) - self.debug = True - return self - - def set_debug(self, flag: bool = True) -> "ParserElement": - """ - Enable display of debugging messages while doing pattern matching. - Set ``flag`` to ``True`` to enable, ``False`` to disable. - - Example:: - - wd = Word(alphas).set_name("alphaword") - integer = Word(nums).set_name("numword") - term = wd | integer - - # turn on debugging for wd - wd.set_debug() - - term[1, ...].parse_string("abc 123 xyz 890") - - prints:: - - Match alphaword at loc 0(1,1) - Matched alphaword -> ['abc'] - Match alphaword at loc 3(1,4) - Exception raised:Expected alphaword (at char 4), (line:1, col:5) - Match alphaword at loc 7(1,8) - Matched alphaword -> ['xyz'] - Match alphaword at loc 11(1,12) - Exception raised:Expected alphaword (at char 12), (line:1, col:13) - Match alphaword at loc 15(1,16) - Exception raised:Expected alphaword (at char 15), (line:1, col:16) - - The output shown is that produced by the default debug actions - custom debug actions can be - specified using :class:`set_debug_actions`. Prior to attempting - to match the ``wd`` expression, the debugging message ``"Match at loc ( , )"`` - is shown. Then if the parse succeeds, a ``"Matched"`` message is shown, or an ``"Exception raised"`` - message is shown. Also note the use of :class:`set_name` to assign a human-readable name to the expression, - which makes debugging and exception messages easier to understand - for instance, the default - name created for the :class:`Word` expression without calling ``set_name`` is ``"W:(A-Za-z)"``. - """ - if flag: - self.set_debug_actions( - _default_start_debug_action, - _default_success_debug_action, - _default_exception_debug_action, - ) - else: - self.debug = False - return self - - @property - def default_name(self) -> str: - if self._defaultName is None: - self._defaultName = self._generateDefaultName() - return self._defaultName - - @abstractmethod - def _generateDefaultName(self): - """ - Child classes must define this method, which defines how the ``default_name`` is set. - """ - - def set_name(self, name: str) -> "ParserElement": - """ - Define name for this expression, makes debugging and exception messages clearer. - Example:: - Word(nums).parse_string("ABC") # -> Exception: Expected W:(0-9) (at char 0), (line:1, col:1) - Word(nums).set_name("integer").parse_string("ABC") # -> Exception: Expected integer (at char 0), (line:1, col:1) - """ - self.customName = name - self.errmsg = "Expected " + self.name - if __diag__.enable_debug_on_named_expressions: - self.set_debug() - return self - - @property - def name(self) -> str: - # This will use a user-defined name if available, but otherwise defaults back to the auto-generated name - return self.customName if self.customName is not None else self.default_name - - def __str__(self) -> str: - return self.name - - def __repr__(self) -> str: - return str(self) - - def streamline(self) -> "ParserElement": - self.streamlined = True - self._defaultName = None - return self - - def recurse(self) -> Sequence["ParserElement"]: - return [] - - def _checkRecursion(self, parseElementList): - subRecCheckList = parseElementList[:] + [self] - for e in self.recurse(): - e._checkRecursion(subRecCheckList) - - def validate(self, validateTrace=None) -> None: - """ - Check defined expressions for valid structure, check for infinite recursive definitions. - """ - self._checkRecursion([]) - - def parse_file( - self, - file_or_filename: Union[str, Path, TextIO], - encoding: str = "utf-8", - parse_all: bool = False, - *, - parseAll: bool = False, - ) -> ParseResults: - """ - Execute the parse expression on the given file or filename. - If a filename is specified (instead of a file object), - the entire file is opened, read, and closed before parsing. - """ - parseAll = parseAll or parse_all - try: - file_contents = file_or_filename.read() - except AttributeError: - with open(file_or_filename, "r", encoding=encoding) as f: - file_contents = f.read() - try: - return self.parse_string(file_contents, parseAll) - except ParseBaseException as exc: - if ParserElement.verbose_stacktrace: - raise - else: - # catch and re-raise exception from here, clears out pyparsing internal stack trace - raise exc.with_traceback(None) - - def __eq__(self, other): - if self is other: - return True - elif isinstance(other, str_type): - return self.matches(other, parse_all=True) - elif isinstance(other, ParserElement): - return vars(self) == vars(other) - return False - - def __hash__(self): - return id(self) - - def matches( - self, test_string: str, parse_all: bool = True, *, parseAll: bool = True - ) -> bool: - """ - Method for quick testing of a parser against a test string. Good for simple - inline microtests of sub expressions while building up larger parser. - - Parameters: - - ``test_string`` - to test against this expression for a match - - ``parse_all`` - (default= ``True``) - flag to pass to :class:`parse_string` when running tests - - Example:: - - expr = Word(nums) - assert expr.matches("100") - """ - parseAll = parseAll and parse_all - try: - self.parse_string(str(test_string), parse_all=parseAll) - return True - except ParseBaseException: - return False - - def run_tests( - self, - tests: Union[str, List[str]], - parse_all: bool = True, - comment: typing.Optional[Union["ParserElement", str]] = "#", - full_dump: bool = True, - print_results: bool = True, - failure_tests: bool = False, - post_parse: Callable[[str, ParseResults], str] = None, - file: typing.Optional[TextIO] = None, - with_line_numbers: bool = False, - *, - parseAll: bool = True, - fullDump: bool = True, - printResults: bool = True, - failureTests: bool = False, - postParse: Callable[[str, ParseResults], str] = None, - ) -> Tuple[bool, List[Tuple[str, Union[ParseResults, Exception]]]]: - """ - Execute the parse expression on a series of test strings, showing each - test, the parsed results or where the parse failed. Quick and easy way to - run a parse expression against a list of sample strings. - - Parameters: - - ``tests`` - a list of separate test strings, or a multiline string of test strings - - ``parse_all`` - (default= ``True``) - flag to pass to :class:`parse_string` when running tests - - ``comment`` - (default= ``'#'``) - expression for indicating embedded comments in the test - string; pass None to disable comment filtering - - ``full_dump`` - (default= ``True``) - dump results as list followed by results names in nested outline; - if False, only dump nested list - - ``print_results`` - (default= ``True``) prints test output to stdout - - ``failure_tests`` - (default= ``False``) indicates if these tests are expected to fail parsing - - ``post_parse`` - (default= ``None``) optional callback for successful parse results; called as - `fn(test_string, parse_results)` and returns a string to be added to the test output - - ``file`` - (default= ``None``) optional file-like object to which test output will be written; - if None, will default to ``sys.stdout`` - - ``with_line_numbers`` - default= ``False``) show test strings with line and column numbers - - Returns: a (success, results) tuple, where success indicates that all tests succeeded - (or failed if ``failure_tests`` is True), and the results contain a list of lines of each - test's output - - Example:: - - number_expr = pyparsing_common.number.copy() - - result = number_expr.run_tests(''' - # unsigned integer - 100 - # negative integer - -100 - # float with scientific notation - 6.02e23 - # integer with scientific notation - 1e-12 - ''') - print("Success" if result[0] else "Failed!") - - result = number_expr.run_tests(''' - # stray character - 100Z - # missing leading digit before '.' - -.100 - # too many '.' - 3.14.159 - ''', failure_tests=True) - print("Success" if result[0] else "Failed!") - - prints:: - - # unsigned integer - 100 - [100] - - # negative integer - -100 - [-100] - - # float with scientific notation - 6.02e23 - [6.02e+23] - - # integer with scientific notation - 1e-12 - [1e-12] - - Success - - # stray character - 100Z - ^ - FAIL: Expected end of text (at char 3), (line:1, col:4) - - # missing leading digit before '.' - -.100 - ^ - FAIL: Expected {real number with scientific notation | real number | signed integer} (at char 0), (line:1, col:1) - - # too many '.' - 3.14.159 - ^ - FAIL: Expected end of text (at char 4), (line:1, col:5) - - Success - - Each test string must be on a single line. If you want to test a string that spans multiple - lines, create a test like this:: - - expr.run_tests(r"this is a test\\n of strings that spans \\n 3 lines") - - (Note that this is a raw string literal, you must include the leading ``'r'``.) - """ - from .testing import pyparsing_test - - parseAll = parseAll and parse_all - fullDump = fullDump and full_dump - printResults = printResults and print_results - failureTests = failureTests or failure_tests - postParse = postParse or post_parse - if isinstance(tests, str_type): - line_strip = type(tests).strip - tests = [line_strip(test_line) for test_line in tests.rstrip().splitlines()] - if isinstance(comment, str_type): - comment = Literal(comment) - if file is None: - file = sys.stdout - print_ = file.write - - result: Union[ParseResults, Exception] - allResults = [] - comments = [] - success = True - NL = Literal(r"\n").add_parse_action(replace_with("\n")).ignore(quoted_string) - BOM = "\ufeff" - for t in tests: - if comment is not None and comment.matches(t, False) or comments and not t: - comments.append( - pyparsing_test.with_line_numbers(t) if with_line_numbers else t - ) - continue - if not t: - continue - out = [ - "\n" + "\n".join(comments) if comments else "", - pyparsing_test.with_line_numbers(t) if with_line_numbers else t, - ] - comments = [] - try: - # convert newline marks to actual newlines, and strip leading BOM if present - t = NL.transform_string(t.lstrip(BOM)) - result = self.parse_string(t, parse_all=parseAll) - except ParseBaseException as pe: - fatal = "(FATAL)" if isinstance(pe, ParseFatalException) else "" - out.append(pe.explain()) - out.append("FAIL: " + str(pe)) - if ParserElement.verbose_stacktrace: - out.extend(traceback.format_tb(pe.__traceback__)) - success = success and failureTests - result = pe - except Exception as exc: - out.append("FAIL-EXCEPTION: {}: {}".format(type(exc).__name__, exc)) - if ParserElement.verbose_stacktrace: - out.extend(traceback.format_tb(exc.__traceback__)) - success = success and failureTests - result = exc - else: - success = success and not failureTests - if postParse is not None: - try: - pp_value = postParse(t, result) - if pp_value is not None: - if isinstance(pp_value, ParseResults): - out.append(pp_value.dump()) - else: - out.append(str(pp_value)) - else: - out.append(result.dump()) - except Exception as e: - out.append(result.dump(full=fullDump)) - out.append( - "{} failed: {}: {}".format( - postParse.__name__, type(e).__name__, e - ) - ) - else: - out.append(result.dump(full=fullDump)) - out.append("") - - if printResults: - print_("\n".join(out)) - - allResults.append((t, result)) - - return success, allResults - - def create_diagram( - self, - output_html: Union[TextIO, Path, str], - vertical: int = 3, - show_results_names: bool = False, - show_groups: bool = False, - **kwargs, - ) -> None: - """ - Create a railroad diagram for the parser. - - Parameters: - - output_html (str or file-like object) - output target for generated - diagram HTML - - vertical (int) - threshold for formatting multiple alternatives vertically - instead of horizontally (default=3) - - show_results_names - bool flag whether diagram should show annotations for - defined results names - - show_groups - bool flag whether groups should be highlighted with an unlabeled surrounding box - Additional diagram-formatting keyword arguments can also be included; - see railroad.Diagram class. - """ - - try: - from .diagram import to_railroad, railroad_to_html - except ImportError as ie: - raise Exception( - "must ``pip install pyparsing[diagrams]`` to generate parser railroad diagrams" - ) from ie - - self.streamline() - - railroad = to_railroad( - self, - vertical=vertical, - show_results_names=show_results_names, - show_groups=show_groups, - diagram_kwargs=kwargs, - ) - if isinstance(output_html, (str, Path)): - with open(output_html, "w", encoding="utf-8") as diag_file: - diag_file.write(railroad_to_html(railroad)) - else: - # we were passed a file-like object, just write to it - output_html.write(railroad_to_html(railroad)) - - setDefaultWhitespaceChars = set_default_whitespace_chars - inlineLiteralsUsing = inline_literals_using - setResultsName = set_results_name - setBreak = set_break - setParseAction = set_parse_action - addParseAction = add_parse_action - addCondition = add_condition - setFailAction = set_fail_action - tryParse = try_parse - canParseNext = can_parse_next - resetCache = reset_cache - enableLeftRecursion = enable_left_recursion - enablePackrat = enable_packrat - parseString = parse_string - scanString = scan_string - searchString = search_string - transformString = transform_string - setWhitespaceChars = set_whitespace_chars - parseWithTabs = parse_with_tabs - setDebugActions = set_debug_actions - setDebug = set_debug - defaultName = default_name - setName = set_name - parseFile = parse_file - runTests = run_tests - ignoreWhitespace = ignore_whitespace - leaveWhitespace = leave_whitespace - - -class _PendingSkip(ParserElement): - # internal placeholder class to hold a place were '...' is added to a parser element, - # once another ParserElement is added, this placeholder will be replaced with a SkipTo - def __init__(self, expr: ParserElement, must_skip: bool = False): - super().__init__() - self.anchor = expr - self.must_skip = must_skip - - def _generateDefaultName(self): - return str(self.anchor + Empty()).replace("Empty", "...") - - def __add__(self, other) -> "ParserElement": - skipper = SkipTo(other).set_name("...")("_skipped*") - if self.must_skip: - - def must_skip(t): - if not t._skipped or t._skipped.as_list() == [""]: - del t[0] - t.pop("_skipped", None) - - def show_skip(t): - if t._skipped.as_list()[-1:] == [""]: - t.pop("_skipped") - t["_skipped"] = "missing <" + repr(self.anchor) + ">" - - return ( - self.anchor + skipper().add_parse_action(must_skip) - | skipper().add_parse_action(show_skip) - ) + other - - return self.anchor + skipper + other - - def __repr__(self): - return self.defaultName - - def parseImpl(self, *args): - raise Exception( - "use of `...` expression without following SkipTo target expression" - ) - - -class Token(ParserElement): - """Abstract :class:`ParserElement` subclass, for defining atomic - matching patterns. - """ - - def __init__(self): - super().__init__(savelist=False) - - def _generateDefaultName(self): - return type(self).__name__ - - -class Empty(Token): - """ - An empty token, will always match. - """ - - def __init__(self): - super().__init__() - self.mayReturnEmpty = True - self.mayIndexError = False - - -class NoMatch(Token): - """ - A token that will never match. - """ - - def __init__(self): - super().__init__() - self.mayReturnEmpty = True - self.mayIndexError = False - self.errmsg = "Unmatchable token" - - def parseImpl(self, instring, loc, doActions=True): - raise ParseException(instring, loc, self.errmsg, self) - - -class Literal(Token): - """ - Token to exactly match a specified string. - - Example:: - - Literal('blah').parse_string('blah') # -> ['blah'] - Literal('blah').parse_string('blahfooblah') # -> ['blah'] - Literal('blah').parse_string('bla') # -> Exception: Expected "blah" - - For case-insensitive matching, use :class:`CaselessLiteral`. - - For keyword matching (force word break before and after the matched string), - use :class:`Keyword` or :class:`CaselessKeyword`. - """ - - def __init__(self, match_string: str = "", *, matchString: str = ""): - super().__init__() - match_string = matchString or match_string - self.match = match_string - self.matchLen = len(match_string) - try: - self.firstMatchChar = match_string[0] - except IndexError: - raise ValueError("null string passed to Literal; use Empty() instead") - self.errmsg = "Expected " + self.name - self.mayReturnEmpty = False - self.mayIndexError = False - - # Performance tuning: modify __class__ to select - # a parseImpl optimized for single-character check - if self.matchLen == 1 and type(self) is Literal: - self.__class__ = _SingleCharLiteral - - def _generateDefaultName(self): - return repr(self.match) - - def parseImpl(self, instring, loc, doActions=True): - if instring[loc] == self.firstMatchChar and instring.startswith( - self.match, loc - ): - return loc + self.matchLen, self.match - raise ParseException(instring, loc, self.errmsg, self) - - -class _SingleCharLiteral(Literal): - def parseImpl(self, instring, loc, doActions=True): - if instring[loc] == self.firstMatchChar: - return loc + 1, self.match - raise ParseException(instring, loc, self.errmsg, self) - - -ParserElement._literalStringClass = Literal - - -class Keyword(Token): - """ - Token to exactly match a specified string as a keyword, that is, - it must be immediately followed by a non-keyword character. Compare - with :class:`Literal`: - - - ``Literal("if")`` will match the leading ``'if'`` in - ``'ifAndOnlyIf'``. - - ``Keyword("if")`` will not; it will only match the leading - ``'if'`` in ``'if x=1'``, or ``'if(y==2)'`` - - Accepts two optional constructor arguments in addition to the - keyword string: - - - ``identChars`` is a string of characters that would be valid - identifier characters, defaulting to all alphanumerics + "_" and - "$" - - ``caseless`` allows case-insensitive matching, default is ``False``. - - Example:: - - Keyword("start").parse_string("start") # -> ['start'] - Keyword("start").parse_string("starting") # -> Exception - - For case-insensitive matching, use :class:`CaselessKeyword`. - """ - - DEFAULT_KEYWORD_CHARS = alphanums + "_$" - - def __init__( - self, - match_string: str = "", - ident_chars: typing.Optional[str] = None, - caseless: bool = False, - *, - matchString: str = "", - identChars: typing.Optional[str] = None, - ): - super().__init__() - identChars = identChars or ident_chars - if identChars is None: - identChars = Keyword.DEFAULT_KEYWORD_CHARS - match_string = matchString or match_string - self.match = match_string - self.matchLen = len(match_string) - try: - self.firstMatchChar = match_string[0] - except IndexError: - raise ValueError("null string passed to Keyword; use Empty() instead") - self.errmsg = "Expected {} {}".format(type(self).__name__, self.name) - self.mayReturnEmpty = False - self.mayIndexError = False - self.caseless = caseless - if caseless: - self.caselessmatch = match_string.upper() - identChars = identChars.upper() - self.identChars = set(identChars) - - def _generateDefaultName(self): - return repr(self.match) - - def parseImpl(self, instring, loc, doActions=True): - errmsg = self.errmsg - errloc = loc - if self.caseless: - if instring[loc : loc + self.matchLen].upper() == self.caselessmatch: - if loc == 0 or instring[loc - 1].upper() not in self.identChars: - if ( - loc >= len(instring) - self.matchLen - or instring[loc + self.matchLen].upper() not in self.identChars - ): - return loc + self.matchLen, self.match - else: - # followed by keyword char - errmsg += ", was immediately followed by keyword character" - errloc = loc + self.matchLen - else: - # preceded by keyword char - errmsg += ", keyword was immediately preceded by keyword character" - errloc = loc - 1 - # else no match just raise plain exception - - else: - if ( - instring[loc] == self.firstMatchChar - and self.matchLen == 1 - or instring.startswith(self.match, loc) - ): - if loc == 0 or instring[loc - 1] not in self.identChars: - if ( - loc >= len(instring) - self.matchLen - or instring[loc + self.matchLen] not in self.identChars - ): - return loc + self.matchLen, self.match - else: - # followed by keyword char - errmsg += ( - ", keyword was immediately followed by keyword character" - ) - errloc = loc + self.matchLen - else: - # preceded by keyword char - errmsg += ", keyword was immediately preceded by keyword character" - errloc = loc - 1 - # else no match just raise plain exception - - raise ParseException(instring, errloc, errmsg, self) - - @staticmethod - def set_default_keyword_chars(chars) -> None: - """ - Overrides the default characters used by :class:`Keyword` expressions. - """ - Keyword.DEFAULT_KEYWORD_CHARS = chars - - setDefaultKeywordChars = set_default_keyword_chars - - -class CaselessLiteral(Literal): - """ - Token to match a specified string, ignoring case of letters. - Note: the matched results will always be in the case of the given - match string, NOT the case of the input text. - - Example:: - - CaselessLiteral("CMD")[1, ...].parse_string("cmd CMD Cmd10") - # -> ['CMD', 'CMD', 'CMD'] - - (Contrast with example for :class:`CaselessKeyword`.) - """ - - def __init__(self, match_string: str = "", *, matchString: str = ""): - match_string = matchString or match_string - super().__init__(match_string.upper()) - # Preserve the defining literal. - self.returnString = match_string - self.errmsg = "Expected " + self.name - - def parseImpl(self, instring, loc, doActions=True): - if instring[loc : loc + self.matchLen].upper() == self.match: - return loc + self.matchLen, self.returnString - raise ParseException(instring, loc, self.errmsg, self) - - -class CaselessKeyword(Keyword): - """ - Caseless version of :class:`Keyword`. - - Example:: - - CaselessKeyword("CMD")[1, ...].parse_string("cmd CMD Cmd10") - # -> ['CMD', 'CMD'] - - (Contrast with example for :class:`CaselessLiteral`.) - """ - - def __init__( - self, - match_string: str = "", - ident_chars: typing.Optional[str] = None, - *, - matchString: str = "", - identChars: typing.Optional[str] = None, - ): - identChars = identChars or ident_chars - match_string = matchString or match_string - super().__init__(match_string, identChars, caseless=True) - - -class CloseMatch(Token): - """A variation on :class:`Literal` which matches "close" matches, - that is, strings with at most 'n' mismatching characters. - :class:`CloseMatch` takes parameters: - - - ``match_string`` - string to be matched - - ``caseless`` - a boolean indicating whether to ignore casing when comparing characters - - ``max_mismatches`` - (``default=1``) maximum number of - mismatches allowed to count as a match - - The results from a successful parse will contain the matched text - from the input string and the following named results: - - - ``mismatches`` - a list of the positions within the - match_string where mismatches were found - - ``original`` - the original match_string used to compare - against the input string - - If ``mismatches`` is an empty list, then the match was an exact - match. - - Example:: - - patt = CloseMatch("ATCATCGAATGGA") - patt.parse_string("ATCATCGAAXGGA") # -> (['ATCATCGAAXGGA'], {'mismatches': [[9]], 'original': ['ATCATCGAATGGA']}) - patt.parse_string("ATCAXCGAAXGGA") # -> Exception: Expected 'ATCATCGAATGGA' (with up to 1 mismatches) (at char 0), (line:1, col:1) - - # exact match - patt.parse_string("ATCATCGAATGGA") # -> (['ATCATCGAATGGA'], {'mismatches': [[]], 'original': ['ATCATCGAATGGA']}) - - # close match allowing up to 2 mismatches - patt = CloseMatch("ATCATCGAATGGA", max_mismatches=2) - patt.parse_string("ATCAXCGAAXGGA") # -> (['ATCAXCGAAXGGA'], {'mismatches': [[4, 9]], 'original': ['ATCATCGAATGGA']}) - """ - - def __init__( - self, - match_string: str, - max_mismatches: int = None, - *, - maxMismatches: int = 1, - caseless=False, - ): - maxMismatches = max_mismatches if max_mismatches is not None else maxMismatches - super().__init__() - self.match_string = match_string - self.maxMismatches = maxMismatches - self.errmsg = "Expected {!r} (with up to {} mismatches)".format( - self.match_string, self.maxMismatches - ) - self.caseless = caseless - self.mayIndexError = False - self.mayReturnEmpty = False - - def _generateDefaultName(self): - return "{}:{!r}".format(type(self).__name__, self.match_string) - - def parseImpl(self, instring, loc, doActions=True): - start = loc - instrlen = len(instring) - maxloc = start + len(self.match_string) - - if maxloc <= instrlen: - match_string = self.match_string - match_stringloc = 0 - mismatches = [] - maxMismatches = self.maxMismatches - - for match_stringloc, s_m in enumerate( - zip(instring[loc:maxloc], match_string) - ): - src, mat = s_m - if self.caseless: - src, mat = src.lower(), mat.lower() - - if src != mat: - mismatches.append(match_stringloc) - if len(mismatches) > maxMismatches: - break - else: - loc = start + match_stringloc + 1 - results = ParseResults([instring[start:loc]]) - results["original"] = match_string - results["mismatches"] = mismatches - return loc, results - - raise ParseException(instring, loc, self.errmsg, self) - - -class Word(Token): - """Token for matching words composed of allowed character sets. - Parameters: - - ``init_chars`` - string of all characters that should be used to - match as a word; "ABC" will match "AAA", "ABAB", "CBAC", etc.; - if ``body_chars`` is also specified, then this is the string of - initial characters - - ``body_chars`` - string of characters that - can be used for matching after a matched initial character as - given in ``init_chars``; if omitted, same as the initial characters - (default=``None``) - - ``min`` - minimum number of characters to match (default=1) - - ``max`` - maximum number of characters to match (default=0) - - ``exact`` - exact number of characters to match (default=0) - - ``as_keyword`` - match as a keyword (default=``False``) - - ``exclude_chars`` - characters that might be - found in the input ``body_chars`` string but which should not be - accepted for matching ;useful to define a word of all - printables except for one or two characters, for instance - (default=``None``) - - :class:`srange` is useful for defining custom character set strings - for defining :class:`Word` expressions, using range notation from - regular expression character sets. - - A common mistake is to use :class:`Word` to match a specific literal - string, as in ``Word("Address")``. Remember that :class:`Word` - uses the string argument to define *sets* of matchable characters. - This expression would match "Add", "AAA", "dAred", or any other word - made up of the characters 'A', 'd', 'r', 'e', and 's'. To match an - exact literal string, use :class:`Literal` or :class:`Keyword`. - - pyparsing includes helper strings for building Words: - - - :class:`alphas` - - :class:`nums` - - :class:`alphanums` - - :class:`hexnums` - - :class:`alphas8bit` (alphabetic characters in ASCII range 128-255 - - accented, tilded, umlauted, etc.) - - :class:`punc8bit` (non-alphabetic characters in ASCII range - 128-255 - currency, symbols, superscripts, diacriticals, etc.) - - :class:`printables` (any non-whitespace character) - - ``alphas``, ``nums``, and ``printables`` are also defined in several - Unicode sets - see :class:`pyparsing_unicode``. - - Example:: - - # a word composed of digits - integer = Word(nums) # equivalent to Word("0123456789") or Word(srange("0-9")) - - # a word with a leading capital, and zero or more lowercase - capital_word = Word(alphas.upper(), alphas.lower()) - - # hostnames are alphanumeric, with leading alpha, and '-' - hostname = Word(alphas, alphanums + '-') - - # roman numeral (not a strict parser, accepts invalid mix of characters) - roman = Word("IVXLCDM") - - # any string of non-whitespace characters, except for ',' - csv_value = Word(printables, exclude_chars=",") - """ - - def __init__( - self, - init_chars: str = "", - body_chars: typing.Optional[str] = None, - min: int = 1, - max: int = 0, - exact: int = 0, - as_keyword: bool = False, - exclude_chars: typing.Optional[str] = None, - *, - initChars: typing.Optional[str] = None, - bodyChars: typing.Optional[str] = None, - asKeyword: bool = False, - excludeChars: typing.Optional[str] = None, - ): - initChars = initChars or init_chars - bodyChars = bodyChars or body_chars - asKeyword = asKeyword or as_keyword - excludeChars = excludeChars or exclude_chars - super().__init__() - if not initChars: - raise ValueError( - "invalid {}, initChars cannot be empty string".format( - type(self).__name__ - ) - ) - - initChars = set(initChars) - self.initChars = initChars - if excludeChars: - excludeChars = set(excludeChars) - initChars -= excludeChars - if bodyChars: - bodyChars = set(bodyChars) - excludeChars - self.initCharsOrig = "".join(sorted(initChars)) - - if bodyChars: - self.bodyCharsOrig = "".join(sorted(bodyChars)) - self.bodyChars = set(bodyChars) - else: - self.bodyCharsOrig = "".join(sorted(initChars)) - self.bodyChars = set(initChars) - - self.maxSpecified = max > 0 - - if min < 1: - raise ValueError( - "cannot specify a minimum length < 1; use Opt(Word()) if zero-length word is permitted" - ) - - self.minLen = min - - if max > 0: - self.maxLen = max - else: - self.maxLen = _MAX_INT - - if exact > 0: - self.maxLen = exact - self.minLen = exact - - self.errmsg = "Expected " + self.name - self.mayIndexError = False - self.asKeyword = asKeyword - - # see if we can make a regex for this Word - if " " not in self.initChars | self.bodyChars and (min == 1 and exact == 0): - if self.bodyChars == self.initChars: - if max == 0: - repeat = "+" - elif max == 1: - repeat = "" - else: - repeat = "{{{},{}}}".format( - self.minLen, "" if self.maxLen == _MAX_INT else self.maxLen - ) - self.reString = "[{}]{}".format( - _collapse_string_to_ranges(self.initChars), - repeat, - ) - elif len(self.initChars) == 1: - if max == 0: - repeat = "*" - else: - repeat = "{{0,{}}}".format(max - 1) - self.reString = "{}[{}]{}".format( - re.escape(self.initCharsOrig), - _collapse_string_to_ranges(self.bodyChars), - repeat, - ) - else: - if max == 0: - repeat = "*" - elif max == 2: - repeat = "" - else: - repeat = "{{0,{}}}".format(max - 1) - self.reString = "[{}][{}]{}".format( - _collapse_string_to_ranges(self.initChars), - _collapse_string_to_ranges(self.bodyChars), - repeat, - ) - if self.asKeyword: - self.reString = r"\b" + self.reString + r"\b" - - try: - self.re = re.compile(self.reString) - except re.error: - self.re = None - else: - self.re_match = self.re.match - self.__class__ = _WordRegex - - def _generateDefaultName(self): - def charsAsStr(s): - max_repr_len = 16 - s = _collapse_string_to_ranges(s, re_escape=False) - if len(s) > max_repr_len: - return s[: max_repr_len - 3] + "..." - else: - return s - - if self.initChars != self.bodyChars: - base = "W:({}, {})".format( - charsAsStr(self.initChars), charsAsStr(self.bodyChars) - ) - else: - base = "W:({})".format(charsAsStr(self.initChars)) - - # add length specification - if self.minLen > 1 or self.maxLen != _MAX_INT: - if self.minLen == self.maxLen: - if self.minLen == 1: - return base[2:] - else: - return base + "{{{}}}".format(self.minLen) - elif self.maxLen == _MAX_INT: - return base + "{{{},...}}".format(self.minLen) - else: - return base + "{{{},{}}}".format(self.minLen, self.maxLen) - return base - - def parseImpl(self, instring, loc, doActions=True): - if instring[loc] not in self.initChars: - raise ParseException(instring, loc, self.errmsg, self) - - start = loc - loc += 1 - instrlen = len(instring) - bodychars = self.bodyChars - maxloc = start + self.maxLen - maxloc = min(maxloc, instrlen) - while loc < maxloc and instring[loc] in bodychars: - loc += 1 - - throwException = False - if loc - start < self.minLen: - throwException = True - elif self.maxSpecified and loc < instrlen and instring[loc] in bodychars: - throwException = True - elif self.asKeyword: - if ( - start > 0 - and instring[start - 1] in bodychars - or loc < instrlen - and instring[loc] in bodychars - ): - throwException = True - - if throwException: - raise ParseException(instring, loc, self.errmsg, self) - - return loc, instring[start:loc] - - -class _WordRegex(Word): - def parseImpl(self, instring, loc, doActions=True): - result = self.re_match(instring, loc) - if not result: - raise ParseException(instring, loc, self.errmsg, self) - - loc = result.end() - return loc, result.group() - - -class Char(_WordRegex): - """A short-cut class for defining :class:`Word` ``(characters, exact=1)``, - when defining a match of any single character in a string of - characters. - """ - - def __init__( - self, - charset: str, - as_keyword: bool = False, - exclude_chars: typing.Optional[str] = None, - *, - asKeyword: bool = False, - excludeChars: typing.Optional[str] = None, - ): - asKeyword = asKeyword or as_keyword - excludeChars = excludeChars or exclude_chars - super().__init__( - charset, exact=1, asKeyword=asKeyword, excludeChars=excludeChars - ) - self.reString = "[{}]".format(_collapse_string_to_ranges(self.initChars)) - if asKeyword: - self.reString = r"\b{}\b".format(self.reString) - self.re = re.compile(self.reString) - self.re_match = self.re.match - - -class Regex(Token): - r"""Token for matching strings that match a given regular - expression. Defined with string specifying the regular expression in - a form recognized by the stdlib Python `re module ...)``), - these will be preserved as named :class:`ParseResults`. - - If instead of the Python stdlib ``re`` module you wish to use a different RE module - (such as the ``regex`` module), you can do so by building your ``Regex`` object with - a compiled RE that was compiled using ``regex``. - - Example:: - - realnum = Regex(r"[+-]?\d+\.\d*") - # ref: https://stackoverflow.com/questions/267399/how-do-you-match-only-valid-roman-numerals-with-a-regular-expression - roman = Regex(r"M{0,4}(CM|CD|D?{0,3})(XC|XL|L?X{0,3})(IX|IV|V?I{0,3})") - - # named fields in a regex will be returned as named results - date = Regex(r'(?P \d{4})-(?P \d\d?)-(?P \d\d?)') - - # the Regex class will accept re's compiled using the regex module - import regex - parser = pp.Regex(regex.compile(r'[0-9]')) - """ - - def __init__( - self, - pattern: Any, - flags: Union[re.RegexFlag, int] = 0, - as_group_list: bool = False, - as_match: bool = False, - *, - asGroupList: bool = False, - asMatch: bool = False, - ): - """The parameters ``pattern`` and ``flags`` are passed - to the ``re.compile()`` function as-is. See the Python - `re module main title
" - """ - if self.asGroupList: - raise TypeError("cannot use sub() with Regex(asGroupList=True)") - - if self.asMatch and callable(repl): - raise TypeError("cannot use sub() with a callable with Regex(asMatch=True)") - - if self.asMatch: - - def pa(tokens): - return tokens[0].expand(repl) - - else: - - def pa(tokens): - return self.re.sub(repl, tokens[0]) - - return self.add_parse_action(pa) - - -class QuotedString(Token): - r""" - Token for matching strings that are delimited by quoting characters. - - Defined with the following parameters: - - - ``quote_char`` - string of one or more characters defining the - quote delimiting string - - ``esc_char`` - character to re_escape quotes, typically backslash - (default= ``None``) - - ``esc_quote`` - special quote sequence to re_escape an embedded quote - string (such as SQL's ``""`` to re_escape an embedded ``"``) - (default= ``None``) - - ``multiline`` - boolean indicating whether quotes can span - multiple lines (default= ``False``) - - ``unquote_results`` - boolean indicating whether the matched text - should be unquoted (default= ``True``) - - ``end_quote_char`` - string of one or more characters defining the - end of the quote delimited string (default= ``None`` => same as - quote_char) - - ``convert_whitespace_escapes`` - convert escaped whitespace - (``'\t'``, ``'\n'``, etc.) to actual whitespace - (default= ``True``) - - Example:: - - qs = QuotedString('"') - print(qs.search_string('lsjdf "This is the quote" sldjf')) - complex_qs = QuotedString('{{', end_quote_char='}}') - print(complex_qs.search_string('lsjdf {{This is the "quote"}} sldjf')) - sql_qs = QuotedString('"', esc_quote='""') - print(sql_qs.search_string('lsjdf "This is the quote with ""embedded"" quotes" sldjf')) - - prints:: - - [['This is the quote']] - [['This is the "quote"']] - [['This is the quote with "embedded" quotes']] - """ - ws_map = ((r"\t", "\t"), (r"\n", "\n"), (r"\f", "\f"), (r"\r", "\r")) - - def __init__( - self, - quote_char: str = "", - esc_char: typing.Optional[str] = None, - esc_quote: typing.Optional[str] = None, - multiline: bool = False, - unquote_results: bool = True, - end_quote_char: typing.Optional[str] = None, - convert_whitespace_escapes: bool = True, - *, - quoteChar: str = "", - escChar: typing.Optional[str] = None, - escQuote: typing.Optional[str] = None, - unquoteResults: bool = True, - endQuoteChar: typing.Optional[str] = None, - convertWhitespaceEscapes: bool = True, - ): - super().__init__() - escChar = escChar or esc_char - escQuote = escQuote or esc_quote - unquoteResults = unquoteResults and unquote_results - endQuoteChar = endQuoteChar or end_quote_char - convertWhitespaceEscapes = ( - convertWhitespaceEscapes and convert_whitespace_escapes - ) - quote_char = quoteChar or quote_char - - # remove white space from quote chars - wont work anyway - quote_char = quote_char.strip() - if not quote_char: - raise ValueError("quote_char cannot be the empty string") - - if endQuoteChar is None: - endQuoteChar = quote_char - else: - endQuoteChar = endQuoteChar.strip() - if not endQuoteChar: - raise ValueError("endQuoteChar cannot be the empty string") - - self.quoteChar = quote_char - self.quoteCharLen = len(quote_char) - self.firstQuoteChar = quote_char[0] - self.endQuoteChar = endQuoteChar - self.endQuoteCharLen = len(endQuoteChar) - self.escChar = escChar - self.escQuote = escQuote - self.unquoteResults = unquoteResults - self.convertWhitespaceEscapes = convertWhitespaceEscapes - - sep = "" - inner_pattern = "" - - if escQuote: - inner_pattern += r"{}(?:{})".format(sep, re.escape(escQuote)) - sep = "|" - - if escChar: - inner_pattern += r"{}(?:{}.)".format(sep, re.escape(escChar)) - sep = "|" - self.escCharReplacePattern = re.escape(self.escChar) + "(.)" - - if len(self.endQuoteChar) > 1: - inner_pattern += ( - "{}(?:".format(sep) - + "|".join( - "(?:{}(?!{}))".format( - re.escape(self.endQuoteChar[:i]), - re.escape(self.endQuoteChar[i:]), - ) - for i in range(len(self.endQuoteChar) - 1, 0, -1) - ) - + ")" - ) - sep = "|" - - if multiline: - self.flags = re.MULTILINE | re.DOTALL - inner_pattern += r"{}(?:[^{}{}])".format( - sep, - _escape_regex_range_chars(self.endQuoteChar[0]), - (_escape_regex_range_chars(escChar) if escChar is not None else ""), - ) - else: - self.flags = 0 - inner_pattern += r"{}(?:[^{}\n\r{}])".format( - sep, - _escape_regex_range_chars(self.endQuoteChar[0]), - (_escape_regex_range_chars(escChar) if escChar is not None else ""), - ) - - self.pattern = "".join( - [ - re.escape(self.quoteChar), - "(?:", - inner_pattern, - ")*", - re.escape(self.endQuoteChar), - ] - ) - - try: - self.re = re.compile(self.pattern, self.flags) - self.reString = self.pattern - self.re_match = self.re.match - except re.error: - raise ValueError( - "invalid pattern {!r} passed to Regex".format(self.pattern) - ) - - self.errmsg = "Expected " + self.name - self.mayIndexError = False - self.mayReturnEmpty = True - - def _generateDefaultName(self): - if self.quoteChar == self.endQuoteChar and isinstance(self.quoteChar, str_type): - return "string enclosed in {!r}".format(self.quoteChar) - - return "quoted string, starting with {} ending with {}".format( - self.quoteChar, self.endQuoteChar - ) - - def parseImpl(self, instring, loc, doActions=True): - result = ( - instring[loc] == self.firstQuoteChar - and self.re_match(instring, loc) - or None - ) - if not result: - raise ParseException(instring, loc, self.errmsg, self) - - loc = result.end() - ret = result.group() - - if self.unquoteResults: - - # strip off quotes - ret = ret[self.quoteCharLen : -self.endQuoteCharLen] - - if isinstance(ret, str_type): - # replace escaped whitespace - if "\\" in ret and self.convertWhitespaceEscapes: - for wslit, wschar in self.ws_map: - ret = ret.replace(wslit, wschar) - - # replace escaped characters - if self.escChar: - ret = re.sub(self.escCharReplacePattern, r"\g<1>", ret) - - # replace escaped quotes - if self.escQuote: - ret = ret.replace(self.escQuote, self.endQuoteChar) - - return loc, ret - - -class CharsNotIn(Token): - """Token for matching words composed of characters *not* in a given - set (will include whitespace in matched characters if not listed in - the provided exclusion set - see example). Defined with string - containing all disallowed characters, and an optional minimum, - maximum, and/or exact length. The default value for ``min`` is - 1 (a minimum value < 1 is not valid); the default values for - ``max`` and ``exact`` are 0, meaning no maximum or exact - length restriction. - - Example:: - - # define a comma-separated-value as anything that is not a ',' - csv_value = CharsNotIn(',') - print(delimited_list(csv_value).parse_string("dkls,lsdkjf,s12 34,@!#,213")) - - prints:: - - ['dkls', 'lsdkjf', 's12 34', '@!#', '213'] - """ - - def __init__( - self, - not_chars: str = "", - min: int = 1, - max: int = 0, - exact: int = 0, - *, - notChars: str = "", - ): - super().__init__() - self.skipWhitespace = False - self.notChars = not_chars or notChars - self.notCharsSet = set(self.notChars) - - if min < 1: - raise ValueError( - "cannot specify a minimum length < 1; use " - "Opt(CharsNotIn()) if zero-length char group is permitted" - ) - - self.minLen = min - - if max > 0: - self.maxLen = max - else: - self.maxLen = _MAX_INT - - if exact > 0: - self.maxLen = exact - self.minLen = exact - - self.errmsg = "Expected " + self.name - self.mayReturnEmpty = self.minLen == 0 - self.mayIndexError = False - - def _generateDefaultName(self): - not_chars_str = _collapse_string_to_ranges(self.notChars) - if len(not_chars_str) > 16: - return "!W:({}...)".format(self.notChars[: 16 - 3]) - else: - return "!W:({})".format(self.notChars) - - def parseImpl(self, instring, loc, doActions=True): - notchars = self.notCharsSet - if instring[loc] in notchars: - raise ParseException(instring, loc, self.errmsg, self) - - start = loc - loc += 1 - maxlen = min(start + self.maxLen, len(instring)) - while loc < maxlen and instring[loc] not in notchars: - loc += 1 - - if loc - start < self.minLen: - raise ParseException(instring, loc, self.errmsg, self) - - return loc, instring[start:loc] - - -class White(Token): - """Special matching class for matching whitespace. Normally, - whitespace is ignored by pyparsing grammars. This class is included - when some whitespace structures are significant. Define with - a string containing the whitespace characters to be matched; default - is ``" \\t\\r\\n"``. Also takes optional ``min``, - ``max``, and ``exact`` arguments, as defined for the - :class:`Word` class. - """ - - whiteStrs = { - " ": "", - "\t": " ", - "\n": " ", - "\r": " ", - "\f": " ", - "\u00A0": " ", - "\u1680": " ", - "\u180E": " ", - "\u2000": " ", - "\u2001": " ", - "\u2002": " ", - "\u2003": " ", - "\u2004": " ", - "\u2005": " ", - "\u2006": " ", - "\u2007": " ", - "\u2008": " ", - "\u2009": " ", - "\u200A": " ", - "\u200B": " ", - "\u202F": " ", - "\u205F": " ", - "\u3000": " ", - } - - def __init__(self, ws: str = " \t\r\n", min: int = 1, max: int = 0, exact: int = 0): - super().__init__() - self.matchWhite = ws - self.set_whitespace_chars( - "".join(c for c in self.whiteStrs if c not in self.matchWhite), - copy_defaults=True, - ) - # self.leave_whitespace() - self.mayReturnEmpty = True - self.errmsg = "Expected " + self.name - - self.minLen = min - - if max > 0: - self.maxLen = max - else: - self.maxLen = _MAX_INT - - if exact > 0: - self.maxLen = exact - self.minLen = exact - - def _generateDefaultName(self): - return "".join(White.whiteStrs[c] for c in self.matchWhite) - - def parseImpl(self, instring, loc, doActions=True): - if instring[loc] not in self.matchWhite: - raise ParseException(instring, loc, self.errmsg, self) - start = loc - loc += 1 - maxloc = start + self.maxLen - maxloc = min(maxloc, len(instring)) - while loc < maxloc and instring[loc] in self.matchWhite: - loc += 1 - - if loc - start < self.minLen: - raise ParseException(instring, loc, self.errmsg, self) - - return loc, instring[start:loc] - - -class PositionToken(Token): - def __init__(self): - super().__init__() - self.mayReturnEmpty = True - self.mayIndexError = False - - -class GoToColumn(PositionToken): - """Token to advance to a specific column of input text; useful for - tabular report scraping. - """ - - def __init__(self, colno: int): - super().__init__() - self.col = colno - - def preParse(self, instring, loc): - if col(loc, instring) != self.col: - instrlen = len(instring) - if self.ignoreExprs: - loc = self._skipIgnorables(instring, loc) - while ( - loc < instrlen - and instring[loc].isspace() - and col(loc, instring) != self.col - ): - loc += 1 - return loc - - def parseImpl(self, instring, loc, doActions=True): - thiscol = col(loc, instring) - if thiscol > self.col: - raise ParseException(instring, loc, "Text not in expected column", self) - newloc = loc + self.col - thiscol - ret = instring[loc:newloc] - return newloc, ret - - -class LineStart(PositionToken): - r"""Matches if current position is at the beginning of a line within - the parse string - - Example:: - - test = '''\ - AAA this line - AAA and this line - AAA but not this one - B AAA and definitely not this one - ''' - - for t in (LineStart() + 'AAA' + restOfLine).search_string(test): - print(t) - - prints:: - - ['AAA', ' this line'] - ['AAA', ' and this line'] - - """ - - def __init__(self): - super().__init__() - self.leave_whitespace() - self.orig_whiteChars = set() | self.whiteChars - self.whiteChars.discard("\n") - self.skipper = Empty().set_whitespace_chars(self.whiteChars) - self.errmsg = "Expected start of line" - - def preParse(self, instring, loc): - if loc == 0: - return loc - else: - ret = self.skipper.preParse(instring, loc) - if "\n" in self.orig_whiteChars: - while instring[ret : ret + 1] == "\n": - ret = self.skipper.preParse(instring, ret + 1) - return ret - - def parseImpl(self, instring, loc, doActions=True): - if col(loc, instring) == 1: - return loc, [] - raise ParseException(instring, loc, self.errmsg, self) - - -class LineEnd(PositionToken): - """Matches if current position is at the end of a line within the - parse string - """ - - def __init__(self): - super().__init__() - self.whiteChars.discard("\n") - self.set_whitespace_chars(self.whiteChars, copy_defaults=False) - self.errmsg = "Expected end of line" - - def parseImpl(self, instring, loc, doActions=True): - if loc < len(instring): - if instring[loc] == "\n": - return loc + 1, "\n" - else: - raise ParseException(instring, loc, self.errmsg, self) - elif loc == len(instring): - return loc + 1, [] - else: - raise ParseException(instring, loc, self.errmsg, self) - - -class StringStart(PositionToken): - """Matches if current position is at the beginning of the parse - string - """ - - def __init__(self): - super().__init__() - self.errmsg = "Expected start of text" - - def parseImpl(self, instring, loc, doActions=True): - if loc != 0: - # see if entire string up to here is just whitespace and ignoreables - if loc != self.preParse(instring, 0): - raise ParseException(instring, loc, self.errmsg, self) - return loc, [] - - -class StringEnd(PositionToken): - """ - Matches if current position is at the end of the parse string - """ - - def __init__(self): - super().__init__() - self.errmsg = "Expected end of text" - - def parseImpl(self, instring, loc, doActions=True): - if loc < len(instring): - raise ParseException(instring, loc, self.errmsg, self) - elif loc == len(instring): - return loc + 1, [] - elif loc > len(instring): - return loc, [] - else: - raise ParseException(instring, loc, self.errmsg, self) - - -class WordStart(PositionToken): - """Matches if the current position is at the beginning of a - :class:`Word`, and is not preceded by any character in a given - set of ``word_chars`` (default= ``printables``). To emulate the - ``\b`` behavior of regular expressions, use - ``WordStart(alphanums)``. ``WordStart`` will also match at - the beginning of the string being parsed, or at the beginning of - a line. - """ - - def __init__(self, word_chars: str = printables, *, wordChars: str = printables): - wordChars = word_chars if wordChars == printables else wordChars - super().__init__() - self.wordChars = set(wordChars) - self.errmsg = "Not at the start of a word" - - def parseImpl(self, instring, loc, doActions=True): - if loc != 0: - if ( - instring[loc - 1] in self.wordChars - or instring[loc] not in self.wordChars - ): - raise ParseException(instring, loc, self.errmsg, self) - return loc, [] - - -class WordEnd(PositionToken): - """Matches if the current position is at the end of a :class:`Word`, - and is not followed by any character in a given set of ``word_chars`` - (default= ``printables``). To emulate the ``\b`` behavior of - regular expressions, use ``WordEnd(alphanums)``. ``WordEnd`` - will also match at the end of the string being parsed, or at the end - of a line. - """ - - def __init__(self, word_chars: str = printables, *, wordChars: str = printables): - wordChars = word_chars if wordChars == printables else wordChars - super().__init__() - self.wordChars = set(wordChars) - self.skipWhitespace = False - self.errmsg = "Not at the end of a word" - - def parseImpl(self, instring, loc, doActions=True): - instrlen = len(instring) - if instrlen > 0 and loc < instrlen: - if ( - instring[loc] in self.wordChars - or instring[loc - 1] not in self.wordChars - ): - raise ParseException(instring, loc, self.errmsg, self) - return loc, [] - - -class ParseExpression(ParserElement): - """Abstract subclass of ParserElement, for combining and - post-processing parsed tokens. - """ - - def __init__(self, exprs: typing.Iterable[ParserElement], savelist: bool = False): - super().__init__(savelist) - self.exprs: List[ParserElement] - if isinstance(exprs, _generatorType): - exprs = list(exprs) - - if isinstance(exprs, str_type): - self.exprs = [self._literalStringClass(exprs)] - elif isinstance(exprs, ParserElement): - self.exprs = [exprs] - elif isinstance(exprs, Iterable): - exprs = list(exprs) - # if sequence of strings provided, wrap with Literal - if any(isinstance(expr, str_type) for expr in exprs): - exprs = ( - self._literalStringClass(e) if isinstance(e, str_type) else e - for e in exprs - ) - self.exprs = list(exprs) - else: - try: - self.exprs = list(exprs) - except TypeError: - self.exprs = [exprs] - self.callPreparse = False - - def recurse(self) -> Sequence[ParserElement]: - return self.exprs[:] - - def append(self, other) -> ParserElement: - self.exprs.append(other) - self._defaultName = None - return self - - def leave_whitespace(self, recursive: bool = True) -> ParserElement: - """ - Extends ``leave_whitespace`` defined in base class, and also invokes ``leave_whitespace`` on - all contained expressions. - """ - super().leave_whitespace(recursive) - - if recursive: - self.exprs = [e.copy() for e in self.exprs] - for e in self.exprs: - e.leave_whitespace(recursive) - return self - - def ignore_whitespace(self, recursive: bool = True) -> ParserElement: - """ - Extends ``ignore_whitespace`` defined in base class, and also invokes ``leave_whitespace`` on - all contained expressions. - """ - super().ignore_whitespace(recursive) - if recursive: - self.exprs = [e.copy() for e in self.exprs] - for e in self.exprs: - e.ignore_whitespace(recursive) - return self - - def ignore(self, other) -> ParserElement: - if isinstance(other, Suppress): - if other not in self.ignoreExprs: - super().ignore(other) - for e in self.exprs: - e.ignore(self.ignoreExprs[-1]) - else: - super().ignore(other) - for e in self.exprs: - e.ignore(self.ignoreExprs[-1]) - return self - - def _generateDefaultName(self): - return "{}:({})".format(self.__class__.__name__, str(self.exprs)) - - def streamline(self) -> ParserElement: - if self.streamlined: - return self - - super().streamline() - - for e in self.exprs: - e.streamline() - - # collapse nested :class:`And`'s of the form ``And(And(And(a, b), c), d)`` to ``And(a, b, c, d)`` - # but only if there are no parse actions or resultsNames on the nested And's - # (likewise for :class:`Or`'s and :class:`MatchFirst`'s) - if len(self.exprs) == 2: - other = self.exprs[0] - if ( - isinstance(other, self.__class__) - and not other.parseAction - and other.resultsName is None - and not other.debug - ): - self.exprs = other.exprs[:] + [self.exprs[1]] - self._defaultName = None - self.mayReturnEmpty |= other.mayReturnEmpty - self.mayIndexError |= other.mayIndexError - - other = self.exprs[-1] - if ( - isinstance(other, self.__class__) - and not other.parseAction - and other.resultsName is None - and not other.debug - ): - self.exprs = self.exprs[:-1] + other.exprs[:] - self._defaultName = None - self.mayReturnEmpty |= other.mayReturnEmpty - self.mayIndexError |= other.mayIndexError - - self.errmsg = "Expected " + str(self) - - return self - - def validate(self, validateTrace=None) -> None: - tmp = (validateTrace if validateTrace is not None else [])[:] + [self] - for e in self.exprs: - e.validate(tmp) - self._checkRecursion([]) - - def copy(self) -> ParserElement: - ret = super().copy() - ret.exprs = [e.copy() for e in self.exprs] - return ret - - def _setResultsName(self, name, listAllMatches=False): - if ( - __diag__.warn_ungrouped_named_tokens_in_collection - and Diagnostics.warn_ungrouped_named_tokens_in_collection - not in self.suppress_warnings_ - ): - for e in self.exprs: - if ( - isinstance(e, ParserElement) - and e.resultsName - and Diagnostics.warn_ungrouped_named_tokens_in_collection - not in e.suppress_warnings_ - ): - warnings.warn( - "{}: setting results name {!r} on {} expression " - "collides with {!r} on contained expression".format( - "warn_ungrouped_named_tokens_in_collection", - name, - type(self).__name__, - e.resultsName, - ), - stacklevel=3, - ) - - return super()._setResultsName(name, listAllMatches) - - ignoreWhitespace = ignore_whitespace - leaveWhitespace = leave_whitespace - - -class And(ParseExpression): - """ - Requires all given :class:`ParseExpression` s to be found in the given order. - Expressions may be separated by whitespace. - May be constructed using the ``'+'`` operator. - May also be constructed using the ``'-'`` operator, which will - suppress backtracking. - - Example:: - - integer = Word(nums) - name_expr = Word(alphas)[1, ...] - - expr = And([integer("id"), name_expr("name"), integer("age")]) - # more easily written as: - expr = integer("id") + name_expr("name") + integer("age") - """ - - class _ErrorStop(Empty): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.leave_whitespace() - - def _generateDefaultName(self): - return "-" - - def __init__( - self, exprs_arg: typing.Iterable[ParserElement], savelist: bool = True - ): - exprs: List[ParserElement] = list(exprs_arg) - if exprs and Ellipsis in exprs: - tmp = [] - for i, expr in enumerate(exprs): - if expr is Ellipsis: - if i < len(exprs) - 1: - skipto_arg: ParserElement = (Empty() + exprs[i + 1]).exprs[-1] - tmp.append(SkipTo(skipto_arg)("_skipped*")) - else: - raise Exception( - "cannot construct And with sequence ending in ..." - ) - else: - tmp.append(expr) - exprs[:] = tmp - super().__init__(exprs, savelist) - if self.exprs: - self.mayReturnEmpty = all(e.mayReturnEmpty for e in self.exprs) - if not isinstance(self.exprs[0], White): - self.set_whitespace_chars( - self.exprs[0].whiteChars, - copy_defaults=self.exprs[0].copyDefaultWhiteChars, - ) - self.skipWhitespace = self.exprs[0].skipWhitespace - else: - self.skipWhitespace = False - else: - self.mayReturnEmpty = True - self.callPreparse = True - - def streamline(self) -> ParserElement: - # collapse any _PendingSkip's - if self.exprs: - if any( - isinstance(e, ParseExpression) - and e.exprs - and isinstance(e.exprs[-1], _PendingSkip) - for e in self.exprs[:-1] - ): - for i, e in enumerate(self.exprs[:-1]): - if e is None: - continue - if ( - isinstance(e, ParseExpression) - and e.exprs - and isinstance(e.exprs[-1], _PendingSkip) - ): - e.exprs[-1] = e.exprs[-1] + self.exprs[i + 1] - self.exprs[i + 1] = None - self.exprs = [e for e in self.exprs if e is not None] - - super().streamline() - - # link any IndentedBlocks to the prior expression - for prev, cur in zip(self.exprs, self.exprs[1:]): - # traverse cur or any first embedded expr of cur looking for an IndentedBlock - # (but watch out for recursive grammar) - seen = set() - while cur: - if id(cur) in seen: - break - seen.add(id(cur)) - if isinstance(cur, IndentedBlock): - prev.add_parse_action( - lambda s, l, t, cur_=cur: setattr( - cur_, "parent_anchor", col(l, s) - ) - ) - break - subs = cur.recurse() - cur = next(iter(subs), None) - - self.mayReturnEmpty = all(e.mayReturnEmpty for e in self.exprs) - return self - - def parseImpl(self, instring, loc, doActions=True): - # pass False as callPreParse arg to _parse for first element, since we already - # pre-parsed the string as part of our And pre-parsing - loc, resultlist = self.exprs[0]._parse( - instring, loc, doActions, callPreParse=False - ) - errorStop = False - for e in self.exprs[1:]: - # if isinstance(e, And._ErrorStop): - if type(e) is And._ErrorStop: - errorStop = True - continue - if errorStop: - try: - loc, exprtokens = e._parse(instring, loc, doActions) - except ParseSyntaxException: - raise - except ParseBaseException as pe: - pe.__traceback__ = None - raise ParseSyntaxException._from_exception(pe) - except IndexError: - raise ParseSyntaxException( - instring, len(instring), self.errmsg, self - ) - else: - loc, exprtokens = e._parse(instring, loc, doActions) - if exprtokens or exprtokens.haskeys(): - resultlist += exprtokens - return loc, resultlist - - def __iadd__(self, other): - if isinstance(other, str_type): - other = self._literalStringClass(other) - return self.append(other) # And([self, other]) - - def _checkRecursion(self, parseElementList): - subRecCheckList = parseElementList[:] + [self] - for e in self.exprs: - e._checkRecursion(subRecCheckList) - if not e.mayReturnEmpty: - break - - def _generateDefaultName(self): - inner = " ".join(str(e) for e in self.exprs) - # strip off redundant inner {}'s - while len(inner) > 1 and inner[0 :: len(inner) - 1] == "{}": - inner = inner[1:-1] - return "{" + inner + "}" - - -class Or(ParseExpression): - """Requires that at least one :class:`ParseExpression` is found. If - two expressions match, the expression that matches the longest - string will be used. May be constructed using the ``'^'`` - operator. - - Example:: - - # construct Or using '^' operator - - number = Word(nums) ^ Combine(Word(nums) + '.' + Word(nums)) - print(number.search_string("123 3.1416 789")) - - prints:: - - [['123'], ['3.1416'], ['789']] - """ - - def __init__(self, exprs: typing.Iterable[ParserElement], savelist: bool = False): - super().__init__(exprs, savelist) - if self.exprs: - self.mayReturnEmpty = any(e.mayReturnEmpty for e in self.exprs) - self.skipWhitespace = all(e.skipWhitespace for e in self.exprs) - else: - self.mayReturnEmpty = True - - def streamline(self) -> ParserElement: - super().streamline() - if self.exprs: - self.mayReturnEmpty = any(e.mayReturnEmpty for e in self.exprs) - self.saveAsList = any(e.saveAsList for e in self.exprs) - self.skipWhitespace = all( - e.skipWhitespace and not isinstance(e, White) for e in self.exprs - ) - else: - self.saveAsList = False - return self - - def parseImpl(self, instring, loc, doActions=True): - maxExcLoc = -1 - maxException = None - matches = [] - fatals = [] - if all(e.callPreparse for e in self.exprs): - loc = self.preParse(instring, loc) - for e in self.exprs: - try: - loc2 = e.try_parse(instring, loc, raise_fatal=True) - except ParseFatalException as pfe: - pfe.__traceback__ = None - pfe.parserElement = e - fatals.append(pfe) - maxException = None - maxExcLoc = -1 - except ParseException as err: - if not fatals: - err.__traceback__ = None - if err.loc > maxExcLoc: - maxException = err - maxExcLoc = err.loc - except IndexError: - if len(instring) > maxExcLoc: - maxException = ParseException( - instring, len(instring), e.errmsg, self - ) - maxExcLoc = len(instring) - else: - # save match among all matches, to retry longest to shortest - matches.append((loc2, e)) - - if matches: - # re-evaluate all matches in descending order of length of match, in case attached actions - # might change whether or how much they match of the input. - matches.sort(key=itemgetter(0), reverse=True) - - if not doActions: - # no further conditions or parse actions to change the selection of - # alternative, so the first match will be the best match - best_expr = matches[0][1] - return best_expr._parse(instring, loc, doActions) - - longest = -1, None - for loc1, expr1 in matches: - if loc1 <= longest[0]: - # already have a longer match than this one will deliver, we are done - return longest - - try: - loc2, toks = expr1._parse(instring, loc, doActions) - except ParseException as err: - err.__traceback__ = None - if err.loc > maxExcLoc: - maxException = err - maxExcLoc = err.loc - else: - if loc2 >= loc1: - return loc2, toks - # didn't match as much as before - elif loc2 > longest[0]: - longest = loc2, toks - - if longest != (-1, None): - return longest - - if fatals: - if len(fatals) > 1: - fatals.sort(key=lambda e: -e.loc) - if fatals[0].loc == fatals[1].loc: - fatals.sort(key=lambda e: (-e.loc, -len(str(e.parserElement)))) - max_fatal = fatals[0] - raise max_fatal - - if maxException is not None: - maxException.msg = self.errmsg - raise maxException - else: - raise ParseException( - instring, loc, "no defined alternatives to match", self - ) - - def __ixor__(self, other): - if isinstance(other, str_type): - other = self._literalStringClass(other) - return self.append(other) # Or([self, other]) - - def _generateDefaultName(self): - return "{" + " ^ ".join(str(e) for e in self.exprs) + "}" - - def _setResultsName(self, name, listAllMatches=False): - if ( - __diag__.warn_multiple_tokens_in_named_alternation - and Diagnostics.warn_multiple_tokens_in_named_alternation - not in self.suppress_warnings_ - ): - if any( - isinstance(e, And) - and Diagnostics.warn_multiple_tokens_in_named_alternation - not in e.suppress_warnings_ - for e in self.exprs - ): - warnings.warn( - "{}: setting results name {!r} on {} expression " - "will return a list of all parsed tokens in an And alternative, " - "in prior versions only the first token was returned; enclose " - "contained argument in Group".format( - "warn_multiple_tokens_in_named_alternation", - name, - type(self).__name__, - ), - stacklevel=3, - ) - - return super()._setResultsName(name, listAllMatches) - - -class MatchFirst(ParseExpression): - """Requires that at least one :class:`ParseExpression` is found. If - more than one expression matches, the first one listed is the one that will - match. May be constructed using the ``'|'`` operator. - - Example:: - - # construct MatchFirst using '|' operator - - # watch the order of expressions to match - number = Word(nums) | Combine(Word(nums) + '.' + Word(nums)) - print(number.search_string("123 3.1416 789")) # Fail! -> [['123'], ['3'], ['1416'], ['789']] - - # put more selective expression first - number = Combine(Word(nums) + '.' + Word(nums)) | Word(nums) - print(number.search_string("123 3.1416 789")) # Better -> [['123'], ['3.1416'], ['789']] - """ - - def __init__(self, exprs: typing.Iterable[ParserElement], savelist: bool = False): - super().__init__(exprs, savelist) - if self.exprs: - self.mayReturnEmpty = any(e.mayReturnEmpty for e in self.exprs) - self.skipWhitespace = all(e.skipWhitespace for e in self.exprs) - else: - self.mayReturnEmpty = True - - def streamline(self) -> ParserElement: - if self.streamlined: - return self - - super().streamline() - if self.exprs: - self.saveAsList = any(e.saveAsList for e in self.exprs) - self.mayReturnEmpty = any(e.mayReturnEmpty for e in self.exprs) - self.skipWhitespace = all( - e.skipWhitespace and not isinstance(e, White) for e in self.exprs - ) - else: - self.saveAsList = False - self.mayReturnEmpty = True - return self - - def parseImpl(self, instring, loc, doActions=True): - maxExcLoc = -1 - maxException = None - - for e in self.exprs: - try: - return e._parse( - instring, - loc, - doActions, - ) - except ParseFatalException as pfe: - pfe.__traceback__ = None - pfe.parserElement = e - raise - except ParseException as err: - if err.loc > maxExcLoc: - maxException = err - maxExcLoc = err.loc - except IndexError: - if len(instring) > maxExcLoc: - maxException = ParseException( - instring, len(instring), e.errmsg, self - ) - maxExcLoc = len(instring) - - if maxException is not None: - maxException.msg = self.errmsg - raise maxException - else: - raise ParseException( - instring, loc, "no defined alternatives to match", self - ) - - def __ior__(self, other): - if isinstance(other, str_type): - other = self._literalStringClass(other) - return self.append(other) # MatchFirst([self, other]) - - def _generateDefaultName(self): - return "{" + " | ".join(str(e) for e in self.exprs) + "}" - - def _setResultsName(self, name, listAllMatches=False): - if ( - __diag__.warn_multiple_tokens_in_named_alternation - and Diagnostics.warn_multiple_tokens_in_named_alternation - not in self.suppress_warnings_ - ): - if any( - isinstance(e, And) - and Diagnostics.warn_multiple_tokens_in_named_alternation - not in e.suppress_warnings_ - for e in self.exprs - ): - warnings.warn( - "{}: setting results name {!r} on {} expression " - "will return a list of all parsed tokens in an And alternative, " - "in prior versions only the first token was returned; enclose " - "contained argument in Group".format( - "warn_multiple_tokens_in_named_alternation", - name, - type(self).__name__, - ), - stacklevel=3, - ) - - return super()._setResultsName(name, listAllMatches) - - -class Each(ParseExpression): - """Requires all given :class:`ParseExpression` s to be found, but in - any order. Expressions may be separated by whitespace. - - May be constructed using the ``'&'`` operator. - - Example:: - - color = one_of("RED ORANGE YELLOW GREEN BLUE PURPLE BLACK WHITE BROWN") - shape_type = one_of("SQUARE CIRCLE TRIANGLE STAR HEXAGON OCTAGON") - integer = Word(nums) - shape_attr = "shape:" + shape_type("shape") - posn_attr = "posn:" + Group(integer("x") + ',' + integer("y"))("posn") - color_attr = "color:" + color("color") - size_attr = "size:" + integer("size") - - # use Each (using operator '&') to accept attributes in any order - # (shape and posn are required, color and size are optional) - shape_spec = shape_attr & posn_attr & Opt(color_attr) & Opt(size_attr) - - shape_spec.run_tests(''' - shape: SQUARE color: BLACK posn: 100, 120 - shape: CIRCLE size: 50 color: BLUE posn: 50,80 - color:GREEN size:20 shape:TRIANGLE posn:20,40 - ''' - ) - - prints:: - - shape: SQUARE color: BLACK posn: 100, 120 - ['shape:', 'SQUARE', 'color:', 'BLACK', 'posn:', ['100', ',', '120']] - - color: BLACK - - posn: ['100', ',', '120'] - - x: 100 - - y: 120 - - shape: SQUARE - - - shape: CIRCLE size: 50 color: BLUE posn: 50,80 - ['shape:', 'CIRCLE', 'size:', '50', 'color:', 'BLUE', 'posn:', ['50', ',', '80']] - - color: BLUE - - posn: ['50', ',', '80'] - - x: 50 - - y: 80 - - shape: CIRCLE - - size: 50 - - - color: GREEN size: 20 shape: TRIANGLE posn: 20,40 - ['color:', 'GREEN', 'size:', '20', 'shape:', 'TRIANGLE', 'posn:', ['20', ',', '40']] - - color: GREEN - - posn: ['20', ',', '40'] - - x: 20 - - y: 40 - - shape: TRIANGLE - - size: 20 - """ - - def __init__(self, exprs: typing.Iterable[ParserElement], savelist: bool = True): - super().__init__(exprs, savelist) - if self.exprs: - self.mayReturnEmpty = all(e.mayReturnEmpty for e in self.exprs) - else: - self.mayReturnEmpty = True - self.skipWhitespace = True - self.initExprGroups = True - self.saveAsList = True - - def streamline(self) -> ParserElement: - super().streamline() - if self.exprs: - self.mayReturnEmpty = all(e.mayReturnEmpty for e in self.exprs) - else: - self.mayReturnEmpty = True - return self - - def parseImpl(self, instring, loc, doActions=True): - if self.initExprGroups: - self.opt1map = dict( - (id(e.expr), e) for e in self.exprs if isinstance(e, Opt) - ) - opt1 = [e.expr for e in self.exprs if isinstance(e, Opt)] - opt2 = [ - e - for e in self.exprs - if e.mayReturnEmpty and not isinstance(e, (Opt, Regex, ZeroOrMore)) - ] - self.optionals = opt1 + opt2 - self.multioptionals = [ - e.expr.set_results_name(e.resultsName, list_all_matches=True) - for e in self.exprs - if isinstance(e, _MultipleMatch) - ] - self.multirequired = [ - e.expr.set_results_name(e.resultsName, list_all_matches=True) - for e in self.exprs - if isinstance(e, OneOrMore) - ] - self.required = [ - e for e in self.exprs if not isinstance(e, (Opt, ZeroOrMore, OneOrMore)) - ] - self.required += self.multirequired - self.initExprGroups = False - - tmpLoc = loc - tmpReqd = self.required[:] - tmpOpt = self.optionals[:] - multis = self.multioptionals[:] - matchOrder = [] - - keepMatching = True - failed = [] - fatals = [] - while keepMatching: - tmpExprs = tmpReqd + tmpOpt + multis - failed.clear() - fatals.clear() - for e in tmpExprs: - try: - tmpLoc = e.try_parse(instring, tmpLoc, raise_fatal=True) - except ParseFatalException as pfe: - pfe.__traceback__ = None - pfe.parserElement = e - fatals.append(pfe) - failed.append(e) - except ParseException: - failed.append(e) - else: - matchOrder.append(self.opt1map.get(id(e), e)) - if e in tmpReqd: - tmpReqd.remove(e) - elif e in tmpOpt: - tmpOpt.remove(e) - if len(failed) == len(tmpExprs): - keepMatching = False - - # look for any ParseFatalExceptions - if fatals: - if len(fatals) > 1: - fatals.sort(key=lambda e: -e.loc) - if fatals[0].loc == fatals[1].loc: - fatals.sort(key=lambda e: (-e.loc, -len(str(e.parserElement)))) - max_fatal = fatals[0] - raise max_fatal - - if tmpReqd: - missing = ", ".join([str(e) for e in tmpReqd]) - raise ParseException( - instring, - loc, - "Missing one or more required elements ({})".format(missing), - ) - - # add any unmatched Opts, in case they have default values defined - matchOrder += [e for e in self.exprs if isinstance(e, Opt) and e.expr in tmpOpt] - - total_results = ParseResults([]) - for e in matchOrder: - loc, results = e._parse(instring, loc, doActions) - total_results += results - - return loc, total_results - - def _generateDefaultName(self): - return "{" + " & ".join(str(e) for e in self.exprs) + "}" - - -class ParseElementEnhance(ParserElement): - """Abstract subclass of :class:`ParserElement`, for combining and - post-processing parsed tokens. - """ - - def __init__(self, expr: Union[ParserElement, str], savelist: bool = False): - super().__init__(savelist) - if isinstance(expr, str_type): - if issubclass(self._literalStringClass, Token): - expr = self._literalStringClass(expr) - elif issubclass(type(self), self._literalStringClass): - expr = Literal(expr) - else: - expr = self._literalStringClass(Literal(expr)) - self.expr = expr - if expr is not None: - self.mayIndexError = expr.mayIndexError - self.mayReturnEmpty = expr.mayReturnEmpty - self.set_whitespace_chars( - expr.whiteChars, copy_defaults=expr.copyDefaultWhiteChars - ) - self.skipWhitespace = expr.skipWhitespace - self.saveAsList = expr.saveAsList - self.callPreparse = expr.callPreparse - self.ignoreExprs.extend(expr.ignoreExprs) - - def recurse(self) -> Sequence[ParserElement]: - return [self.expr] if self.expr is not None else [] - - def parseImpl(self, instring, loc, doActions=True): - if self.expr is not None: - return self.expr._parse(instring, loc, doActions, callPreParse=False) - else: - raise ParseException(instring, loc, "No expression defined", self) - - def leave_whitespace(self, recursive: bool = True) -> ParserElement: - super().leave_whitespace(recursive) - - if recursive: - self.expr = self.expr.copy() - if self.expr is not None: - self.expr.leave_whitespace(recursive) - return self - - def ignore_whitespace(self, recursive: bool = True) -> ParserElement: - super().ignore_whitespace(recursive) - - if recursive: - self.expr = self.expr.copy() - if self.expr is not None: - self.expr.ignore_whitespace(recursive) - return self - - def ignore(self, other) -> ParserElement: - if isinstance(other, Suppress): - if other not in self.ignoreExprs: - super().ignore(other) - if self.expr is not None: - self.expr.ignore(self.ignoreExprs[-1]) - else: - super().ignore(other) - if self.expr is not None: - self.expr.ignore(self.ignoreExprs[-1]) - return self - - def streamline(self) -> ParserElement: - super().streamline() - if self.expr is not None: - self.expr.streamline() - return self - - def _checkRecursion(self, parseElementList): - if self in parseElementList: - raise RecursiveGrammarException(parseElementList + [self]) - subRecCheckList = parseElementList[:] + [self] - if self.expr is not None: - self.expr._checkRecursion(subRecCheckList) - - def validate(self, validateTrace=None) -> None: - if validateTrace is None: - validateTrace = [] - tmp = validateTrace[:] + [self] - if self.expr is not None: - self.expr.validate(tmp) - self._checkRecursion([]) - - def _generateDefaultName(self): - return "{}:({})".format(self.__class__.__name__, str(self.expr)) - - ignoreWhitespace = ignore_whitespace - leaveWhitespace = leave_whitespace - - -class IndentedBlock(ParseElementEnhance): - """ - Expression to match one or more expressions at a given indentation level. - Useful for parsing text where structure is implied by indentation (like Python source code). - """ - - class _Indent(Empty): - def __init__(self, ref_col: int): - super().__init__() - self.errmsg = "expected indent at column {}".format(ref_col) - self.add_condition(lambda s, l, t: col(l, s) == ref_col) - - class _IndentGreater(Empty): - def __init__(self, ref_col: int): - super().__init__() - self.errmsg = "expected indent at column greater than {}".format(ref_col) - self.add_condition(lambda s, l, t: col(l, s) > ref_col) - - def __init__( - self, expr: ParserElement, *, recursive: bool = False, grouped: bool = True - ): - super().__init__(expr, savelist=True) - # if recursive: - # raise NotImplementedError("IndentedBlock with recursive is not implemented") - self._recursive = recursive - self._grouped = grouped - self.parent_anchor = 1 - - def parseImpl(self, instring, loc, doActions=True): - # advance parse position to non-whitespace by using an Empty() - # this should be the column to be used for all subsequent indented lines - anchor_loc = Empty().preParse(instring, loc) - - # see if self.expr matches at the current location - if not it will raise an exception - # and no further work is necessary - self.expr.try_parse(instring, anchor_loc, doActions) - - indent_col = col(anchor_loc, instring) - peer_detect_expr = self._Indent(indent_col) - - inner_expr = Empty() + peer_detect_expr + self.expr - if self._recursive: - sub_indent = self._IndentGreater(indent_col) - nested_block = IndentedBlock( - self.expr, recursive=self._recursive, grouped=self._grouped - ) - nested_block.set_debug(self.debug) - nested_block.parent_anchor = indent_col - inner_expr += Opt(sub_indent + nested_block) - - inner_expr.set_name(f"inner {hex(id(inner_expr))[-4:].upper()}@{indent_col}") - block = OneOrMore(inner_expr) - - trailing_undent = self._Indent(self.parent_anchor) | StringEnd() - - if self._grouped: - wrapper = Group - else: - wrapper = lambda expr: expr - return (wrapper(block) + Optional(trailing_undent)).parseImpl( - instring, anchor_loc, doActions - ) - - -class AtStringStart(ParseElementEnhance): - """Matches if expression matches at the beginning of the parse - string:: - - AtStringStart(Word(nums)).parse_string("123") - # prints ["123"] - - AtStringStart(Word(nums)).parse_string(" 123") - # raises ParseException - """ - - def __init__(self, expr: Union[ParserElement, str]): - super().__init__(expr) - self.callPreparse = False - - def parseImpl(self, instring, loc, doActions=True): - if loc != 0: - raise ParseException(instring, loc, "not found at string start") - return super().parseImpl(instring, loc, doActions) - - -class AtLineStart(ParseElementEnhance): - r"""Matches if an expression matches at the beginning of a line within - the parse string - - Example:: - - test = '''\ - AAA this line - AAA and this line - AAA but not this one - B AAA and definitely not this one - ''' - - for t in (AtLineStart('AAA') + restOfLine).search_string(test): - print(t) - - prints:: - - ['AAA', ' this line'] - ['AAA', ' and this line'] - - """ - - def __init__(self, expr: Union[ParserElement, str]): - super().__init__(expr) - self.callPreparse = False - - def parseImpl(self, instring, loc, doActions=True): - if col(loc, instring) != 1: - raise ParseException(instring, loc, "not found at line start") - return super().parseImpl(instring, loc, doActions) - - -class FollowedBy(ParseElementEnhance): - """Lookahead matching of the given parse expression. - ``FollowedBy`` does *not* advance the parsing position within - the input string, it only verifies that the specified parse - expression matches at the current position. ``FollowedBy`` - always returns a null token list. If any results names are defined - in the lookahead expression, those *will* be returned for access by - name. - - Example:: - - # use FollowedBy to match a label only if it is followed by a ':' - data_word = Word(alphas) - label = data_word + FollowedBy(':') - attr_expr = Group(label + Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join)) - - attr_expr[1, ...].parse_string("shape: SQUARE color: BLACK posn: upper left").pprint() - - prints:: - - [['shape', 'SQUARE'], ['color', 'BLACK'], ['posn', 'upper left']] - """ - - def __init__(self, expr: Union[ParserElement, str]): - super().__init__(expr) - self.mayReturnEmpty = True - - def parseImpl(self, instring, loc, doActions=True): - # by using self._expr.parse and deleting the contents of the returned ParseResults list - # we keep any named results that were defined in the FollowedBy expression - _, ret = self.expr._parse(instring, loc, doActions=doActions) - del ret[:] - - return loc, ret - - -class PrecededBy(ParseElementEnhance): - """Lookbehind matching of the given parse expression. - ``PrecededBy`` does not advance the parsing position within the - input string, it only verifies that the specified parse expression - matches prior to the current position. ``PrecededBy`` always - returns a null token list, but if a results name is defined on the - given expression, it is returned. - - Parameters: - - - expr - expression that must match prior to the current parse - location - - retreat - (default= ``None``) - (int) maximum number of characters - to lookbehind prior to the current parse location - - If the lookbehind expression is a string, :class:`Literal`, - :class:`Keyword`, or a :class:`Word` or :class:`CharsNotIn` - with a specified exact or maximum length, then the retreat - parameter is not required. Otherwise, retreat must be specified to - give a maximum number of characters to look back from - the current parse position for a lookbehind match. - - Example:: - - # VB-style variable names with type prefixes - int_var = PrecededBy("#") + pyparsing_common.identifier - str_var = PrecededBy("$") + pyparsing_common.identifier - - """ - - def __init__( - self, expr: Union[ParserElement, str], retreat: typing.Optional[int] = None - ): - super().__init__(expr) - self.expr = self.expr().leave_whitespace() - self.mayReturnEmpty = True - self.mayIndexError = False - self.exact = False - if isinstance(expr, str_type): - retreat = len(expr) - self.exact = True - elif isinstance(expr, (Literal, Keyword)): - retreat = expr.matchLen - self.exact = True - elif isinstance(expr, (Word, CharsNotIn)) and expr.maxLen != _MAX_INT: - retreat = expr.maxLen - self.exact = True - elif isinstance(expr, PositionToken): - retreat = 0 - self.exact = True - self.retreat = retreat - self.errmsg = "not preceded by " + str(expr) - self.skipWhitespace = False - self.parseAction.append(lambda s, l, t: t.__delitem__(slice(None, None))) - - def parseImpl(self, instring, loc=0, doActions=True): - if self.exact: - if loc < self.retreat: - raise ParseException(instring, loc, self.errmsg) - start = loc - self.retreat - _, ret = self.expr._parse(instring, start) - else: - # retreat specified a maximum lookbehind window, iterate - test_expr = self.expr + StringEnd() - instring_slice = instring[max(0, loc - self.retreat) : loc] - last_expr = ParseException(instring, loc, self.errmsg) - for offset in range(1, min(loc, self.retreat + 1) + 1): - try: - # print('trying', offset, instring_slice, repr(instring_slice[loc - offset:])) - _, ret = test_expr._parse( - instring_slice, len(instring_slice) - offset - ) - except ParseBaseException as pbe: - last_expr = pbe - else: - break - else: - raise last_expr - return loc, ret - - -class Located(ParseElementEnhance): - """ - Decorates a returned token with its starting and ending - locations in the input string. - - This helper adds the following results names: - - - ``locn_start`` - location where matched expression begins - - ``locn_end`` - location where matched expression ends - - ``value`` - the actual parsed results - - Be careful if the input text contains `` `` characters, you - may want to call :class:`ParserElement.parse_with_tabs` - - Example:: - - wd = Word(alphas) - for match in Located(wd).search_string("ljsdf123lksdjjf123lkkjj1222"): - print(match) - - prints:: - - [0, ['ljsdf'], 5] - [8, ['lksdjjf'], 15] - [18, ['lkkjj'], 23] - - """ - - def parseImpl(self, instring, loc, doActions=True): - start = loc - loc, tokens = self.expr._parse(instring, start, doActions, callPreParse=False) - ret_tokens = ParseResults([start, tokens, loc]) - ret_tokens["locn_start"] = start - ret_tokens["value"] = tokens - ret_tokens["locn_end"] = loc - if self.resultsName: - # must return as a list, so that the name will be attached to the complete group - return loc, [ret_tokens] - else: - return loc, ret_tokens - - -class NotAny(ParseElementEnhance): - """ - Lookahead to disallow matching with the given parse expression. - ``NotAny`` does *not* advance the parsing position within the - input string, it only verifies that the specified parse expression - does *not* match at the current position. Also, ``NotAny`` does - *not* skip over leading whitespace. ``NotAny`` always returns - a null token list. May be constructed using the ``'~'`` operator. - - Example:: - - AND, OR, NOT = map(CaselessKeyword, "AND OR NOT".split()) - - # take care not to mistake keywords for identifiers - ident = ~(AND | OR | NOT) + Word(alphas) - boolean_term = Opt(NOT) + ident - - # very crude boolean expression - to support parenthesis groups and - # operation hierarchy, use infix_notation - boolean_expr = boolean_term + ((AND | OR) + boolean_term)[...] - - # integers that are followed by "." are actually floats - integer = Word(nums) + ~Char(".") - """ - - def __init__(self, expr: Union[ParserElement, str]): - super().__init__(expr) - # do NOT use self.leave_whitespace(), don't want to propagate to exprs - # self.leave_whitespace() - self.skipWhitespace = False - - self.mayReturnEmpty = True - self.errmsg = "Found unwanted token, " + str(self.expr) - - def parseImpl(self, instring, loc, doActions=True): - if self.expr.can_parse_next(instring, loc): - raise ParseException(instring, loc, self.errmsg, self) - return loc, [] - - def _generateDefaultName(self): - return "~{" + str(self.expr) + "}" - - -class _MultipleMatch(ParseElementEnhance): - def __init__( - self, - expr: ParserElement, - stop_on: typing.Optional[Union[ParserElement, str]] = None, - *, - stopOn: typing.Optional[Union[ParserElement, str]] = None, - ): - super().__init__(expr) - stopOn = stopOn or stop_on - self.saveAsList = True - ender = stopOn - if isinstance(ender, str_type): - ender = self._literalStringClass(ender) - self.stopOn(ender) - - def stopOn(self, ender) -> ParserElement: - if isinstance(ender, str_type): - ender = self._literalStringClass(ender) - self.not_ender = ~ender if ender is not None else None - return self - - def parseImpl(self, instring, loc, doActions=True): - self_expr_parse = self.expr._parse - self_skip_ignorables = self._skipIgnorables - check_ender = self.not_ender is not None - if check_ender: - try_not_ender = self.not_ender.tryParse - - # must be at least one (but first see if we are the stopOn sentinel; - # if so, fail) - if check_ender: - try_not_ender(instring, loc) - loc, tokens = self_expr_parse(instring, loc, doActions) - try: - hasIgnoreExprs = not not self.ignoreExprs - while 1: - if check_ender: - try_not_ender(instring, loc) - if hasIgnoreExprs: - preloc = self_skip_ignorables(instring, loc) - else: - preloc = loc - loc, tmptokens = self_expr_parse(instring, preloc, doActions) - if tmptokens or tmptokens.haskeys(): - tokens += tmptokens - except (ParseException, IndexError): - pass - - return loc, tokens - - def _setResultsName(self, name, listAllMatches=False): - if ( - __diag__.warn_ungrouped_named_tokens_in_collection - and Diagnostics.warn_ungrouped_named_tokens_in_collection - not in self.suppress_warnings_ - ): - for e in [self.expr] + self.expr.recurse(): - if ( - isinstance(e, ParserElement) - and e.resultsName - and Diagnostics.warn_ungrouped_named_tokens_in_collection - not in e.suppress_warnings_ - ): - warnings.warn( - "{}: setting results name {!r} on {} expression " - "collides with {!r} on contained expression".format( - "warn_ungrouped_named_tokens_in_collection", - name, - type(self).__name__, - e.resultsName, - ), - stacklevel=3, - ) - - return super()._setResultsName(name, listAllMatches) - - -class OneOrMore(_MultipleMatch): - """ - Repetition of one or more of the given expression. - - Parameters: - - expr - expression that must match one or more times - - stop_on - (default= ``None``) - expression for a terminating sentinel - (only required if the sentinel would ordinarily match the repetition - expression) - - Example:: - - data_word = Word(alphas) - label = data_word + FollowedBy(':') - attr_expr = Group(label + Suppress(':') + OneOrMore(data_word).set_parse_action(' '.join)) - - text = "shape: SQUARE posn: upper left color: BLACK" - attr_expr[1, ...].parse_string(text).pprint() # Fail! read 'color' as data instead of next label -> [['shape', 'SQUARE color']] - - # use stop_on attribute for OneOrMore to avoid reading label string as part of the data - attr_expr = Group(label + Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join)) - OneOrMore(attr_expr).parse_string(text).pprint() # Better -> [['shape', 'SQUARE'], ['posn', 'upper left'], ['color', 'BLACK']] - - # could also be written as - (attr_expr * (1,)).parse_string(text).pprint() - """ - - def _generateDefaultName(self): - return "{" + str(self.expr) + "}..." - - -class ZeroOrMore(_MultipleMatch): - """ - Optional repetition of zero or more of the given expression. - - Parameters: - - ``expr`` - expression that must match zero or more times - - ``stop_on`` - expression for a terminating sentinel - (only required if the sentinel would ordinarily match the repetition - expression) - (default= ``None``) - - Example: similar to :class:`OneOrMore` - """ - - def __init__( - self, - expr: ParserElement, - stop_on: typing.Optional[Union[ParserElement, str]] = None, - *, - stopOn: typing.Optional[Union[ParserElement, str]] = None, - ): - super().__init__(expr, stopOn=stopOn or stop_on) - self.mayReturnEmpty = True - - def parseImpl(self, instring, loc, doActions=True): - try: - return super().parseImpl(instring, loc, doActions) - except (ParseException, IndexError): - return loc, ParseResults([], name=self.resultsName) - - def _generateDefaultName(self): - return "[" + str(self.expr) + "]..." - - -class _NullToken: - def __bool__(self): - return False - - def __str__(self): - return "" - - -class Opt(ParseElementEnhance): - """ - Optional matching of the given expression. - - Parameters: - - ``expr`` - expression that must match zero or more times - - ``default`` (optional) - value to be returned if the optional expression is not found. - - Example:: - - # US postal code can be a 5-digit zip, plus optional 4-digit qualifier - zip = Combine(Word(nums, exact=5) + Opt('-' + Word(nums, exact=4))) - zip.run_tests(''' - # traditional ZIP code - 12345 - - # ZIP+4 form - 12101-0001 - - # invalid ZIP - 98765- - ''') - - prints:: - - # traditional ZIP code - 12345 - ['12345'] - - # ZIP+4 form - 12101-0001 - ['12101-0001'] - - # invalid ZIP - 98765- - ^ - FAIL: Expected end of text (at char 5), (line:1, col:6) - """ - - __optionalNotMatched = _NullToken() - - def __init__( - self, expr: Union[ParserElement, str], default: Any = __optionalNotMatched - ): - super().__init__(expr, savelist=False) - self.saveAsList = self.expr.saveAsList - self.defaultValue = default - self.mayReturnEmpty = True - - def parseImpl(self, instring, loc, doActions=True): - self_expr = self.expr - try: - loc, tokens = self_expr._parse(instring, loc, doActions, callPreParse=False) - except (ParseException, IndexError): - default_value = self.defaultValue - if default_value is not self.__optionalNotMatched: - if self_expr.resultsName: - tokens = ParseResults([default_value]) - tokens[self_expr.resultsName] = default_value - else: - tokens = [default_value] - else: - tokens = [] - return loc, tokens - - def _generateDefaultName(self): - inner = str(self.expr) - # strip off redundant inner {}'s - while len(inner) > 1 and inner[0 :: len(inner) - 1] == "{}": - inner = inner[1:-1] - return "[" + inner + "]" - - -Optional = Opt - - -class SkipTo(ParseElementEnhance): - """ - Token for skipping over all undefined text until the matched - expression is found. - - Parameters: - - ``expr`` - target expression marking the end of the data to be skipped - - ``include`` - if ``True``, the target expression is also parsed - (the skipped text and target expression are returned as a 2-element - list) (default= ``False``). - - ``ignore`` - (default= ``None``) used to define grammars (typically quoted strings and - comments) that might contain false matches to the target expression - - ``fail_on`` - (default= ``None``) define expressions that are not allowed to be - included in the skipped test; if found before the target expression is found, - the :class:`SkipTo` is not a match - - Example:: - - report = ''' - Outstanding Issues Report - 1 Jan 2000 - - # | Severity | Description | Days Open - -----+----------+-------------------------------------------+----------- - 101 | Critical | Intermittent system crash | 6 - 94 | Cosmetic | Spelling error on Login ('log|n') | 14 - 79 | Minor | System slow when running too many reports | 47 - ''' - integer = Word(nums) - SEP = Suppress('|') - # use SkipTo to simply match everything up until the next SEP - # - ignore quoted strings, so that a '|' character inside a quoted string does not match - # - parse action will call token.strip() for each matched token, i.e., the description body - string_data = SkipTo(SEP, ignore=quoted_string) - string_data.set_parse_action(token_map(str.strip)) - ticket_expr = (integer("issue_num") + SEP - + string_data("sev") + SEP - + string_data("desc") + SEP - + integer("days_open")) - - for tkt in ticket_expr.search_string(report): - print tkt.dump() - - prints:: - - ['101', 'Critical', 'Intermittent system crash', '6'] - - days_open: '6' - - desc: 'Intermittent system crash' - - issue_num: '101' - - sev: 'Critical' - ['94', 'Cosmetic', "Spelling error on Login ('log|n')", '14'] - - days_open: '14' - - desc: "Spelling error on Login ('log|n')" - - issue_num: '94' - - sev: 'Cosmetic' - ['79', 'Minor', 'System slow when running too many reports', '47'] - - days_open: '47' - - desc: 'System slow when running too many reports' - - issue_num: '79' - - sev: 'Minor' - """ - - def __init__( - self, - other: Union[ParserElement, str], - include: bool = False, - ignore: bool = None, - fail_on: typing.Optional[Union[ParserElement, str]] = None, - *, - failOn: Union[ParserElement, str] = None, - ): - super().__init__(other) - failOn = failOn or fail_on - self.ignoreExpr = ignore - self.mayReturnEmpty = True - self.mayIndexError = False - self.includeMatch = include - self.saveAsList = False - if isinstance(failOn, str_type): - self.failOn = self._literalStringClass(failOn) - else: - self.failOn = failOn - self.errmsg = "No match found for " + str(self.expr) - - def parseImpl(self, instring, loc, doActions=True): - startloc = loc - instrlen = len(instring) - self_expr_parse = self.expr._parse - self_failOn_canParseNext = ( - self.failOn.canParseNext if self.failOn is not None else None - ) - self_ignoreExpr_tryParse = ( - self.ignoreExpr.tryParse if self.ignoreExpr is not None else None - ) - - tmploc = loc - while tmploc <= instrlen: - if self_failOn_canParseNext is not None: - # break if failOn expression matches - if self_failOn_canParseNext(instring, tmploc): - break - - if self_ignoreExpr_tryParse is not None: - # advance past ignore expressions - while 1: - try: - tmploc = self_ignoreExpr_tryParse(instring, tmploc) - except ParseBaseException: - break - - try: - self_expr_parse(instring, tmploc, doActions=False, callPreParse=False) - except (ParseException, IndexError): - # no match, advance loc in string - tmploc += 1 - else: - # matched skipto expr, done - break - - else: - # ran off the end of the input string without matching skipto expr, fail - raise ParseException(instring, loc, self.errmsg, self) - - # build up return values - loc = tmploc - skiptext = instring[startloc:loc] - skipresult = ParseResults(skiptext) - - if self.includeMatch: - loc, mat = self_expr_parse(instring, loc, doActions, callPreParse=False) - skipresult += mat - - return loc, skipresult - - -class Forward(ParseElementEnhance): - """ - Forward declaration of an expression to be defined later - - used for recursive grammars, such as algebraic infix notation. - When the expression is known, it is assigned to the ``Forward`` - variable using the ``'<<'`` operator. - - Note: take care when assigning to ``Forward`` not to overlook - precedence of operators. - - Specifically, ``'|'`` has a lower precedence than ``'<<'``, so that:: - - fwd_expr << a | b | c - - will actually be evaluated as:: - - (fwd_expr << a) | b | c - - thereby leaving b and c out as parseable alternatives. It is recommended that you - explicitly group the values inserted into the ``Forward``:: - - fwd_expr << (a | b | c) - - Converting to use the ``'<<='`` operator instead will avoid this problem. - - See :class:`ParseResults.pprint` for an example of a recursive - parser created using ``Forward``. - """ - - def __init__(self, other: typing.Optional[Union[ParserElement, str]] = None): - self.caller_frame = traceback.extract_stack(limit=2)[0] - super().__init__(other, savelist=False) - self.lshift_line = None - - def __lshift__(self, other): - if hasattr(self, "caller_frame"): - del self.caller_frame - if isinstance(other, str_type): - other = self._literalStringClass(other) - self.expr = other - self.mayIndexError = self.expr.mayIndexError - self.mayReturnEmpty = self.expr.mayReturnEmpty - self.set_whitespace_chars( - self.expr.whiteChars, copy_defaults=self.expr.copyDefaultWhiteChars - ) - self.skipWhitespace = self.expr.skipWhitespace - self.saveAsList = self.expr.saveAsList - self.ignoreExprs.extend(self.expr.ignoreExprs) - self.lshift_line = traceback.extract_stack(limit=2)[-2] - return self - - def __ilshift__(self, other): - return self << other - - def __or__(self, other): - caller_line = traceback.extract_stack(limit=2)[-2] - if ( - __diag__.warn_on_match_first_with_lshift_operator - and caller_line == self.lshift_line - and Diagnostics.warn_on_match_first_with_lshift_operator - not in self.suppress_warnings_ - ): - warnings.warn( - "using '<<' operator with '|' is probably an error, use '<<='", - stacklevel=2, - ) - ret = super().__or__(other) - return ret - - def __del__(self): - # see if we are getting dropped because of '=' reassignment of var instead of '<<=' or '<<' - if ( - self.expr is None - and __diag__.warn_on_assignment_to_Forward - and Diagnostics.warn_on_assignment_to_Forward not in self.suppress_warnings_ - ): - warnings.warn_explicit( - "Forward defined here but no expression attached later using '<<=' or '<<'", - UserWarning, - filename=self.caller_frame.filename, - lineno=self.caller_frame.lineno, - ) - - def parseImpl(self, instring, loc, doActions=True): - if ( - self.expr is None - and __diag__.warn_on_parse_using_empty_Forward - and Diagnostics.warn_on_parse_using_empty_Forward - not in self.suppress_warnings_ - ): - # walk stack until parse_string, scan_string, search_string, or transform_string is found - parse_fns = [ - "parse_string", - "scan_string", - "search_string", - "transform_string", - ] - tb = traceback.extract_stack(limit=200) - for i, frm in enumerate(reversed(tb), start=1): - if frm.name in parse_fns: - stacklevel = i + 1 - break - else: - stacklevel = 2 - warnings.warn( - "Forward expression was never assigned a value, will not parse any input", - stacklevel=stacklevel, - ) - if not ParserElement._left_recursion_enabled: - return super().parseImpl(instring, loc, doActions) - # ## Bounded Recursion algorithm ## - # Recursion only needs to be processed at ``Forward`` elements, since they are - # the only ones that can actually refer to themselves. The general idea is - # to handle recursion stepwise: We start at no recursion, then recurse once, - # recurse twice, ..., until more recursion offers no benefit (we hit the bound). - # - # The "trick" here is that each ``Forward`` gets evaluated in two contexts - # - to *match* a specific recursion level, and - # - to *search* the bounded recursion level - # and the two run concurrently. The *search* must *match* each recursion level - # to find the best possible match. This is handled by a memo table, which - # provides the previous match to the next level match attempt. - # - # See also "Left Recursion in Parsing Expression Grammars", Medeiros et al. - # - # There is a complication since we not only *parse* but also *transform* via - # actions: We do not want to run the actions too often while expanding. Thus, - # we expand using `doActions=False` and only run `doActions=True` if the next - # recursion level is acceptable. - with ParserElement.recursion_lock: - memo = ParserElement.recursion_memos - try: - # we are parsing at a specific recursion expansion - use it as-is - prev_loc, prev_result = memo[loc, self, doActions] - if isinstance(prev_result, Exception): - raise prev_result - return prev_loc, prev_result.copy() - except KeyError: - act_key = (loc, self, True) - peek_key = (loc, self, False) - # we are searching for the best recursion expansion - keep on improving - # both `doActions` cases must be tracked separately here! - prev_loc, prev_peek = memo[peek_key] = ( - loc - 1, - ParseException( - instring, loc, "Forward recursion without base case", self - ), - ) - if doActions: - memo[act_key] = memo[peek_key] - while True: - try: - new_loc, new_peek = super().parseImpl(instring, loc, False) - except ParseException: - # we failed before getting any match – do not hide the error - if isinstance(prev_peek, Exception): - raise - new_loc, new_peek = prev_loc, prev_peek - # the match did not get better: we are done - if new_loc <= prev_loc: - if doActions: - # replace the match for doActions=False as well, - # in case the action did backtrack - prev_loc, prev_result = memo[peek_key] = memo[act_key] - del memo[peek_key], memo[act_key] - return prev_loc, prev_result.copy() - del memo[peek_key] - return prev_loc, prev_peek.copy() - # the match did get better: see if we can improve further - else: - if doActions: - try: - memo[act_key] = super().parseImpl(instring, loc, True) - except ParseException as e: - memo[peek_key] = memo[act_key] = (new_loc, e) - raise - prev_loc, prev_peek = memo[peek_key] = new_loc, new_peek - - def leave_whitespace(self, recursive: bool = True) -> ParserElement: - self.skipWhitespace = False - return self - - def ignore_whitespace(self, recursive: bool = True) -> ParserElement: - self.skipWhitespace = True - return self - - def streamline(self) -> ParserElement: - if not self.streamlined: - self.streamlined = True - if self.expr is not None: - self.expr.streamline() - return self - - def validate(self, validateTrace=None) -> None: - if validateTrace is None: - validateTrace = [] - - if self not in validateTrace: - tmp = validateTrace[:] + [self] - if self.expr is not None: - self.expr.validate(tmp) - self._checkRecursion([]) - - def _generateDefaultName(self): - # Avoid infinite recursion by setting a temporary _defaultName - self._defaultName = ": ..." - - # Use the string representation of main expression. - retString = "..." - try: - if self.expr is not None: - retString = str(self.expr)[:1000] - else: - retString = "None" - finally: - return self.__class__.__name__ + ": " + retString - - def copy(self) -> ParserElement: - if self.expr is not None: - return super().copy() - else: - ret = Forward() - ret <<= self - return ret - - def _setResultsName(self, name, list_all_matches=False): - if ( - __diag__.warn_name_set_on_empty_Forward - and Diagnostics.warn_name_set_on_empty_Forward - not in self.suppress_warnings_ - ): - if self.expr is None: - warnings.warn( - "{}: setting results name {!r} on {} expression " - "that has no contained expression".format( - "warn_name_set_on_empty_Forward", name, type(self).__name__ - ), - stacklevel=3, - ) - - return super()._setResultsName(name, list_all_matches) - - ignoreWhitespace = ignore_whitespace - leaveWhitespace = leave_whitespace - - -class TokenConverter(ParseElementEnhance): - """ - Abstract subclass of :class:`ParseExpression`, for converting parsed results. - """ - - def __init__(self, expr: Union[ParserElement, str], savelist=False): - super().__init__(expr) # , savelist) - self.saveAsList = False - - -class Combine(TokenConverter): - """Converter to concatenate all matching tokens to a single string. - By default, the matching patterns must also be contiguous in the - input string; this can be disabled by specifying - ``'adjacent=False'`` in the constructor. - - Example:: - - real = Word(nums) + '.' + Word(nums) - print(real.parse_string('3.1416')) # -> ['3', '.', '1416'] - # will also erroneously match the following - print(real.parse_string('3. 1416')) # -> ['3', '.', '1416'] - - real = Combine(Word(nums) + '.' + Word(nums)) - print(real.parse_string('3.1416')) # -> ['3.1416'] - # no match when there are internal spaces - print(real.parse_string('3. 1416')) # -> Exception: Expected W:(0123...) - """ - - def __init__( - self, - expr: ParserElement, - join_string: str = "", - adjacent: bool = True, - *, - joinString: typing.Optional[str] = None, - ): - super().__init__(expr) - joinString = joinString if joinString is not None else join_string - # suppress whitespace-stripping in contained parse expressions, but re-enable it on the Combine itself - if adjacent: - self.leave_whitespace() - self.adjacent = adjacent - self.skipWhitespace = True - self.joinString = joinString - self.callPreparse = True - - def ignore(self, other) -> ParserElement: - if self.adjacent: - ParserElement.ignore(self, other) - else: - super().ignore(other) - return self - - def postParse(self, instring, loc, tokenlist): - retToks = tokenlist.copy() - del retToks[:] - retToks += ParseResults( - ["".join(tokenlist._asStringList(self.joinString))], modal=self.modalResults - ) - - if self.resultsName and retToks.haskeys(): - return [retToks] - else: - return retToks - - -class Group(TokenConverter): - """Converter to return the matched tokens as a list - useful for - returning tokens of :class:`ZeroOrMore` and :class:`OneOrMore` expressions. - - The optional ``aslist`` argument when set to True will return the - parsed tokens as a Python list instead of a pyparsing ParseResults. - - Example:: - - ident = Word(alphas) - num = Word(nums) - term = ident | num - func = ident + Opt(delimited_list(term)) - print(func.parse_string("fn a, b, 100")) - # -> ['fn', 'a', 'b', '100'] - - func = ident + Group(Opt(delimited_list(term))) - print(func.parse_string("fn a, b, 100")) - # -> ['fn', ['a', 'b', '100']] - """ - - def __init__(self, expr: ParserElement, aslist: bool = False): - super().__init__(expr) - self.saveAsList = True - self._asPythonList = aslist - - def postParse(self, instring, loc, tokenlist): - if self._asPythonList: - return ParseResults.List( - tokenlist.asList() - if isinstance(tokenlist, ParseResults) - else list(tokenlist) - ) - else: - return [tokenlist] - - -class Dict(TokenConverter): - """Converter to return a repetitive expression as a list, but also - as a dictionary. Each element can also be referenced using the first - token in the expression as its key. Useful for tabular report - scraping when the first column can be used as a item key. - - The optional ``asdict`` argument when set to True will return the - parsed tokens as a Python dict instead of a pyparsing ParseResults. - - Example:: - - data_word = Word(alphas) - label = data_word + FollowedBy(':') - - text = "shape: SQUARE posn: upper left color: light blue texture: burlap" - attr_expr = (label + Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join)) - - # print attributes as plain groups - print(attr_expr[1, ...].parse_string(text).dump()) - - # instead of OneOrMore(expr), parse using Dict(Group(expr)[1, ...]) - Dict will auto-assign names - result = Dict(Group(attr_expr)[1, ...]).parse_string(text) - print(result.dump()) - - # access named fields as dict entries, or output as dict - print(result['shape']) - print(result.as_dict()) - - prints:: - - ['shape', 'SQUARE', 'posn', 'upper left', 'color', 'light blue', 'texture', 'burlap'] - [['shape', 'SQUARE'], ['posn', 'upper left'], ['color', 'light blue'], ['texture', 'burlap']] - - color: 'light blue' - - posn: 'upper left' - - shape: 'SQUARE' - - texture: 'burlap' - SQUARE - {'color': 'light blue', 'posn': 'upper left', 'texture': 'burlap', 'shape': 'SQUARE'} - - See more examples at :class:`ParseResults` of accessing fields by results name. - """ - - def __init__(self, expr: ParserElement, asdict: bool = False): - super().__init__(expr) - self.saveAsList = True - self._asPythonDict = asdict - - def postParse(self, instring, loc, tokenlist): - for i, tok in enumerate(tokenlist): - if len(tok) == 0: - continue - - ikey = tok[0] - if isinstance(ikey, int): - ikey = str(ikey).strip() - - if len(tok) == 1: - tokenlist[ikey] = _ParseResultsWithOffset("", i) - - elif len(tok) == 2 and not isinstance(tok[1], ParseResults): - tokenlist[ikey] = _ParseResultsWithOffset(tok[1], i) - - else: - try: - dictvalue = tok.copy() # ParseResults(i) - except Exception: - exc = TypeError( - "could not extract dict values from parsed results" - " - Dict expression must contain Grouped expressions" - ) - raise exc from None - - del dictvalue[0] - - if len(dictvalue) != 1 or ( - isinstance(dictvalue, ParseResults) and dictvalue.haskeys() - ): - tokenlist[ikey] = _ParseResultsWithOffset(dictvalue, i) - else: - tokenlist[ikey] = _ParseResultsWithOffset(dictvalue[0], i) - - if self._asPythonDict: - return [tokenlist.as_dict()] if self.resultsName else tokenlist.as_dict() - else: - return [tokenlist] if self.resultsName else tokenlist - - -class Suppress(TokenConverter): - """Converter for ignoring the results of a parsed expression. - - Example:: - - source = "a, b, c,d" - wd = Word(alphas) - wd_list1 = wd + (',' + wd)[...] - print(wd_list1.parse_string(source)) - - # often, delimiters that are useful during parsing are just in the - # way afterward - use Suppress to keep them out of the parsed output - wd_list2 = wd + (Suppress(',') + wd)[...] - print(wd_list2.parse_string(source)) - - # Skipped text (using '...') can be suppressed as well - source = "lead in START relevant text END trailing text" - start_marker = Keyword("START") - end_marker = Keyword("END") - find_body = Suppress(...) + start_marker + ... + end_marker - print(find_body.parse_string(source) - - prints:: - - ['a', ',', 'b', ',', 'c', ',', 'd'] - ['a', 'b', 'c', 'd'] - ['START', 'relevant text ', 'END'] - - (See also :class:`delimited_list`.) - """ - - def __init__(self, expr: Union[ParserElement, str], savelist: bool = False): - if expr is ...: - expr = _PendingSkip(NoMatch()) - super().__init__(expr) - - def __add__(self, other) -> "ParserElement": - if isinstance(self.expr, _PendingSkip): - return Suppress(SkipTo(other)) + other - else: - return super().__add__(other) - - def __sub__(self, other) -> "ParserElement": - if isinstance(self.expr, _PendingSkip): - return Suppress(SkipTo(other)) - other - else: - return super().__sub__(other) - - def postParse(self, instring, loc, tokenlist): - return [] - - def suppress(self) -> ParserElement: - return self - - -def trace_parse_action(f: ParseAction) -> ParseAction: - """Decorator for debugging parse actions. - - When the parse action is called, this decorator will print - ``">> entering method-name(line: , , )"``. - When the parse action completes, the decorator will print - ``"<<"`` followed by the returned value, or any exception that the parse action raised. - - Example:: - - wd = Word(alphas) - - @trace_parse_action - def remove_duplicate_chars(tokens): - return ''.join(sorted(set(''.join(tokens)))) - - wds = wd[1, ...].set_parse_action(remove_duplicate_chars) - print(wds.parse_string("slkdjs sld sldd sdlf sdljf")) - - prints:: - - >>entering remove_duplicate_chars(line: 'slkdjs sld sldd sdlf sdljf', 0, (['slkdjs', 'sld', 'sldd', 'sdlf', 'sdljf'], {})) - < 3: - thisFunc = paArgs[0].__class__.__name__ + "." + thisFunc - sys.stderr.write( - ">>entering {}(line: {!r}, {}, {!r})\n".format(thisFunc, line(l, s), l, t) - ) - try: - ret = f(*paArgs) - except Exception as exc: - sys.stderr.write("< str: - r"""Helper to easily define string ranges for use in :class:`Word` - construction. Borrows syntax from regexp ``'[]'`` string range - definitions:: - - srange("[0-9]") -> "0123456789" - srange("[a-z]") -> "abcdefghijklmnopqrstuvwxyz" - srange("[a-z$_]") -> "abcdefghijklmnopqrstuvwxyz$_" - - The input string must be enclosed in []'s, and the returned string - is the expanded character set joined into a single string. The - values enclosed in the []'s may be: - - - a single character - - an escaped character with a leading backslash (such as ``\-`` - or ``\]``) - - an escaped hex character with a leading ``'\x'`` - (``\x21``, which is a ``'!'`` character) (``\0x##`` - is also supported for backwards compatibility) - - an escaped octal character with a leading ``'\0'`` - (``\041``, which is a ``'!'`` character) - - a range of any of the above, separated by a dash (``'a-z'``, - etc.) - - any combination of the above (``'aeiouy'``, - ``'a-zA-Z0-9_$'``, etc.) - """ - _expanded = ( - lambda p: p - if not isinstance(p, ParseResults) - else "".join(chr(c) for c in range(ord(p[0]), ord(p[1]) + 1)) - ) - try: - return "".join(_expanded(part) for part in _reBracketExpr.parse_string(s).body) - except Exception: - return "" - - -def token_map(func, *args) -> ParseAction: - """Helper to define a parse action by mapping a function to all - elements of a :class:`ParseResults` list. If any additional args are passed, - they are forwarded to the given function as additional arguments - after the token, as in - ``hex_integer = Word(hexnums).set_parse_action(token_map(int, 16))``, - which will convert the parsed data to an integer using base 16. - - Example (compare the last to example in :class:`ParserElement.transform_string`:: - - hex_ints = Word(hexnums)[1, ...].set_parse_action(token_map(int, 16)) - hex_ints.run_tests(''' - 00 11 22 aa FF 0a 0d 1a - ''') - - upperword = Word(alphas).set_parse_action(token_map(str.upper)) - upperword[1, ...].run_tests(''' - my kingdom for a horse - ''') - - wd = Word(alphas).set_parse_action(token_map(str.title)) - wd[1, ...].set_parse_action(' '.join).run_tests(''' - now is the winter of our discontent made glorious summer by this sun of york - ''') - - prints:: - - 00 11 22 aa FF 0a 0d 1a - [0, 17, 34, 170, 255, 10, 13, 26] - - my kingdom for a horse - ['MY', 'KINGDOM', 'FOR', 'A', 'HORSE'] - - now is the winter of our discontent made glorious summer by this sun of york - ['Now Is The Winter Of Our Discontent Made Glorious Summer By This Sun Of York'] - """ - - def pa(s, l, t): - return [func(tokn, *args) for tokn in t] - - func_name = getattr(func, "__name__", getattr(func, "__class__").__name__) - pa.__name__ = func_name - - return pa - - -def autoname_elements() -> None: - """ - Utility to simplify mass-naming of parser elements, for - generating railroad diagram with named subdiagrams. - """ - for name, var in sys._getframe().f_back.f_locals.items(): - if isinstance(var, ParserElement) and not var.customName: - var.set_name(name) - - -dbl_quoted_string = Combine( - Regex(r'"(?:[^"\n\r\\]|(?:"")|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*') + '"' -).set_name("string enclosed in double quotes") - -sgl_quoted_string = Combine( - Regex(r"'(?:[^'\n\r\\]|(?:'')|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*") + "'" -).set_name("string enclosed in single quotes") - -quoted_string = Combine( - Regex(r'"(?:[^"\n\r\\]|(?:"")|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*') + '"' - | Regex(r"'(?:[^'\n\r\\]|(?:'')|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*") + "'" -).set_name("quotedString using single or double quotes") - -unicode_string = Combine("u" + quoted_string.copy()).set_name("unicode string literal") - - -alphas8bit = srange(r"[\0xc0-\0xd6\0xd8-\0xf6\0xf8-\0xff]") -punc8bit = srange(r"[\0xa1-\0xbf\0xd7\0xf7]") - -# build list of built-in expressions, for future reference if a global default value -# gets updated -_builtin_exprs: List[ParserElement] = [ - v for v in vars().values() if isinstance(v, ParserElement) -] - -# backward compatibility names -tokenMap = token_map -conditionAsParseAction = condition_as_parse_action -nullDebugAction = null_debug_action -sglQuotedString = sgl_quoted_string -dblQuotedString = dbl_quoted_string -quotedString = quoted_string -unicodeString = unicode_string -lineStart = line_start -lineEnd = line_end -stringStart = string_start -stringEnd = string_end -traceParseAction = trace_parse_action diff --git a/spaces/Audio-AGI/AudioSep/callbacks/base.py b/spaces/Audio-AGI/AudioSep/callbacks/base.py deleted file mode 100644 index 9caad4bacba02b46c6668563f58c95b45d370c06..0000000000000000000000000000000000000000 --- a/spaces/Audio-AGI/AudioSep/callbacks/base.py +++ /dev/null @@ -1,35 +0,0 @@ -import os -import lightning.pytorch as pl -from lightning.pytorch.utilities import rank_zero_only - - -class CheckpointEveryNSteps(pl.Callback): - def __init__( - self, - checkpoints_dir, - save_step_frequency, - ) -> None: - r"""Save a checkpoint every N steps. - - Args: - checkpoints_dir (str): directory to save checkpoints - save_step_frequency (int): save checkpoint every N step - """ - - self.checkpoints_dir = checkpoints_dir - self.save_step_frequency = save_step_frequency - - @rank_zero_only - def on_train_batch_end(self, *args, **kwargs) -> None: - r"""Save a checkpoint every N steps.""" - - trainer = args[0] - global_step = trainer.global_step - - if global_step == 1 or global_step % self.save_step_frequency == 0: - - ckpt_path = os.path.join( - self.checkpoints_dir, - "step={}.ckpt".format(global_step)) - trainer.save_checkpoint(ckpt_path) - print("Save checkpoint to {}".format(ckpt_path)) diff --git a/spaces/AyakuraMei/Real-CUGAN/README.md b/spaces/AyakuraMei/Real-CUGAN/README.md deleted file mode 100644 index fabd7e43bcbbd3717afbcde561ba4ab307b30c74..0000000000000000000000000000000000000000 --- a/spaces/AyakuraMei/Real-CUGAN/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Real CUGAN -emoji: 🐢 -colorFrom: gray -colorTo: green -sdk: gradio -sdk_version: 3.6 -app_file: app.py -pinned: false -license: gpl-3.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Banbri/zcvzcv/CONTRIBUTORS.md b/spaces/Banbri/zcvzcv/CONTRIBUTORS.md deleted file mode 100644 index 421a1fb031dc63e7e10e590abed81e0ecbf1c21b..0000000000000000000000000000000000000000 --- a/spaces/Banbri/zcvzcv/CONTRIBUTORS.md +++ /dev/null @@ -1,10 +0,0 @@ -This project was developed by Julian Bilcke (@jbilcke-hf), as part of his work at Hugging Face. - ------------------------------------------- - -A huge thanks to external developers for their contributions! - -艾逗笔 (@idoubi): -- [feature] Added support for OpenAI: https://github.com/jbilcke-hf/ai-comic-factory/pull/6 -- [bug] predict import error (use dynamic imports for the LLM provider): https://github.com/jbilcke-hf/ai-comic-factory/pull/9 - diff --git a/spaces/Banbri/zcvzcv/src/components/ui/badge.tsx b/spaces/Banbri/zcvzcv/src/components/ui/badge.tsx deleted file mode 100644 index 8a05c5e844f6551efb3b35a0a23c748a9a6639b4..0000000000000000000000000000000000000000 --- a/spaces/Banbri/zcvzcv/src/components/ui/badge.tsx +++ /dev/null @@ -1,36 +0,0 @@ -import * as React from "react" -import { cva, type VariantProps } from "class-variance-authority" - -import { cn } from "@/lib/utils" - -const badgeVariants = cva( - "inline-flex items-center rounded-full border border-stone-200 px-2.5 py-0.5 text-xs font-semibold transition-colors focus:outline-none focus:ring-2 focus:ring-stone-400 focus:ring-offset-2 dark:border-stone-800 dark:focus:ring-stone-800", - { - variants: { - variant: { - default: - "border-transparent bg-stone-900 text-stone-50 hover:bg-stone-900/80 dark:bg-stone-50 dark:text-stone-900 dark:hover:bg-stone-50/80", - secondary: - "border-transparent bg-stone-100 text-stone-900 hover:bg-stone-100/80 dark:bg-stone-800 dark:text-stone-50 dark:hover:bg-stone-800/80", - destructive: - "border-transparent bg-red-500 text-stone-50 hover:bg-red-500/80 dark:bg-red-900 dark:text-red-50 dark:hover:bg-red-900/80", - outline: "text-stone-950 dark:text-stone-50", - }, - }, - defaultVariants: { - variant: "default", - }, - } -) - -export interface BadgeProps - extends React.HTMLAttributes , - VariantProps {} - -function Badge({ className, variant, ...props }: BadgeProps) { - return ( - - ) -} - -export { Badge, badgeVariants } diff --git a/spaces/Benson/text-generation/Examples/6tv Download Apk.md b/spaces/Benson/text-generation/Examples/6tv Download Apk.md deleted file mode 100644 index 80dd584a7507e1b35881be95865353118db364cf..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/6tv Download Apk.md +++ /dev/null @@ -1,64 +0,0 @@ - - 6TV APK: Cómo descargar y disfrutar de la transmisión de canales de televisión de Malasia e Indonesia
-¿Te encanta ver programas de televisión, películas, noticias, deportes y eventos en vivo desde Malasia e Indonesia? Si es así, entonces es posible que desee probar 6TV APK, una aplicación gratuita para Android que le permite transmitir varios canales de televisión y estaciones de radio de estos dos países. En este artículo, le diremos qué es 6TV APK, cómo descargarlo e instalarlo en su dispositivo Android, cuáles son los beneficios y desventajas de usarlo, y cuáles son algunas alternativas a ella. Al final de este artículo, usted será capaz de decidir si 6TV APK es la aplicación adecuada para usted o no.
-6tv download apk
Download File --->>> https://bltlly.com/2v6LPy
-¿Qué es 6TV APK?
-6TV APK es una aplicación para Android que le permite ver o escuchar la transmisión en vivo de varios canales de televisión y estaciones de radio de Malasia e Indonesia. Es desarrollado por Korzhuck, una compañía de software que se especializa en la creación de aplicaciones de streaming. 6TV APK no está disponible en la Google Play Store, pero se puede descargar desde otras fuentes en línea. Sin embargo, debe tener cuidado al descargar 6TV APK de sitios web de terceros, ya que algunos de ellos pueden contener malware o virus que pueden dañar su dispositivo o comprometer su privacidad.
-Características de 6TV APK
-Algunas de las características de 6TV APK son:
--
-
- Ofrece una amplia gama de canales de televisión y estaciones de radio de Malasia e Indonesia, incluyendo noticias, deportes, entretenimiento, películas, música, educación, religión y más. -
- Tiene una interfaz fácil de usar que hace que sea fácil navegar y encontrar sus canales o estaciones favoritas. -
- Soporta diferentes opciones de calidad de vídeo, de baja a alta, dependiendo de su velocidad y preferencia de Internet. -
- No requiere ningún registro o suscripción para usar. Simplemente puede descargar la aplicación y comenzar a transmitir. -
- Es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 4.1 o versiones posteriores. -
Para descargar e instalar 6TV APK en su dispositivo Android, siga estos pasos:
- --
-
- Ir a un sitio web de confianza que proporciona el enlace de descarga para 6TV APK. Por ejemplo, puede utilizar [este enlace]( 1 ) para descargar la última versión de la aplicación. -
- Toque en el botón de descarga y espere a que el archivo se descargue en su dispositivo. -
- Una vez completada la descarga, vaya a la configuración del dispositivo y habilite la opción de instalar aplicaciones de fuentes desconocidas. Esto le permitirá instalar 6TV APK sin ningún problema. -
- Localice el archivo descargado en el almacenamiento del dispositivo y toque en él para iniciar el proceso de instalación. -
- Siga las instrucciones en la pantalla y conceda los permisos necesarios a la aplicación. -
- Espere a que la instalación termine y luego inicie la aplicación desde el cajón de aplicaciones o la pantalla de inicio. -
- Disfrute de la transmisión de sus canales de televisión favoritos y estaciones de radio de Malasia e Indonesia. -
Beneficios de usar 6TV APK
-Algunos de los beneficios de usar 6TV APK son:
-Acceso a varios canales de televisión y estaciones de radio de Malasia e Indonesia
-Si usted es un fan de Malasia o Indonesia programas de televisión, películas, noticias, deportes, o eventos en vivo, entonces 6TV APK es una gran aplicación para usted. Puede acceder a cientos de canales de televisión y estaciones de radio de estos dos países con solo unos toques en la pantalla. Puedes ver o escuchar todo lo que quieras, desde series de drama, programas de comedia, documentales, reality shows, dibujos animados, películas, videos musicales, partidos deportivos, actualizaciones de noticias, eventos en vivo y más. También puedes descubrir nuevos canales o estaciones de los que no hayas oído hablar antes. 6TV APK le da la oportunidad de disfrutar de la rica y diversa cultura y entretenimiento de Malasia e Indonesia.
-Interfaz fácil de usar y transmisión de alta calidad
- -Libre y seguro de usar
-Una ventaja más de usar 6TV APK es que es gratis y seguro de usar. Usted no necesita pagar ninguna cuota o suscripciones para usar la aplicación. Simplemente puede descargar la aplicación y comenzar a transmitir sin limitaciones o restricciones. Tampoco es necesario que se registre o proporcione información personal para usar la aplicación. Puede transmitir de forma anónima y privada sin preocuparse de que sus datos o privacidad se vean comprometidos. 6TV APK no contiene ningún malware o virus que puede dañar su dispositivo o robar su información.
-Desventajas de usar 6TV APK
-Sin embargo, 6TV APK también tiene algunos inconvenientes que usted debe ser consciente de antes de usarlo. Algunos de los inconvenientes son:
-Disponibilidad y compatibilidad limitadas
-Uno de los inconvenientes de usar 6TV APK es que no está disponible en la Google Play Store, lo que significa que tienes que descargarlo de otras fuentes en línea. Esto puede ser arriesgado como algunos de los sitios web que proporcionan el enlace de descarga para 6TV APK puede contener malware o virus que pueden dañar su dispositivo o comprometer su privacidad. También debe habilitar la opción de instalar aplicaciones de fuentes desconocidas en la configuración del dispositivo, lo que puede exponer el dispositivo a amenazas potenciales. Por otra parte, 6TV APK solo es compatible con dispositivos Android que se ejecutan en Android 4.1 o versiones posteriores. Si usted tiene un dispositivo iOS o un dispositivo Android más antiguo, usted no será capaz de utilizar 6TV APK.
-Posibles problemas y riesgos legales
- -Dependencia de la conexión a Internet y el uso de datos
-Un inconveniente final de usar 6TV APK es que depende de su conexión a Internet y el uso de datos. Para transmitir los canales de televisión y las estaciones de radio de Malasia e Indonesia, necesita tener una conexión a Internet estable y rápida. Si su conexión a Internet es lenta o inestable, puede experimentar problemas de almacenamiento en búfer o retraso durante la transmisión. También puede perderse algún contenido importante o interesante debido a la mala calidad de la transmisión o las interrupciones. Además, la transmisión de los canales de televisión y las estaciones de radio de Malasia e Indonesia puede consumir mucho de su uso de datos. Si tienes un plan de datos limitado o una velocidad de datos lenta, puedes terminar gastando mucho dinero o tiempo en streaming.
-Alternativas a 6TV APK
-Si usted está buscando algunas alternativas a 6TV APK, aquí están algunos de ellos:
-Mobdro
-Mobdro es una popular aplicación para Android que le permite transmitir varios canales de televisión y estaciones de radio de todo el mundo. Tiene una gran colección de canales y estaciones de diferentes categorías, como noticias, deportes, entretenimiento, películas, música, educación, religión y más. También tiene una interfaz fácil de usar y soporta diferentes opciones de calidad de vídeo. Puede descargar Mobdro desde su sitio web oficial [aquí].
-NetTV en vivo
-Live NetTV es otra aplicación para Android que le permite transmitir varios canales de televisión y estaciones de radio de todo el mundo. Tiene que 6TV APK, puede probar Mobdro, Live NetTV, o RedBox TV, que son aplicaciones similares que le permiten transmitir varios canales de TV y estaciones de radio de todo el mundo. Esperamos que este artículo le ha ayudado a aprender más acerca de 6TV APK y cómo descargar y disfrutar de streaming Malasia e Indonesia canales de televisión.
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre 6TV APK:
--
-
- ¿Es 6TV APK legal? - -
- ¿Es seguro 6TV APK? -
- Cómo actualizar 6TV APK? -
- Cómo desinstalar 6TV APK? -
- ¿Cómo ponerse en contacto con el soporte 6TV APK? -
- Modo de mundo abierto multijugador: Puede caminar libremente, conducir libremente e interactuar libremente con estaciones de servicio y servicios de automóviles reales. También puedes competir contra jugadores reales en carreras multijugador, intercambiar coches con otros jugadores, hacer amigos, chat de voz y jugar como oficial de policía o criminal. -
- Personalización del automóvil: Puede ajustar la suspensión, el ángulo de la rueda, el motor, el turbo, la caja de cambios, el escape y más de su automóvil. También puede cambiar la apariencia visual de su automóvil con vinilos dinámicos, partes del cuerpo del automóvil y tipos de placas. -
- Mundo abierto de alta calidad: Puedes disfrutar de entornos muy detallados con 100 coches con interiores reales. También puede elegir entre 16 pieles de jugador y entrar en edificios con interiores. -
- Juego interesante: Puede desafiarse a sí mismo con 82 escenarios de estacionamiento y conducción en la vida real. También puede conducir diferentes vehículos como remolques, camionetas, camiones, coches deportivos y coches clásicos. -
- Para dispositivos iOS: Puedes descargar el juego desde App Store o desde otras fuentes de terceros como Panda Helper . Sin embargo, tenga cuidado al descargar de fuentes desconocidas, ya que pueden requerir jailbreak o sideloading que pueden anular su garantía o comprometer su seguridad. -
- Para dispositivos PC: Puede descargar el juego desde BlueStacks o desde otras fuentes de terceros como LDPlayer . Sin embargo, tenga cuidado al descargar de fuentes desconocidas, ya que pueden contener malware o virus. También necesitará un emulador para ejecutar el juego en su PC. -
- Aprender a la deriva: La deriva es una técnica que le permite deslizar su coche hacia los lados mientras gira. Puede ayudarle a evitar obstáculos, tomar esquinas afiladas, e impresionar a otros jugadores. Para la deriva, es necesario presionar el botón del freno de mano mientras se dirige en la dirección que desea ir. También puede ajustar la relación de transmisión y la suspensión de su coche para que sea más fácil a la deriva. -
- Bloquea tus puertas: Si no quieres que personas al azar salten en tu auto mientras estás conduciendo o estacionado, puedes bloquear tus puertas tocando el icono de bloqueo en la pantalla. Esto evitará que otros jugadores entren o roben tu auto. -
- Usa el chat de voz: El chat de voz es una función que te permite comunicarte con otros jugadores usando tu micrófono. Puedes usar el chat de voz para hacer amigos, pedir ayuda o coordinar tus acciones. Para usar el chat de voz, debe habilitarlo en la configuración y presionar el botón del micrófono en la pantalla. También puedes silenciar o bloquear a otros jugadores si son molestos o abusivos. - -
- Completando escenarios de estacionamiento y conducción -
- Ganar carreras multijugador -
- Venta o intercambio de coches con otros jugadores -
- Ver anuncios de recompensas -
- Comprarlos con dinero real (opcional) -
6TV APK no es seguro, ya que no está disponible en la Google Play Store, lo que significa que usted tiene que descargar de otras fuentes en línea. Esto puede ser arriesgado como algunos de los sitios web que proporcionan el enlace de descarga para 6TV APK puede contener malware o virus que pueden dañar su dispositivo o comprometer su privacidad. También debe habilitar la opción de instalar aplicaciones de fuentes desconocidas en la configuración del dispositivo, lo que puede exponer el dispositivo a amenazas potenciales. Además, 6TV APK no protege sus datos o privacidad durante la transmisión, lo que significa que su actividad en línea puede ser rastreada o monitoreada por terceros.
-Para actualizar 6TV APK, es necesario comprobar la última versión de la aplicación en el sitio web que lo descargó desde. Si hay una nueva versión disponible, debe descargarla e instalarla en su dispositivo. Sin embargo, debe tener cuidado al descargar 6TV APK de sitios web de terceros, ya que algunos de ellos pueden contener malware o virus que pueden dañar su dispositivo o comprometer su privacidad.
-Para desinstalar 6TV APK, es necesario ir a la configuración del dispositivo y encontrar la aplicación en la lista de aplicaciones instaladas. Luego, debe tocar en la aplicación y seleccionar la opción para desinstalarla. También es posible que tenga que eliminar el archivo descargado del almacenamiento del dispositivo y borrar la caché y los datos.
-Para contactar con el soporte de 6TV APK, debe visitar su sitio web oficial [aquí] y llenar el formulario de contacto con su nombre, dirección de correo electrónico, asunto y mensaje. También puedes seguirlos en sus cuentas de redes sociales, como Facebook, Twitter, Instagram y YouTube.
64aa2da5cf
-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Camionero Camino Loco.md b/spaces/Benson/text-generation/Examples/Camionero Camino Loco.md deleted file mode 100644 index 9e8c0ed3a783041d4baf642adaf29920a2c19be7..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Camionero Camino Loco.md +++ /dev/null @@ -1,47 +0,0 @@ -
-Truck Driver Crazy Road: Cómo dominar el juego de conducción más desafiante
-Si estás buscando un juego de conducción que te ponga al límite, deberías probar Truck Driver Crazy Road. Este juego pondrá a prueba sus habilidades de equilibrio, su paciencia y sus nervios a medida que conduce a través de la cuesta arriba con un montón de rocas y escombros dispersos a lo largo del camino áspero y lleno de baches. Tendrá que entregar todos los productos completos sin perder ninguno en el camino, o aparcar su camión en lugares estrechos sin estrellarse. ¿Suena fácil? Piénsalo de nuevo. Este juego no es para los débiles de corazón. Es uno de los juegos de conducción de camiones más desafiantes y divertidos que podrás jugar.
-camionero camino loco
Download ►►► https://bltlly.com/2v6KFr
-Introducción
-¿Qué es el conductor de camiones Crazy Road?
-Truck Driver Crazy Road es un juego de conducción de camiones en 3D que fue desarrollado por Falco Software y publicado por Y8.com. Tiene dos modos para elegir: Entrega y Estacionamiento. En el modo de entrega, tienes que transportar varias cargas de un punto a otro, a través de diferentes estaciones y terrenos. En el modo de estacionamiento, tienes que aparcar tu camión en áreas designadas, siguiendo las flechas y evitando obstáculos. El juego tiene 10 etapas en cada temporada y 14 niveles en modo Parking, cada uno con dificultad y complejidad crecientes.
-¿Por qué es tan desafiante y divertido?
-Truck Driver Crazy Road no es tu típico juego de conducción. Requiere mucha habilidad, concentración y perseverancia para completar cada nivel. Tienes que lidiar con física realista, clima impredecible, carreteras resbaladizas, puentes estrechos, colinas empinadas, giros bruscos y más. También tienes que vigilar tu velocidad, tu combustible, tus daños y tu carga. Si va demasiado rápido, puede perder el control de su camión o dejar caer parte de su carga. Si va demasiado lento, es posible que se quede sin gasolina o tiempo. Si golpeas algo, puedes dañar tu camión o fallar el nivel. Y si pierdes toda tu carga, tienes que empezar de nuevo.
- -Cómo jugar Truck Driver Crazy Road
-Elige tu modo y temporada
-Lo primero que tienes que hacer es elegir el modo que quieres jugar: Entrega o Estacionamiento. El modo de entrega es más sobre el transporte de mercancías, mientras que el modo de estacionamiento es más sobre la maniobra de su camión. Puede cambiar entre modos en cualquier momento desde el menú principal.
- -A continuación, debe elegir qué temporada desea jugar: Verano, Invierno o Desierto. Cada temporada tiene diferentes condiciones climáticas, superficies de carreteras y paisajes que afectan su experiencia de conducción. Por ejemplo, en invierno, tienes que lidiar con nieve y hielo que hacen que la carretera sea resbaladiza y reduce tu visibilidad. En el desierto, tienes que lidiar con tormentas de arena y el calor que hacen que el camino polvoriento y seco. Y en verano, tienes que lidiar con la lluvia y el barro que hacen que el camino sea húmedo y pegajoso.
-Conduce con cuidado y equilibra tu carga
-Una vez que haya elegido su modo y temporada, está listo para comenzar a conducir. Utilice las teclas de flecha o las teclas WASD para controlar su camión. La flecha arriba o la tecla W es para acelerar, la flecha abajo o la tecla S es para frenar o invertir, la flecha izquierda o la tecla A es para girar a la izquierda, y la flecha derecha o la tecla D es para girar a la derecha. También puede utilizar la barra espaciadora para activar el freno de mano. Tenga cuidado de no presionar las teclas demasiado fuerte o demasiado tiempo, ya que esto podría causar que su camión se deslice, se voltee o se caiga.
-A medida que conduce, tiene que equilibrar su carga y asegurarse de que no se caiga de su camión. Puedes ver cuánta carga te queda en la esquina superior izquierda de la pantalla. Si pierdes toda tu carga, tienes que reiniciar el nivel. También debe prestar atención al límite de tiempo, el medidor de combustible y el medidor de daños en la esquina superior derecha de la pantalla. Si te quedas sin tiempo, gas o salud, tienes que reiniciar el nivel también.
-Usar la cámara y opciones de pausa
- -Si necesita tomar un descanso o ajustar algunos ajustes, puede presionar la tecla P para pausar el juego. A continuación, puede reanudar el juego, reiniciar el nivel, volver al menú principal, o cambiar las opciones de sonido y gráficos.
-Consejos y trucos para el conductor de camiones Crazy Road
-Práctica en el modo de estacionamiento
-Si usted es nuevo en este juego o quiere mejorar sus habilidades de conducción, usted debe practicar en el modo de estacionamiento primero. Este modo te ayudará a familiarizarte con los controles, la física y las características de este juego. También aprenderá a maniobrar su camión en espacios reducidos, evitar obstáculos y aparcar con precisión. El modo de aparcamiento tiene 14 niveles con diferentes escenarios y desafíos. Puedes desbloquear nuevos niveles completando los anteriores.
-Cuidado con los obstáculos y peligros
-Una de las principales dificultades de este juego es que hay muchos obstáculos y peligros en la carretera que pueden ralentizarte, dañar tu camión o hacerte perder tu carga. Algunos de estos incluyen rocas, troncos, barriles, conos, cercas, automóviles, autobuses, trenes, aviones, helicópteros, animales, personas y más. Usted tiene que estar alerta y cuidadoso al conducir a través de estos obstáculos y peligros. También tienes que estar atento a las señales que te advierten de los peligros o direcciones que se avecinan. Por ejemplo, una señal roja con un signo de exclamación significa que hay algo peligroso por delante. Un signo amarillo con una flecha significa que tienes que girar a la izquierda o a la derecha.
-Actualiza tu camión y desbloquea nuevos niveles
-A medida que avanzas en el juego, ganarás dinero para completar cada nivel. Puede utilizar este dinero para actualizar su camión y hacerlo más rápido, más fuerte y más eficiente. Puede actualizar su motor, transmisión, frenos, neumáticos, suspensión, tanque de combustible y capacidad de carga. Actualizar su camión le ayudará a hacer frente a la creciente dificultad y complejidad de cada nivel.
- -Conclusión
-Resumen de los puntos principales
-Truck Driver Crazy Road es un juego de conducción de camiones en 3D que desafiará sus habilidades de equilibrio, su paciencia y sus nervios. Puede elegir entre el modo de entrega y el modo de estacionamiento, y entre las temporadas de verano, invierno y desierto. Tienes que conducir con cuidado y equilibrar su carga, utilizar la cámara y las opciones de pausa, practicar en el modo de estacionamiento, cuidado con los obstáculos y peligros, y actualizar su camión y desbloquear nuevos niveles. Este juego no es para los débiles de corazón, pero es uno de los juegos de conducción de camiones más divertidos y adictivos que nunca jugará.
-Llamada a la acción para los lectores
-Si estás listo para asumir este desafío, puedes jugar Truck Driver Crazy Road online gratis en Y8.com. También puede descargar el juego para dispositivos Windows o Android desde el mismo sitio web. También puedes ver otros juegos de conducción de camiones en Y8.com, como Truck Driver Simulator, Russian Car Driver ZIL 130, o Offroad Cargo Drive Simulator. ¡Diviértete y buena suerte!
-Preguntas frecuentes
-P: ¿Cómo puedo guardar mi progreso en Truck Driver Crazy Road?
-A: El juego guarda automáticamente tu progreso después de cada nivel. Puedes reanudar tu juego desde el menú principal haciendo clic en el botón Continuar.
-P: ¿Cómo puedo cambiar el lenguaje de Truck Driver Crazy Road?
-A: El juego es compatible con 10 idiomas: inglés, ruso, español, portugués, francés, alemán, italiano, turco, árabe y chino. Puede cambiar el idioma desde el menú principal haciendo clic en el botón Idioma.
-P: ¿Cómo silencio el sonido o la música de Truck Driver Crazy Road?
-A: Puede silenciar el sonido o la música desde el menú principal haciendo clic en el botón Sonido o Música. También puede ajustar el volumen desde el menú de configuración haciendo clic en el botón Configuración.
-P: ¿Cómo informo de un error o un problema con Truck Driver Crazy Road?
- -Q: ¿Cómo evalúo o reviso Truck Driver Crazy Road?
-A: Puede calificar o revisar el juego en Y8.com haciendo clic en el botón Tasa o en el botón Reseña. También puedes compartir tus comentarios o sugerencias con otros jugadores en la sección de comentarios.
64aa2da5cf
-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Aparcamiento Gratuito Multijugador.md b/spaces/Benson/text-generation/Examples/Descargar Aparcamiento Gratuito Multijugador.md deleted file mode 100644 index b81127302fa00e6fde8fc799e8c06b549730926a..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Aparcamiento Gratuito Multijugador.md +++ /dev/null @@ -1,78 +0,0 @@ - -Descargar Aparcamiento gratuito Multijugador
-Parking multijugador es un juego de simulación que te permite experimentar la emoción de conducir, aparcar y personalizar diferentes coches en un entorno de mundo abierto. Puede jugar solo o unirse a millones de otros jugadores en línea en varios modos como carreras, policía, juegos de rol y más. También puedes explorar diferentes ubicaciones, interactuar con otros jugadores y chatear por voz con tus amigos. Aparcamiento multijugador es más que solo aparcamiento: es un juego divertido y realista que te mantendrá entretenido durante horas.
-Características de Aparcamiento Multijugador
-El multijugador de estacionamiento tiene muchas características que lo hacen destacar de otros juegos de estacionamiento. Aquí están algunas de ellas:
-descargar aparcamiento gratuito multijugador
Download ⇔ https://bltlly.com/2v6Mm1
--
-
Cómo descargar el multijugador de estacionamiento de coches gratis
-Si quieres descargar el multijugador de estacionamiento gratis, tienes varias opciones dependiendo de tu dispositivo. Estas son algunas de ellas:
--
-
-
Consejos y trucos para el estacionamiento de coches multijugador
-Si quieres mejorar tus habilidades y disfrutar más del juego, aquí hay algunos consejos y trucos que puedes usar:
--
-
Conclusión
-Aparcamiento multijugador es un juego divertido y realista que le permite conducir, aparcar y personalizar diferentes coches en un entorno de mundo abierto. Puede jugar solo o unirse a millones de otros jugadores en línea en varios modos como carreras, policía, juegos de rol y más. También puedes explorar diferentes ubicaciones, interactuar con otros jugadores y chatear por voz con tus amigos. Aparcamiento multijugador es más que solo aparcamiento: es un juego que te mantendrá entretenido durante horas.
-Si quieres descargar el multijugador de estacionamiento gratis, tienes varias opciones dependiendo de tu dispositivo. Sin embargo, tenga cuidado al descargar de fuentes desconocidas, ya que pueden contener malware o virus. También necesitarás un emulador para ejecutar el juego en tu PC.
-Si quieres mejorar tus habilidades y disfrutar más del juego, puedes utilizar algunos consejos y trucos como aprender a la deriva, cerrar las puertas, usar el chat de voz y ver anuncios para obtener recompensas. También puede ajustar la configuración y los controles del juego para adaptarse a sus preferencias.
-Entonces, ¿qué estás esperando? Descarga el estacionamiento de coches multijugador hoy y experimenta la emoción de conducir y estacionar en un mundo abierto realista!
-Preguntas frecuentes
-¿Cuáles son los requisitos del sistema para el aparcamiento multijugador?
-Los requisitos del sistema para el multijugador de estacionamiento son los siguientes:
-- -Plataforma Requisitos mínimos Android Android 4.4 o superior, 1 GB de RAM, 300 MB de espacio libre iOS iOS 9.0 o superior, iPhone 5s o posterior, iPad Air o posterior, iPod touch de sexta generación o posterior, 300 MB de espacio libre PC Windows 7 o superior, 4 GB de RAM, 2 GB de espacio libre, DirectX 9.0c o superior, emulador de BlueStacks o similar ¿Cómo puedo obtener más dinero o monedas en el aparcamiento multijugador?
-Puedes obtener más dinero o monedas en el multijugador de estacionamiento haciendo lo siguiente:
- - -
¿Cómo puedo cambiar mi nombre o avatar en el multijugador de estacionamiento?
-Puedes cambiar tu nombre o avatar en el multijugador de estacionamiento haciendo lo siguiente:
--
-
- Pulsando en el icono de perfil en la esquina superior izquierda de la pantalla -
- Pulsando en el icono de edición en la esquina superior derecha de la pantalla -
- Introducir un nuevo nombre o elegir un nuevo avatar de la lista -
- Pulsando en el icono de guardar en la esquina superior derecha de la pantalla -
¿Cómo puedo reportar un error o un problema en el multijugador de estacionamiento?
-Puedes reportar un error o un problema en el multijugador de estacionamiento haciendo lo siguiente:
--
-
- Pulsando en el icono de configuración en la esquina superior derecha de la pantalla -
- Pulsando en la opción de retroalimentación -
- Llenar el formulario con su nombre, correo electrónico, modelo de dispositivo, versión del juego, y la descripción de la cuestión -
- Pulsando en el botón enviar -
¿Cómo puedo contactar a los desarrolladores del multijugador de aparcamiento?
-Puede ponerse en contacto con los desarrolladores de aparcamiento multijugador haciendo lo siguiente:
--
-
- Enviarlos por correo electrónico a olzhassgames@gmail.com -
- Visitar su sitio web en https:/olzhass.com/ -
- Siguiéndolos en Facebook en https://www.facebook.com/olzhassgames/ -
- Siguiéndolos en Instagram en https://www.instagram.com/olzhassgames/ -
- Siguiéndolos en Twitter en https://twitter.com/olzhassgames -
-
-
\ No newline at end of file diff --git a/spaces/Bidwill/Sanskrit-asr/app.py b/spaces/Bidwill/Sanskrit-asr/app.py deleted file mode 100644 index 5f3120f3ea132d3c2fca77c8f541846cada8d421..0000000000000000000000000000000000000000 --- a/spaces/Bidwill/Sanskrit-asr/app.py +++ /dev/null @@ -1,33 +0,0 @@ -from transformers import pipeline -import gradio as gr - -pipe = pipeline(model="Bidwill/whisper-small-sanskrit_4") # change to "your-username/the-name-you-picked" - -def transcribe(audio): - text = pipe(audio)["text"] - return text - -demo = gr.Blocks() - -mic_transcribe = gr.Interface( - fn=transcribe, - inputs=gr.Audio(source="microphone", type="filepath"), - outputs="text", - title="Sanskrit Speech to Text", - description="Realtime demo for Sanskrit speech recognition.", -) - -file_transcribe = gr.Interface( - fn=transcribe, - inputs=gr.Audio(source="upload", type="filepath"), - outputs=gr.outputs.Textbox(), - title="Sanskrit STT", - description= "Realtime demo for Sanskrit speech recognition." -) -with demo: - gr.TabbedInterface( - [mic_transcribe, file_transcribe], - ["Transcribe Microphone", "Transcribe Audio File"], - ) - -demo.launch() \ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/requests/models.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/requests/models.py deleted file mode 100644 index 76e6f199c0042cec6500f53c062ff9ea1033e79d..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/requests/models.py +++ /dev/null @@ -1,1034 +0,0 @@ -""" -requests.models -~~~~~~~~~~~~~~~ - -This module contains the primary objects that power Requests. -""" - -import datetime - -# Import encoding now, to avoid implicit import later. -# Implicit import within threads may cause LookupError when standard library is in a ZIP, -# such as in Embedded Python. See https://github.com/psf/requests/issues/3578. -import encodings.idna # noqa: F401 -from io import UnsupportedOperation - -from pip._vendor.urllib3.exceptions import ( - DecodeError, - LocationParseError, - ProtocolError, - ReadTimeoutError, - SSLError, -) -from pip._vendor.urllib3.fields import RequestField -from pip._vendor.urllib3.filepost import encode_multipart_formdata -from pip._vendor.urllib3.util import parse_url - -from ._internal_utils import to_native_string, unicode_is_ascii -from .auth import HTTPBasicAuth -from .compat import ( - Callable, - JSONDecodeError, - Mapping, - basestring, - builtin_str, - chardet, - cookielib, -) -from .compat import json as complexjson -from .compat import urlencode, urlsplit, urlunparse -from .cookies import _copy_cookie_jar, cookiejar_from_dict, get_cookie_header -from .exceptions import ( - ChunkedEncodingError, - ConnectionError, - ContentDecodingError, - HTTPError, - InvalidJSONError, - InvalidURL, -) -from .exceptions import JSONDecodeError as RequestsJSONDecodeError -from .exceptions import MissingSchema -from .exceptions import SSLError as RequestsSSLError -from .exceptions import StreamConsumedError -from .hooks import default_hooks -from .status_codes import codes -from .structures import CaseInsensitiveDict -from .utils import ( - check_header_validity, - get_auth_from_url, - guess_filename, - guess_json_utf, - iter_slices, - parse_header_links, - requote_uri, - stream_decode_response_unicode, - super_len, - to_key_val_list, -) - -#: The set of HTTP status codes that indicate an automatically -#: processable redirect. -REDIRECT_STATI = ( - codes.moved, # 301 - codes.found, # 302 - codes.other, # 303 - codes.temporary_redirect, # 307 - codes.permanent_redirect, # 308 -) - -DEFAULT_REDIRECT_LIMIT = 30 -CONTENT_CHUNK_SIZE = 10 * 1024 -ITER_CHUNK_SIZE = 512 - - -class RequestEncodingMixin: - @property - def path_url(self): - """Build the path URL to use.""" - - url = [] - - p = urlsplit(self.url) - - path = p.path - if not path: - path = "/" - - url.append(path) - - query = p.query - if query: - url.append("?") - url.append(query) - - return "".join(url) - - @staticmethod - def _encode_params(data): - """Encode parameters in a piece of data. - - Will successfully encode parameters when passed as a dict or a list of - 2-tuples. Order is retained if data is a list of 2-tuples but arbitrary - if parameters are supplied as a dict. - """ - - if isinstance(data, (str, bytes)): - return data - elif hasattr(data, "read"): - return data - elif hasattr(data, "__iter__"): - result = [] - for k, vs in to_key_val_list(data): - if isinstance(vs, basestring) or not hasattr(vs, "__iter__"): - vs = [vs] - for v in vs: - if v is not None: - result.append( - ( - k.encode("utf-8") if isinstance(k, str) else k, - v.encode("utf-8") if isinstance(v, str) else v, - ) - ) - return urlencode(result, doseq=True) - else: - return data - - @staticmethod - def _encode_files(files, data): - """Build the body for a multipart/form-data request. - - Will successfully encode files when passed as a dict or a list of - tuples. Order is retained if data is a list of tuples but arbitrary - if parameters are supplied as a dict. - The tuples may be 2-tuples (filename, fileobj), 3-tuples (filename, fileobj, contentype) - or 4-tuples (filename, fileobj, contentype, custom_headers). - """ - if not files: - raise ValueError("Files must be provided.") - elif isinstance(data, basestring): - raise ValueError("Data must not be a string.") - - new_fields = [] - fields = to_key_val_list(data or {}) - files = to_key_val_list(files or {}) - - for field, val in fields: - if isinstance(val, basestring) or not hasattr(val, "__iter__"): - val = [val] - for v in val: - if v is not None: - # Don't call str() on bytestrings: in Py3 it all goes wrong. - if not isinstance(v, bytes): - v = str(v) - - new_fields.append( - ( - field.decode("utf-8") - if isinstance(field, bytes) - else field, - v.encode("utf-8") if isinstance(v, str) else v, - ) - ) - - for (k, v) in files: - # support for explicit filename - ft = None - fh = None - if isinstance(v, (tuple, list)): - if len(v) == 2: - fn, fp = v - elif len(v) == 3: - fn, fp, ft = v - else: - fn, fp, ft, fh = v - else: - fn = guess_filename(v) or k - fp = v - - if isinstance(fp, (str, bytes, bytearray)): - fdata = fp - elif hasattr(fp, "read"): - fdata = fp.read() - elif fp is None: - continue - else: - fdata = fp - - rf = RequestField(name=k, data=fdata, filename=fn, headers=fh) - rf.make_multipart(content_type=ft) - new_fields.append(rf) - - body, content_type = encode_multipart_formdata(new_fields) - - return body, content_type - - -class RequestHooksMixin: - def register_hook(self, event, hook): - """Properly register a hook.""" - - if event not in self.hooks: - raise ValueError(f'Unsupported event specified, with event name "{event}"') - - if isinstance(hook, Callable): - self.hooks[event].append(hook) - elif hasattr(hook, "__iter__"): - self.hooks[event].extend(h for h in hook if isinstance(h, Callable)) - - def deregister_hook(self, event, hook): - """Deregister a previously registered hook. - Returns True if the hook existed, False if not. - """ - - try: - self.hooks[event].remove(hook) - return True - except ValueError: - return False - - -class Request(RequestHooksMixin): - """A user-created :class:`Request
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "620o1BxdNbgq"
- },
- "source": [
- "# **Stable Diffusion 2.1**\n",
- "Gradio app for [Stable Diffusion 2](https://huggingface.co/stabilityai/stable-diffusion-2) by [Stability AI](https://stability.ai/) (v2-1_768-ema-pruned.ckpt).\n",
- "It uses [Hugging Face](https://huggingface.co/) Diffusers🧨 implementation.\n",
- "\n",
- "Currently supported pipelines are `text-to-image`, `image-to-image`, `inpainting`, `4x upscaling` and `depth-to-image`.\n",
- "\n",
- "\n", - "\n", - "Colab by [anzorq](https://twitter.com/hahahahohohe). If you like it, please consider supporting me:\n", - "\n", - "[
![\"Buy](\"https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png\")
](https://www.buymeacoffee.com/anzorq)\n",
- "\n", - "[](https://github.com/qunash/stable-diffusion-2-gui)\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KQI4RX20DW_8" - }, - "source": [ - "# Install dependencies (~1.5 mins)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "78HoqRAB-cES", - "cellView": "form" - }, - "outputs": [], - "source": [ - "!pip install --upgrade git+https://github.com/huggingface/diffusers.git\n", - "# !pip install diffusers\n", - "!pip install --upgrade git+https://github.com/huggingface/transformers/\n", - "# !pip install transformers\n", - "!pip install accelerate==0.12.0\n", - "!pip install scipy\n", - "!pip install ftfy\n", - "!pip install gradio -q\n", - "\n", - "#@markdown ### ⬅️ Run this cell\n", - "#@markdown ---\n", - "#@markdown ### Install **xformers**?\n", - "#@markdown This will take an additional ~3.5 mins.
But images will generate 25-40% faster.\n", - "install_xformers = False #@param {type:\"boolean\"}\n", - "\n", - "if install_xformers:\n", - " import os\n", - " from subprocess import getoutput\n", - "\n", - " os.system(\"pip install --extra-index-url https://download.pytorch.org/whl/cu113 torch torchvision==0.13.1+cu113\")\n", - " os.system(\"pip install triton==2.0.0.dev20220701\")\n", - " gpu_info = getoutput('nvidia-smi')\n", - " if(\"A10G\" in gpu_info):\n", - " os.system(f\"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+4c06c79.d20221205-cp38-cp38-linux_x86_64.whl\")\n", - " elif(\"T4\" in gpu_info):\n", - " os.system(f\"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+1515f77.d20221130-cp38-cp38-linux_x86_64.whl\")\n", - "\n", - "\n", - "# ### install xformers\n", - "# from IPython.utils import capture\n", - "# from subprocess import getoutput\n", - "# from re import search\n", - "\n", - "# with capture.capture_output() as cap:\n", - " \n", - "# smi_out = getoutput('nvidia-smi')\n", - "# supported = search('(T4|P100|V100|A100|K80)', smi_out)\n", - "\n", - "# if not supported:\n", - "# while True:\n", - "# print(\"\\x1b[1;31mThe current GPU is not supported, try starting a new session.\\x1b[0m\")\n", - "# else:\n", - "# supported = supported.group(0)\n", - "\n", - "# !pip install -q https://github.com/TheLastBen/fast-stable-diffusion/raw/main/precompiled/{supported}/xformers-0.0.13.dev0-py3-none-any.whl\n", - "# !pip install -q https://github.com/ShivamShrirao/xformers-wheels/releases/download/4c06c79/xformers-0.0.15.dev0+4c06c79.d20221201-cp38-cp38-linux_x86_64.whl" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OOPHNsFYDbc0" - }, - "source": [ - "# Run the app" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "gId0-asCBVwL" - }, - "outputs": [], - "source": [ - "#@title ⬇️🖼️\n", - "from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, StableDiffusionUpscalePipeline, DiffusionPipeline, StableDiffusionDepth2ImgPipeline, DPMSolverMultistepScheduler\n", - "import gradio as gr\n", - "import torch\n", - "from PIL import Image\n", - "import random\n", - "\n", - "state = None\n", - "current_steps = 25\n", - "attn_slicing_enabled = True\n", - "mem_eff_attn_enabled = install_xformers\n", - "\n", - "# model_id = 'stabilityai/stable-diffusion-2'\n", - "model_id = 'stabilityai/stable-diffusion-2-1'\n", - "\n", - "scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder=\"scheduler\")\n", - "\n", - "pipe = StableDiffusionPipeline.from_pretrained(\n", - " model_id,\n", - " revision=\"fp16\" if torch.cuda.is_available() else \"fp32\",\n", - " torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,\n", - " scheduler=scheduler\n", - " ).to(\"cuda\")\n", - "pipe.enable_attention_slicing()\n", - "if mem_eff_attn_enabled:\n", - " pipe.enable_xformers_memory_efficient_attention()\n", - "\n", - "pipe_i2i = None\n", - "pipe_upscale = None\n", - "pipe_inpaint = None\n", - "pipe_depth2img = None\n", - "\n", - "\n", - "modes = {\n", - " 'txt2img': 'Text to Image',\n", - " 'img2img': 'Image to Image',\n", - " 'inpaint': 'Inpainting',\n", - " 'upscale4x': 'Upscale 4x',\n", - " 'depth2img': 'Depth to Image'\n", - "}\n", - "current_mode = modes['txt2img']\n", - "\n", - "def error_str(error, title=\"Error\"):\n", - " return f\"\"\"#### {title}\n", - " {error}\"\"\" if error else \"\"\n", - "\n", - "def update_state(new_state):\n", - " global state\n", - " state = new_state\n", - "\n", - "def update_state_info(old_state):\n", - " if state and state != old_state:\n", - " return gr.update(value=state)\n", - "\n", - "def set_mem_optimizations(pipe):\n", - " if attn_slicing_enabled:\n", - " pipe.enable_attention_slicing()\n", - " else:\n", - " pipe.disable_attention_slicing()\n", - " \n", - " if mem_eff_attn_enabled:\n", - " pipe.enable_xformers_memory_efficient_attention()\n", - " else:\n", - " pipe.disable_xformers_memory_efficient_attention()\n", - "\n", - "def get_i2i_pipe(scheduler):\n", - " \n", - " update_state(\"Loading image to image model...\")\n", - "\n", - " pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\n", - " model_id,\n", - " revision=\"fp16\" if torch.cuda.is_available() else \"fp32\",\n", - " torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,\n", - " scheduler=scheduler\n", - " )\n", - " set_mem_optimizations(pipe)\n", - " pipe.to(\"cuda\")\n", - " return pipe\n", - "\n", - "def get_inpaint_pipe():\n", - " \n", - " update_state(\"Loading inpainting model...\")\n", - "\n", - " pipe = DiffusionPipeline.from_pretrained(\n", - " \"stabilityai/stable-diffusion-2-inpainting\",\n", - " revision=\"fp16\" if torch.cuda.is_available() else \"fp32\",\n", - " torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,\n", - " # scheduler=scheduler # TODO currently setting scheduler here messes up the end result. A bug in Diffusers🧨\n", - " ).to(\"cuda\")\n", - " pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n", - " pipe.enable_attention_slicing()\n", - " pipe.enable_xformers_memory_efficient_attention()\n", - " return pipe\n", - "\n", - "def get_upscale_pipe(scheduler):\n", - " \n", - " update_state(\"Loading upscale model...\")\n", - "\n", - " pipe = StableDiffusionUpscalePipeline.from_pretrained(\n", - " \"stabilityai/stable-diffusion-x4-upscaler\",\n", - " revision=\"fp16\" if torch.cuda.is_available() else \"fp32\",\n", - " torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,\n", - " # scheduler=scheduler\n", - " )\n", - " # pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n", - " set_mem_optimizations(pipe)\n", - " pipe.to(\"cuda\")\n", - " return pipe\n", - " \n", - "def get_depth2img_pipe():\n", - " \n", - " update_state(\"Loading depth to image model...\")\n", - "\n", - " pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(\n", - " \"stabilityai/stable-diffusion-2-depth\",\n", - " revision=\"fp16\" if torch.cuda.is_available() else \"fp32\",\n", - " torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,\n", - " # scheduler=scheduler\n", - " )\n", - " pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n", - " set_mem_optimizations(pipe)\n", - " pipe.to(\"cuda\")\n", - " return pipe\n", - "\n", - "def switch_attention_slicing(attn_slicing):\n", - " global attn_slicing_enabled\n", - " attn_slicing_enabled = attn_slicing\n", - "\n", - "def switch_mem_eff_attn(mem_eff_attn):\n", - " global mem_eff_attn_enabled\n", - " mem_eff_attn_enabled = mem_eff_attn\n", - "\n", - "def pipe_callback(step: int, timestep: int, latents: torch.FloatTensor):\n", - " update_state(f\"{step}/{current_steps} steps\")#\\nTime left, sec: {timestep/100:.0f}\")\n", - "\n", - "def inference(inf_mode, prompt, n_images, guidance, steps, width=768, height=768, seed=0, img=None, strength=0.5, neg_prompt=\"\"):\n", - "\n", - " update_state(\" \")\n", - "\n", - " global current_mode\n", - " if inf_mode != current_mode:\n", - " pipe.to(\"cuda\" if inf_mode == modes['txt2img'] else \"cpu\")\n", - "\n", - " if pipe_i2i is not None:\n", - " pipe_i2i.to(\"cuda\" if inf_mode == modes['img2img'] else \"cpu\")\n", - "\n", - " if pipe_inpaint is not None:\n", - " pipe_inpaint.to(\"cuda\" if inf_mode == modes['inpaint'] else \"cpu\")\n", - "\n", - " if pipe_upscale is not None:\n", - " pipe_upscale.to(\"cuda\" if inf_mode == modes['upscale4x'] else \"cpu\")\n", - " \n", - " if pipe_depth2img is not None:\n", - " pipe_depth2img.to(\"cuda\" if inf_mode == modes['depth2img'] else \"cpu\")\n", - "\n", - " current_mode = inf_mode\n", - " \n", - " if seed == 0:\n", - " seed = random.randint(0, 2147483647)\n", - "\n", - " generator = torch.Generator('cuda').manual_seed(seed)\n", - " prompt = prompt\n", - "\n", - " try:\n", - " \n", - " if inf_mode == modes['txt2img']:\n", - " return txt_to_img(prompt, n_images, neg_prompt, guidance, steps, width, height, generator, seed), gr.update(visible=False, value=None)\n", - " \n", - " elif inf_mode == modes['img2img']:\n", - " if img is None:\n", - " return None, gr.update(visible=True, value=error_str(\"Image is required for Image to Image mode\"))\n", - "\n", - " return img_to_img(prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator, seed), gr.update(visible=False, value=None)\n", - " \n", - " elif inf_mode == modes['inpaint']:\n", - " if img is None:\n", - " return None, gr.update(visible=True, value=error_str(\"Image is required for Inpainting mode\"))\n", - "\n", - " return inpaint(prompt, n_images, neg_prompt, img, guidance, steps, width, height, generator, seed), gr.update(visible=False, value=None)\n", - "\n", - " elif inf_mode == modes['upscale4x']:\n", - " if img is None:\n", - " return None, gr.update(visible=True, value=error_str(\"Image is required for Upscale mode\"))\n", - "\n", - " return upscale(prompt, n_images, neg_prompt, img, guidance, steps, generator), gr.update(visible=False, value=None)\n", - "\n", - " elif inf_mode == modes['depth2img']:\n", - " if img is None:\n", - " return None, gr.update(visible=True, value=error_str(\"Image is required for Depth to Image mode\"))\n", - "\n", - " return depth2img(prompt, n_images, neg_prompt, img, guidance, steps, generator, seed), gr.update(visible=False, value=None)\n", - "\n", - " except Exception as e:\n", - " return None, gr.update(visible=True, value=error_str(e))\n", - "\n", - "def txt_to_img(prompt, n_images, neg_prompt, guidance, steps, width, height, generator, seed):\n", - "\n", - " result = pipe(\n", - " prompt,\n", - " num_images_per_prompt = n_images,\n", - " negative_prompt = neg_prompt,\n", - " num_inference_steps = int(steps),\n", - " guidance_scale = guidance,\n", - " width = width,\n", - " height = height,\n", - " generator = generator,\n", - " callback=pipe_callback).images\n", - "\n", - " update_state(f\"Done. Seed: {seed}\")\n", - "\n", - " return result\n", - "\n", - "def img_to_img(prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator, seed):\n", - "\n", - " global pipe_i2i\n", - " if pipe_i2i is None:\n", - " pipe_i2i = get_i2i_pipe(scheduler)\n", - "\n", - " img = img['image']\n", - " ratio = min(height / img.height, width / img.width)\n", - " img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)\n", - " result = pipe_i2i(\n", - " prompt,\n", - " num_images_per_prompt = n_images,\n", - " negative_prompt = neg_prompt,\n", - " image = img,\n", - " num_inference_steps = int(steps),\n", - " strength = strength,\n", - " guidance_scale = guidance,\n", - " # width = width,\n", - " # height = height,\n", - " generator = generator,\n", - " callback=pipe_callback).images\n", - "\n", - " update_state(f\"Done. Seed: {seed}\")\n", - " \n", - " return result\n", - "\n", - "# TODO Currently supports only 512x512 images\n", - "def inpaint(prompt, n_images, neg_prompt, img, guidance, steps, width, height, generator, seed):\n", - "\n", - " global pipe_inpaint\n", - " if pipe_inpaint is None:\n", - " pipe_inpaint = get_inpaint_pipe()\n", - "\n", - " inp_img = img['image']\n", - " mask = img['mask']\n", - " inp_img = square_padding(inp_img)\n", - " mask = square_padding(mask)\n", - "\n", - " # # ratio = min(height / inp_img.height, width / inp_img.width)\n", - " # ratio = min(512 / inp_img.height, 512 / inp_img.width)\n", - " # inp_img = inp_img.resize((int(inp_img.width * ratio), int(inp_img.height * ratio)), Image.LANCZOS)\n", - " # mask = mask.resize((int(mask.width * ratio), int(mask.height * ratio)), Image.LANCZOS)\n", - "\n", - " inp_img = inp_img.resize((512, 512))\n", - " mask = mask.resize((512, 512))\n", - "\n", - " result = pipe_inpaint(\n", - " prompt,\n", - " image = inp_img,\n", - " mask_image = mask,\n", - " num_images_per_prompt = n_images,\n", - " negative_prompt = neg_prompt,\n", - " num_inference_steps = int(steps),\n", - " guidance_scale = guidance,\n", - " # width = width,\n", - " # height = height,\n", - " generator = generator,\n", - " callback=pipe_callback).images\n", - " \n", - " update_state(f\"Done. Seed: {seed}\")\n", - "\n", - " return result\n", - "\n", - "def depth2img(prompt, n_images, neg_prompt, img, guidance, steps, generator, seed):\n", - "\n", - " global pipe_depth2img\n", - " if pipe_depth2img is None:\n", - " pipe_depth2img = get_depth2img_pipe()\n", - "\n", - " img = img['image']\n", - " result = pipe_depth2img(\n", - " prompt,\n", - " num_images_per_prompt = n_images,\n", - " negative_prompt = neg_prompt,\n", - " image = img,\n", - " num_inference_steps = int(steps),\n", - " guidance_scale = guidance,\n", - " # width = width,\n", - " # height = height,\n", - " generator = generator,\n", - " callback=pipe_callback).images\n", - "\n", - " update_state(f\"Done. Seed: {seed}\")\n", - " \n", - " return result\n", - "\n", - "def square_padding(img):\n", - " width, height = img.size\n", - " if width == height:\n", - " return img\n", - " new_size = max(width, height)\n", - " new_img = Image.new('RGB', (new_size, new_size), (0, 0, 0, 255))\n", - " new_img.paste(img, ((new_size - width) // 2, (new_size - height) // 2))\n", - " return new_img\n", - "\n", - "def upscale(prompt, n_images, neg_prompt, img, guidance, steps, generator):\n", - "\n", - " global pipe_upscale\n", - " if pipe_upscale is None:\n", - " pipe_upscale = get_upscale_pipe(scheduler)\n", - "\n", - " img = img['image']\n", - " return upscale_tiling(prompt, neg_prompt, img, guidance, steps, generator)\n", - "\n", - " # result = pipe_upscale(\n", - " # prompt,\n", - " # image = img,\n", - " # num_inference_steps = int(steps),\n", - " # guidance_scale = guidance,\n", - " # negative_prompt = neg_prompt,\n", - " # num_images_per_prompt = n_images,\n", - " # generator = generator).images[0]\n", - "\n", - " # return result\n", - "\n", - "def upscale_tiling(prompt, neg_prompt, img, guidance, steps, generator):\n", - "\n", - " width, height = img.size\n", - "\n", - " # calculate the padding needed to make the image dimensions a multiple of 128\n", - " padding_x = 128 - (width % 128) if width % 128 != 0 else 0\n", - " padding_y = 128 - (height % 128) if height % 128 != 0 else 0\n", - "\n", - " # create a white image of the right size to be used as padding\n", - " padding_img = Image.new('RGB', (padding_x, padding_y), color=(255, 255, 255, 0))\n", - "\n", - " # paste the padding image onto the original image to add the padding\n", - " img.paste(padding_img, (width, height))\n", - "\n", - " # update the image dimensions to include the padding\n", - " width += padding_x\n", - " height += padding_y\n", - "\n", - " if width > 128 or height > 128:\n", - "\n", - " num_tiles_x = int(width / 128)\n", - " num_tiles_y = int(height / 128)\n", - "\n", - " upscaled_img = Image.new('RGB', (img.size[0] * 4, img.size[1] * 4))\n", - " for x in range(num_tiles_x):\n", - " for y in range(num_tiles_y):\n", - " update_state(f\"Upscaling tile {x * num_tiles_y + y + 1}/{num_tiles_x * num_tiles_y}\")\n", - " tile = img.crop((x * 128, y * 128, (x + 1) * 128, (y + 1) * 128))\n", - "\n", - " upscaled_tile = pipe_upscale(\n", - " prompt=\"\",\n", - " image=tile,\n", - " num_inference_steps=steps,\n", - " guidance_scale=guidance,\n", - " # negative_prompt = neg_prompt,\n", - " generator=generator,\n", - " ).images[0]\n", - "\n", - " upscaled_img.paste(upscaled_tile, (x * upscaled_tile.size[0], y * upscaled_tile.size[1]))\n", - "\n", - " return [upscaled_img]\n", - " else:\n", - " return pipe_upscale(\n", - " prompt=prompt,\n", - " image=img,\n", - " num_inference_steps=steps,\n", - " guidance_scale=guidance,\n", - " negative_prompt = neg_prompt,\n", - " generator=generator,\n", - " ).images\n", - "\n", - "\n", - "\n", - "def on_mode_change(mode):\n", - " return gr.update(visible = mode in (modes['img2img'], modes['inpaint'], modes['upscale4x'], modes['depth2img'])), \\\n", - " gr.update(visible = mode == modes['inpaint']), \\\n", - " gr.update(visible = mode == modes['upscale4x']), \\\n", - " gr.update(visible = mode == modes['img2img'])\n", - "\n", - "def on_steps_change(steps):\n", - " global current_steps\n", - " current_steps = steps\n", - "\n", - "css = \"\"\".main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}\n", - "\"\"\"\n", - "with gr.Blocks(css=css) as demo:\n", - " gr.HTML(\n", - " f\"\"\"\n", - "
\n",
- "
\n", - "
\n",
- " \"\"\"\n",
- " )\n",
- " with gr.Row():\n",
- " \n",
- " with gr.Column(scale=70):\n",
- " with gr.Group():\n",
- " with gr.Row():\n",
- " prompt = gr.Textbox(label=\"Prompt\", show_label=False, max_lines=2,placeholder=f\"Enter prompt\").style(container=False)\n",
- " generate = gr.Button(value=\"Generate\").style(rounded=(False, True, True, False))\n",
- "\n",
- " gallery = gr.Gallery(label=\"Generated images\", show_label=False).style(grid=[2], height=\"auto\")\n",
- " state_info = gr.Textbox(label=\"State\", show_label=False, max_lines=2).style(container=False)\n",
- " error_output = gr.Markdown(visible=False)\n",
- "\n",
- " with gr.Column(scale=30):\n",
- " inf_mode = gr.Radio(label=\"Inference Mode\", choices=list(modes.values()), value=modes['txt2img'])\n",
- " \n",
- " with gr.Group(visible=False) as i2i_options:\n",
- " image = gr.Image(label=\"Image\", height=128, type=\"pil\", tool='sketch')\n",
- " inpaint_info = gr.Markdown(\"Inpainting resizes and pads images to 512x512\", visible=False)\n",
- " upscale_info = gr.Markdown(\"\"\"Best for small images (128x128 or smaller).\n",
- "
Stable Diffusion 2.1
\n", - "\n", - "
Model used: v2-1_768-ema-pruned.ckpt
\n", - " Running on {\"GPU 🔥\" if torch.cuda.is_available() else \"CPU 🥶\"}\n", - "\n", - " Bigger images will be sliced into 128x128 tiles which will be upscaled individually.
\n", - " This is done to avoid running out of GPU memory.\"\"\", visible=False)\n", - " strength = gr.Slider(label=\"Transformation strength\", minimum=0, maximum=1, step=0.01, value=0.5)\n", - "\n", - " with gr.Group():\n", - " neg_prompt = gr.Textbox(label=\"Negative prompt\", placeholder=\"What to exclude from the image\")\n", - "\n", - " n_images = gr.Slider(label=\"Number of images\", value=1, minimum=1, maximum=4, step=1)\n", - " with gr.Row():\n", - " guidance = gr.Slider(label=\"Guidance scale\", value=7.5, maximum=15)\n", - " steps = gr.Slider(label=\"Steps\", value=current_steps, minimum=2, maximum=100, step=1)\n", - "\n", - " with gr.Row():\n", - " width = gr.Slider(label=\"Width\", value=768, minimum=64, maximum=1024, step=8)\n", - " height = gr.Slider(label=\"Height\", value=768, minimum=64, maximum=1024, step=8)\n", - "\n", - " seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)\n", - " with gr.Accordion(\"Memory optimization\"):\n", - " attn_slicing = gr.Checkbox(label=\"Attention slicing (a bit slower, but uses less memory)\", value=attn_slicing_enabled)\n", - " # mem_eff_attn = gr.Checkbox(label=\"Memory efficient attention (xformers)\", value=mem_eff_attn_enabled)\n", - "\n", - " inf_mode.change(on_mode_change, inputs=[inf_mode], outputs=[i2i_options, inpaint_info, upscale_info, strength], queue=False)\n", - " steps.change(on_steps_change, inputs=[steps], outputs=[], queue=False)\n", - " attn_slicing.change(lambda x: switch_attention_slicing(x), inputs=[attn_slicing], queue=False)\n", - " # mem_eff_attn.change(lambda x: switch_mem_eff_attn(x), inputs=[mem_eff_attn], queue=False)\n", - "\n", - " inputs = [inf_mode, prompt, n_images, guidance, steps, width, height, seed, image, strength, neg_prompt]\n", - " outputs = [gallery, error_output]\n", - " prompt.submit(inference, inputs=inputs, outputs=outputs)\n", - " generate.click(inference, inputs=inputs, outputs=outputs)\n", - "\n", - " demo.load(update_state_info, inputs=state_info, outputs=state_info, every=0.5, show_progress=False)\n", - "\n", - " gr.HTML(\"\"\"\n", - "
\n",
- "
\n", - "
\n", - "
\n", - "![\"GitHub](\"https://img.shields.io/github/stars/qunash/stable-diffusion-2-gui?style=social\")
\n",
- "
\n",
- " \"\"\")\n",
- "\n",
- "demo.queue()\n",
- "demo.launch(debug=True, share=True, height=768)\n"
- ]
- }
- ],
- "metadata": {
- "accelerator": "GPU",
- "colab": {
- "private_outputs": true,
- "provenance": [],
- "toc_visible": true,
- "include_colab_link": true
- },
- "gpuClass": "standard",
- "kernelspec": {
- "display_name": "Python 3",
- "name": "python3"
- },
- "language_info": {
- "name": "python"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/spaces/CVPR/DualStyleGAN/style.css b/spaces/CVPR/DualStyleGAN/style.css
deleted file mode 100644
index 472b3df9e5e4936157c53a570a8875992ccac7a3..0000000000000000000000000000000000000000
--- a/spaces/CVPR/DualStyleGAN/style.css
+++ /dev/null
@@ -1,17 +0,0 @@
-h1 {
- text-align: center;
-}
-
-img#overview {
- max-width: 1000px;
- max-height: 600px;
- display: block;
- margin: auto;
-}
-
-img#style-image {
- max-width: 1000px;
- max-height: 600px;
- display: block;
- margin: auto;
-}
diff --git a/spaces/Chris4K/llms_compare/CabelasDangerousHunts2013-SKIDROW-REPACK-Crack-Fix-Torrent-Download.md b/spaces/Chris4K/llms_compare/CabelasDangerousHunts2013-SKIDROW-REPACK-Crack-Fix-Torrent-Download.md
deleted file mode 100644
index b1fdd48d206a702292508c6ce3d71b4f504d0f20..0000000000000000000000000000000000000000
--- a/spaces/Chris4K/llms_compare/CabelasDangerousHunts2013-SKIDROW-REPACK-Crack-Fix-Torrent-Download.md
+++ /dev/null
@@ -1,64 +0,0 @@
-## Cabelas.Dangerous.Hunts.2013. -SKIDROW -Crack Fix Torrent Download
-
-
-
-
-
-
-
-
-
-**LINK ->>->>->> [https://urluso.com/2tBNzD](https://urluso.com/2tBNzD)**
-
-
-
-
-
-
-
-
-
-
-
-
-
-# Cabela's Dangerous Hunts 2013: A Hunting Game with Kill or Be Killed Consequences
-
-
-
-Cabela's Dangerous Hunts 2013 is a first-person shooter hunting game developed by FUN Labs and published by Activision in 2013. The game features a new Prowler animal AI engine that simulates pack social hierarchies, coordinates complex group tactics and sets up deadly ambushes. The game also introduces a new Maneater co-op mode, where two players can join together to take on wave after wave of increasingly deadly beasts in a split screen mode.
-
-
-
-The game is set in 12 exotic locations throughout the world, where players can hunt 27 big game animals, such as lions, bears, wolves, crocodiles and more. The game offers various modes of gameplay, such as Quick Hunt, Action Hunt and Career Hunt. The game also allows players to customize their weapons and gear with thousands of authentic options, including rifles, handguns, bows, crossbows, knives and various scopes.
-
-
-
-The game received mixed reviews from critics, who praised the graphics, sound effects and co-op mode, but criticized the repetitive gameplay, poor AI and texture glitches. The game is available for PC, Xbox 360, PlayStation 3 and Wii U platforms.
-
-
-
-If you are interested in downloading Cabela's Dangerous Hunts 2013 for PC, you can find a torrent link here[^1^]. You will need to mount or burn the image file, install the game, copy everything from the SKIDROW folder into the game installation folder, block the game in your firewall and antivirus program, and play the game. You can also find a crack fix here[^3^], which will solve some of the issues with the game.
-
-
-
-However, please note that downloading torrents is illegal and may expose you to viruses and malware. We do not condone or support piracy in any way. If you like the game, please support the developers and buy it from official sources.
-
-
-
-The game's main mode is the Story Mode, where the player takes the role of Cole Rainsford, a young hunter who joins his estranged father on an African safari. However, their trip turns into a nightmare when they encounter a mysterious cult that unleashes a horde of deadly animals on them. The player must survive the attacks of lions, hyenas, leopards, rhinos and more, while uncovering the truth behind the cult's motives.
-
-
-
-The game's gameplay is based on quick-time events and shooting sequences, where the player must react to the animal attacks and aim for their vital organs. The game also features a Fearmaster controller for some platforms, which measures the player's heart rate and motion. The higher the fear level, the harder it is to aim and shoot accurately. The game also has a dynamic weather system and day-night cycle, which affect the visibility and behavior of the animals.
-
-
-
-The game's graphics and sound effects are realistic and immersive, creating a tense and thrilling atmosphere. The game also features voice acting by Scott Eastwood and Rob Lowe as Cole and his father respectively. The game's co-op mode is also fun and challenging, allowing two players to team up and face different scenarios and objectives. However, the game also has some flaws, such as repetitive gameplay, poor AI and texture glitches. The game also received some criticism for its depiction of animal violence and hunting ethics.
-
- 145887f19f
-
-
-
-
-
diff --git a/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/model/tool.js b/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/model/tool.js
deleted file mode 100644
index 0f03dc352d36e2c4aefa36caf48481743f24c650..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/model/tool.js
+++ /dev/null
@@ -1,163 +0,0 @@
-import _ from 'lodash'
-import fs from 'fs'
-import { Version } from '../components/index.js'
-
-async function CreateMusicShare(data) {
- let appid, appname, appsign, style = 4;
- switch (data.subType) {
- case 'bilibili':
- appid = 100951776, appname = 'tv.danmaku.bili', appsign = '7194d531cbe7960a22007b9f6bdaa38b';
- break;
- case 'netease':
- appid = 100495085, appname = "com.netease.cloudmusic", appsign = "da6b069da1e2982db3e386233f68d76d";
- break;
- case 'kuwo':
- appid = 100243533, appname = "cn.kuwo.player", appsign = "bf9ff4ffb4c558a34ee3fd52c223ebf5";
- break;
- case 'kugou':
- appid = 205141, appname = "com.kugou.android", appsign = "fe4a24d80fcf253a00676a808f62c2c6";
- break;
- case 'migu':
- appid = 1101053067, appname = "cmccwm.mobilemusic", appsign = "6cdc72a439cef99a3418d2a78aa28c73";
- break;
- case 'qq':
- default:
- appid = 100497308, appname = "com.tencent.qqmusic", appsign = "cbd27cd7c861227d013a25b2d10f0799";
- break;
- }
-
- var text = '', title = data.title, singer = data.content, prompt = '[分享]', jumpUrl = data.url, preview = data.image, musicUrl = data.voice;
-
- prompt = '[分享]' + title + '-' + singer;
-
- let recv_uin = 0;
- let send_type = 0;
- let recv_guild_id = 0;
-
- if (data.message_type === 'group') {//群聊
- recv_uin = data.group_id;
- send_type = 1;
- } else if (data.message_type === 'guild') {//频道
- recv_uin = Number(data.channel_id);
- recv_guild_id = BigInt(data.guild_id);
- send_type = 3;
- } else if (data.message_type === 'private') {//私聊
- recv_uin = data.user_id;
- send_type = 0;
- }
-
- let body = {
- 1: appid,
- 2: 1,
- 3: style,
- 5: {
- 1: 1,
- 2: "0.0.0",
- 3: appname,
- 4: appsign,
- },
- 6: text,
- 10: send_type,
- 11: recv_uin,
- 12: {
- 10: title,
- 11: singer,
- 12: prompt,
- 13: jumpUrl,
- 14: preview,
- 16: musicUrl,
- },
- 19: recv_guild_id
- };
- return body;
-}
-
-async function SendMusicShare(data) {
- let core, bot
- if (Version.isTrss) {
- bot = Bot[data.bot_id]
- core = bot?.core
- } else {
- bot = Bot
- try {
- core = (await import('oicq')).core
- } catch (error) {
- core = null
- }
- }
- if (!core) {
- const msg = [data.url]
- if (data.message_type === 'group') {//群聊
- await bot?.pickGroup?.(data.group_id)?.sendMsg?.(msg)
- } else if (data.message_type === 'private') {//私聊
- await bot?.pickFriend?.(data.user_id)?.sendMsg?.(msg)
- }
- return
- }
- try {
- let body = await CreateMusicShare(data)
- let payload = await bot.sendOidb("OidbSvc.0xb77_9", core.pb.encode(body));
- let result = core.pb.decode(payload);
- if (result[3] != 0) {
- if (data.message_type === 'group') {//群聊
- await bot?.pickGroup(data.group_id).sendMsg('歌曲分享失败：' + result[3])
- } else if (data.message_type === 'private') {//私聊
- await bot?.pickFriend(data.user_id).sendMsg('歌曲分享失败：' + result[3])
- }
- // e.reply('歌曲分享失败：' + result[3], true);
- }
- } catch (error) {
- const msg = [data.url]
- if (data.message_type === 'group') {//群聊
- await bot?.pickGroup?.(data.group_id)?.sendMsg?.(msg)
- } else if (data.message_type === 'private') {//私聊
- await bot?.pickFriend?.(data.user_id)?.sendMsg?.(msg)
- }
- return
- }
-}
-
-function sleep(ms) {
- return new Promise((resolve) => setTimeout(resolve, ms))
-}
-
-const TMP_DIR = process.cwd() + '/plugins/ws-plugin/Temp'
-if (!fs.existsSync(TMP_DIR)) fs.mkdirSync(TMP_DIR)
-
-const mimeTypes = {
- '.html': 'text/html',
- '.js': 'text/javascript',
- '.css': 'text/css',
- '.json': 'application/json',
- '.png': 'image/png',
- '.jpg': 'image/jpg',
- '.gif': 'image/gif',
- '.ico': 'image/x-icon',
- '.txt': 'text/plain',
- '.xlsx': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet',
-}
-
-function decodeHtml(html) {
- var map = {
- '&': '&',
- '[': '[',
- ']': ']',
- ',': ','
- };
-
- for (var key in map) {
- const value = map[key];
- const regex = new RegExp(key, 'g');
- html = html.replace(regex, value);
- }
- return html;
-}
-
-
-export {
- SendMusicShare,
- sleep,
- TMP_DIR,
- mimeTypes,
- decodeHtml
-}
\ No newline at end of file
diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/keep_away/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/keep_away/__init__.py
deleted file mode 100644
index ed47b292dd9d1b70d4f248a65e394c21e03d3c88..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/meme_generator/memes/keep_away/__init__.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from typing import List
-
-from PIL.Image import Transpose
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-
-
-def keep_away(images: List[BuildImage], texts: List[str], args):
- def trans(img: BuildImage, n: int) -> BuildImage:
- img = img.convert("RGBA").square().resize((100, 100))
- if n < 4:
- return img.rotate(n * 90)
- else:
- return img.transpose(Transpose.FLIP_LEFT_RIGHT).rotate((n - 4) * 90)
-
- def paste(img: BuildImage):
- nonlocal count
- y = 90 if count < 4 else 190
- frame.paste(img, ((count % 4) * 100, y))
- count += 1
-
- text = texts[0] if texts else "如何提高社交质量 : \n远离以下头像的人"
- frame = BuildImage.new("RGB", (400, 290), "white")
- frame.draw_text((10, 10, 390, 80), text, max_fontsize=40, halign="left")
- count = 0
- num_per_user = 8 // len(images)
- for image in images:
- for n in range(num_per_user):
- paste(trans(image, n))
- num_left = 8 - num_per_user * len(images)
- for n in range(num_left):
- paste(trans(images[-1], n + num_per_user))
-
- return frame.save_jpg()
-
-
-add_meme(
- "keep_away",
- keep_away,
- min_images=1,
- max_images=8,
- min_texts=0,
- max_texts=1,
- default_texts=["如何提高社交质量 : \n远离以下头像的人"],
- keywords=["远离"],
-)
diff --git a/spaces/CjangCjengh/Sanskrit-TTS/commons.py b/spaces/CjangCjengh/Sanskrit-TTS/commons.py
deleted file mode 100644
index 2153153f527d94e2abb641ea00c80b518ff6c5bd..0000000000000000000000000000000000000000
--- a/spaces/CjangCjengh/Sanskrit-TTS/commons.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
- return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
- return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-def init_weights(m, mean=0.0, std=0.01):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
- return int((kernel_size*dilation - dilation)/2)
-
-
-def intersperse(lst, item):
- result = [item] * (len(lst) * 2 + 1)
- result[1::2] = lst
- return result
-
-
-def slice_segments(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, :, idx_str:idx_end]
- return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size + 1
- ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- return ret, ids_str
-
-
-def subsequent_mask(length):
- mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
- return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
- n_channels_int = n_channels[0]
- in_act = input_a + input_b
- t_act = torch.tanh(in_act[:, :n_channels_int, :])
- s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
- acts = t_act * s_act
- return acts
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def sequence_mask(length, max_length=None):
- if max_length is None:
- max_length = length.max()
- x = torch.arange(max_length, dtype=length.dtype, device=length.device)
- return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
- """
- duration: [b, 1, t_x]
- mask: [b, 1, t_y, t_x]
- """
- device = duration.device
-
- b, _, t_y, t_x = mask.shape
- cum_duration = torch.cumsum(duration, -1)
-
- cum_duration_flat = cum_duration.view(b * t_x)
- path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
- path = path.view(b, t_x, t_y)
- path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
- path = path.unsqueeze(1).transpose(2,3) * mask
- return path
diff --git a/spaces/CofAI/chat/g4f/Provider/Provider.py b/spaces/CofAI/chat/g4f/Provider/Provider.py
deleted file mode 100644
index d24df76b6a6ccfc9b244f13a51bfc124b398a271..0000000000000000000000000000000000000000
--- a/spaces/CofAI/chat/g4f/Provider/Provider.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import os
-from ..typing import sha256, Dict, get_type_hints
-
-url = None
-model = None
-supports_stream = False
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
- return
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join(
- [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
diff --git a/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/models/diffusion/sampling_util.py b/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/models/diffusion/sampling_util.py
deleted file mode 100644
index 7eff02be6d7c54d43ee6680636ac0698dd3b3f33..0000000000000000000000000000000000000000
--- a/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/models/diffusion/sampling_util.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import torch
-import numpy as np
-
-
-def append_dims(x, target_dims):
- """Appends dimensions to the end of a tensor until it has target_dims dimensions.
- From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
- dims_to_append = target_dims - x.ndim
- if dims_to_append < 0:
- raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
- return x[(...,) + (None,) * dims_to_append]
-
-
-def norm_thresholding(x0, value):
- s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim)
- return x0 * (value / s)
-
-
-def spatial_norm_thresholding(x0, value):
- # b c h w
- s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value)
- return x0 * (value / s)
\ No newline at end of file
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/middleware/trustedhost.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/middleware/trustedhost.py
deleted file mode 100644
index 08d7e035315677856fd2cd0be2044689b57619bf..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/middleware/trustedhost.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from starlette.middleware.trustedhost import ( # noqa
- TrustedHostMiddleware as TrustedHostMiddleware,
-)
diff --git a/spaces/Deci/DeciDiffusion-v1-0/app.py b/spaces/Deci/DeciDiffusion-v1-0/app.py
deleted file mode 100644
index 69c5871b0b098ab1cd1a1e6b5ee2290bb8517b53..0000000000000000000000000000000000000000
--- a/spaces/Deci/DeciDiffusion-v1-0/app.py
+++ /dev/null
@@ -1,115 +0,0 @@
-import gradio as gr
-import torch
-from PIL.ImageDraw import Draw
-from diffusers import StableDiffusionPipeline
-from PIL import Image, ImageOps
-
-
-# Load pipeline once
-model_id = 'Deci/DeciDiffusion-v1-0'
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = StableDiffusionPipeline.from_pretrained(model_id, custom_pipeline=model_id, torch_dtype=torch.float32)
-pipe.unet = pipe.unet.from_pretrained(model_id, subfolder='flexible_unet', torch_dtype=torch.float32)
-pipe = pipe.to(device)
-
-
-def read_content(file_path: str) -> str:
- """read the content of target file
- """
- with open(file_path, 'r', encoding='utf-8') as f:
- content = f.read()
-
- return content
-
-
-def predict(_prompt: str, _steps: int = 30, _seed: int = 42, _guidance_scale: float = 7.5, _negative_prompt: str = ""):
- _negative_prompt = [_negative_prompt] if _negative_prompt else None
-
- output = pipe(prompt=[_prompt],
- negative_prompt=_negative_prompt,
- num_inference_steps=int(_steps),
- guidance_scale=_guidance_scale,
- generator=torch.Generator(device).manual_seed(_seed),
- )
- output_image = output.images[0]
-
- # Add border beneath the image with Deci logo + prompt
- if len(_prompt) > 52:
- _prompt = _prompt[:52] + "..."
-
- original_image_height = output_image.size[1]
- output_image = ImageOps.expand(output_image, border=(0, 0, 0, 64), fill='white')
- deci_logo = Image.open('./deci_logo_white.png')
- output_image.paste(deci_logo, (0, original_image_height))
- Draw(output_image).text((deci_logo.size[0], original_image_height + 26), _prompt, (127, 127, 127))
- return output_image
-
-
-css = '''
-.gradio-container {
- max-width: 1100px !important;
- background-image: url(https://huggingface.co/spaces/Deci/Deci-DeciDiffusionClean/resolve/main/background-image.png);
- background-size: cover;
- background-position: center center;
- background-repeat: no-repeat;
-}
-
-.footer {margin-bottom: 45px;margin-top: 35px !important;text-align: center;border-bottom: 1px solid #e5e5e5}
-.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
-.dark .footer {border-color: #303030}
-.dark .footer>p {background: #0b0f19}
-.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
-@keyframes spin {
- from {
- transform: rotate(0deg);
- }
- to {
- transform: rotate(360deg);
- }
-}
-'''
-
-demo = gr.Blocks(css=css, elem_id="total-container")
-with demo:
- gr.HTML(read_content("header.html"))
- with gr.Row():
- with gr.Column():
- with gr.Row(mobile_collapse=False, equal_height=True):
- prompt = gr.Textbox(placeholder="Your prompt", show_label=False, elem_id="prompt", autofocus=True, lines=3, )
-
- with gr.Accordion(label="Advanced Settings", open=False):
- with gr.Row(mobile_collapse=False, equal_height=True):
- steps = gr.Slider(value=30, minimum=15, maximum=50, step=1, label="steps", interactive=True)
- seed = gr.Slider(value=42, minimum=1, maximum=100, step=1, label="seed", interactive=True)
- guidance_scale = gr.Slider(value=7.5, minimum=1, maximum=15, step=0.1, label='guidance_scale', interactive=True)
-
- with gr.Row(mobile_collapse=False, equal_height=True):
- negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt",
- info="what you don't want to see in the image", lines=3)
- with gr.Row():
- btn = gr.Button(value="Generate!", elem_id="run_button")
-
- with gr.Column():
- image_out = gr.Image(label="Output", elem_id="output-img", height=400)
-
- btn.click(fn=predict,
- inputs=[prompt, steps, seed, guidance_scale, negative_prompt],
- outputs=[image_out],
- api_name='run')
-
- gr.HTML(
- """
-
- \n", - "
Space by:
![\"Twitter](\"https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social\")
\n", - "
Enjoying this app? Please consider supporting me
\n", - "\n", - "
\n",
- "
-
- """
- )
-
-demo.queue(max_size=50).launch()
diff --git a/spaces/ElainaFanBoy/MusicGen/tests/common_utils/temp_utils.py b/spaces/ElainaFanBoy/MusicGen/tests/common_utils/temp_utils.py
deleted file mode 100644
index d1e0367e979c8b9fea65472c373916d956ad5aaa..0000000000000000000000000000000000000000
--- a/spaces/ElainaFanBoy/MusicGen/tests/common_utils/temp_utils.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import os
-import tempfile
-
-
-class TempDirMixin:
- """Mixin to provide easy access to temp dir.
- """
-
- temp_dir_ = None
-
- @classmethod
- def get_base_temp_dir(cls):
- # If AUDIOCRAFT_TEST_DIR is set, use it instead of temporary directory.
- # this is handy for debugging.
- key = "AUDIOCRAFT_TEST_DIR"
- if key in os.environ:
- return os.environ[key]
- if cls.temp_dir_ is None:
- cls.temp_dir_ = tempfile.TemporaryDirectory()
- return cls.temp_dir_.name
-
- @classmethod
- def tearDownClass(cls):
- if cls.temp_dir_ is not None:
- try:
- cls.temp_dir_.cleanup()
- cls.temp_dir_ = None
- except PermissionError:
- # On Windows there is a know issue with `shutil.rmtree`,
- # which fails intermittenly.
- # https://github.com/python/cpython/issues/74168
- # Following the above thread, we ignore it.
- pass
- super().tearDownClass()
-
- @property
- def id(self):
- return self.__class__.__name__
-
- def get_temp_path(self, *paths):
- temp_dir = os.path.join(self.get_base_temp_dir(), self.id)
- path = os.path.join(temp_dir, *paths)
- os.makedirs(os.path.dirname(path), exist_ok=True)
- return path
-
- def get_temp_dir(self, *paths):
- temp_dir = os.path.join(self.get_base_temp_dir(), self.id)
- path = os.path.join(temp_dir, *paths)
- os.makedirs(path, exist_ok=True)
- return path
diff --git a/spaces/EngAbod/Liveness_Detection/README.md b/spaces/EngAbod/Liveness_Detection/README.md
deleted file mode 100644
index 9953cf60eea94b7bc20089a281c8c4f7babb2704..0000000000000000000000000000000000000000
--- a/spaces/EngAbod/Liveness_Detection/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Liveness Detection
-emoji: 👀
-colorFrom: blue
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.27.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/FL33TW00D/whisper-turbo/README.md b/spaces/FL33TW00D/whisper-turbo/README.md
deleted file mode 100644
index 24523341c9ccf937083af6141f73cf5c684ecc19..0000000000000000000000000000000000000000
--- a/spaces/FL33TW00D/whisper-turbo/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Whisper Turbo
-emoji: 🗣️🏎️
-colorFrom: blue
-colorTo: gray
-sdk: static
-pinned: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Fadil369/docker/README.md b/spaces/Fadil369/docker/README.md
deleted file mode 100644
index c4f71628142aeafb0cd15d54c9890d373ebb61cb..0000000000000000000000000000000000000000
--- a/spaces/Fadil369/docker/README.md
+++ /dev/null
@@ -1,20 +0,0 @@
----
-title: Shiny for Python template
-emoji: 🌍
-colorFrom: yellow
-colorTo: indigo
-sdk: docker
-pinned: false
-license: mit
----
-
-This is a templated Space for [Shiny for Python](https://shiny.rstudio.com/py/).
-
-
-To get started with a new app do the following:
-
-1) Install Shiny with `pip install shiny`
-2) Create a new app with `shiny create .`
-3) Then run the app with `shiny run --reload`
-
-To learn more about this framework please see the [Documentation](https://shiny.rstudio.com/py/docs/overview.html).
diff --git a/spaces/Feifei315/flax-midjourney-v4-diffusion/app.py b/spaces/Feifei315/flax-midjourney-v4-diffusion/app.py
deleted file mode 100644
index a7e777fc5c7f3e31a491e4bd016b8948b6a260f4..0000000000000000000000000000000000000000
--- a/spaces/Feifei315/flax-midjourney-v4-diffusion/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/flax/midjourney-v4-diffusion").launch()
\ No newline at end of file
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/modules/F0Predictor/DioF0Predictor.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/modules/F0Predictor/DioF0Predictor.py
deleted file mode 100644
index 4ab27de23cae4dbc282e30f84501afebd1a37518..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/modules/F0Predictor/DioF0Predictor.py
+++ /dev/null
@@ -1,85 +0,0 @@
-from modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-class DioF0Predictor(F0Predictor):
- def __init__(self,hop_length=512,f0_min=50,f0_max=1100,sampling_rate=44100):
- self.hop_length = hop_length
- self.f0_min = f0_min
- self.f0_max = f0_max
- self.sampling_rate = sampling_rate
-
- def interpolate_f0(self,f0):
- '''
- 对F0进行插值处理
- '''
-
- data = np.reshape(f0, (f0.size, 1))
-
- vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
- vuv_vector[data > 0.0] = 1.0
- vuv_vector[data <= 0.0] = 0.0
-
- ip_data = data
-
- frame_number = data.size
- last_value = 0.0
- for i in range(frame_number):
- if data[i] <= 0.0:
- j = i + 1
- for j in range(i + 1, frame_number):
- if data[j] > 0.0:
- break
- if j < frame_number - 1:
- if last_value > 0.0:
- step = (data[j] - data[i - 1]) / float(j - i)
- for k in range(i, j):
- ip_data[k] = data[i - 1] + step * (k - i + 1)
- else:
- for k in range(i, j):
- ip_data[k] = data[j]
- else:
- for k in range(i, frame_number):
- ip_data[k] = last_value
- else:
- ip_data[i] = data[i] #这里可能存在一个没有必要的拷贝
- last_value = data[i]
-
- return ip_data[:,0], vuv_vector[:,0]
-
- def resize_f0(self,x, target_len):
- source = np.array(x)
- source[source<0.001] = np.nan
- target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source)
- res = np.nan_to_num(target)
- return res
-
- def compute_f0(self,wav,p_len=None):
- if p_len is None:
- p_len = wav.shape[0]//self.hop_length
- f0, t = pyworld.dio(
- wav.astype(np.double),
- fs=self.sampling_rate,
- f0_floor=self.f0_min,
- f0_ceil=self.f0_max,
- frame_period=1000 * self.hop_length / self.sampling_rate,
- )
- f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
- for index, pitch in enumerate(f0):
- f0[index] = round(pitch, 1)
- return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
- def compute_f0_uv(self,wav,p_len=None):
- if p_len is None:
- p_len = wav.shape[0]//self.hop_length
- f0, t = pyworld.dio(
- wav.astype(np.double),
- fs=self.sampling_rate,
- f0_floor=self.f0_min,
- f0_ceil=self.f0_max,
- frame_period=1000 * self.hop_length / self.sampling_rate,
- )
- f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
- for index, pitch in enumerate(f0):
- f0[index] = round(pitch, 1)
- return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/FridaZuley/RVC_HFKawaii/Applio-RVC-Fork/utils/backups_test.py b/spaces/FridaZuley/RVC_HFKawaii/Applio-RVC-Fork/utils/backups_test.py
deleted file mode 100644
index f3edf15811b5035ee82f21e54e87b7e87ce413eb..0000000000000000000000000000000000000000
--- a/spaces/FridaZuley/RVC_HFKawaii/Applio-RVC-Fork/utils/backups_test.py
+++ /dev/null
@@ -1,138 +0,0 @@
-
-import os
-import shutil
-import hashlib
-import time
-
-LOGS_FOLDER = '/content/Applio-RVC-Fork/logs'
-WEIGHTS_FOLDER = '/content/Applio-RVC-Fork/weights'
-GOOGLE_DRIVE_PATH = '/content/drive/MyDrive/RVC_Backup'
-
-def import_google_drive_backup():
- print("Importing Google Drive backup...")
- GOOGLE_DRIVE_PATH = '/content/drive/MyDrive/RVC_Backup' # change this to your Google Drive path
- LOGS_FOLDER = '/content/Applio-RVC-Fork/logs'
- WEIGHTS_FOLDER = '/content/Applio-RVC-Fork/weights'
- weights_exist = False
- files_to_copy = []
- weights_to_copy = []
-
- def handle_files(root, files, is_weight_files=False):
- for filename in files:
- filepath = os.path.join(root, filename)
- if filename.endswith('.pth') and is_weight_files:
- weights_exist = True
- backup_filepath = os.path.join(WEIGHTS_FOLDER, os.path.relpath(filepath, GOOGLE_DRIVE_PATH))
- else:
- backup_filepath = os.path.join(LOGS_FOLDER, os.path.relpath(filepath, GOOGLE_DRIVE_PATH))
- backup_folderpath = os.path.dirname(backup_filepath)
- if not os.path.exists(backup_folderpath):
- os.makedirs(backup_folderpath)
- print(f'Created folder: {backup_folderpath}', flush=True)
- if is_weight_files:
- weights_to_copy.append((filepath, backup_filepath))
- else:
- files_to_copy.append((filepath, backup_filepath))
-
- for root, dirs, files in os.walk(os.path.join(GOOGLE_DRIVE_PATH, 'logs')):
- handle_files(root, files)
-
- for root, dirs, files in os.walk(os.path.join(GOOGLE_DRIVE_PATH, 'weights')):
- handle_files(root, files, True)
-
- # Copy files in batches
- total_files = len(files_to_copy)
- start_time = time.time()
- for i, (source, dest) in enumerate(files_to_copy, start=1):
- with open(source, 'rb') as src, open(dest, 'wb') as dst:
- shutil.copyfileobj(src, dst, 1024*1024) # 1MB buffer size
- # Report progress every 5 seconds or after every 100 files, whichever is less frequent
- if time.time() - start_time > 5 or i % 100 == 0:
- print(f'\rCopying file {i} of {total_files} ({i * 100 / total_files:.2f}%)', end="")
- start_time = time.time()
- print(f'\nImported {len(files_to_copy)} files from Google Drive backup')
-
- # Copy weights in batches
- total_weights = len(weights_to_copy)
- start_time = time.time()
- for i, (source, dest) in enumerate(weights_to_copy, start=1):
- with open(source, 'rb') as src, open(dest, 'wb') as dst:
- shutil.copyfileobj(src, dst, 1024*1024) # 1MB buffer size
- # Report progress every 5 seconds or after every 100 files, whichever is less frequent
- if time.time() - start_time > 5 or i % 100 == 0:
- print(f'\rCopying weight file {i} of {total_weights} ({i * 100 / total_weights:.2f}%)', end="")
- start_time = time.time()
- if weights_exist:
- print(f'\nImported {len(weights_to_copy)} weight files')
- print("Copied weights from Google Drive backup to local weights folder.")
- else:
- print("\nNo weights found in Google Drive backup.")
- print("Google Drive backup import completed.")
-
-def backup_files():
- print("\n Starting backup loop...")
- last_backup_timestamps_path = os.path.join(LOGS_FOLDER, 'last_backup_timestamps.txt')
- fully_updated = False # boolean to track if all files are up to date
- try:
- with open(last_backup_timestamps_path, 'r') as f:
- last_backup_timestamps = dict(line.strip().split(':') for line in f)
- except:
- last_backup_timestamps = {}
-
- while True:
- updated = False
- files_to_copy = []
- files_to_delete = []
-
- for root, dirs, files in os.walk(LOGS_FOLDER):
- for filename in files:
- if filename != 'last_backup_timestamps.txt':
- filepath = os.path.join(root, filename)
- if os.path.isfile(filepath):
- backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
- backup_folderpath = os.path.dirname(backup_filepath)
-
- if not os.path.exists(backup_folderpath):
- os.makedirs(backup_folderpath)
- print(f'Created backup folder: {backup_folderpath}', flush=True)
-
- # check if file has changed since last backup
- last_backup_timestamp = last_backup_timestamps.get(filepath)
- current_timestamp = os.path.getmtime(filepath)
- if last_backup_timestamp is None or float(last_backup_timestamp) < current_timestamp:
- files_to_copy.append((filepath, backup_filepath)) # add to list of files to copy
- last_backup_timestamps[filepath] = str(current_timestamp) # update last backup timestamp
- updated = True
- fully_updated = False # if a file is updated, all files are not up to date
-
- # check if any files were deleted in Colab and delete them from the backup drive
- for filepath in list(last_backup_timestamps.keys()):
- if not os.path.exists(filepath):
- backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
- if os.path.exists(backup_filepath):
- files_to_delete.append(backup_filepath) # add to list of files to delete
- del last_backup_timestamps[filepath]
- updated = True
- fully_updated = False # if a file is deleted, all files are not up to date
-
- # Copy files in batches
- if files_to_copy:
- for source, dest in files_to_copy:
- shutil.copy2(source, dest)
- print(f'Copied or updated {len(files_to_copy)} files')
-
- # Delete files in batches
- if files_to_delete:
- for file in files_to_delete:
- os.remove(file)
- print(f'Deleted {len(files_to_delete)} files')
-
- if not updated and not fully_updated:
- print("Files are up to date.")
- fully_updated = True # if all files are up to date, set the boolean to True
- copy_weights_folder_to_drive()
-
- with open(last_backup_timestamps_path, 'w') as f:
- for filepath, timestamp in last_backup_timestamps.items():
- f.write(f'{filepath}:{timestamp}\n')
- time.sleep(15) # wait for 15 seconds before checking again
diff --git a/spaces/FridaZuley/RVC_HFKawaii/infer_batch_rvc.py b/spaces/FridaZuley/RVC_HFKawaii/infer_batch_rvc.py
deleted file mode 100644
index 15c862a3d6bf815fa68003cc7054b694cae50c2a..0000000000000000000000000000000000000000
--- a/spaces/FridaZuley/RVC_HFKawaii/infer_batch_rvc.py
+++ /dev/null
@@ -1,215 +0,0 @@
-"""
-v1
-runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\logs\mi-test\added_IVF677_Flat_nprobe_7.index" harvest "E:\codes\py39\RVC-beta\output" "E:\codes\py39\test-20230416b\weights\mi-test.pth" 0.66 cuda:0 True 3 0 1 0.33
-v2
-runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\test-20230416b\logs\mi-test-v2\aadded_IVF677_Flat_nprobe_1_v2.index" harvest "E:\codes\py39\RVC-beta\output_v2" "E:\codes\py39\test-20230416b\weights\mi-test-v2.pth" 0.66 cuda:0 True 3 0 1 0.33
-"""
-import os, sys, pdb, torch
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-import sys
-import torch
-import tqdm as tq
-from multiprocessing import cpu_count
-
-
-class Config:
- def __init__(self, device, is_half):
- self.device = device
- self.is_half = is_half
- self.n_cpu = 0
- self.gpu_name = None
- self.gpu_mem = None
- self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-
- def device_config(self) -> tuple:
- if torch.cuda.is_available():
- i_device = int(self.device.split(":")[-1])
- self.gpu_name = torch.cuda.get_device_name(i_device)
- if (
- ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
- or "P40" in self.gpu_name.upper()
- or "1060" in self.gpu_name
- or "1070" in self.gpu_name
- or "1080" in self.gpu_name
- ):
- print("16系/10系显卡和P40强制单精度")
- self.is_half = False
- for config_file in ["32k.json", "40k.json", "48k.json"]:
- with open(f"configs/{config_file}", "r") as f:
- strr = f.read().replace("true", "false")
- with open(f"configs/{config_file}", "w") as f:
- f.write(strr)
- with open("infer/modules/train/preprocess.py", "r") as f:
- strr = f.read().replace("3.7", "3.0")
- with open("infer/modules/train/preprocess.py", "w") as f:
- f.write(strr)
- else:
- self.gpu_name = None
- self.gpu_mem = int(
- torch.cuda.get_device_properties(i_device).total_memory
- / 1024
- / 1024
- / 1024
- + 0.4
- )
- if self.gpu_mem <= 4:
- with open("infer/modules/train/preprocess.py", "r") as f:
- strr = f.read().replace("3.7", "3.0")
- with open("infer/modules/train/preprocess.py", "w") as f:
- f.write(strr)
- elif torch.backends.mps.is_available():
- print("没有发现支持的N卡, 使用MPS进行推理")
- self.device = "mps"
- else:
- print("没有发现支持的N卡, 使用CPU进行推理")
- self.device = "cpu"
- self.is_half = True
-
- if self.n_cpu == 0:
- self.n_cpu = cpu_count()
-
- if self.is_half:
- # 6G显存配置
- x_pad = 3
- x_query = 10
- x_center = 60
- x_max = 65
- else:
- # 5G显存配置
- x_pad = 1
- x_query = 6
- x_center = 38
- x_max = 41
-
- if self.gpu_mem != None and self.gpu_mem <= 4:
- x_pad = 1
- x_query = 5
- x_center = 30
- x_max = 32
-
- return x_pad, x_query, x_center, x_max
-
-
-f0up_key = sys.argv[1]
-input_path = sys.argv[2]
-index_path = sys.argv[3]
-f0method = sys.argv[4] # harvest or pm
-opt_path = sys.argv[5]
-model_path = sys.argv[6]
-index_rate = float(sys.argv[7])
-device = sys.argv[8]
-is_half = sys.argv[9].lower() != "false"
-filter_radius = int(sys.argv[10])
-resample_sr = int(sys.argv[11])
-rms_mix_rate = float(sys.argv[12])
-protect = float(sys.argv[13])
-print(sys.argv)
-config = Config(device, is_half)
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from infer.modules.vc.modules import VC
-from lib.infer_pack.models import (
- SynthesizerTrnMs256NSFsid,
- SynthesizerTrnMs256NSFsid_nono,
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono,
-)
-from infer.lib.audio import load_audio
-from fairseq import checkpoint_utils
-from scipy.io import wavfile
-
-hubert_model = None
-
-
-def load_hubert():
- global hubert_model
- models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
- ["hubert_base.pt"],
- suffix="",
- )
- hubert_model = models[0]
- hubert_model = hubert_model.to(device)
- if is_half:
- hubert_model = hubert_model.half()
- else:
- hubert_model = hubert_model.float()
- hubert_model.eval()
-
-
-def vc_single(sid, input_audio, f0_up_key, f0_file, f0_method, file_index, index_rate):
- global tgt_sr, net_g, vc, hubert_model, version
- if input_audio is None:
- return "You need to upload an audio", None
- f0_up_key = int(f0_up_key)
- audio = load_audio(input_audio, 16000)
- times = [0, 0, 0]
- if hubert_model == None:
- load_hubert()
- if_f0 = cpt.get("f0", 1)
- # audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,times,f0_up_key,f0_method,file_index,file_big_npy,index_rate,if_f0,f0_file=f0_file)
- audio_opt = vc.pipeline(
- hubert_model,
- net_g,
- sid,
- audio,
- input_audio,
- times,
- f0_up_key,
- f0_method,
- file_index,
- index_rate,
- if_f0,
- filter_radius,
- tgt_sr,
- resample_sr,
- rms_mix_rate,
- version,
- protect,
- f0_file=f0_file,
- )
- print(times)
- return audio_opt
-
-
-def get_vc(model_path):
- global n_spk, tgt_sr, net_g, vc, cpt, device, is_half, version
- print("loading pth %s" % model_path)
- cpt = torch.load(model_path, map_location="cpu")
- tgt_sr = cpt["config"][-1]
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- if_f0 = cpt.get("f0", 1)
- version = cpt.get("version", "v1")
- if version == "v1":
- if if_f0 == 1:
- net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
- else:
- net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
- elif version == "v2":
- if if_f0 == 1: #
- net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
- else:
- net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
- del net_g.enc_q
- print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净，真奇葩
- net_g.eval().to(device)
- if is_half:
- net_g = net_g.half()
- else:
- net_g = net_g.float()
- vc = VC(tgt_sr, config)
- n_spk = cpt["config"][-3]
- # return {"visible": True,"maximum": n_spk, "__type__": "update"}
-
-
-get_vc(model_path)
-audios = os.listdir(input_path)
-for file in tq.tqdm(audios):
- if file.endswith(".wav"):
- file_path = input_path + "/" + file
- wav_opt = vc_single(
- 0, file_path, f0up_key, None, f0method, index_path, index_rate
- )
- out_path = opt_path + "/" + file
- wavfile.write(out_path, tgt_sr, wav_opt)
diff --git a/spaces/GMFTBY/PandaGPT/datasets/samplers.py b/spaces/GMFTBY/PandaGPT/datasets/samplers.py
deleted file mode 100644
index d3ce1e90b2177940acb911d31d1c5245d74a6119..0000000000000000000000000000000000000000
--- a/spaces/GMFTBY/PandaGPT/datasets/samplers.py
+++ /dev/null
@@ -1,166 +0,0 @@
-# coding=utf-8
-# Copyright (c) 2019, NVIDIA CORPORATION. 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.
-"""batch samplers that work with either random or sequential data samplers"""
-import math
-import os
-import sys
-
-import torch
-from torch.utils import data
-import numpy as np
-
-
-class RandomSampler(data.sampler.Sampler):
- r"""
- Based off of pytorch RandomSampler and DistributedSampler. Essentially a RandomSampler,
- but this class lets the user set an epoch like DistributedSampler
- Samples elements randomly. If without replacement, then sample from a shuffled dataset.
- If with replacement, then user can specify ``num_samples`` to draw.
- Arguments:
- data_source (Dataset): dataset to sample from
- num_samples (int): number of samples to draw, default=len(dataset)
- replacement (bool): samples are drawn with replacement if ``True``, default=False
- """
-
- def __init__(self, data_source, replacement=False, num_samples=None):
- super(RandomSampler, self).__init__(data_source)
- self.data_source = data_source
- self.replacement = replacement
- self._num_samples = num_samples
- self.epoch = -1
-
- if self._num_samples is not None and replacement is False:
- raise ValueError("With replacement=False, num_samples should not be specified, "
- "since a random permute will be performed.")
-
- if not isinstance(self.num_samples, int) or self.num_samples <= 0:
- raise ValueError("num_samples should be a positive integer "
- "value, but got num_samples={}".format(self.num_samples))
- if not isinstance(self.replacement, bool):
- raise ValueError("replacement should be a boolean value, but got "
- "replacement={}".format(self.replacement))
-
- @property
- def num_samples(self):
- # dataset size might change at runtime
- if self._num_samples is None:
- return len(self.data_source)
- return self._num_samples
-
- def __iter__(self):
- n = len(self.data_source)
- g = torch.Generator()
- if self.epoch >= 0:
- g.manual_seed(self.epoch)
- if self.replacement:
- for _ in range(self.num_samples // 32):
- yield from torch.randint(high=n, size=(32,), dtype=torch.int64, generator=g).tolist()
- yield from torch.randint(high=n, size=(self.num_samples % 32,), dtype=torch.int64,
- generator=g).tolist()
- else:
- yield from torch.randperm(n, generator=self.generator).tolist()
-
- def __len__(self):
- return self.num_samples
-
- def set_epoch(self, epoch):
- self.epoch = epoch
-
-
-class DistributedSequentialSampler(data.sampler.Sampler):
- def __init__(self, num_samples, train_iters, batch_size, rank=-1, world_size=2):
- super().__init__(num_samples)
- if rank == -1:
- rank = 0
- world_size = 1
- self.num_samples = num_samples
- self.rank = rank
- self.world_size = world_size
- self.start_iter = 0
- self.train_iters = train_iters
- self.batch_size = batch_size
- self.batch_bias = [i * (num_samples // batch_size) for i in range(batch_size)]
-
- def __iter__(self):
- for idx in range(self.start_iter, self.train_iters * 10):
- batch = [(idx + bias) % self.num_samples for bias in self.batch_bias]
- tbatch = self._batch(batch)
- yield tbatch
-
- def __len__(self):
- return self.train_iters
-
- def _batch(self, batch):
- """extracts samples only pertaining to this worker's batch"""
- start = self.rank*self.batch_size//self.world_size
- end = (self.rank+1)*self.batch_size//self.world_size
- return batch[start:end]
-
-
-class DistributedBatchSampler(data.sampler.BatchSampler):
- """
- similar to normal implementation of distributed sampler, except implementation is at the
- batch sampler level, instead of just the sampler level. This allows wrapping of arbitrary
- data samplers (sequential, random, WeightedRandomSampler, etc.) with this batch sampler.
- """
- def __init__(self, sampler, batch_size, drop_last, rank=-1, world_size=2, wrap_last=False, gradient_accumulation_steps=None):
- super(DistributedBatchSampler, self).__init__(sampler, batch_size, drop_last)
- if rank == -1:
- assert False, 'should not be here'
- self.rank = rank
- self.world_size = world_size
- self.sampler.wrap_around = 0
- self.wrap_around = 0
- self.wrap_last = wrap_last
- self.start_iter = 0
- self.effective_batch_size = batch_size if gradient_accumulation_steps is None else batch_size * gradient_accumulation_steps
-
- def __iter__(self):
- batch = []
- i = 0
- for idx in self.data_iterator(self.sampler, wrap_around=False):
- batch.append(idx)
- if len(batch) == self.batch_size:
- tbatch = self._batch(batch)
- if i >= self.start_iter * self.effective_batch_size:
- yield tbatch
- self.start_iter = 0
- i += len(batch)
- batch = []
- batch_len = len(batch)
- if batch_len > 0 and not self.drop_last:
- if self.wrap_last:
- self.sampler.wrap_around -= (self.batch_size)
- self.wrap_around += (len(batch))
- self.wrap_around %= self.batch_size
- yield self._batch(batch)
- if self.wrap_last:
- self.sampler.wrap_around += self.batch_size
-
- def data_iterator(self, _iter, wrap_around=False):
- """iterates through data and handles wrap around"""
- for i, idx in enumerate(_iter):
- if i < self.wrap_around%self.batch_size:
- continue
- if wrap_around:
- self.wrap_around += 1
- self.wrap_around %= self.batch_size
- yield idx
-
- def _batch(self, batch):
- """extracts samples only pertaining to this worker's batch"""
- start = self.rank*self.batch_size//self.world_size
- end = (self.rank+1)*self.batch_size//self.world_size
- return batch[start:end]
diff --git a/spaces/GT4SD/paccmann_rl/README.md b/spaces/GT4SD/paccmann_rl/README.md
deleted file mode 100644
index 06d8cff6e50add08db1cd5d6a1cea3c75c6a0e18..0000000000000000000000000000000000000000
--- a/spaces/GT4SD/paccmann_rl/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: PaccMann^RL
-emoji: 💡
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.46.0
-app_file: app.py
-pinned: false
-python_version: 3.8.13
-pypi_version: 20.2.4
-duplicated_from: jannisborn/gt4sd-torchdrug
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/GeekedReals/jonatasgrosman-wav2vec2-large-xlsr-53-english/app.py b/spaces/GeekedReals/jonatasgrosman-wav2vec2-large-xlsr-53-english/app.py
deleted file mode 100644
index f7bc16986ba92aee8660a44201cba56f67447114..0000000000000000000000000000000000000000
--- a/spaces/GeekedReals/jonatasgrosman-wav2vec2-large-xlsr-53-english/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/jonatasgrosman/wav2vec2-large-xlsr-53-english").launch()
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/anime-colorization/scripts/super_res_sample.py b/spaces/Gradio-Blocks/anime-colorization/scripts/super_res_sample.py
deleted file mode 100644
index d7e4e3374073945a4f34d92c0caab164c45eac3a..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/anime-colorization/scripts/super_res_sample.py
+++ /dev/null
@@ -1,117 +0,0 @@
-"""
-Generate a large batch of samples from a super resolution model, given a batch
-of samples from a regular model from image_sample.py.
-"""
-
-import argparse
-import os
-
-import blobfile as bf
-import numpy as np
-import torch as th
-import torch.distributed as dist
-
-from pixel_guide_diffusion import dist_util, logger
-from pixel_guide_diffusion.script_util import (
- sr_model_and_diffusion_defaults,
- sr_create_model_and_diffusion,
- args_to_dict,
- add_dict_to_argparser,
-)
-
-
-def main():
- args = create_argparser().parse_args()
-
- dist_util.setup_dist()
- logger.configure()
-
- logger.log("creating model...")
- model, diffusion = sr_create_model_and_diffusion(
- **args_to_dict(args, sr_model_and_diffusion_defaults().keys())
- )
- model.load_state_dict(
- dist_util.load_state_dict(args.model_path, map_location="cpu")
- )
- model.to(dist_util.dev())
- model.eval()
-
- logger.log("loading data...")
- data = load_data_for_worker(args.base_samples, args.batch_size, args.class_cond)
-
- logger.log("creating samples...")
- all_images = []
- while len(all_images) * args.batch_size < args.num_samples:
- model_kwargs = next(data)
- model_kwargs = {k: v.to(dist_util.dev()) for k, v in model_kwargs.items()}
- sample = diffusion.p_sample_loop(
- model,
- (args.batch_size, 3, args.large_size, args.large_size),
- clip_denoised=args.clip_denoised,
- model_kwargs=model_kwargs,
- )
- sample = ((sample + 1) * 127.5).clamp(0, 255).to(th.uint8)
- sample = sample.permute(0, 2, 3, 1)
- sample = sample.contiguous()
-
- all_samples = [th.zeros_like(sample) for _ in range(dist.get_world_size())]
- dist.all_gather(all_samples, sample) # gather not supported with NCCL
- for sample in all_samples:
- all_images.append(sample.cpu().numpy())
- logger.log(f"created {len(all_images) * args.batch_size} samples")
-
- arr = np.concatenate(all_images, axis=0)
- arr = arr[: args.num_samples]
- if dist.get_rank() == 0:
- shape_str = "x".join([str(x) for x in arr.shape])
- out_path = os.path.join(logger.get_dir(), f"samples_{shape_str}.npz")
- logger.log(f"saving to {out_path}")
- np.savez(out_path, arr)
-
- dist.barrier()
- logger.log("sampling complete")
-
-
-def load_data_for_worker(base_samples, batch_size, class_cond):
- with bf.BlobFile(base_samples, "rb") as f:
- obj = np.load(f)
- image_arr = obj["arr_0"]
- if class_cond:
- label_arr = obj["arr_1"]
- rank = dist.get_rank()
- num_ranks = dist.get_world_size()
- buffer = []
- label_buffer = []
- while True:
- for i in range(rank, len(image_arr), num_ranks):
- buffer.append(image_arr[i])
- if class_cond:
- label_buffer.append(label_arr[i])
- if len(buffer) == batch_size:
- batch = th.from_numpy(np.stack(buffer)).float()
- batch = batch / 127.5 - 1.0
- batch = batch.permute(0, 3, 1, 2)
- res = dict(low_res=batch)
- if class_cond:
- res["y"] = th.from_numpy(np.stack(label_buffer))
- yield res
- buffer, label_buffer = [], []
-
-
-def create_argparser():
- defaults = dict(
- clip_denoised=True,
- num_samples=10000,
- batch_size=16,
- use_ddim=False,
- base_samples="",
- model_path="",
- )
- defaults.update(sr_model_and_diffusion_defaults())
- parser = argparse.ArgumentParser()
- add_dict_to_argparser(parser, defaults)
- return parser
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py
deleted file mode 100644
index 9b8118b4b633c78120c370f877f47e951c2fdb38..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,23 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(
- roi_head=dict(
- type='DoubleHeadRoIHead',
- reg_roi_scale_factor=1.3,
- bbox_head=dict(
- _delete_=True,
- type='DoubleConvFCBBoxHead',
- num_convs=4,
- num_fcs=2,
- in_channels=256,
- conv_out_channels=1024,
- fc_out_channels=1024,
- roi_feat_size=7,
- num_classes=80,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0., 0., 0., 0.],
- target_stds=[0.1, 0.1, 0.2, 0.2]),
- reg_class_agnostic=False,
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=2.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=2.0))))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py
deleted file mode 100644
index 504ed5ec5040559b3d10f7caf8a970005a1a92d7..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py
+++ /dev/null
@@ -1,53 +0,0 @@
-_base_ = '../nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py'
-
-norm_cfg = dict(type='BN', requires_grad=True)
-model = dict(
- neck=dict(
- _delete_=True,
- type='FPG',
- in_channels=[256, 512, 1024, 2048],
- out_channels=256,
- inter_channels=256,
- num_outs=5,
- add_extra_convs=True,
- start_level=1,
- stack_times=9,
- paths=['bu'] * 9,
- same_down_trans=None,
- same_up_trans=dict(
- type='conv',
- kernel_size=3,
- stride=2,
- padding=1,
- norm_cfg=norm_cfg,
- inplace=False,
- order=('act', 'conv', 'norm')),
- across_lateral_trans=dict(
- type='conv',
- kernel_size=1,
- norm_cfg=norm_cfg,
- inplace=False,
- order=('act', 'conv', 'norm')),
- across_down_trans=dict(
- type='interpolation_conv',
- mode='nearest',
- kernel_size=3,
- norm_cfg=norm_cfg,
- order=('act', 'conv', 'norm'),
- inplace=False),
- across_up_trans=None,
- across_skip_trans=dict(
- type='conv',
- kernel_size=1,
- norm_cfg=norm_cfg,
- inplace=False,
- order=('act', 'conv', 'norm')),
- output_trans=dict(
- type='last_conv',
- kernel_size=3,
- order=('act', 'conv', 'norm'),
- inplace=False),
- norm_cfg=norm_cfg,
- skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()]))
-
-evaluation = dict(interval=2)
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py
deleted file mode 100644
index 5b10c166cf36601bdb895de81874970aebc83310..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py
+++ /dev/null
@@ -1,10 +0,0 @@
-_base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py'
-model = dict(
- pretrained='open-mmlab://msra/hrnetv2_w40',
- backbone=dict(
- type='HRNet',
- extra=dict(
- stage2=dict(num_channels=(40, 80)),
- stage3=dict(num_channels=(40, 80, 160)),
- stage4=dict(num_channels=(40, 80, 160, 320)))),
- neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/retinanet/retinanet_r101_fpn_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/retinanet/retinanet_r101_fpn_2x_coco.py
deleted file mode 100644
index c12088a266d7ccad31bd2233ee5a9ee90f4c2b14..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/retinanet/retinanet_r101_fpn_2x_coco.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './retinanet_r50_fpn_2x_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/utils/__init__.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/utils/__init__.py
deleted file mode 100644
index 5c51dac6d648f41d5c5f46dbf703f19469a7bb6c..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/utils/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from .dist_utils import DistOptimizerHook, allreduce_grads, reduce_mean
-from .misc import mask2ndarray, multi_apply, unmap
-
-__all__ = [
- 'allreduce_grads', 'DistOptimizerHook', 'reduce_mean', 'multi_apply',
- 'unmap', 'mask2ndarray'
-]
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/models/lm.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/models/lm.py
deleted file mode 100644
index 8cefd2c58c3a337378579d6cd6469fd038cbb1ee..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/models/lm.py
+++ /dev/null
@@ -1,531 +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 dataclasses import dataclass
-from functools import partial
-import logging
-import math
-import typing as tp
-
-import torch
-from torch import nn
-
-from ..utils import utils
-from ..modules.streaming import StreamingModule, State
-from ..modules.transformer import StreamingTransformer, create_norm_fn
-from ..modules.conditioners import (
- ConditionFuser,
- ClassifierFreeGuidanceDropout,
- AttributeDropout,
- ConditioningProvider,
- ConditioningAttributes,
- ConditionType,
-)
-from ..modules.codebooks_patterns import CodebooksPatternProvider
-from ..modules.activations import get_activation_fn
-
-
-logger = logging.getLogger(__name__)
-ConditionTensors = tp.Dict[str, ConditionType]
-CFGConditions = tp.Union[ConditionTensors, tp.Tuple[ConditionTensors, ConditionTensors]]
-
-
-def get_init_fn(method: str, input_dim: int, init_depth: tp.Optional[int] = None):
- """LM layer initialization.
- Inspired from xlformers: https://github.com/fairinternal/xlformers
-
- Args:
- method (str): Method name for init function. Valid options are:
- 'gaussian', 'uniform'.
- input_dim (int): Input dimension of the initialized module.
- init_depth (int, optional): Optional init depth value used to rescale
- the standard deviation if defined.
- """
- # Compute std
- std = 1 / math.sqrt(input_dim)
- # Rescale with depth
- if init_depth is not None:
- std = std / math.sqrt(2 * init_depth)
-
- if method == 'gaussian':
- return partial(
- torch.nn.init.trunc_normal_, mean=0.0, std=std, a=-3 * std, b=3 * std
- )
- elif method == 'uniform':
- bound = math.sqrt(3) * std # ensure the standard deviation is `std`
- return partial(torch.nn.init.uniform_, a=-bound, b=bound)
- else:
- raise ValueError("Unsupported layer initialization method")
-
-
-def init_layer(m: nn.Module,
- method: str,
- init_depth: tp.Optional[int] = None,
- zero_bias_init: bool = False):
- """Wrapper around ``get_init_fn`` for proper initialization of LM modules.
-
- Args:
- m (nn.Module): Module to initialize.
- method (str): Method name for the init function.
- init_depth (int, optional): Optional init depth value used to rescale
- the standard deviation if defined.
- zero_bias_init (bool): Whether to initialize the bias to 0 or not.
- """
- if isinstance(m, nn.Linear):
- init_fn = get_init_fn(method, m.in_features, init_depth=init_depth)
- if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
- weight = m.weight.float()
- init_fn(weight)
- m.weight.data[:] = weight.half()
- else:
- init_fn(m.weight)
- if zero_bias_init and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.Embedding):
- init_fn = get_init_fn(method, m.embedding_dim, init_depth=None)
- if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
- weight = m.weight.float()
- init_fn(weight)
- m.weight.data[:] = weight.half()
- else:
- init_fn(m.weight)
-
-
-class ScaledEmbedding(nn.Embedding):
- """Boost learning rate for embeddings (with `scale`).
- """
- def __init__(self, *args, lr=None, **kwargs):
- super().__init__(*args, **kwargs)
- self.lr = lr
-
- def make_optim_group(self):
- group = {"params": list(self.parameters())}
- if self.lr is not None:
- group["lr"] = self.lr
- return group
-
-
-@dataclass
-class LMOutput:
- # The logits are already re-aligned with the input codes
- # hence no extra shift is required, e.g. when computing CE
- logits: torch.Tensor # [B, K, T, card]
- mask: torch.Tensor # [B, K, T]
-
-
-class LMModel(StreamingModule):
- """Transformer-based language model on multiple streams of codes.
-
- Args:
- pattern_provider (CodebooksPatternProvider): Pattern provider for codebook interleaving.
- condition_provider (MusicConditioningProvider): Conditioning provider from metadata.
- fuser (ConditionFuser): Fuser handling the fusing of conditions with language model input.
- n_q (int): Number of parallel streams to model.
- card (int): Cardinality, vocabulary size.
- dim (int): Dimension of the transformer encoder.
- num_heads (int): Number of heads for the transformer encoder.
- hidden_scale (int): Scale for hidden feed forward dimension of the transformer encoder.
- norm (str): Normalization method.
- norm_first (bool): Use pre-norm instead of post-norm.
- emb_lr (float, optional): Embedding-specific learning rate.
- bias_proj (bool): Use bias for output projections.
- weight_init (str, optional): Method for weight initialization.
- depthwise_init (str, optional): Method for depthwise weight initialization.
- zero_bias_init (bool): If true and bias in Linears, initialize bias to zeros.
- cfg_dropout (float): Classifier-free guidance dropout.
- cfg_coef (float): Classifier-free guidance coefficient.
- attribute_dropout (dict): Attribute dropout probabilities.
- two_step_cfg (bool): Whether to run classifier free-guidance with 2 distinct steps.
- **kwargs: Additional parameters for the transformer encoder.
- """
- def __init__(self, pattern_provider: CodebooksPatternProvider, condition_provider: ConditioningProvider,
- fuser: ConditionFuser, n_q: int = 8, card: int = 1024, dim: int = 128, num_heads: int = 8,
- hidden_scale: int = 4, norm: str = 'layer_norm', norm_first: bool = False,
- emb_lr: tp.Optional[float] = None, bias_proj: bool = True,
- weight_init: tp.Optional[str] = None, depthwise_init: tp.Optional[str] = None,
- zero_bias_init: bool = False, cfg_dropout: float = 0, cfg_coef: float = 1.0,
- attribute_dropout: tp.Dict[str, tp.Dict[str, float]] = {}, two_step_cfg: bool = False,
- **kwargs):
- super().__init__()
- self.cfg_coef = cfg_coef
- self.cfg_dropout = ClassifierFreeGuidanceDropout(p=cfg_dropout)
- self.att_dropout = AttributeDropout(p=attribute_dropout)
- self.condition_provider = condition_provider
- self.fuser = fuser
- self.card = card
- embed_dim = self.card + 1
- self.n_q = n_q
- self.dim = dim
- self.pattern_provider = pattern_provider
- self.two_step_cfg = two_step_cfg
- self.emb = nn.ModuleList([ScaledEmbedding(embed_dim, dim, lr=emb_lr) for _ in range(n_q)])
- if 'activation' in kwargs:
- kwargs['activation'] = get_activation_fn(kwargs['activation'])
- self.transformer = StreamingTransformer(
- d_model=dim, num_heads=num_heads, dim_feedforward=int(hidden_scale * dim),
- norm=norm, norm_first=norm_first, **kwargs)
- self.out_norm: tp.Optional[nn.Module] = None
- if norm_first:
- self.out_norm = create_norm_fn(norm, dim)
- self.linears = nn.ModuleList([nn.Linear(dim, self.card, bias=bias_proj) for _ in range(n_q)])
- self._init_weights(weight_init, depthwise_init, zero_bias_init)
- self._fsdp: tp.Optional[nn.Module]
- self.__dict__['_fsdp'] = None
-
- def _init_weights(self, weight_init: tp.Optional[str], depthwise_init: tp.Optional[str], zero_bias_init: bool):
- """Initialization of the transformer module weights.
-
- Args:
- weight_init (str, optional): Weight initialization strategy. See ``get_init_fn`` for valid options.
- depthwise_init (str, optional): Depthwise initialization strategy. The following options are valid:
- 'current' where the depth corresponds to the current layer index or 'global' where the total number
- of layer is used as depth. If not set, no depthwise initialization strategy is used.
- zero_bias_init (bool): Whether to initialize bias to zero or not.
- """
- assert depthwise_init is None or depthwise_init in ['current', 'global']
- assert depthwise_init is None or weight_init is not None, \
- "If 'depthwise_init' is defined, a 'weight_init' method should be provided."
- assert not zero_bias_init or weight_init is not None, \
- "If 'zero_bias_init', a 'weight_init' method should be provided"
-
- if weight_init is None:
- return
-
- for emb_layer in self.emb:
- init_layer(emb_layer, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
- for layer_idx, tr_layer in enumerate(self.transformer.layers):
- depth = None
- if depthwise_init == 'current':
- depth = layer_idx + 1
- elif depthwise_init == 'global':
- depth = len(self.transformer.layers)
- init_fn = partial(init_layer, method=weight_init, init_depth=depth, zero_bias_init=zero_bias_init)
- tr_layer.apply(init_fn)
-
- for linear in self.linears:
- init_layer(linear, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
- @property
- def special_token_id(self) -> int:
- return self.card
-
- @property
- def num_codebooks(self) -> int:
- return self.n_q
-
- def forward(self, sequence: torch.Tensor,
- conditions: tp.List[ConditioningAttributes],
- condition_tensors: tp.Optional[ConditionTensors] = None) -> torch.Tensor:
- """Apply language model on sequence and conditions.
- Given a tensor of sequence of shape [B, K, S] with K the number of codebooks and
- S the sequence steps, return the logits with shape [B, card, K, S].
-
- Args:
- indices (torch.Tensor): Indices of the codes to model.
- conditions (list of ConditioningAttributes): Conditions to use when modeling
- the given codes. Note that when evaluating multiple time with the same conditioning
- you should pre-compute those and pass them as `condition_tensors`.
- condition_tensors (dict[str, ConditionType], optional): Pre-computed conditioning
- tensors, see `conditions`.
- Returns:
- torch.Tensor: Logits.
- """
- B, K, S = sequence.shape
- assert K == self.num_codebooks, "Sequence shape must match the specified number of codebooks"
- input_ = sum([self.emb[k](sequence[:, k]) for k in range(K)])
- if condition_tensors is None:
- assert not self._is_streaming, "Conditions tensors should be precomputed when streaming."
- # apply dropout modules
- conditions = self.cfg_dropout(conditions)
- conditions = self.att_dropout(conditions)
- tokenized = self.condition_provider.tokenize(conditions)
- # encode conditions and fuse, both have a streaming cache to not recompute when generating.
- condition_tensors = self.condition_provider(tokenized)
- else:
- assert not conditions, "Shouldn't pass both conditions and condition_tensors."
-
- input_, cross_attention_input = self.fuser(input_, condition_tensors)
-
- out = self.transformer(input_, cross_attention_src=cross_attention_input)
- if self.out_norm:
- out = self.out_norm(out)
- logits = torch.stack([self.linears[k](out) for k in range(K)], dim=1) # [B, K, S, card]
-
- # remove the prefix from the model outputs
- if len(self.fuser.fuse2cond['prepend']) > 0:
- logits = logits[:, :, -S:]
-
- return logits # [B, K, S, card]
-
- def compute_predictions(
- self, codes: torch.Tensor,
- conditions: tp.List[ConditioningAttributes],
- condition_tensors: tp.Optional[ConditionTensors] = None) -> LMOutput:
- """Given an input tensor of codes [B, K, T] and list of conditions, runs the model
- forward using the specified codes interleaving pattern.
-
- Args:
- codes (torch.Tensor): Input codes of shape [B, K, T] with B the batch size,
- K the number of codebooks and T the number of timesteps.
- conditions (list of ConditioningAttributes): conditionings to use when modeling
- the given codes. Note that when evaluating multiple time with the same conditioning
- you should pre-compute those and pass them as `condition_tensors`.
- condition_tensors (dict[str, ConditionType], optional): pre-computed conditioning
- tensors, see `conditions`.
- Returns:
- LMOutput: Language model outputs
- logits (torch.Tensor) of shape [B, K, T, card] corresponding to the provided codes,
- i.e. the first item corresponds to logits to predict the first code, meaning that
- no additional shifting of codes and logits is required.
- mask (torch.Tensor) of shape [B, K, T], mask over valid and invalid positions.
- Given the specified interleaving strategies, parts of the logits and codes should
- not be considered as valid predictions because of invalid context.
- """
- B, K, T = codes.shape
- codes = codes.contiguous()
- # map codes [B, K, T] into pattern sequence [B, K, S] using special_token_id for masked tokens
- pattern = self.pattern_provider.get_pattern(T)
- sequence_codes, sequence_indexes, sequence_mask = pattern.build_pattern_sequence(
- codes, self.special_token_id, keep_only_valid_steps=True
- )
- # apply model on pattern sequence
- model = self if self._fsdp is None else self._fsdp
- logits = model(sequence_codes, conditions, condition_tensors) # [B, K, S, card]
- # map back the logits on pattern sequence to logits on original codes: [B, K, S, card] -> [B, K, T, card]
- # and provide the corresponding mask over invalid positions of tokens
- logits = logits.permute(0, 3, 1, 2) # [B, card, K, S]
- # note: we use nans as special token to make it obvious if we feed unexpected logits
- logits, logits_indexes, logits_mask = pattern.revert_pattern_logits(
- logits, float('nan'), keep_only_valid_steps=True
- )
- logits = logits.permute(0, 2, 3, 1) # [B, K, T, card]
- logits_mask = logits_mask[None, :, :].expand(B, -1, -1) # [K, T] -> [B, K, T]
- return LMOutput(logits, logits_mask)
-
- def _sample_next_token(self,
- sequence: torch.Tensor,
- cfg_conditions: CFGConditions,
- unconditional_state: State,
- use_sampling: bool = False,
- temp: float = 1.0,
- top_k: int = 0,
- top_p: float = 0.0,
- cfg_coef: tp.Optional[float] = None) -> torch.Tensor:
- """Sample next token from the model given a sequence and a set of conditions. The model supports
- multiple sampling strategies (greedy sampling, softmax, top-k, top-p...).
-
- Args:
- sequence (torch.Tensor): Current sequence of shape [B, K, S]
- with K corresponding to the number of codebooks and S the number of sequence steps.
- S = 1 in streaming mode, except for the first step that contains a bigger prompt.
- condition_tensors (dict[str, ConditionType): Set of conditions. If CFG is used,
- should be twice the batch size, being the concatenation of the conditions + null conditions.
- use_sampling (bool): Whether to use a sampling strategy or not.
- temp (float): Sampling temperature.
- top_k (int): K for "top-k" sampling.
- top_p (float): P for "top-p" sampling.
- cfg_coef (float, optional): classifier free guidance coefficient
- Returns:
- next_token (torch.Tensor): Next token tensor of shape [B, K, 1].
- """
- B = sequence.shape[0]
- cfg_coef = self.cfg_coef if cfg_coef is None else cfg_coef
- model = self if self._fsdp is None else self._fsdp
- if self.two_step_cfg and cfg_conditions != {}:
- assert isinstance(cfg_conditions, tuple), type(cfg_conditions)
- condition_tensors, null_condition_tensors = cfg_conditions
- cond_logits = model(sequence, conditions=[], condition_tensors=condition_tensors)
- state = self.get_streaming_state()
- self.set_streaming_state(unconditional_state)
- uncond_logits = model(sequence, conditions=[], condition_tensors=null_condition_tensors)
- unconditional_state.update(self.get_streaming_state())
- self.set_streaming_state(state)
- logits = uncond_logits + (cond_logits - uncond_logits) * self.cfg_coef
- else:
- assert isinstance(cfg_conditions, dict)
- condition_tensors = cfg_conditions
- if condition_tensors:
- # Preparing for CFG, predicting both conditional and unconditional logits.
- sequence = torch.cat([sequence, sequence], dim=0)
- all_logits = model(
- sequence,
- conditions=[], condition_tensors=condition_tensors)
- if condition_tensors:
- cond_logits, uncond_logits = all_logits.split(B, dim=0) # [B, K, T, card]
- logits = uncond_logits + (cond_logits - uncond_logits) * cfg_coef
- else:
- logits = all_logits
-
- logits = logits.permute(0, 1, 3, 2) # [B, K, card, T]
- logits = logits[..., -1] # [B x K x card]
-
- # Apply softmax for sampling if temp > 0. Else, do greedy sampling to avoid zero division error.
- if use_sampling and temp > 0.0:
- probs = torch.softmax(logits / temp, dim=-1)
- if top_p > 0.0:
- next_token = utils.sample_top_p(probs, p=top_p)
- elif top_k > 0:
- next_token = utils.sample_top_k(probs, k=top_k)
- else:
- next_token = utils.multinomial(probs, num_samples=1)
- else:
- next_token = torch.argmax(logits, dim=-1, keepdim=True)
-
- return next_token
-
- @torch.no_grad()
- def generate(self,
- prompt: tp.Optional[torch.Tensor] = None,
- conditions: tp.List[ConditioningAttributes] = [],
- num_samples: tp.Optional[int] = None,
- max_gen_len: int = 256,
- use_sampling: bool = True,
- temp: float = 1.0,
- top_k: int = 250,
- top_p: float = 0.0,
- cfg_coef: tp.Optional[float] = None,
- two_step_cfg: tp.Optional[bool] = None,
- remove_prompts: bool = False,
- check: bool = False,
- callback: tp.Optional[tp.Callable[[int, int], None]] = None) -> torch.Tensor:
- """Generate tokens sampling from the model given a prompt or unconditionally. Generation can
- be perform in a greedy fashion or using sampling with top K and top P strategies.
-
- Args:
- prompt (torch.Tensor, optional): Prompt tokens of shape [B, K, T].
- conditions_tensors (list of ConditioningAttributes, optional): List of conditions.
- num_samples (int, optional): Number of samples to generate when no prompt and no conditions are given.
- max_gen_len (int): Maximum generation length.
- use_sampling (bool): Whether to use a sampling strategy or not.
- temp (float): Sampling temperature.
- top_k (int): K for "top-k" sampling.
- top_p (float): P for "top-p" sampling.
- cfg_coeff (float, optional): Classifier-free guidance coefficient.
- two_step_cfg (bool, optional): Whether to perform classifier-free guidance with two steps generation.
- remove_prompts (bool): Whether to remove prompts from generation or not.
- check (bool): Whether to apply further checks on generated sequence.
- callback (Callback, optional): Callback function to report generation progress.
- Returns:
- torch.Tensor: Generated tokens.
- """
- assert not self.training, "generation shouldn't be used in training mode."
- first_param = next(iter(self.parameters()))
- device = first_param.device
-
- # Checking all input shapes are consistent.
- possible_num_samples = []
- if num_samples is not None:
- possible_num_samples.append(num_samples)
- elif prompt is not None:
- possible_num_samples.append(prompt.shape[0])
- elif conditions:
- possible_num_samples.append(len(conditions))
- else:
- possible_num_samples.append(1)
- assert [x == possible_num_samples[0] for x in possible_num_samples], "Inconsistent inputs shapes"
- num_samples = possible_num_samples[0]
-
- # below we create set of conditions: one conditional and one unconditional
- # to do that we merge the regular condition together with the null condition
- # we then do 1 forward pass instead of 2.
- # the reason for that is two-fold:
- # 1. it is about x2 faster than doing 2 forward passes
- # 2. avoid the streaming API treating the 2 passes as part of different time steps
- # We also support doing two different passes, in particular to ensure that
- # the padding structure is exactly the same between train and test.
- # With a batch size of 1, this can be slower though.
- cfg_conditions: CFGConditions
- two_step_cfg = self.two_step_cfg if two_step_cfg is None else two_step_cfg
- if conditions:
- null_conditions = ClassifierFreeGuidanceDropout(p=1.0)(conditions)
- if two_step_cfg:
- cfg_conditions = (
- self.condition_provider(self.condition_provider.tokenize(conditions)),
- self.condition_provider(self.condition_provider.tokenize(null_conditions)),
- )
- else:
- conditions = conditions + null_conditions
- tokenized = self.condition_provider.tokenize(conditions)
- cfg_conditions = self.condition_provider(tokenized)
- else:
- cfg_conditions = {}
-
- if prompt is None:
- assert num_samples > 0
- prompt = torch.zeros((num_samples, self.num_codebooks, 0), dtype=torch.long, device=device)
-
- B, K, T = prompt.shape
- start_offset = T
- assert start_offset < max_gen_len
-
- pattern = self.pattern_provider.get_pattern(max_gen_len)
- # this token is used as default value for codes that are not generated yet
- unknown_token = -1
-
- # we generate codes up to the max_gen_len that will be mapped to the pattern sequence
- gen_codes = torch.full((B, K, max_gen_len), unknown_token, dtype=torch.long, device=device)
- # filling the gen_codes with the prompt if needed
- gen_codes[..., :start_offset] = prompt
- # create the gen_sequence with proper interleaving from the pattern: [B, K, S]
- gen_sequence, indexes, mask = pattern.build_pattern_sequence(gen_codes, self.special_token_id)
- # retrieve the start_offset in the sequence:
- # it is the first sequence step that contains the `start_offset` timestep
- start_offset_sequence = pattern.get_first_step_with_timesteps(start_offset)
- assert start_offset_sequence is not None
-
- with self.streaming():
- unconditional_state = self.get_streaming_state()
- prev_offset = 0
- gen_sequence_len = gen_sequence.shape[-1] # gen_sequence shape is [B, K, S]
- for offset in range(start_offset_sequence, gen_sequence_len):
- # get current sequence (note that the streaming API is providing the caching over previous offsets)
- curr_sequence = gen_sequence[..., prev_offset:offset]
- curr_mask = mask[None, ..., prev_offset:offset].expand(B, -1, -1)
- if check:
- # check coherence between mask and sequence
- assert (curr_sequence == torch.where(curr_mask, curr_sequence, self.special_token_id)).all()
- # should never happen as gen_sequence is filled progressively
- assert not (curr_sequence == unknown_token).any()
- # sample next token from the model, next token shape is [B, K, 1]
- next_token = self._sample_next_token(
- curr_sequence, cfg_conditions, unconditional_state, use_sampling, temp, top_k, top_p,
- cfg_coef=cfg_coef)
- # ensure the tokens that should be masked are properly set to special_token_id
- # as the model never output special_token_id
- valid_mask = mask[..., offset:offset+1].expand(B, -1, -1)
- next_token[~valid_mask] = self.special_token_id
- # ensure we don't overwrite prompt tokens, we only write over unknown tokens
- # (then mask tokens should be left as is as well, which is correct)
- gen_sequence[..., offset:offset+1] = torch.where(
- gen_sequence[..., offset:offset+1] == unknown_token,
- next_token, gen_sequence[..., offset:offset+1]
- )
- prev_offset = offset
- if callback is not None:
- callback(1 + offset - start_offset_sequence, gen_sequence_len - start_offset_sequence)
- unconditional_state.clear()
-
- # ensure sequence has been entirely filled
- assert not (gen_sequence == unknown_token).any()
- # ensure gen_sequence pattern and mask are matching
- # which means the gen_sequence is valid according to the pattern
- assert (
- gen_sequence == torch.where(mask[None, ...].expand(B, -1, -1), gen_sequence, self.special_token_id)
- ).all()
- # get back the codes, trimming the prompt if needed and cutting potentially incomplete timesteps
- out_codes, out_indexes, out_mask = pattern.revert_pattern_sequence(gen_sequence, special_token=unknown_token)
-
- # sanity checks over the returned codes and corresponding masks
- assert (out_codes[..., :max_gen_len] != unknown_token).all()
- assert (out_mask[..., :max_gen_len] == 1).all()
-
- out_start_offset = start_offset if remove_prompts else 0
- out_codes = out_codes[..., out_start_offset:max_gen_len]
-
- # ensure the returned codes are all valid
- assert (out_codes >= 0).all() and (out_codes <= self.card).all()
- return out_codes
diff --git a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/upsegmodel/prroi_pool/README.md b/spaces/HaHaBill/LandShapes-Antarctica/netdissect/upsegmodel/prroi_pool/README.md
deleted file mode 100644
index bb98946d3b48a2069a58f179eb6da63e009c3849..0000000000000000000000000000000000000000
--- a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/upsegmodel/prroi_pool/README.md
+++ /dev/null
@@ -1,66 +0,0 @@
-# PreciseRoIPooling
-This repo implements the **Precise RoI Pooling** (PrRoI Pooling), proposed in the paper **Acquisition of Localization Confidence for Accurate Object Detection** published at ECCV 2018 (Oral Presentation).
-
-**Acquisition of Localization Confidence for Accurate Object Detection**
-
-_Borui Jiang*, Ruixuan Luo*, Jiayuan Mao*, Tete Xiao, Yuning Jiang_ (* indicates equal contribution.)
-
-https://arxiv.org/abs/1807.11590
-
-## Brief
-
-In short, Precise RoI Pooling is an integration-based (bilinear interpolation) average pooling method for RoI Pooling. It avoids any quantization and has a continuous gradient on bounding box coordinates. It is:
-
-- different from the original RoI Pooling proposed in [Fast R-CNN](https://arxiv.org/abs/1504.08083). PrRoI Pooling uses average pooling instead of max pooling for each bin and has a continuous gradient on bounding box coordinates. That is, one can take the derivatives of some loss function w.r.t the coordinates of each RoI and optimize the RoI coordinates.
-- different from the RoI Align proposed in [Mask R-CNN](https://arxiv.org/abs/1703.06870). PrRoI Pooling uses a full integration-based average pooling instead of sampling a constant number of points. This makes the gradient w.r.t. the coordinates continuous.
-
-For a better illustration, we illustrate RoI Pooling, RoI Align and PrRoI Pooing in the following figure. More details including the gradient computation can be found in our paper.
-
-LICENSE
-The model is licensed with a CreativeML Open RAIL-M license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please read the license -Biases and content acknowledgment
-Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on the LAION-5B dataset, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. You can read more in the model card -
' - + nh3.clean(message_pair[0]) - + "", - self._process_chat_messages(message_pair[1]), - ) - ) - return processed_messages - - def style(self, height: int | None = None, **kwargs): - """ - This method can be used to change the appearance of the Chatbot component. - """ - if height is not None: - self._style["height"] = height - if kwargs.get("color_map") is not None: - warnings.warn("The 'color_map' parameter has been deprecated.") - - Component.style( - self, - **kwargs, - ) - return self diff --git a/spaces/Intoval/privateChatGPT/modules/presets.py b/spaces/Intoval/privateChatGPT/modules/presets.py deleted file mode 100644 index 2b6d5f5a0bc95e18612f99503ba19f0a6ac00f2c..0000000000000000000000000000000000000000 --- a/spaces/Intoval/privateChatGPT/modules/presets.py +++ /dev/null @@ -1,226 +0,0 @@ -# -*- coding:utf-8 -*- -import os -from pathlib import Path -import gradio as gr -from .webui_locale import I18nAuto - -i18n = I18nAuto() # internationalization - -CHATGLM_MODEL = None -CHATGLM_TOKENIZER = None -LLAMA_MODEL = None -LLAMA_INFERENCER = None - -# ChatGPT 设置 -INITIAL_SYSTEM_PROMPT = "You are a helpful assistant." -API_HOST = "api.openai.com" -COMPLETION_URL = "https://api.openai.com/v1/chat/completions" -BALANCE_API_URL="https://api.openai.com/dashboard/billing/credit_grants" -USAGE_API_URL="https://api.openai.com/dashboard/billing/usage" -HISTORY_DIR = Path("history") -HISTORY_DIR = "history" -TEMPLATES_DIR = "templates" - -# 错误信息 -STANDARD_ERROR_MSG = i18n("☹️发生了错误：") # 错误信息的标准前缀 -GENERAL_ERROR_MSG = i18n("获取对话时发生错误，请查看后台日志") -ERROR_RETRIEVE_MSG = i18n("请检查网络连接，或者API-Key是否有效。") -CONNECTION_TIMEOUT_MSG = i18n("连接超时，无法获取对话。") # 连接超时 -READ_TIMEOUT_MSG = i18n("读取超时，无法获取对话。") # 读取超时 -PROXY_ERROR_MSG = i18n("代理错误，无法获取对话。") # 代理错误 -SSL_ERROR_PROMPT = i18n("SSL错误，无法获取对话。") # SSL 错误 -NO_APIKEY_MSG = i18n("API key为空，请检查是否输入正确。") # API key 长度不足 51 位 -NO_INPUT_MSG = i18n("请输入对话内容。") # 未输入对话内容 -BILLING_NOT_APPLICABLE_MSG = i18n("账单信息不适用") # 本地运行的模型返回的账单信息 - -TIMEOUT_STREAMING = 60 # 流式对话时的超时时间 -TIMEOUT_ALL = 200 # 非流式对话时的超时时间 -ENABLE_STREAMING_OPTION = True # 是否启用选择选择是否实时显示回答的勾选框 -HIDE_MY_KEY = False # 如果你想在UI中隐藏你的 API 密钥，将此值设置为 True -CONCURRENT_COUNT = 100 # 允许同时使用的用户数量 - -SIM_K = 5 -INDEX_QUERY_TEMPRATURE = 1.0 - -CHUANHU_TITLE = i18n("Intoval Private ChatGPT 🚀") - -CHUANHU_DESCRIPTION = i18n("由Bilibili [土川虎虎虎](https://space.bilibili.com/29125536) 和 [明昭MZhao](https://space.bilibili.com/24807452)开发
访问川虎Chat的 [GitHub项目](https://github.com/GaiZhenbiao/ChuanhuChatGPT) 下载最新版脚本") - -FOOTER = """
{versions}
"""
-
-APPEARANCE_SWITCHER = """
-
-"""+ i18n("切换亮暗色主题") + """
-
-
-"""
-
-SUMMARIZE_PROMPT = "你是谁？我们刚才聊了什么？" # 总结对话时的 prompt
-
-ONLINE_MODELS = [
- "gpt-3.5-turbo",
- "gpt-3.5-turbo-0301",
- "gpt-4",
- "gpt-4-0314",
- "gpt-4-32k",
- "gpt-4-32k-0314",
- "xmchat",
-]
-
-LOCAL_MODELS = [
- "chatglm-6b",
- "chatglm-6b-int4",
- "chatglm-6b-int4-qe",
- "llama-7b-hf",
- "llama-7b-hf-int4",
- "llama-7b-hf-int8",
- "llama-13b-hf",
- "llama-13b-hf-int4",
- "llama-30b-hf",
- "llama-30b-hf-int4",
- "llama-65b-hf"
-]
-
-if os.environ.get('HIDE_LOCAL_MODELS', 'false') == 'true':
- MODELS = ONLINE_MODELS
-else:
- MODELS = ONLINE_MODELS + LOCAL_MODELS
-
-DEFAULT_MODEL = 1
-
-os.makedirs("models", exist_ok=True)
-os.makedirs("lora", exist_ok=True)
-os.makedirs("history", exist_ok=True)
-for dir_name in os.listdir("models"):
- if os.path.isdir(os.path.join("models", dir_name)):
- if dir_name not in MODELS:
- MODELS.append(dir_name)
-
-MODEL_TOKEN_LIMIT = {
- "gpt-3.5-turbo": 4096,
- "gpt-3.5-turbo-0301": 4096,
- "gpt-4": 8192,
- "gpt-4-0314": 8192,
- "gpt-4-32k": 32768,
- "gpt-4-32k-0314": 32768
-}
-
-TOKEN_OFFSET = 1000 # 模型的token上限减去这个值，得到软上限。到达软上限之后，自动尝试减少token占用。
-DEFAULT_TOKEN_LIMIT = 3000 # 默认的token上限
-REDUCE_TOKEN_FACTOR = 0.5 # 与模型token上限想乘，得到目标token数。减少token占用时，将token占用减少到目标token数以下。
-
-REPLY_LANGUAGES = [
- "简体中文",
- "繁體中文",
- "English",
- "日本語",
- "Español",
- "Français",
- "Deutsch",
- "跟随问题语言（不稳定）"
-]
-
-
-WEBSEARCH_PTOMPT_TEMPLATE = """\
-Web search results:
-
-{web_results}
-Current date: {current_date}
-
-Instructions: Using the provided web search results, write a comprehensive reply to the given query. Make sure to cite results using [[number](URL)] notation after the reference. If the provided search results refer to multiple subjects with the same name, write separate answers for each subject.
-Query: {query}
-Reply in {reply_language}
-"""
-
-PROMPT_TEMPLATE = """\
-Context information is below.
----------------------
-{context_str}
----------------------
-Current date: {current_date}.
-Using the provided context information, write a comprehensive reply to the given query.
-Make sure to cite results using [number] notation after the reference.
-If the provided context information refer to multiple subjects with the same name, write separate answers for each subject.
-Use prior knowledge only if the given context didn't provide enough information.
-Answer the question: {query_str}
-Reply in {reply_language}
-"""
-
-REFINE_TEMPLATE = """\
-The original question is as follows: {query_str}
-We have provided an existing answer: {existing_answer}
-We have the opportunity to refine the existing answer
-(only if needed) with some more context below.
-------------
-{context_msg}
-------------
-Given the new context, refine the original answer to better
-Reply in {reply_language}
-If the context isn't useful, return the original answer.
-"""
-
-ALREADY_CONVERTED_MARK = ""
-
-small_and_beautiful_theme = gr.themes.Soft(
- primary_hue=gr.themes.Color(
- c50="#02C160",
- c100="rgba(2, 193, 96, 0.2)",
- c200="#02C160",
- c300="rgba(2, 193, 96, 0.32)",
- c400="rgba(2, 193, 96, 0.32)",
- c500="rgba(2, 193, 96, 1.0)",
- c600="rgba(2, 193, 96, 1.0)",
- c700="rgba(2, 193, 96, 0.32)",
- c800="rgba(2, 193, 96, 0.32)",
- c900="#02C160",
- c950="#02C160",
- ),
- secondary_hue=gr.themes.Color(
- c50="#576b95",
- c100="#576b95",
- c200="#576b95",
- c300="#576b95",
- c400="#576b95",
- c500="#576b95",
- c600="#576b95",
- c700="#576b95",
- c800="#576b95",
- c900="#576b95",
- c950="#576b95",
- ),
- neutral_hue=gr.themes.Color(
- name="gray",
- c50="#f9fafb",
- c100="#f3f4f6",
- c200="#e5e7eb",
- c300="#d1d5db",
- c400="#B2B2B2",
- c500="#808080",
- c600="#636363",
- c700="#515151",
- c800="#393939",
- c900="#272727",
- c950="#171717",
- ),
- radius_size=gr.themes.sizes.radius_sm,
- ).set(
- button_primary_background_fill="#06AE56",
- button_primary_background_fill_dark="#06AE56",
- button_primary_background_fill_hover="#07C863",
- button_primary_border_color="#06AE56",
- button_primary_border_color_dark="#06AE56",
- button_primary_text_color="#FFFFFF",
- button_primary_text_color_dark="#FFFFFF",
- button_secondary_background_fill="#F2F2F2",
- button_secondary_background_fill_dark="#2B2B2B",
- button_secondary_text_color="#393939",
- button_secondary_text_color_dark="#FFFFFF",
- # background_fill_primary="#F7F7F7",
- # background_fill_primary_dark="#1F1F1F",
- block_title_text_color="*primary_500",
- block_title_background_fill="*primary_100",
- input_background_fill="#F6F6F6",
- )
diff --git a/spaces/Jack-Ahan/fruit-vegetable-classifier/README.md b/spaces/Jack-Ahan/fruit-vegetable-classifier/README.md
deleted file mode 100644
index ee17f01df361f6f139aa1d16f9f99e40ad16ed8b..0000000000000000000000000000000000000000
--- a/spaces/Jack-Ahan/fruit-vegetable-classifier/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Fruit Vegetable Classifier
-emoji: 👀
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.1.7
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Jikiwi/sovits-models/vdecoder/hifigan/nvSTFT.py b/spaces/Jikiwi/sovits-models/vdecoder/hifigan/nvSTFT.py
deleted file mode 100644
index 88597d62a505715091f9ba62d38bf0a85a31b95a..0000000000000000000000000000000000000000
--- a/spaces/Jikiwi/sovits-models/vdecoder/hifigan/nvSTFT.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import math
-import os
-os.environ["LRU_CACHE_CAPACITY"] = "3"
-import random
-import torch
-import torch.utils.data
-import numpy as np
-import librosa
-from librosa.util import normalize
-from librosa.filters import mel as librosa_mel_fn
-from scipy.io.wavfile import read
-import soundfile as sf
-
-def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
- sampling_rate = None
- try:
- data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
- except Exception as ex:
- print(f"'{full_path}' failed to load.\nException:")
- print(ex)
- if return_empty_on_exception:
- return [], sampling_rate or target_sr or 32000
- else:
- raise Exception(ex)
-
- if len(data.shape) > 1:
- data = data[:, 0]
- assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
-
- if np.issubdtype(data.dtype, np.integer): # if audio data is type int
- max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
- else: # if audio data is type fp32
- max_mag = max(np.amax(data), -np.amin(data))
- max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
-
- data = torch.FloatTensor(data.astype(np.float32))/max_mag
-
- if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
- return [], sampling_rate or target_sr or 32000
- if target_sr is not None and sampling_rate != target_sr:
- data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
- sampling_rate = target_sr
-
- return data, sampling_rate
-
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
- return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
-
-def dynamic_range_decompression(x, C=1):
- return np.exp(x) / C
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-def dynamic_range_decompression_torch(x, C=1):
- return torch.exp(x) / C
-
-class STFT():
- def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
- self.target_sr = sr
-
- self.n_mels = n_mels
- self.n_fft = n_fft
- self.win_size = win_size
- self.hop_length = hop_length
- self.fmin = fmin
- self.fmax = fmax
- self.clip_val = clip_val
- self.mel_basis = {}
- self.hann_window = {}
-
- def get_mel(self, y, center=False):
- sampling_rate = self.target_sr
- n_mels = self.n_mels
- n_fft = self.n_fft
- win_size = self.win_size
- hop_length = self.hop_length
- fmin = self.fmin
- fmax = self.fmax
- clip_val = self.clip_val
-
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- if fmax not in self.mel_basis:
- mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
- self.mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
- self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_length)/2), int((n_fft-hop_length)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)],
- center=center, pad_mode='reflect', normalized=False, onesided=True)
- # print(111,spec)
- spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
- # print(222,spec)
- spec = torch.matmul(self.mel_basis[str(fmax)+'_'+str(y.device)], spec)
- # print(333,spec)
- spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
- # print(444,spec)
- return spec
-
- def __call__(self, audiopath):
- audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
- spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
- return spect
-
-stft = STFT()
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/infer/modules/onnx/export.py b/spaces/Kangarroar/ApplioRVC-Inference/infer/modules/onnx/export.py
deleted file mode 100644
index ed4a4162ff04b7e12642fcbe96847f8ea9db06aa..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/infer/modules/onnx/export.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import torch
-
-from infer.lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
-
-
-def export_onnx(ModelPath, ExportedPath):
- cpt = torch.load(ModelPath, map_location="cpu")
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
- vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768
-
- test_phone = torch.rand(1, 200, vec_channels) # hidden unit
- test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用）
- test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频（单位赫兹）
- test_pitchf = torch.rand(1, 200) # nsf基频
- test_ds = torch.LongTensor([0]) # 说话人ID
- test_rnd = torch.rand(1, 192, 200) # 噪声（加入随机因子）
-
- device = "cpu" # 导出时设备（不影响使用模型）
-
- net_g = SynthesizerTrnMsNSFsidM(
- *cpt["config"], is_half=False, version=cpt.get("version", "v1")
- ) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
- net_g.load_state_dict(cpt["weight"], strict=False)
- input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
- output_names = [
- "audio",
- ]
- # net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出
- torch.onnx.export(
- net_g,
- (
- test_phone.to(device),
- test_phone_lengths.to(device),
- test_pitch.to(device),
- test_pitchf.to(device),
- test_ds.to(device),
- test_rnd.to(device),
- ),
- ExportedPath,
- dynamic_axes={
- "phone": [1],
- "pitch": [1],
- "pitchf": [1],
- "rnd": [2],
- },
- do_constant_folding=False,
- opset_version=13,
- verbose=False,
- input_names=input_names,
- output_names=output_names,
- )
- return "Finished"
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/utils/backups.py b/spaces/Kangarroar/ApplioRVC-Inference/utils/backups.py
deleted file mode 100644
index b814f8184792e80e2324685436053d61487110b1..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/utils/backups.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import os
-import shutil
-import hashlib
-import time
-import base64
-
-
-
-
-LOGS_FOLDER = '/content/Applio-RVC-Fork/logs'
-WEIGHTS_FOLDER = '/content/Applio-RVC-Fork/weights'
-GOOGLE_DRIVE_PATH = '/content/drive/MyDrive/RVC_Backup'
-
-def import_google_drive_backup():
- print("Importing Google Drive backup...")
- weights_exist = False
- for root, dirs, files in os.walk(GOOGLE_DRIVE_PATH):
- for filename in files:
- filepath = os.path.join(root, filename)
- if os.path.isfile(filepath) and not filepath.startswith(os.path.join(GOOGLE_DRIVE_PATH, 'weights')):
- backup_filepath = os.path.join(LOGS_FOLDER, os.path.relpath(filepath, GOOGLE_DRIVE_PATH))
- backup_folderpath = os.path.dirname(backup_filepath)
- if not os.path.exists(backup_folderpath):
- os.makedirs(backup_folderpath)
- print(f'Created backup folder: {backup_folderpath}', flush=True)
- shutil.copy2(filepath, backup_filepath) # copy file with metadata
- print(f'Imported file from Google Drive backup: {filename}')
- elif filepath.startswith(os.path.join(GOOGLE_DRIVE_PATH, 'weights')) and filename.endswith('.pth'):
- weights_exist = True
- weights_filepath = os.path.join(WEIGHTS_FOLDER, os.path.relpath(filepath, os.path.join(GOOGLE_DRIVE_PATH, 'weights')))
- weights_folderpath = os.path.dirname(weights_filepath)
- if not os.path.exists(weights_folderpath):
- os.makedirs(weights_folderpath)
- print(f'Created weights folder: {weights_folderpath}', flush=True)
- shutil.copy2(filepath, weights_filepath) # copy file with metadata
- print(f'Imported file from weights: {filename}')
- if weights_exist:
- print("Copied weights from Google Drive backup to local weights folder.")
- else:
- print("No weights found in Google Drive backup.")
- print("Google Drive backup import completed.")
-
-def get_md5_hash(file_path):
- hash_md5 = hashlib.md5()
- with open(file_path, "rb") as f:
- for chunk in iter(lambda: f.read(4096), b""):
- hash_md5.update(chunk)
- return hash_md5.hexdigest()
-
-def copy_weights_folder_to_drive():
- destination_folder = os.path.join(GOOGLE_DRIVE_PATH, 'weights')
- try:
- if not os.path.exists(destination_folder):
- os.makedirs(destination_folder)
-
- num_copied = 0
- for filename in os.listdir(WEIGHTS_FOLDER):
- if filename.endswith('.pth'):
- source_file = os.path.join(WEIGHTS_FOLDER, filename)
- destination_file = os.path.join(destination_folder, filename)
- if not os.path.exists(destination_file):
- shutil.copy2(source_file, destination_file)
- num_copied += 1
- print(f"Copied {filename} to Google Drive!")
-
- if num_copied == 0:
- print("No new finished models found for copying.")
- else:
- print(f"Finished copying {num_copied} files to Google Drive!")
-
- except Exception as e:
- print(f"An error occurred while copying weights: {str(e)}")
- # You can log the error or take appropriate actions here.
-
-def backup_files():
- print("\nStarting backup loop...")
- last_backup_timestamps_path = os.path.join(LOGS_FOLDER, 'last_backup_timestamps.txt')
- fully_updated = False # boolean to track if all files are up to date
-
- while True:
- try:
- updated = False # flag to check if any files were updated
- last_backup_timestamps = {}
-
- try:
- with open(last_backup_timestamps_path, 'r') as f:
- last_backup_timestamps = dict(line.strip().split(':') for line in f)
- except FileNotFoundError:
- pass # File does not exist yet, which is fine
-
- for root, dirs, files in os.walk(LOGS_FOLDER):
- for filename in files:
- if filename != 'last_backup_timestamps.txt':
- filepath = os.path.join(root, filename)
- if os.path.isfile(filepath):
- backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
- backup_folderpath = os.path.dirname(backup_filepath)
- if not os.path.exists(backup_folderpath):
- os.makedirs(backup_folderpath)
- print(f'Created backup folder: {backup_folderpath}', flush=True)
- # check if file has changed since last backup
- last_backup_timestamp = last_backup_timestamps.get(filepath)
- current_timestamp = os.path.getmtime(filepath)
- if last_backup_timestamp is None or float(last_backup_timestamp) < current_timestamp:
- shutil.copy2(filepath, backup_filepath) # copy file with metadata
- last_backup_timestamps[filepath] = str(current_timestamp) # update last backup timestamp
- if last_backup_timestamp is None:
- print(f'Backed up file: {filename}')
- else:
- print(f'Updating backed up file: {filename}')
- updated = True
- fully_updated = False # if a file is updated, all files are not up to date
-
- # check if any files were deleted in Colab and delete them from the backup drive
- for filepath in list(last_backup_timestamps.keys()):
- if not os.path.exists(filepath):
- backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
- if os.path.exists(backup_filepath):
- os.remove(backup_filepath)
- print(f'Deleted file: {filepath}')
- del last_backup_timestamps[filepath]
- updated = True
- fully_updated = False # if a file is deleted, all files are not up to date
-
- if not updated and not fully_updated:
- print("Files are up to date.")
- fully_updated = True # if all files are up to date, set the boolean to True
- copy_weights_folder_to_drive()
- sleep_time = 15
- else:
- sleep_time = 0.1
-
- with open(last_backup_timestamps_path, 'w') as f:
- for filepath, timestamp in last_backup_timestamps.items():
- f.write(f'{filepath}:{timestamp}\n')
-
- time.sleep(sleep_time) # wait for 15 seconds before checking again, or 0.1s if not fully up to date to speed up backups
-
- except Exception as e:
- print(f"An error occurred: {str(e)}")
- # You can log the error or take appropriate actions here.
diff --git a/spaces/KarmKarma/genshinimpact-rvc-models-v2/lib/infer_pack/modules/F0Predictor/F0Predictor.py b/spaces/KarmKarma/genshinimpact-rvc-models-v2/lib/infer_pack/modules/F0Predictor/F0Predictor.py
deleted file mode 100644
index f56e49e7f0e6eab3babf0711cae2933371b9f9cc..0000000000000000000000000000000000000000
--- a/spaces/KarmKarma/genshinimpact-rvc-models-v2/lib/infer_pack/modules/F0Predictor/F0Predictor.py
+++ /dev/null
@@ -1,16 +0,0 @@
-class F0Predictor(object):
- def compute_f0(self, wav, p_len):
- """
- input: wav:[signal_length]
- p_len:int
- output: f0:[signal_length//hop_length]
- """
- pass
-
- def compute_f0_uv(self, wav, p_len):
- """
- input: wav:[signal_length]
- p_len:int
- output: f0:[signal_length//hop_length],uv:[signal_length//hop_length]
- """
- pass
diff --git a/spaces/KbL19/invokeAI/README.md b/spaces/KbL19/invokeAI/README.md
deleted file mode 100644
index 1c909c0308cf151e4a976a44b93da580957bba60..0000000000000000000000000000000000000000
--- a/spaces/KbL19/invokeAI/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: InvokeAI
-emoji: 🌖
-colorFrom: green
-colorTo: blue
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/encoder_preprocess.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/encoder_preprocess.py
deleted file mode 100644
index 853c6cb6c5cdda5c2e53ce3370d2570f2925f01a..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/encoder_preprocess.py
+++ /dev/null
@@ -1,61 +0,0 @@
-from encoder.preprocess import preprocess_librispeech, preprocess_voxceleb1, preprocess_voxceleb2, preprocess_aidatatang_200zh
-from utils.argutils import print_args
-from pathlib import Path
-import argparse
-
-if __name__ == "__main__":
- class MyFormatter(argparse.ArgumentDefaultsHelpFormatter, argparse.RawDescriptionHelpFormatter):
- pass
-
- parser = argparse.ArgumentParser(
- description="Preprocesses audio files from datasets, encodes them as mel spectrograms and "
- "writes them to the disk. This will allow you to train the encoder. The "
- "datasets required are at least one of LibriSpeech, VoxCeleb1, VoxCeleb2, aidatatang_200zh. ",
- formatter_class=MyFormatter
- )
- parser.add_argument("datasets_root", type=Path, help=\
- "Path to the directory containing your LibriSpeech/TTS and VoxCeleb datasets.")
- parser.add_argument("-o", "--out_dir", type=Path, default=argparse.SUPPRESS, help=\
- "Path to the output directory that will contain the mel spectrograms. If left out, "
- "defaults to 
-
-[Website](https://hubs.ly/Q01CNgsd0) • [Docs](https://hubs.ly/Q01CN9Yq0) • [12K+ GitHub ⭐️!](https://hubs.ly/Q01CNbPQ0) • [Slack Community](https://hubs.ly/Q01CNb9H0)
-
-## What is Label Studio?
-
-Label Studio is an open source data labeling platform. It lets you label audio,
-text, images, videos, and time series data with a simple, straightforward, and
-highly-configurable user interface. Label Studio can prepare new data or
-improve existing training data to get more accurate ML models.
-
-
-## Label Studio in Hugging Face Spaces
-
-The Label Studio community is thrilled to offer Label Studio as a Hugging Face
-Spaces application. You can try the data-annotation interface, connect popular
-machine learning models, and share the application with collaborators. You can
-start immediately by creating an account or replicate the space and work in
-your own environment.
-
-## Creating a Use Account and Logging In
-
-Begin by creating a new account in the Label Studio space, then log in with your
-credentials.
-
-**By default, these spaces permit anyone to create a new login
-account, allowing them to view and modify project configuration, data sets, and
-annotations. Without any modifications, treat this space like a demo environment.**
-
-## Creating a Labeling Project
-
-After logging in, Label Studio will present you with a project view. Here you
-can create a new project with prompts to upload data and set up a custom
-configuration interface.
-
-**Note that in the default configuration, storage is local and temporary. Any
-projects, annotations, and configurations will be lost if the space is restarted.**
-
-## Next Steps and Additional Resources
-
-To help with getting started, the Label Studio community curated a list of
-resources including tutorials and documentation.
-
-- 🚀 [Zero to One with Label Studio Tutorial](https://labelstud.io/blog/introduction-to-label-studio-in-hugging-face-spaces/)
-- 📈 [Try Label Studio Enterprise](https://hubs.ly/Q01CMLll0)
-- 🤗 [Tutorial: Using Label Studio with Hugging Face Datasets Hub](https://danielvanstrien.xyz/huggingface/huggingface-datasets/annotation/full%20stack%20deep%20learning%20notes/2022/09/07/label-studio-annotations-hub.html)
-- 💡 [Label Studio Docs](https://hubs.ly/Q01CN9Yq0)
-
-
-
-
-### Making your Label Studio Hugging Face Space production-ready
-
-By default this space allows for the unrestricted creation of new accounts
-will full access to all projects and data. This is great for trying out
-Label Studio and collaborating on projects, but you may want to restrict
-access to your space to only authorized users. Add the following environment
-variable to your spaces Dockerfile to disable public account creation for
-this space.
-
- ENV LABEL_STUDIO_DISABLE_SIGNUP_WITHOUT_LINK=true
-
-Set secrets in your space to create an inital user, and log in with your
-provided username and password. Do not set these in your Dockerfile, as they
-globally visible on a public space.
-
- LABEL_STUDIO_USERNAME
- LABEL_STUDIO_PASSWORD
-
-You will need to provide new users with an invitation link to join the space,
-which can be found in the Organizations interface of Label Studio.
-
-By default this space stores all project configuration and data annotations
-in local storage with Sqlite. If the space is reset, all configuration and
-annotation data in the space will be lost. You can enable configuration
-persistence in one of two ways:
-
-1. Enabling Persistent Storage in your Space settings and configuring Label
- Studio to write its database and task storage there.
-
-2. Connecting an external Postgres database and cloud storage to your space,
- guaranteeing that all project and annotation settings are preserved.
-
-### Enabling Hugging Face Persistent Storage
-
-In the Hugging Face Label Studio Space settings, select the appropriate
-Persistent Storage tier. Note that Persistent Storage is a paid add-on.
-By default, persistent storage is mounted to /data. In your Space settings,
-set the following variables:
-
- LABEL_STUDIO_BASE_DATA_DIR=/data
- ENV STORAGE_PERSISTENCE=1
-
-Your space will restart. NOTE: if you have existing settings and data,
-they will be lost in this first restart. Data and setting will only be
-preserved on subsequent restarts of the space.
-
-### Enabling Postgres Database and Cloud Storage
-
-Set the following secret variables to match your own hosted instance of
-Postgres. We strongly recommend setting these as secrets to prevent leaking
-information about your database service to the public in your spaces
-definition.
-
- DJANGO_DB=default
- POSTGRE_NAME=
-
- )
-}
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/csrc/vision.cpp b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/csrc/vision.cpp
deleted file mode 100644
index d7a663718eae9d5615c155fe1e4cf9c5bb0d6d6b..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/csrc/vision.cpp
+++ /dev/null
@@ -1,27 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-#include "nms.h"
-#include "ml_nms.h"
-#include "ROIAlign.h"
-#include "ROIPool.h"
-#include "SigmoidFocalLoss.h"
-#include "deform_conv.h"
-#include "deform_pool.h"
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def("nms", &nms, "non-maximum suppression");
- m.def("ml_nms", &ml_nms, "multi-label non-maximum suppression");
- m.def("soft_nms", &soft_nms, "soft non-maximum suppression");
- m.def("roi_align_forward", &ROIAlign_forward, "ROIAlign_forward");
- m.def("roi_align_backward", &ROIAlign_backward, "ROIAlign_backward");
- m.def("roi_pool_forward", &ROIPool_forward, "ROIPool_forward");
- m.def("roi_pool_backward", &ROIPool_backward, "ROIPool_backward");
- m.def("sigmoid_focalloss_forward", &SigmoidFocalLoss_forward, "SigmoidFocalLoss_forward");
- m.def("sigmoid_focalloss_backward", &SigmoidFocalLoss_backward, "SigmoidFocalLoss_backward");
- m.def("deform_conv_forward", &deform_conv_forward, "deform_conv_forward");
- m.def("deform_conv_backward_input", &deform_conv_backward_input, "deform_conv_backward_input");
- m.def("deform_conv_backward_parameters", &deform_conv_backward_parameters, "deform_conv_backward_parameters");
- m.def("modulated_deform_conv_forward", &modulated_deform_conv_forward, "modulated_deform_conv_forward");
- m.def("modulated_deform_conv_backward", &modulated_deform_conv_backward, "modulated_deform_conv_backward");
- m.def("deform_psroi_pooling_forward", &deform_psroi_pooling_forward, "deform_psroi_pooling_forward");
- m.def("deform_psroi_pooling_backward", &deform_psroi_pooling_backward, "deform_psroi_pooling_backward");
-}
diff --git a/spaces/Prathap/summarization/app.py b/spaces/Prathap/summarization/app.py
deleted file mode 100644
index d658cd0fd6877e07b5c50655452cefbc4439ced5..0000000000000000000000000000000000000000
--- a/spaces/Prathap/summarization/app.py
+++ /dev/null
@@ -1,88 +0,0 @@
-
-from transformers import pipeline
-import base64
-import time
-from bs4 import BeautifulSoup
-import requests
-import streamlit as st
-import warnings
-warnings.filterwarnings("ignore")
-
-
-timestr = time.strftime("%Y%m%d-%H%M%S")
-
-st.markdown(' Created by **_Prathap_**. :baby_chick:')
-
-st.title("Automatic text summarization")
-
-@st.cache(allow_output_mutation=True)
-def pipen():
- summarizer = pipeline("summarization")
- return summarizer
-
-
-def text_downloader(raw_text):
- b64 = base64.b64encode(raw_text.encode()).decode()
- new_filename = "new_text_file_{}_.txt".format(timestr)
- st.markdown("#### Download File ###")
- href = f'Click Here!!'
- st.markdown(href,unsafe_allow_html=True)
-
-
-
-url = st.text_input('Paste URL ⤵️')
-
-
-if st.button("Submit"):
- r = requests.get(url)
- soup = BeautifulSoup(r.text, 'html.parser')
- results = soup.find_all(['h1', 'p'])
- text = [result.text for result in results]
- ARTICLE = ' '.join(text)
- max_chunk = 400
- ARTICLE = ARTICLE.replace('.', '.
- 选择对话样式
-
-
-
- -
- {
- ToneList.map(tone => (
-
- onChange?.(tone.type)}> - - - )) - } -
Prompt en Español!
' -article = """ -El modelo usa: -- Texto a Imagen [Stable Diffusion 2.0](https://huggingface.co/stabilityai/stable-diffusion-2), -- Para la traduccion [Helsinki-NLP](https://huggingface.co/Helsinki-NLP) -\n ... y mucha magia ☺ -""" - -text_imagen = gr.Interface.load("models/stabilityai/stable-diffusion-2") -text_translate = gr.Interface.load("models/Helsinki-NLP/opus-mt-es-en") - -gr.Series(text_translate, text_imagen, title=title, description = description, article=article).launch(debug=True) \ No newline at end of file diff --git a/spaces/Vegecken/sovits4dzl/utils.py b/spaces/Vegecken/sovits4dzl/utils.py deleted file mode 100644 index f13d3526d514be71c77bebb17a5af8831b9c6a36..0000000000000000000000000000000000000000 --- a/spaces/Vegecken/sovits4dzl/utils.py +++ /dev/null @@ -1,508 +0,0 @@ -import os -import glob -import re -import sys -import argparse -import logging -import json -import subprocess -import random - -import librosa -import numpy as np -from scipy.io.wavfile import read -import torch -from torch.nn import functional as F -from modules.commons import sequence_mask -from hubert import hubert_model -MATPLOTLIB_FLAG = False - -logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) -logger = logging - -f0_bin = 256 -f0_max = 1100.0 -f0_min = 50.0 -f0_mel_min = 1127 * np.log(1 + f0_min / 700) -f0_mel_max = 1127 * np.log(1 + f0_max / 700) - - -# def normalize_f0(f0, random_scale=True): -# f0_norm = f0.clone() # create a copy of the input Tensor -# batch_size, _, frame_length = f0_norm.shape -# for i in range(batch_size): -# means = torch.mean(f0_norm[i, 0, :]) -# if random_scale: -# factor = random.uniform(0.8, 1.2) -# else: -# factor = 1 -# f0_norm[i, 0, :] = (f0_norm[i, 0, :] - means) * factor -# return f0_norm -# def normalize_f0(f0, random_scale=True): -# means = torch.mean(f0[:, 0, :], dim=1, keepdim=True) -# if random_scale: -# factor = torch.Tensor(f0.shape[0],1).uniform_(0.8, 1.2).to(f0.device) -# else: -# factor = torch.ones(f0.shape[0], 1, 1).to(f0.device) -# f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1) -# return f0_norm -def normalize_f0(f0, x_mask, uv, random_scale=True): - # calculate means based on x_mask - uv_sum = torch.sum(uv, dim=1, keepdim=True) - uv_sum[uv_sum == 0] = 9999 - means = torch.sum(f0[:, 0, :] * uv, dim=1, keepdim=True) / uv_sum - - if random_scale: - factor = torch.Tensor(f0.shape[0], 1).uniform_(0.8, 1.2).to(f0.device) - else: - factor = torch.ones(f0.shape[0], 1).to(f0.device) - # normalize f0 based on means and factor - f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1) - if torch.isnan(f0_norm).any(): - exit(0) - return f0_norm * x_mask - - -def plot_data_to_numpy(x, y): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(10, 2)) - plt.plot(x) - plt.plot(y) - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - - -def interpolate_f0(f0): - ''' - 对F0进行插值处理 - ''' - - data = np.reshape(f0, (f0.size, 1)) - - vuv_vector = np.zeros((data.size, 1), dtype=np.float32) - vuv_vector[data > 0.0] = 1.0 - vuv_vector[data <= 0.0] = 0.0 - - ip_data = data - - frame_number = data.size - last_value = 0.0 - for i in range(frame_number): - if data[i] <= 0.0: - j = i + 1 - for j in range(i + 1, frame_number): - if data[j] > 0.0: - break - if j < frame_number - 1: - if last_value > 0.0: - step = (data[j] - data[i - 1]) / float(j - i) - for k in range(i, j): - ip_data[k] = data[i - 1] + step * (k - i + 1) - else: - for k in range(i, j): - ip_data[k] = data[j] - else: - for k in range(i, frame_number): - ip_data[k] = last_value - else: - ip_data[i] = data[i] - last_value = data[i] - - return ip_data[:,0], vuv_vector[:,0] - - -def compute_f0_parselmouth(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512): - import parselmouth - x = wav_numpy - if p_len is None: - p_len = x.shape[0]//hop_length - else: - assert abs(p_len-x.shape[0]//hop_length) < 4, "pad length error" - time_step = hop_length / sampling_rate * 1000 - f0_min = 50 - f0_max = 1100 - f0 = parselmouth.Sound(x, sampling_rate).to_pitch_ac( - time_step=time_step / 1000, voicing_threshold=0.6, - pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency'] - - pad_size=(p_len - len(f0) + 1) // 2 - if(pad_size>0 or p_len - len(f0) - pad_size>0): - f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant') - return f0 - -def resize_f0(x, target_len): - source = np.array(x) - source[source<0.001] = np.nan - target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source) - res = np.nan_to_num(target) - return res - -def compute_f0_dio(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512): - import pyworld - if p_len is None: - p_len = wav_numpy.shape[0]//hop_length - f0, t = pyworld.dio( - wav_numpy.astype(np.double), - fs=sampling_rate, - f0_ceil=800, - frame_period=1000 * hop_length / sampling_rate, - ) - f0 = pyworld.stonemask(wav_numpy.astype(np.double), f0, t, sampling_rate) - for index, pitch in enumerate(f0): - f0[index] = round(pitch, 1) - return resize_f0(f0, p_len) - -def f0_to_coarse(f0): - is_torch = isinstance(f0, torch.Tensor) - f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700) - f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1 - - f0_mel[f0_mel <= 1] = 1 - f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1 - f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int) - assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min()) - return f0_coarse - - -def get_hubert_model(): - vec_path = "hubert/checkpoint_best_legacy_500.pt" - print("load model(s) from {}".format(vec_path)) - from fairseq import checkpoint_utils - models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( - [vec_path], - suffix="", - ) - model = models[0] - model.eval() - return model - -def get_hubert_content(hmodel, wav_16k_tensor): - feats = wav_16k_tensor - if feats.dim() == 2: # double channels - feats = feats.mean(-1) - assert feats.dim() == 1, feats.dim() - feats = feats.view(1, -1) - padding_mask = torch.BoolTensor(feats.shape).fill_(False) - inputs = { - "source": feats.to(wav_16k_tensor.device), - "padding_mask": padding_mask.to(wav_16k_tensor.device), - "output_layer": 9, # layer 9 - } - with torch.no_grad(): - logits = hmodel.extract_features(**inputs) - feats = hmodel.final_proj(logits[0]) - return feats.transpose(1, 2) - - -def get_content(cmodel, y): - with torch.no_grad(): - c = cmodel.extract_features(y.squeeze(1))[0] - c = c.transpose(1, 2) - return c - - - -def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False): - assert os.path.isfile(checkpoint_path) - checkpoint_dict = torch.load(checkpoint_path, map_location='cpu') - iteration = checkpoint_dict['iteration'] - learning_rate = checkpoint_dict['learning_rate'] - if optimizer is not None and not skip_optimizer: - optimizer.load_state_dict(checkpoint_dict['optimizer']) - saved_state_dict = checkpoint_dict['model'] - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - new_state_dict = {} - for k, v in state_dict.items(): - try: - # assert "dec" in k or "disc" in k - # print("load", k) - new_state_dict[k] = saved_state_dict[k] - assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape) - except: - print("error, %s is not in the checkpoint" % k) - logger.info("%s is not in the checkpoint" % k) - new_state_dict[k] = v - if hasattr(model, 'module'): - model.module.load_state_dict(new_state_dict) - else: - model.load_state_dict(new_state_dict) - print("load ") - logger.info("Loaded checkpoint '{}' (iteration {})".format( - checkpoint_path, iteration)) - return model, optimizer, learning_rate, iteration - - -def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path, val_steps, current_step): - logger.info("Saving model and optimizer state at iteration {} to {}".format( - iteration, checkpoint_path)) - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - torch.save({'model': state_dict, - 'iteration': iteration, - 'optimizer': optimizer.state_dict(), - 'learning_rate': learning_rate}, checkpoint_path) - if current_step >= val_steps * 3: - to_del_ckptname = checkpoint_path.replace(str(current_step), str(current_step - val_steps * 3)) - if os.path.exists(to_del_ckptname): - os.remove(to_del_ckptname) - print("Removing ", to_del_ckptname) - - -def clean_checkpoints(path_to_models='logs/48k/', n_ckpts_to_keep=2, sort_by_time=True): - """Freeing up space by deleting saved ckpts - - Arguments: - path_to_models -- Path to the model directory - n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth - sort_by_time -- True -> chronologically delete ckpts - False -> lexicographically delete ckpts - """ - ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))] - name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1))) - time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f))) - sort_key = time_key if sort_by_time else name_key - x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')], key=sort_key) - to_del = [os.path.join(path_to_models, fn) for fn in - (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])] - del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}") - del_routine = lambda x: [os.remove(x), del_info(x)] - rs = [del_routine(fn) for fn in to_del] - -def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050): - for k, v in scalars.items(): - writer.add_scalar(k, v, global_step) - for k, v in histograms.items(): - writer.add_histogram(k, v, global_step) - for k, v in images.items(): - writer.add_image(k, v, global_step, dataformats='HWC') - for k, v in audios.items(): - writer.add_audio(k, v, global_step, audio_sampling_rate) - - -def latest_checkpoint_path(dir_path, regex="G_*.pth"): - f_list = glob.glob(os.path.join(dir_path, regex)) - f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f)))) - x = f_list[-1] - print(x) - return x - - -def plot_spectrogram_to_numpy(spectrogram): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(10,2)) - im = ax.imshow(spectrogram, aspect="auto", origin="lower", - interpolation='none') - plt.colorbar(im, ax=ax) - plt.xlabel("Frames") - plt.ylabel("Channels") - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def plot_alignment_to_numpy(alignment, info=None): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(6, 4)) - im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower', - interpolation='none') - fig.colorbar(im, ax=ax) - xlabel = 'Decoder timestep' - if info is not None: - xlabel += '\n\n' + info - plt.xlabel(xlabel) - plt.ylabel('Encoder timestep') - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def load_wav_to_torch(full_path): - sampling_rate, data = read(full_path) - return torch.FloatTensor(data.astype(np.float32)), sampling_rate - - -def load_filepaths_and_text(filename, split="|"): - with open(filename, encoding='utf-8') as f: - filepaths_and_text = [line.strip().split(split) for line in f] - return filepaths_and_text - - -def get_hparams(init=True): - parser = argparse.ArgumentParser() - parser.add_argument('-c', '--config', type=str, default="./configs/base.json", - help='JSON file for configuration') - parser.add_argument('-m', '--model', type=str, required=True, - help='Model name') - - args = parser.parse_args() - model_dir = os.path.join("./logs", args.model) - - if not os.path.exists(model_dir): - os.makedirs(model_dir) - - config_path = args.config - config_save_path = os.path.join(model_dir, "config.json") - if init: - with open(config_path, "r") as f: - data = f.read() - with open(config_save_path, "w") as f: - f.write(data) - else: - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_dir(model_dir): - config_save_path = os.path.join(model_dir, "config.json") - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams =HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_file(config_path): - with open(config_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams =HParams(**config) - return hparams - - -def check_git_hash(model_dir): - source_dir = os.path.dirname(os.path.realpath(__file__)) - if not os.path.exists(os.path.join(source_dir, ".git")): - logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format( - source_dir - )) - return - - cur_hash = subprocess.getoutput("git rev-parse HEAD") - - path = os.path.join(model_dir, "githash") - if os.path.exists(path): - saved_hash = open(path).read() - if saved_hash != cur_hash: - logger.warn("git hash values are different. {}(saved) != {}(current)".format( - saved_hash[:8], cur_hash[:8])) - else: - open(path, "w").write(cur_hash) - - -def get_logger(model_dir, filename="train.log"): - global logger - logger = logging.getLogger(os.path.basename(model_dir)) - logger.setLevel(logging.DEBUG) - - formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s") - if not os.path.exists(model_dir): - os.makedirs(model_dir) - h = logging.FileHandler(os.path.join(model_dir, filename)) - h.setLevel(logging.DEBUG) - h.setFormatter(formatter) - logger.addHandler(h) - return logger - - -def repeat_expand_2d(content, target_len): - # content : [h, t] - - src_len = content.shape[-1] - target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to(content.device) - temp = torch.arange(src_len+1) * target_len / src_len - current_pos = 0 - for i in range(target_len): - if i < temp[current_pos+1]: - target[:, i] = content[:, current_pos] - else: - current_pos += 1 - target[:, i] = content[:, current_pos] - - return target - - -class HParams(): - def __init__(self, **kwargs): - for k, v in kwargs.items(): - if type(v) == dict: - v = HParams(**v) - self[k] = v - - def keys(self): - return self.__dict__.keys() - - def items(self): - return self.__dict__.items() - - def values(self): - return self.__dict__.values() - - def __len__(self): - return len(self.__dict__) - - def __getitem__(self, key): - return getattr(self, key) - - def __setitem__(self, key, value): - return setattr(self, key, value) - - def __contains__(self, key): - return key in self.__dict__ - - def __repr__(self): - return self.__dict__.__repr__() - diff --git a/spaces/Vision-CAIR/minigpt4/minigpt4/datasets/builders/__init__.py b/spaces/Vision-CAIR/minigpt4/minigpt4/datasets/builders/__init__.py deleted file mode 100644 index a1f19e672f951204dc80067f30db368818fa4e00..0000000000000000000000000000000000000000 --- a/spaces/Vision-CAIR/minigpt4/minigpt4/datasets/builders/__init__.py +++ /dev/null @@ -1,72 +0,0 @@ -""" - Copyright (c) 2022, salesforce.com, inc. - All rights reserved. - SPDX-License-Identifier: BSD-3-Clause - For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause -""" - -from minigpt4.datasets.builders.base_dataset_builder import load_dataset_config -from minigpt4.datasets.builders.image_text_pair_builder import ( - CCCombineBuilder, - LaionBuilder, - CCAlignBuilder -) -from minigpt4.common.registry import registry - -__all__ = [ - "CCCombineBuilder", - "LaionBuilder", - "CCAlignBuilder" -] - - -def load_dataset(name, cfg_path=None, vis_path=None, data_type=None): - """ - Example - - >>> dataset = load_dataset("coco_caption", cfg=None) - >>> splits = dataset.keys() - >>> print([len(dataset[split]) for split in splits]) - - """ - if cfg_path is None: - cfg = None - else: - cfg = load_dataset_config(cfg_path) - - try: - builder = registry.get_builder_class(name)(cfg) - except TypeError: - print( - f"Dataset {name} not found. Available datasets:\n" - + ", ".join([str(k) for k in dataset_zoo.get_names()]) - ) - exit(1) - - if vis_path is not None: - if data_type is None: - # use default data type in the config - data_type = builder.config.data_type - - assert ( - data_type in builder.config.build_info - ), f"Invalid data_type {data_type} for {name}." - - builder.config.build_info.get(data_type).storage = vis_path - - dataset = builder.build_datasets() - return dataset - - -class DatasetZoo: - def __init__(self) -> None: - self.dataset_zoo = { - k: list(v.DATASET_CONFIG_DICT.keys()) - for k, v in sorted(registry.mapping["builder_name_mapping"].items()) - } - - def get_names(self): - return list(self.dataset_zoo.keys()) - - -dataset_zoo = DatasetZoo() diff --git a/spaces/VoiceHero69/changer/hubert/hubert_manager.py b/spaces/VoiceHero69/changer/hubert/hubert_manager.py deleted file mode 100644 index 4c62ed7efc95da65f31b540803c0d25dc2a2ff68..0000000000000000000000000000000000000000 --- a/spaces/VoiceHero69/changer/hubert/hubert_manager.py +++ /dev/null @@ -1,46 +0,0 @@ -import os.path -import shutil -import urllib.request - -import huggingface_hub - - -class HuBERTManager: - @staticmethod - def make_sure_hubert_installed(download_url: str = 'https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt', file_name: str = 'hubert.pt'): - install_dir = os.path.join('data', 'models', 'hubert') - if not os.path.isdir(install_dir): - os.makedirs(install_dir, exist_ok=True) - install_file = os.path.join(install_dir, file_name) - if not os.path.isfile(install_file): - print('Downloading HuBERT base model') - urllib.request.urlretrieve(download_url, install_file) - print('Downloaded HuBERT') - return install_file - - - @staticmethod - def make_sure_tokenizer_installed(model: str = 'quantifier_hubert_base_ls960_14.pth', repo: str = 'GitMylo/bark-voice-cloning', local_file: str = 'tokenizer.pth'): - install_dir = os.path.join('data', 'models', 'hubert') - if not os.path.isdir(install_dir): - os.makedirs(install_dir, exist_ok=True) - install_file = os.path.join(install_dir, local_file) - if not os.path.isfile(install_file): - print('Downloading HuBERT custom tokenizer') - huggingface_hub.hf_hub_download(repo, model, local_dir=install_dir, local_dir_use_symlinks=False) - shutil.move(os.path.join(install_dir, model), install_file) - print('Downloaded tokenizer') - return install_file - - @staticmethod - def make_sure_hubert_rvc_installed(model: str = 'hubert_base.pt', repo: str = 'lj1995/VoiceConversionWebUI', local_file: str = 'hubert_rvc.pt'): - install_dir = os.path.join('data', 'models', 'hubert') - if not os.path.isdir(install_dir): - os.makedirs(install_dir, exist_ok=True) - install_file = os.path.join(install_dir, local_file) - if not os.path.isfile(install_file): - print('Downloading HuBERT for RVC') - huggingface_hub.hf_hub_download(repo, model, local_dir=install_dir, local_dir_use_symlinks=False) - shutil.move(os.path.join(install_dir, model), install_file) - print('Downloaded HuBERT for RVC') - return install_file diff --git a/spaces/WZUN666/vits-uma-genshin-honkai/README.md b/spaces/WZUN666/vits-uma-genshin-honkai/README.md deleted file mode 100644 index 1c0aa069bfd980b6b45bb2bf62ff74bd9b0b61c2..0000000000000000000000000000000000000000 --- a/spaces/WZUN666/vits-uma-genshin-honkai/README.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -license: apache-2.0 -title: ' vits-uma-genshin-honkai' -sdk: gradio -sdk_version: 3.7 -emoji: 🐨 -colorTo: yellow -pinned: false -app_file: app.py -duplicated_from: ikechan8370/vits-uma-genshin-honkai ---- diff --git a/spaces/XzJosh/XingTong-Bert-VITS2/monotonic_align/core.py b/spaces/XzJosh/XingTong-Bert-VITS2/monotonic_align/core.py deleted file mode 100644 index dddc688d76172b880054e544b7a217acd013f14f..0000000000000000000000000000000000000000 --- a/spaces/XzJosh/XingTong-Bert-VITS2/monotonic_align/core.py +++ /dev/null @@ -1,35 +0,0 @@ -import numba - - -@numba.jit(numba.void(numba.int32[:,:,::1], numba.float32[:,:,::1], numba.int32[::1], numba.int32[::1]), nopython=True, nogil=True) -def maximum_path_jit(paths, values, t_ys, t_xs): - b = paths.shape[0] - max_neg_val=-1e9 - for i in range(int(b)): - path = paths[i] - value = values[i] - t_y = t_ys[i] - t_x = t_xs[i] - - v_prev = v_cur = 0.0 - index = t_x - 1 - - for y in range(t_y): - for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)): - if x == y: - v_cur = max_neg_val - else: - v_cur = value[y-1, x] - if x == 0: - if y == 0: - v_prev = 0. - else: - v_prev = max_neg_val - else: - v_prev = value[y-1, x-1] - value[y, x] += max(v_prev, v_cur) - - for y in range(t_y - 1, -1, -1): - path[y, index] = 1 - if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]): - index = index - 1 diff --git a/spaces/XzJosh/yoyo-Bert-VITS2/text/chinese_bert.py b/spaces/XzJosh/yoyo-Bert-VITS2/text/chinese_bert.py deleted file mode 100644 index cb84ce0b426cd0a1c7954ddcdf41322c10ed14fa..0000000000000000000000000000000000000000 --- a/spaces/XzJosh/yoyo-Bert-VITS2/text/chinese_bert.py +++ /dev/null @@ -1,50 +0,0 @@ -import torch -from transformers import AutoTokenizer, AutoModelForMaskedLM - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - -tokenizer = AutoTokenizer.from_pretrained("./bert/chinese-roberta-wwm-ext-large") -model = AutoModelForMaskedLM.from_pretrained("./bert/chinese-roberta-wwm-ext-large").to(device) - -def get_bert_feature(text, word2ph): - with torch.no_grad(): - inputs = tokenizer(text, return_tensors='pt') - for i in inputs: - inputs[i] = inputs[i].to(device) - res = model(**inputs, output_hidden_states=True) - res = torch.cat(res['hidden_states'][-3:-2], -1)[0].cpu() - - assert len(word2ph) == len(text)+2 - word2phone = word2ph - phone_level_feature = [] - for i in range(len(word2phone)): - repeat_feature = res[i].repeat(word2phone[i], 1) - phone_level_feature.append(repeat_feature) - - phone_level_feature = torch.cat(phone_level_feature, dim=0) - - - return phone_level_feature.T - -if __name__ == '__main__': - # feature = get_bert_feature('你好,我是说的道理。') - import torch - - word_level_feature = torch.rand(38, 1024) # 12个词,每个词1024维特征 - word2phone = [1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1] - - # 计算总帧数 - total_frames = sum(word2phone) - print(word_level_feature.shape) - print(word2phone) - phone_level_feature = [] - for i in range(len(word2phone)): - print(word_level_feature[i].shape) - - # 对每个词重复word2phone[i]次 - repeat_feature = word_level_feature[i].repeat(word2phone[i], 1) - phone_level_feature.append(repeat_feature) - - phone_level_feature = torch.cat(phone_level_feature, dim=0) - print(phone_level_feature.shape) # torch.Size([36, 1024]) - diff --git a/spaces/YUANAI/DiffspeechResearch/data_gen/tts/wav_processors/__init__.py b/spaces/YUANAI/DiffspeechResearch/data_gen/tts/wav_processors/__init__.py deleted file mode 100644 index 4be97b377dcb95a0e6bceb876ac0ce93c8290249..0000000000000000000000000000000000000000 --- a/spaces/YUANAI/DiffspeechResearch/data_gen/tts/wav_processors/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from . import base_processor -from . import common_processors diff --git a/spaces/YUANAI/DiffspeechResearch/modules/commons/rnn.py b/spaces/YUANAI/DiffspeechResearch/modules/commons/rnn.py deleted file mode 100644 index 205c2c76b8fda2de920bc59228a5eec0a20119a9..0000000000000000000000000000000000000000 --- a/spaces/YUANAI/DiffspeechResearch/modules/commons/rnn.py +++ /dev/null @@ -1,261 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - - -class PreNet(nn.Module): - def __init__(self, in_dims, fc1_dims=256, fc2_dims=128, dropout=0.5): - super().__init__() - self.fc1 = nn.Linear(in_dims, fc1_dims) - self.fc2 = nn.Linear(fc1_dims, fc2_dims) - self.p = dropout - - def forward(self, x): - x = self.fc1(x) - x = F.relu(x) - x = F.dropout(x, self.p, training=self.training) - x = self.fc2(x) - x = F.relu(x) - x = F.dropout(x, self.p, training=self.training) - return x - - -class HighwayNetwork(nn.Module): - def __init__(self, size): - super().__init__() - self.W1 = nn.Linear(size, size) - self.W2 = nn.Linear(size, size) - self.W1.bias.data.fill_(0.) - - def forward(self, x): - x1 = self.W1(x) - x2 = self.W2(x) - g = torch.sigmoid(x2) - y = g * F.relu(x1) + (1. - g) * x - return y - - -class BatchNormConv(nn.Module): - def __init__(self, in_channels, out_channels, kernel, relu=True): - super().__init__() - self.conv = nn.Conv1d(in_channels, out_channels, kernel, stride=1, padding=kernel // 2, bias=False) - self.bnorm = nn.BatchNorm1d(out_channels) - self.relu = relu - - def forward(self, x): - x = self.conv(x) - x = F.relu(x) if self.relu is True else x - return self.bnorm(x) - - -class ConvNorm(torch.nn.Module): - def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, - padding=None, dilation=1, bias=True, w_init_gain='linear'): - super(ConvNorm, self).__init__() - if padding is None: - assert (kernel_size % 2 == 1) - padding = int(dilation * (kernel_size - 1) / 2) - - self.conv = torch.nn.Conv1d(in_channels, out_channels, - kernel_size=kernel_size, stride=stride, - padding=padding, dilation=dilation, - bias=bias) - - torch.nn.init.xavier_uniform_( - self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain)) - - def forward(self, signal): - conv_signal = self.conv(signal) - return conv_signal - - -class CBHG(nn.Module): - def __init__(self, K, in_channels, channels, proj_channels, num_highways): - super().__init__() - - # List of all rnns to call `flatten_parameters()` on - self._to_flatten = [] - - self.bank_kernels = [i for i in range(1, K + 1)] - self.conv1d_bank = nn.ModuleList() - for k in self.bank_kernels: - conv = BatchNormConv(in_channels, channels, k) - self.conv1d_bank.append(conv) - - self.maxpool = nn.MaxPool1d(kernel_size=2, stride=1, padding=1) - - self.conv_project1 = BatchNormConv(len(self.bank_kernels) * channels, proj_channels[0], 3) - self.conv_project2 = BatchNormConv(proj_channels[0], proj_channels[1], 3, relu=False) - - # Fix the highway input if necessary - if proj_channels[-1] != channels: - self.highway_mismatch = True - self.pre_highway = nn.Linear(proj_channels[-1], channels, bias=False) - else: - self.highway_mismatch = False - - self.highways = nn.ModuleList() - for i in range(num_highways): - hn = HighwayNetwork(channels) - self.highways.append(hn) - - self.rnn = nn.GRU(channels, channels, batch_first=True, bidirectional=True) - self._to_flatten.append(self.rnn) - - # Avoid fragmentation of RNN parameters and associated warning - self._flatten_parameters() - - def forward(self, x): - # Although we `_flatten_parameters()` on init, when using DataParallel - # the model gets replicated, making it no longer guaranteed that the - # weights are contiguous in GPU memory. Hence, we must call it again - self._flatten_parameters() - - # Save these for later - residual = x - seq_len = x.size(-1) - conv_bank = [] - - # Convolution Bank - for conv in self.conv1d_bank: - c = conv(x) # Convolution - conv_bank.append(c[:, :, :seq_len]) - - # Stack along the channel axis - conv_bank = torch.cat(conv_bank, dim=1) - - # dump the last padding to fit residual - x = self.maxpool(conv_bank)[:, :, :seq_len] - - # Conv1d projections - x = self.conv_project1(x) - x = self.conv_project2(x) - - # Residual Connect - x = x + residual - - # Through the highways - x = x.transpose(1, 2) - if self.highway_mismatch is True: - x = self.pre_highway(x) - for h in self.highways: - x = h(x) - - # And then the RNN - x, _ = self.rnn(x) - return x - - def _flatten_parameters(self): - """Calls `flatten_parameters` on all the rnns used by the WaveRNN. Used - to improve efficiency and avoid PyTorch yelling at us.""" - [m.flatten_parameters() for m in self._to_flatten] - - -class TacotronEncoder(nn.Module): - def __init__(self, embed_dims, num_chars, cbhg_channels, K, num_highways, dropout): - super().__init__() - self.embedding = nn.Embedding(num_chars, embed_dims) - self.pre_net = PreNet(embed_dims, embed_dims, embed_dims, dropout=dropout) - self.cbhg = CBHG(K=K, in_channels=cbhg_channels, channels=cbhg_channels, - proj_channels=[cbhg_channels, cbhg_channels], - num_highways=num_highways) - self.proj_out = nn.Linear(cbhg_channels * 2, cbhg_channels) - - def forward(self, x): - x = self.embedding(x) - x = self.pre_net(x) - x.transpose_(1, 2) - x = self.cbhg(x) - x = self.proj_out(x) - return x - - -class RNNEncoder(nn.Module): - def __init__(self, num_chars, embedding_dim, n_convolutions=3, kernel_size=5): - super(RNNEncoder, self).__init__() - self.embedding = nn.Embedding(num_chars, embedding_dim, padding_idx=0) - convolutions = [] - for _ in range(n_convolutions): - conv_layer = nn.Sequential( - ConvNorm(embedding_dim, - embedding_dim, - kernel_size=kernel_size, stride=1, - padding=int((kernel_size - 1) / 2), - dilation=1, w_init_gain='relu'), - nn.BatchNorm1d(embedding_dim)) - convolutions.append(conv_layer) - self.convolutions = nn.ModuleList(convolutions) - - self.lstm = nn.LSTM(embedding_dim, int(embedding_dim / 2), 1, - batch_first=True, bidirectional=True) - - def forward(self, x): - input_lengths = (x > 0).sum(-1) - input_lengths = input_lengths.cpu().numpy() - - x = self.embedding(x) - x = x.transpose(1, 2) # [B, H, T] - for conv in self.convolutions: - x = F.dropout(F.relu(conv(x)), 0.5, self.training) + x - x = x.transpose(1, 2) # [B, T, H] - - # pytorch tensor are not reversible, hence the conversion - x = nn.utils.rnn.pack_padded_sequence(x, input_lengths, batch_first=True, enforce_sorted=False) - - self.lstm.flatten_parameters() - outputs, _ = self.lstm(x) - outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs, batch_first=True) - - return outputs - - -class DecoderRNN(torch.nn.Module): - def __init__(self, hidden_size, decoder_rnn_dim, dropout): - super(DecoderRNN, self).__init__() - self.in_conv1d = nn.Sequential( - torch.nn.Conv1d( - in_channels=hidden_size, - out_channels=hidden_size, - kernel_size=9, padding=4, - ), - torch.nn.ReLU(), - torch.nn.Conv1d( - in_channels=hidden_size, - out_channels=hidden_size, - kernel_size=9, padding=4, - ), - ) - self.ln = nn.LayerNorm(hidden_size) - if decoder_rnn_dim == 0: - decoder_rnn_dim = hidden_size * 2 - self.rnn = torch.nn.LSTM( - input_size=hidden_size, - hidden_size=decoder_rnn_dim, - num_layers=1, - batch_first=True, - bidirectional=True, - dropout=dropout - ) - self.rnn.flatten_parameters() - self.conv1d = torch.nn.Conv1d( - in_channels=decoder_rnn_dim * 2, - out_channels=hidden_size, - kernel_size=3, - padding=1, - ) - - def forward(self, x): - input_masks = x.abs().sum(-1).ne(0).data[:, :, None] - input_lengths = input_masks.sum([-1, -2]) - input_lengths = input_lengths.cpu().numpy() - - x = self.in_conv1d(x.transpose(1, 2)).transpose(1, 2) - x = self.ln(x) - x = nn.utils.rnn.pack_padded_sequence(x, input_lengths, batch_first=True, enforce_sorted=False) - self.rnn.flatten_parameters() - x, _ = self.rnn(x) # [B, T, C] - x, _ = nn.utils.rnn.pad_packed_sequence(x, batch_first=True) - x = x * input_masks - pre_mel = self.conv1d(x.transpose(1, 2)).transpose(1, 2) # [B, T, C] - pre_mel = pre_mel * input_masks - return pre_mel diff --git a/spaces/YuAnthony/Audio-Caption/coco_caption/pycocoevalcap/rouge/__init__.py b/spaces/YuAnthony/Audio-Caption/coco_caption/pycocoevalcap/rouge/__init__.py deleted file mode 100644 index 43a773e12ea2e960f9a62fa1c2179a73d8c0dd35..0000000000000000000000000000000000000000 --- a/spaces/YuAnthony/Audio-Caption/coco_caption/pycocoevalcap/rouge/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__author__ = 'vrama91' diff --git a/spaces/Zakia/cat_or_dog_predictor/README.md b/spaces/Zakia/cat_or_dog_predictor/README.md deleted file mode 100644 index 63b4832721a7d3a85c3913824a9bd6167e27f871..0000000000000000000000000000000000000000 --- a/spaces/Zakia/cat_or_dog_predictor/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Cat_or_dog_predictor -emoji: 💩 -colorFrom: blue -colorTo: green -sdk: gradio -sdk_version: 2.9.4 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/ZilliaxOfficial/nyaru-svc-3.0/vdecoder/hifigan/env.py b/spaces/ZilliaxOfficial/nyaru-svc-3.0/vdecoder/hifigan/env.py deleted file mode 100644 index 2bdbc95d4f7a8bad8fd4f5eef657e2b51d946056..0000000000000000000000000000000000000000 --- a/spaces/ZilliaxOfficial/nyaru-svc-3.0/vdecoder/hifigan/env.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import shutil - - -class AttrDict(dict): - def __init__(self, *args, **kwargs): - super(AttrDict, self).__init__(*args, **kwargs) - self.__dict__ = self - - -def build_env(config, config_name, path): - t_path = os.path.join(path, config_name) - if config != t_path: - os.makedirs(path, exist_ok=True) - shutil.copyfile(config, os.path.join(path, config_name)) diff --git a/spaces/abhaskumarsinha/MinimalGPT-Ragdoll/MinimalGPT_2.py b/spaces/abhaskumarsinha/MinimalGPT-Ragdoll/MinimalGPT_2.py deleted file mode 100644 index fb21243a63272d6efa56a289ab6f42333308353a..0000000000000000000000000000000000000000 --- a/spaces/abhaskumarsinha/MinimalGPT-Ragdoll/MinimalGPT_2.py +++ /dev/null @@ -1,401 +0,0 @@ -import os -import json -import tensorflow as tf -from tqdm import tqdm -from GPT import * -import pickle -import argparse -import sys - - - -def save_module(save_weights, model, vectorizer, save_tokenizer): - - # Save the GPT Model - with open(save_weights, 'wb') as file: - pickle.dump(model.weights, file) - - #Save the Vectorizer Model - vocabulary = vectorizer.get_vocabulary() - - # Encode the vocabulary as JSON-compatible strings - encoded_vocabulary = [word.encode('unicode_escape').decode('utf-8') for word in vocabulary] - encoded_vocabulary = encoded_vocabulary[2:] - - # Save the encoded vocabulary to a JSON file - with open(save_tokenizer, 'w') as f: - json.dump(encoded_vocabulary, f) - print("Vocabulary size saved: " + str(len(encoded_vocabulary))) - - - - - -def read_file(f, vectorizer, chunk_size = 1024, starting_chunk = 0, ending_chunk = 5, gpt_input = 10): - i = 0 - chunk = [] - - while True: - data = f.read(chunk_size) - - if not data or i > ending_chunk: - break - - if i >= starting_chunk and i <= ending_chunk: - file_contents = data.split() - input_tokens, output_tokens = [], [] - for j in range(len(file_contents) - gpt_input - 1): - input_tokens += [file_contents[j : j + gpt_input]] - output_tokens += [file_contents[j + gpt_input]] - - - X = [' '.join(input_tokens[j]) for j in range(len(input_tokens))] - Y = output_tokens - - X = vectorizer(X) - Y = vectorizer(Y) - - output = tf.concat([X, Y], 1) - - yield output - - i += 1 - - -def get_model(gpt_input, d_model, h, vocab_size, decoder_stacks, GPT_attention): - input_words = tf.keras.layers.Input((gpt_input)) - embedding = tf.keras.layers.Embedding(vocab_size + 2, d_model)(input_words) - positional_enc = PositionalEmbedding(words = gpt_input, embedding_size = d_model)(embedding) - decoder = Decoder(num_heads = h, key_dim = gpt_input, key_embedding = d_model, GPT_attention = GPT_attention)(positional_enc) - - for _ in range(decoder_stacks - 1): - decoder = Decoder(num_heads = h, key_dim = gpt_input, key_embedding = d_model, GPT_attention = GPT_attention)(decoder) - - decoder = tf.keras.layers.Flatten()(decoder) - linear_layer = tf.keras.layers.Dense(vocab_size + 3)(decoder) - softmax = tf.nn.softmax(linear_layer) - GPT = tf.keras.Model(inputs = input_words, outputs = softmax) - - return GPT - - -def MinimalGPT(data_path='.', - learning_rate=0, - output_length=0, - epochs = 1, - batch_size = 1, - gpt_input=10, - d_model=128, - h=8, - decoder_stacks=1, - starting_chunk = 0, - ending_chunk = 5, - chunk_size = 10, - token_end=40000, - vocabulary_start = 0, - vocabulary_end = 40000, - save=False, - load_tokenizer=None, - load_weights=None, - save_tokenizer=None, - save_weights=None, - optimizer=None, - inference_only = False, - return_model_and_vectorizer = False, - return_model_and_vectorizer_and_output = False, - GPT_attention = False, - TPU = False): - - if chunk_size: - chunk_size *= 1024 - - - if inference_only == False: - with open(data_path, 'r', encoding = 'utf-8') as file: - corpus = file.read() - #file_contents = corpus.split()[token_start : token_end] - #print("Total tokens: " + str(len(file_contents))) - - - if load_tokenizer: - with open(load_tokenizer, 'r') as f: - encoded_vocabulary = json.load(f) - - # Decode the encoded vocabulary to original strings - vocabulary = [word.encode('utf-8').decode('unicode_escape') for word in encoded_vocabulary] - vectorizer = tf.keras.layers.TextVectorization(standardize = None, split = 'whitespace') - vectorizer.set_vocabulary(vocabulary) - vocab_size = vectorizer.vocabulary_size() - - else: - vocab = [] - for word in tqdm(corpus.split()[vocabulary_start : vocabulary_end]): - vocab += [word] - vocab = list(set(vocab)) - vocab_size = len(vocab) - vectorizer = tf.keras.layers.TextVectorization(standardize = None, split = 'whitespace', vocabulary = vocab) - print('New Vectorizer created successfully...') - print("Vocabulary Size: " + str(vocab_size)) - del corpus - - - #if inference_only == False: - # input_tokens, output_tokens = [], [] - # for i in tqdm(range(len(file_contents) - gpt_input - 1)): - # input_tokens += [file_contents[i : i + gpt_input]] - # output_tokens += [file_contents[i + gpt_input]] - - - # X = [' '.join(input_tokens[i]) for i in tqdm(range(len(input_tokens)))] - # Y = output_tokens - - # del corpus - - # X = vectorizer(X) - # Y = vectorizer(Y) - - if load_weights: - model = get_model(gpt_input = gpt_input, d_model = d_model, h = h, decoder_stacks = decoder_stacks, vocab_size = vocab_size - 2, GPT_attention = GPT_attention) - - with open(load_weights, 'rb') as file: - W = pickle.load(file) - model.set_weights(W) - else: - model = get_model(gpt_input = gpt_input, d_model = d_model, h = h, decoder_stacks = decoder_stacks, vocab_size = vocab_size, GPT_attention = GPT_attention) - - - print(model.summary()) - - - if inference_only == False: - # Compile the model - if not optimizer: - model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate), loss='sparse_categorical_crossentropy') - else: - model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy') - - # Train the model - if learning_rate > 0: - - for epoch in tqdm(range(epochs)): - - with open(data_path, 'r', encoding='utf-8') as f: - chunk_number = 1 - for chunk in read_file(f, - vectorizer, - chunk_size, - starting_chunk, - ending_chunk, - gpt_input): - print('Chunk_size: ' + str(chunk.shape[0])) - model.fit(chunk[:, :gpt_input], tf.reshape(chunk[:, -1], (-1, 1)), batch_size = batch_size, epochs=1) - print("Chunk Number " + str(chunk_number) + "/" +str(ending_chunk - starting_chunk + 1) + " processed!") - chunk_number += 1 - - - # Print the output of the Model - output_seq = generate_output(gpt_input = gpt_input, model = model, vectorizer = vectorizer, text_size = output_length, input_sequence = []) - - if save == True and TPU == False: - print('Saveeeeee') - - save_module(save_weights, model, vectorizer, save_tokenizer) - - if save == True and TPU == True: - - return save_weights, model, vectorizer, save_tokenizer, output_seq - # Save the GPT Model - #with open(save_weights, 'wb') as file: - # pickle.dump(model.weights, file) - - #Save the Vectorizer Model - #vocabulary = vectorizer.get_vocabulary() - - # Encode the vocabulary as JSON-compatible strings - #encoded_vocabulary = [word.encode('unicode_escape').decode('utf-8') for word in vocabulary] - #encoded_vocabulary = encoded_vocabulary[2:] - - # Save the encoded vocabulary to a JSON file - #with open(save_tokenizer, 'w') as f: - # json.dump(encoded_vocabulary, f) - # print("Vocabulary size saved: " + str(len(encoded_vocabulary))) - - - if return_model_and_vectorizer: - return model, vectorizer - elif return_model_and_vectorizer_and_output: - return model, vectorizer, output_seq.replace('@@ ', '') - else: - return output_seq.replace('@@ ', '') - - - -# Example code to execute when the script file is called - -def main(): - print("This code is executed when the script file is called directly.") - -# Check if the script is being run as the main module -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('-d', '--data-path', help='File: Corresponding to corpus or training text [String]') - parser.add_argument('-l', '--learning-rate', help='Float: Learning Rate. The model will train ONLY IF the rate is > 0, skip otherwise [Float]', type=float) - parser.add_argument('-ol', '--output-length', help='Length of the output sequence to be generated', type=int) - parser.add_argument('-e', '--epochs', help='Number of training Epochs [Int]', type=int) - parser.add_argument('-b', '--batch-size', help='Size of each batch [Int]', type=int) - parser.add_argument('-s', '--gpt-input', help='Number of Tokens of text the model inputs at a time [Int]', type=int) - parser.add_argument('-dm', '--d-model', help='Embedding layer output dimensions [Int]', type=int) - parser.add_argument('-p', '--multi-head', help='Number of Multi-head Attention layer in parallel [Int]', type=int) - parser.add_argument('-ds', '--decoder-stacks', help='Number of stacked Decoder layer [Int]', type=int) - parser.add_argument('-sc', '--chunk-start', help='The chunk number in the corpus to mark it as the starting point of the training [Int]', type=int) - parser.add_argument('-ec', '--chunk-end', help='The chunk number in the corpus to mark it as the end point of the training [Int]', type=int) - parser.add_argument('-csz', '--chunk-size', help='The size of each chunk in KB.', type=int) - parser.add_argument('-vs', '--vocabulary-start', help='Token number from the corpus to mark the starting point of vocabulary data [Int]', type=int) - parser.add_argument('-ve', '--vocabulary-end', help='Token number from the corpus to mark the end point of vocabulary data [Int]', type=int) - parser.add_argument('-sd', '--save', help='Save the Model and Vectorizer data to disk [True/False]', action='store_true') - parser.add_argument('-lt', '--load-tokenizer', help='File: Vectorization layer [File]') - parser.add_argument('-lw', '--load-weights', help='File: Model Weights [File]') - parser.add_argument('-st', '--save-tokenizer', help='File: Saving Vectorizer File [File]') - parser.add_argument('-sw', '--save-weights', help='File: Saving Model Weights[File]') - parser.add_argument('-ot', '--optimizer', help='Optimizer consistent to TensorFlow optimizer class [tf.keras.optimizers]') - parser.add_argument('-i', '--inference-only', help='Only Print the output of the model in Inference Mode [True/False]', action='store_true') - parser.add_argument('-mv', '--model-vectorizer', help='Return Model, Vectorizer Tuple [True/False]', action='store_true') - parser.add_argument('-mvo', '--model-vectorizer-output', help='Return Model, Vectorizer, Output Tuple [True/False]', action='store_true') - parser.add_argument('-ga', '--gpt-style-attention', help='Uses GPT-styled attention. Note: (d-model) parameter should be divisible by (multi-head), otherwise the program will throw an error! [True/False]', action='store_true') - parser.add_argument('-tpu', '--TPU', help='Use Tensor Processor Units (Distributed Learning)', action='store_true') - - - args = parser.parse_args() - - - data_path = args.data_path - learning_rate = args.learning_rate - output_length = args.output_length - epochs = args.epochs - batch_size = args.batch_size - gpt_input = args.gpt_input - d_model = args.d_model - h = args.multi_head - stacks = args.decoder_stacks - chunk_start = args.chunk_start - chunk_end = args.chunk_end - chunk_size = args.chunk_size - vocabulary_start = args.vocabulary_start - vocabulary_end = args.vocabulary_end - save = args.save - load_tokenizer = args.load_tokenizer - load_weights = args.load_weights - save_tokenizer = args.save_tokenizer - save_weights = args.save_weights - optimizer = args.optimizer - inference_only = args.inference_only - model_and_vectorizer = args.model_vectorizer - GPT_attention = args.gpt_style_attention - model_vectorizer_output = args.model_vectorizer_output - - - - configuration = { - 'data_path': args.data_path, - 'learning_rate': args.learning_rate, - 'output_length': args.output_length, - 'epochs': args.epochs, - 'batch_size': args.batch_size, - 'gpt_input': args.gpt_input, - 'd_model': args.d_model, - 'h': args.multi_head, - 'stacks': args.decoder_stacks, - 'chunk_start': args.chunk_start, - 'chunk_end': args.chunk_end, - 'chunk_size': args.chunk_size, - 'vocabulary_start': args.vocabulary_start, - 'vocabulary_end': args.vocabulary_end, - 'save': args.save, - 'load_tokenizer': args.load_tokenizer, - 'load_weights': args.load_weights, - 'save_tokenizer': args.save_tokenizer, - 'save_weights': args.save_weights, - 'optimizer': args.optimizer, - 'inference_only': args.inference_only, - 'model_and_vectorizer': args.model_vectorizer, - 'model_vectorizer_output': args.model_vectorizer_output, - 'GPT_Attention' : args.gpt_style_attention - } - - # Save the configuration to a JSON file - with open('last-configuration.json', 'w') as file: - json.dump(configuration, file) - - - - if args.TPU == True: - - resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='') - tf.config.experimental_connect_to_cluster(resolver) - # This is the TPU initialization code that has to be at the beginning. - tf.tpu.experimental.initialize_tpu_system(resolver) - print("All devices: ", tf.config.list_logical_devices('TPU')) - - - strategy = tf.distribute.TPUStrategy(resolver) - - with strategy.scope(): - - output = MinimalGPT(data_path = data_path, - learning_rate = learning_rate, - output_length = output_length, - epochs = epochs, - batch_size = batch_size, - gpt_input = gpt_input, - d_model = d_model, - h = h, - decoder_stacks = stacks, - starting_chunk = chunk_start, - ending_chunk = chunk_end, - chunk_size = chunk_size, - vocabulary_start = vocabulary_start, - vocabulary_end = vocabulary_end, - save = save, - load_tokenizer = load_tokenizer, - load_weights = load_weights, - save_tokenizer = save_tokenizer, - save_weights = save_weights, - optimizer = optimizer, - inference_only = inference_only, - return_model_and_vectorizer = model_and_vectorizer, - return_model_and_vectorizer_and_output = model_vectorizer_output, - GPT_attention = GPT_attention, - TPU = True) - - save_module(output[0], output[1], output[2], output[3]) - - print(output[4]) - sys.exit(0) - - - output = MinimalGPT(data_path = data_path, - learning_rate = learning_rate, - output_length = output_length, - epochs = epochs, - batch_size = batch_size, - gpt_input = gpt_input, - d_model = d_model, - h = h, - decoder_stacks = stacks, - starting_chunk = chunk_start, - ending_chunk = chunk_end, - chunk_size = chunk_size, - vocabulary_start = vocabulary_start, - vocabulary_end = vocabulary_end, - save = save, - load_tokenizer = load_tokenizer, - load_weights = load_weights, - save_tokenizer = save_tokenizer, - save_weights = save_weights, - optimizer = optimizer, - inference_only = inference_only, - return_model_and_vectorizer = model_and_vectorizer, - return_model_and_vectorizer_and_output = model_vectorizer_output, - GPT_attention = GPT_attention, - TPU = False) - print(output) \ No newline at end of file diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/utils/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/utils/__init__.py deleted file mode 100644 index 3d3bdd349b9f2ae499a2fcb2ac1d2e3c77befebe..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/utils/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -from .drop import DropPath -from .inverted_residual import InvertedResidual, InvertedResidualV3 -from .make_divisible import make_divisible -from .res_layer import ResLayer -from .se_layer import SELayer -from .self_attention_block import SelfAttentionBlock -from .up_conv_block import UpConvBlock -from .weight_init import trunc_normal_ - -__all__ = [ - 'ResLayer', 'SelfAttentionBlock', 'make_divisible', 'InvertedResidual', - 'UpConvBlock', 'InvertedResidualV3', 'SELayer', 'DropPath', 'trunc_normal_' -] diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/core/evaluation/eval_hooks.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/core/evaluation/eval_hooks.py deleted file mode 100644 index 6fc100c8f96e817a6ed2666f7c9f762af2463b48..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/core/evaluation/eval_hooks.py +++ /dev/null @@ -1,109 +0,0 @@ -import os.path as osp - -from annotator.uniformer.mmcv.runner import DistEvalHook as _DistEvalHook -from annotator.uniformer.mmcv.runner import EvalHook as _EvalHook - - -class EvalHook(_EvalHook): - """Single GPU EvalHook, with efficient test support. - - Args: - by_epoch (bool): Determine perform evaluation by epoch or by iteration. - If set to True, it will perform by epoch. Otherwise, by iteration. - Default: False. - efficient_test (bool): Whether save the results as local numpy files to - save CPU memory during evaluation. Default: False. - Returns: - list: The prediction results. - """ - - greater_keys = ['mIoU', 'mAcc', 'aAcc'] - - def __init__(self, *args, by_epoch=False, efficient_test=False, **kwargs): - super().__init__(*args, by_epoch=by_epoch, **kwargs) - self.efficient_test = efficient_test - - def after_train_iter(self, runner): - """After train epoch hook. - - Override default ``single_gpu_test``. - """ - if self.by_epoch or not self.every_n_iters(runner, self.interval): - return - from annotator.uniformer.mmseg.apis import single_gpu_test - runner.log_buffer.clear() - results = single_gpu_test( - runner.model, - self.dataloader, - show=False, - efficient_test=self.efficient_test) - self.evaluate(runner, results) - - def after_train_epoch(self, runner): - """After train epoch hook. - - Override default ``single_gpu_test``. - """ - if not self.by_epoch or not self.every_n_epochs(runner, self.interval): - return - from annotator.uniformer.mmseg.apis import single_gpu_test - runner.log_buffer.clear() - results = single_gpu_test(runner.model, self.dataloader, show=False) - self.evaluate(runner, results) - - -class DistEvalHook(_DistEvalHook): - """Distributed EvalHook, with efficient test support. - - Args: - by_epoch (bool): Determine perform evaluation by epoch or by iteration. - If set to True, it will perform by epoch. Otherwise, by iteration. - Default: False. - efficient_test (bool): Whether save the results as local numpy files to - save CPU memory during evaluation. Default: False. - Returns: - list: The prediction results. - """ - - greater_keys = ['mIoU', 'mAcc', 'aAcc'] - - def __init__(self, *args, by_epoch=False, efficient_test=False, **kwargs): - super().__init__(*args, by_epoch=by_epoch, **kwargs) - self.efficient_test = efficient_test - - def after_train_iter(self, runner): - """After train epoch hook. - - Override default ``multi_gpu_test``. - """ - if self.by_epoch or not self.every_n_iters(runner, self.interval): - return - from annotator.uniformer.mmseg.apis import multi_gpu_test - runner.log_buffer.clear() - results = multi_gpu_test( - runner.model, - self.dataloader, - tmpdir=osp.join(runner.work_dir, '.eval_hook'), - gpu_collect=self.gpu_collect, - efficient_test=self.efficient_test) - if runner.rank == 0: - print('\n') - self.evaluate(runner, results) - - def after_train_epoch(self, runner): - """After train epoch hook. - - Override default ``multi_gpu_test``. - """ - if not self.by_epoch or not self.every_n_epochs(runner, self.interval): - return - from annotator.uniformer.mmseg.apis import multi_gpu_test - runner.log_buffer.clear() - results = multi_gpu_test( - runner.model, - self.dataloader, - tmpdir=osp.join(runner.work_dir, '.eval_hook'), - gpu_collect=self.gpu_collect) - if runner.rank == 0: - print('\n') - self.evaluate(runner, results) diff --git a/spaces/abrar-lohia/text-2-character-anim/VQTrans/models/resnet.py b/spaces/abrar-lohia/text-2-character-anim/VQTrans/models/resnet.py deleted file mode 100644 index 062346e3ba2fc4d6ae5636f228c5b7565bdb62b7..0000000000000000000000000000000000000000 --- a/spaces/abrar-lohia/text-2-character-anim/VQTrans/models/resnet.py +++ /dev/null @@ -1,82 +0,0 @@ -import torch.nn as nn -import torch - -class nonlinearity(nn.Module): - def __init__(self): - super().__init__() - - def forward(self, x): - # swish - return x * torch.sigmoid(x) - -class ResConv1DBlock(nn.Module): - def __init__(self, n_in, n_state, dilation=1, activation='silu', norm=None, dropout=None): - super().__init__() - padding = dilation - self.norm = norm - if norm == "LN": - self.norm1 = nn.LayerNorm(n_in) - self.norm2 = nn.LayerNorm(n_in) - elif norm == "GN": - self.norm1 = nn.GroupNorm(num_groups=32, num_channels=n_in, eps=1e-6, affine=True) - self.norm2 = nn.GroupNorm(num_groups=32, num_channels=n_in, eps=1e-6, affine=True) - elif norm == "BN": - self.norm1 = nn.BatchNorm1d(num_features=n_in, eps=1e-6, affine=True) - self.norm2 = nn.BatchNorm1d(num_features=n_in, eps=1e-6, affine=True) - - else: - self.norm1 = nn.Identity() - self.norm2 = nn.Identity() - - if activation == "relu": - self.activation1 = nn.ReLU() - self.activation2 = nn.ReLU() - - elif activation == "silu": - self.activation1 = nonlinearity() - self.activation2 = nonlinearity() - - elif activation == "gelu": - self.activation1 = nn.GELU() - self.activation2 = nn.GELU() - - - - self.conv1 = nn.Conv1d(n_in, n_state, 3, 1, padding, dilation) - self.conv2 = nn.Conv1d(n_state, n_in, 1, 1, 0,) - - - def forward(self, x): - x_orig = x - if self.norm == "LN": - x = self.norm1(x.transpose(-2, -1)) - x = self.activation1(x.transpose(-2, -1)) - else: - x = self.norm1(x) - x = self.activation1(x) - - x = self.conv1(x) - - if self.norm == "LN": - x = self.norm2(x.transpose(-2, -1)) - x = self.activation2(x.transpose(-2, -1)) - else: - x = self.norm2(x) - x = self.activation2(x) - - x = self.conv2(x) - x = x + x_orig - return x - -class Resnet1D(nn.Module): - def __init__(self, n_in, n_depth, dilation_growth_rate=1, reverse_dilation=True, activation='relu', norm=None): - super().__init__() - - blocks = [ResConv1DBlock(n_in, n_in, dilation=dilation_growth_rate ** depth, activation=activation, norm=norm) for depth in range(n_depth)] - if reverse_dilation: - blocks = blocks[::-1] - - self.model = nn.Sequential(*blocks) - - def forward(self, x): - return self.model(x) \ No newline at end of file diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/gl/lib_agl.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/gl/lib_agl.py deleted file mode 100644 index a6c258ef6c877a050a11a6fbf038e9f074b973a1..0000000000000000000000000000000000000000 --- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/gl/lib_agl.py +++ /dev/null @@ -1,31 +0,0 @@ -from ctypes import * - -import pyglet.lib -from pyglet.gl.lib import missing_function, decorate_function - -__all__ = ['link_GL', 'link_AGL'] - -gl_lib = pyglet.lib.load_library(framework='OpenGL') -agl_lib = pyglet.lib.load_library(framework='AGL') - - -def link_GL(name, restype, argtypes, requires=None, suggestions=None): - try: - func = getattr(gl_lib, name) - func.restype = restype - func.argtypes = argtypes - decorate_function(func, name) - return func - except AttributeError: - return missing_function(name, requires, suggestions) - - -def link_AGL(name, restype, argtypes, requires=None, suggestions=None): - try: - func = getattr(agl_lib, name) - func.restype = restype - func.argtypes = argtypes - decorate_function(func, name) - return func - except AttributeError: - return missing_function(name, requires, suggestions) diff --git a/spaces/aieye/named_entity_recognition_tutorial/utils/levels.py b/spaces/aieye/named_entity_recognition_tutorial/utils/levels.py deleted file mode 100644 index a37624fa0e95a0a1385a146edb4be97cdbbd54b7..0000000000000000000000000000000000000000 --- a/spaces/aieye/named_entity_recognition_tutorial/utils/levels.py +++ /dev/null @@ -1,36 +0,0 @@ -import streamlit as st -from utils.login import get_login -import os - -def initialize_level(): - if 'level' not in st.session_state: - if get_login()["status"]: - if not os.path.exists(f".sessions/{get_login()['username']}/level.txt"): - with open(f".sessions/{get_login()['username']}/level.txt", "w") as f: - f.write("0") - st.session_state['level'] = 0 - else: - with open(f".sessions/{get_login()['username']}/level.txt", "r") as f: - st.session_state['level'] = int(f.read()) - -def get_level(): - return st.session_state['level'] - -def render_page(page, level): - if get_login()["status"]: - if st.session_state['level'] < level: - st.error(f"You need to complete Level {st.session_state['level']} first!") - else: - page() - else: - st.error("You need to login first!") - -def complete_level(level): - if st.session_state['level'] > level: - st.info(f'You have Already completed Level {level}!') - else: - st.session_state['level'] = level + 1 - with open(f".sessions/{get_login()['username']}/level.txt", "w") as f: - f.write(str(st.session_state['level'])) - st.balloons() - st.success(f'You have completed Level {level}! You can now move on to the next level.') \ No newline at end of file diff --git a/spaces/ajitrajasekharan/NER-Biomedical-PHI-Ensemble/app.py b/spaces/ajitrajasekharan/NER-Biomedical-PHI-Ensemble/app.py deleted file mode 100644 index 09be3e9531f9e1d62de20121aadae4479a9764aa..0000000000000000000000000000000000000000 --- a/spaces/ajitrajasekharan/NER-Biomedical-PHI-Ensemble/app.py +++ /dev/null @@ -1,272 +0,0 @@ -import time -import streamlit as st -import torch -import string -from annotated_text import annotated_text - -from flair.data import Sentence -from flair.models import SequenceTagger -from transformers import BertTokenizer, BertForMaskedLM -import BatchInference as bd -import batched_main_NER as ner -import aggregate_server_json as aggr -import json - - -DEFAULT_TOP_K = 20 -SPECIFIC_TAG=":__entity__" - - -@st.cache(suppress_st_warning=True, allow_output_mutation=True) -def POS_get_model(model_name): - val = SequenceTagger.load(model_name) # Load the model - return val - -def getPos(s: Sentence): - texts = [] - labels = [] - for t in s.tokens: - for label in t.annotation_layers.keys(): - texts.append(t.text) - labels.append(t.get_labels(label)[0].value) - return texts, labels - -def getDictFromPOS(texts, labels): - return [["dummy",t,l,"dummy","dummy" ] for t, l in zip(texts, labels)] - -def decode(tokenizer, pred_idx, top_clean): - ignore_tokens = string.punctuation + '[PAD]' - tokens = [] - for w in pred_idx: - token = ''.join(tokenizer.decode(w).split()) - if token not in ignore_tokens: - tokens.append(token.replace('##', '')) - return '\n'.join(tokens[:top_clean]) - -def encode(tokenizer, text_sentence, add_special_tokens=True): - text_sentence = text_sentence.replace('Biomedical and PHI NER ensemble
", unsafe_allow_html=True) - st.markdown("Using pretrained models with no fine tuning
", unsafe_allow_html=True) - #st.markdown(""" - #App under construction... (not in working condition yet)
- #""", unsafe_allow_html=True) - - - st.markdown(""" -
-
- """, unsafe_allow_html=True) - - st.write("This app uses 3 models. Two Pretrained Bert models (**no fine tuning**) and a POS tagger") - - - with st.form('my_form'): - selected_sentence = init_selectbox() - text_input = st.text_area(label='Type any sentence below',value="") - submit_button = st.form_submit_button('Submit') - input_status_area = st.empty() - display_area = st.empty() - if submit_button: - start = time.time() - if (len(text_input) == 0): - text_input = sent_arr_masked[sent_arr.index(selected_sentence)] - input_status_area.text("Input sentence: " + text_input) - results = perform_inference(text_input,display_area) - display_area.empty() - with display_area.container(): - st.text(f"prediction took {time.time() - start:.2f}s") - st.json(results) - - - - - - st.markdown(""" - Note: The example sentences in the pull-down above test biomedical & PHI entities. To see valid predictions specifically for biomedical entities use this app. For PHI entities only (Person,location, Organization) use this app - """, unsafe_allow_html=True) - - st.markdown(""" -
Models used:
(1) Biomedical model pretrained on Pubmed,Clinical trials and BookCorpus subset.
(2) Bert-base-cased (for PHI entities - Person/location/organization etc.)
(3) Flair POS tagger - #""", unsafe_allow_html=True) - st.markdown(""" -
App link to examine pretrained models used to perform NER without fine tuning
- """, unsafe_allow_html=True) - st.markdown(""" -Github link to same working code (without UI) as separate microservices
- """, unsafe_allow_html=True) - - except Exception as e: - print("Some error occurred in main") - st.exception(e) - -if __name__ == "__main__": - main() diff --git a/spaces/akhaliq/VQMIVC/ParallelWaveGAN/parallel_wavegan/distributed/__init__.py b/spaces/akhaliq/VQMIVC/ParallelWaveGAN/parallel_wavegan/distributed/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/alessveloz/lenssssw-roblox-clothing-ai-maker/README.md b/spaces/alessveloz/lenssssw-roblox-clothing-ai-maker/README.md deleted file mode 100644 index 6c33000c422a30b6b7010740445b2003b01f7b85..0000000000000000000000000000000000000000 --- a/spaces/alessveloz/lenssssw-roblox-clothing-ai-maker/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Lenssssw Roblox Clothing Ai Maker -emoji: 🏃 -colorFrom: yellow -colorTo: yellow -sdk: gradio -sdk_version: 3.33.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/_palettes.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/_palettes.py deleted file mode 100644 index 3c748d33e45bfcdc690ceee490cbb50b516cd2b3..0000000000000000000000000000000000000000 --- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/_palettes.py +++ /dev/null @@ -1,309 +0,0 @@ -from .palette import Palette - - -# Taken from https://en.wikipedia.org/wiki/ANSI_escape_code (Windows 10 column) -WINDOWS_PALETTE = Palette( - [ - (12, 12, 12), - (197, 15, 31), - (19, 161, 14), - (193, 156, 0), - (0, 55, 218), - (136, 23, 152), - (58, 150, 221), - (204, 204, 204), - (118, 118, 118), - (231, 72, 86), - (22, 198, 12), - (249, 241, 165), - (59, 120, 255), - (180, 0, 158), - (97, 214, 214), - (242, 242, 242), - ] -) - -# # The standard ansi colors (including bright variants) -STANDARD_PALETTE = Palette( - [ - (0, 0, 0), - (170, 0, 0), - (0, 170, 0), - (170, 85, 0), - (0, 0, 170), - (170, 0, 170), - (0, 170, 170), - (170, 170, 170), - (85, 85, 85), - (255, 85, 85), - (85, 255, 85), - (255, 255, 85), - (85, 85, 255), - (255, 85, 255), - (85, 255, 255), - (255, 255, 255), - ] -) - - -# The 256 color palette -EIGHT_BIT_PALETTE = Palette( - [ - (0, 0, 0), - (128, 0, 0), - (0, 128, 0), - (128, 128, 0), - (0, 0, 128), - (128, 0, 128), - (0, 128, 128), - (192, 192, 192), - (128, 128, 128), - (255, 0, 0), - (0, 255, 0), - (255, 255, 0), - (0, 0, 255), - (255, 0, 255), - (0, 255, 255), - (255, 255, 255), - (0, 0, 0), - (0, 0, 95), - (0, 0, 135), - (0, 0, 175), - (0, 0, 215), - (0, 0, 255), - (0, 95, 0), - (0, 95, 95), - (0, 95, 135), - (0, 95, 175), - (0, 95, 215), - (0, 95, 255), - (0, 135, 0), - (0, 135, 95), - (0, 135, 135), - (0, 135, 175), - (0, 135, 215), - (0, 135, 255), - (0, 175, 0), - (0, 175, 95), - (0, 175, 135), - (0, 175, 175), - (0, 175, 215), - (0, 175, 255), - (0, 215, 0), - (0, 215, 95), - (0, 215, 135), - (0, 215, 175), - (0, 215, 215), - (0, 215, 255), - (0, 255, 0), - (0, 255, 95), - (0, 255, 135), - (0, 255, 175), - (0, 255, 215), - (0, 255, 255), - (95, 0, 0), - (95, 0, 95), - (95, 0, 135), - (95, 0, 175), - (95, 0, 215), - (95, 0, 255), - (95, 95, 0), - (95, 95, 95), - (95, 95, 135), - (95, 95, 175), - (95, 95, 215), - (95, 95, 255), - (95, 135, 0), - (95, 135, 95), - (95, 135, 135), - (95, 135, 175), - (95, 135, 215), - (95, 135, 255), - (95, 175, 0), - (95, 175, 95), - (95, 175, 135), - (95, 175, 175), - (95, 175, 215), - (95, 175, 255), - (95, 215, 0), - (95, 215, 95), - (95, 215, 135), - (95, 215, 175), - (95, 215, 215), - (95, 215, 255), - (95, 255, 0), - (95, 255, 95), - (95, 255, 135), - (95, 255, 175), - (95, 255, 215), - (95, 255, 255), - (135, 0, 0), - (135, 0, 95), - (135, 0, 135), - (135, 0, 175), - (135, 0, 215), - (135, 0, 255), - (135, 95, 0), - (135, 95, 95), - (135, 95, 135), - (135, 95, 175), - (135, 95, 215), - (135, 95, 255), - (135, 135, 0), - (135, 135, 95), - (135, 135, 135), - (135, 135, 175), - (135, 135, 215), - (135, 135, 255), - (135, 175, 0), - (135, 175, 95), - (135, 175, 135), - (135, 175, 175), - (135, 175, 215), - (135, 175, 255), - (135, 215, 0), - (135, 215, 95), - (135, 215, 135), - (135, 215, 175), - (135, 215, 215), - (135, 215, 255), - (135, 255, 0), - (135, 255, 95), - (135, 255, 135), - (135, 255, 175), - (135, 255, 215), - (135, 255, 255), - (175, 0, 0), - (175, 0, 95), - (175, 0, 135), - (175, 0, 175), - (175, 0, 215), - (175, 0, 255), - (175, 95, 0), - (175, 95, 95), - (175, 95, 135), - (175, 95, 175), - (175, 95, 215), - (175, 95, 255), - (175, 135, 0), - (175, 135, 95), - (175, 135, 135), - (175, 135, 175), - (175, 135, 215), - (175, 135, 255), - (175, 175, 0), - (175, 175, 95), - (175, 175, 135), - (175, 175, 175), - (175, 175, 215), - (175, 175, 255), - (175, 215, 0), - (175, 215, 95), - (175, 215, 135), - (175, 215, 175), - (175, 215, 215), - (175, 215, 255), - (175, 255, 0), - (175, 255, 95), - (175, 255, 135), - (175, 255, 175), - (175, 255, 215), - (175, 255, 255), - (215, 0, 0), - (215, 0, 95), - (215, 0, 135), - (215, 0, 175), - (215, 0, 215), - (215, 0, 255), - (215, 95, 0), - (215, 95, 95), - (215, 95, 135), - (215, 95, 175), - (215, 95, 215), - (215, 95, 255), - (215, 135, 0), - (215, 135, 95), - (215, 135, 135), - (215, 135, 175), - (215, 135, 215), - (215, 135, 255), - (215, 175, 0), - (215, 175, 95), - (215, 175, 135), - (215, 175, 175), - (215, 175, 215), - (215, 175, 255), - (215, 215, 0), - (215, 215, 95), - (215, 215, 135), - (215, 215, 175), - (215, 215, 215), - (215, 215, 255), - (215, 255, 0), - (215, 255, 95), - (215, 255, 135), - (215, 255, 175), - (215, 255, 215), - (215, 255, 255), - (255, 0, 0), - (255, 0, 95), - (255, 0, 135), - (255, 0, 175), - (255, 0, 215), - (255, 0, 255), - (255, 95, 0), - (255, 95, 95), - (255, 95, 135), - (255, 95, 175), - (255, 95, 215), - (255, 95, 255), - (255, 135, 0), - (255, 135, 95), - (255, 135, 135), - (255, 135, 175), - (255, 135, 215), - (255, 135, 255), - (255, 175, 0), - (255, 175, 95), - (255, 175, 135), - (255, 175, 175), - (255, 175, 215), - (255, 175, 255), - (255, 215, 0), - (255, 215, 95), - (255, 215, 135), - (255, 215, 175), - (255, 215, 215), - (255, 215, 255), - (255, 255, 0), - (255, 255, 95), - (255, 255, 135), - (255, 255, 175), - (255, 255, 215), - (255, 255, 255), - (8, 8, 8), - (18, 18, 18), - (28, 28, 28), - (38, 38, 38), - (48, 48, 48), - (58, 58, 58), - (68, 68, 68), - (78, 78, 78), - (88, 88, 88), - (98, 98, 98), - (108, 108, 108), - (118, 118, 118), - (128, 128, 128), - (138, 138, 138), - (148, 148, 148), - (158, 158, 158), - (168, 168, 168), - (178, 178, 178), - (188, 188, 188), - (198, 198, 198), - (208, 208, 208), - (218, 218, 218), - (228, 228, 228), - (238, 238, 238), - ] -) diff --git a/spaces/aliabd/SummerTime/evaluation/__init__.py b/spaces/aliabd/SummerTime/evaluation/__init__.py deleted file mode 100644 index cb5a9bf0790375852ca51750e45a4cbc91968275..0000000000000000000000000000000000000000 --- a/spaces/aliabd/SummerTime/evaluation/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -import site -import os - -# needed so that rouge works -package_path = site.getsitepackages()[0] -os.environ["ROUGE_HOME"] = package_path + "/summ_eval/ROUGE-1.5.5/" - -from .rouge_metric import Rouge -from .bertscore_metric import BertScore -from .rougewe_metric import RougeWe -from .bleu_metric import Bleu -from .meteor_metric import Meteor - -SUPPORTED_EVALUATION_METRICS = [BertScore, Bleu, Rouge, RougeWe, Meteor] diff --git a/spaces/aliabid94/AutoGPT/.devcontainer/Dockerfile b/spaces/aliabid94/AutoGPT/.devcontainer/Dockerfile deleted file mode 100644 index 02f580a02e11f3d711350448c6f5d17f4f74b8c1..0000000000000000000000000000000000000000 --- a/spaces/aliabid94/AutoGPT/.devcontainer/Dockerfile +++ /dev/null @@ -1,28 +0,0 @@ -# [Choice] Python version (use -bullseye variants on local arm64/Apple Silicon): 3, 3.10, 3-bullseye, 3.10-bullseye, 3-buster, 3.10-buster -ARG VARIANT=3-bullseye -FROM --platform=linux/amd64 python:3.10 - -RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \ - # Remove imagemagick due to https://security-tracker.debian.org/tracker/CVE-2019-10131 - && apt-get purge -y imagemagick imagemagick-6-common - -# Temporary: Upgrade python packages due to https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-40897 -# They are installed by the base image (python) which does not have the patch. -RUN python3 -m pip install --upgrade setuptools - -# Install Chrome for web browsing -RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \ - && curl -sSL https://dl.google.com/linux/direct/google-chrome-stable_current_$(dpkg --print-architecture).deb -o /tmp/chrome.deb \ - && apt-get -y install /tmp/chrome.deb - -# [Optional] If your pip requirements rarely change, uncomment this section to add them to the image. -# COPY requirements.txt /tmp/pip-tmp/ -# RUN pip3 --disable-pip-version-check --no-cache-dir install -r /tmp/pip-tmp/requirements.txt \ -# && rm -rf /tmp/pip-tmp - -# [Optional] Uncomment this section to install additional OS packages. -# RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \ -# && apt-get -y install --no-install-recommends
- 
-
-
-In this repository, we release code for TensorMask in Detectron2.
-TensorMask is a dense sliding-window instance segmentation framework that, for the first time, achieves results close to the well-developed Mask R-CNN framework -- both qualitatively and quantitatively. It establishes a conceptually complementary direction for object instance segmentation research.
-
-## Installation
-First install Detectron2 following the [documentation](https://detectron2.readthedocs.io/tutorials/install.html) and
-[setup the dataset](../../datasets). Then compile the TensorMask-specific op (`swap_align2nat`):
-```bash
-pip install -e /path/to/detectron2/projects/TensorMask
-```
-
-## Training
-
-To train a model, run:
-```bash
-python /path/to/detectron2/projects/TensorMask/train_net.py --config-file
-01net T\\u00e9l\\u00e9chargement Gratuit Jeux Pour
DOWNLOAD ··· https://tinurli.com/2uwilR
- - aaccfb2cb3
-
-
- diff --git a/spaces/cihyFjudo/fairness-paper-search/Sony Optiarc Inc. DVD AD-5240S Driver Update Firmware for Better Performance.md b/spaces/cihyFjudo/fairness-paper-search/Sony Optiarc Inc. DVD AD-5240S Driver Update Firmware for Better Performance.md deleted file mode 100644 index 424cbedd9e857fb5c5041983cd8371a24f22c6d2..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/Sony Optiarc Inc. DVD AD-5240S Driver Update Firmware for Better Performance.md +++ /dev/null @@ -1,6 +0,0 @@ -
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- diff --git a/spaces/cleanmaster/so-vits-svc-akagi/data_utils.py b/spaces/cleanmaster/so-vits-svc-akagi/data_utils.py deleted file mode 100644 index 1f5d4e257116871588720f296ad78f0bc31e7b24..0000000000000000000000000000000000000000 --- a/spaces/cleanmaster/so-vits-svc-akagi/data_utils.py +++ /dev/null @@ -1,154 +0,0 @@ -import time -import os -import random -import numpy as np -import torch -import torch.utils.data - -import commons -from mel_processing import spectrogram_torch, spec_to_mel_torch -from utils import load_wav_to_torch, load_filepaths_and_text, transform - -# import h5py - - -"""Multi speaker version""" - - -class TextAudioSpeakerLoader(torch.utils.data.Dataset): - """ - 1) loads audio, speaker_id, text pairs - 2) normalizes text and converts them to sequences of integers - 3) computes spectrograms from audio files. - """ - - def __init__(self, audiopaths, hparams): - self.audiopaths = load_filepaths_and_text(audiopaths) - self.max_wav_value = hparams.data.max_wav_value - self.sampling_rate = hparams.data.sampling_rate - self.filter_length = hparams.data.filter_length - self.hop_length = hparams.data.hop_length - self.win_length = hparams.data.win_length - self.sampling_rate = hparams.data.sampling_rate - self.use_sr = hparams.train.use_sr - self.spec_len = hparams.train.max_speclen - self.spk_map = hparams.spk - - random.seed(1234) - random.shuffle(self.audiopaths) - - def get_audio(self, filename): - filename = filename.replace("\\", "/") - audio, sampling_rate = load_wav_to_torch(filename) - if sampling_rate != self.sampling_rate: - raise ValueError("{} SR doesn't match target {} SR".format( - sampling_rate, self.sampling_rate)) - audio_norm = audio / self.max_wav_value - audio_norm = audio_norm.unsqueeze(0) - spec_filename = filename.replace(".wav", ".spec.pt") - if os.path.exists(spec_filename): - spec = torch.load(spec_filename) - else: - spec = spectrogram_torch(audio_norm, self.filter_length, - self.sampling_rate, self.hop_length, self.win_length, - center=False) - spec = torch.squeeze(spec, 0) - torch.save(spec, spec_filename) - - spk = filename.split("/")[-2] - spk = torch.LongTensor([self.spk_map[spk]]) - - c = torch.load(filename + ".soft.pt").squeeze(0) - c = torch.repeat_interleave(c, repeats=2, dim=1) - - f0 = np.load(filename + ".f0.npy") - f0 = torch.FloatTensor(f0) - lmin = min(c.size(-1), spec.size(-1), f0.shape[0]) - assert abs(c.size(-1) - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape, filename) - assert abs(lmin - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape) - assert abs(lmin - c.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape) - spec, c, f0 = spec[:, :lmin], c[:, :lmin], f0[:lmin] - audio_norm = audio_norm[:, :lmin * self.hop_length] - _spec, _c, _audio_norm, _f0 = spec, c, audio_norm, f0 - while spec.size(-1) < self.spec_len: - spec = torch.cat((spec, _spec), -1) - c = torch.cat((c, _c), -1) - f0 = torch.cat((f0, _f0), -1) - audio_norm = torch.cat((audio_norm, _audio_norm), -1) - start = random.randint(0, spec.size(-1) - self.spec_len) - end = start + self.spec_len - spec = spec[:, start:end] - c = c[:, start:end] - f0 = f0[start:end] - audio_norm = audio_norm[:, start * self.hop_length:end * self.hop_length] - - return c, f0, spec, audio_norm, spk - - def __getitem__(self, index): - return self.get_audio(self.audiopaths[index][0]) - - def __len__(self): - return len(self.audiopaths) - - -class EvalDataLoader(torch.utils.data.Dataset): - """ - 1) loads audio, speaker_id, text pairs - 2) normalizes text and converts them to sequences of integers - 3) computes spectrograms from audio files. - """ - - def __init__(self, audiopaths, hparams): - self.audiopaths = load_filepaths_and_text(audiopaths) - self.max_wav_value = hparams.data.max_wav_value - self.sampling_rate = hparams.data.sampling_rate - self.filter_length = hparams.data.filter_length - self.hop_length = hparams.data.hop_length - self.win_length = hparams.data.win_length - self.sampling_rate = hparams.data.sampling_rate - self.use_sr = hparams.train.use_sr - self.audiopaths = self.audiopaths[:5] - self.spk_map = hparams.spk - - - def get_audio(self, filename): - filename = filename.replace("\\", "/") - audio, sampling_rate = load_wav_to_torch(filename) - if sampling_rate != self.sampling_rate: - raise ValueError("{} SR doesn't match target {} SR".format( - sampling_rate, self.sampling_rate)) - audio_norm = audio / self.max_wav_value - audio_norm = audio_norm.unsqueeze(0) - spec_filename = filename.replace(".wav", ".spec.pt") - if os.path.exists(spec_filename): - spec = torch.load(spec_filename) - else: - spec = spectrogram_torch(audio_norm, self.filter_length, - self.sampling_rate, self.hop_length, self.win_length, - center=False) - spec = torch.squeeze(spec, 0) - torch.save(spec, spec_filename) - - spk = filename.split("/")[-2] - spk = torch.LongTensor([self.spk_map[spk]]) - - c = torch.load(filename + ".soft.pt").squeeze(0) - - c = torch.repeat_interleave(c, repeats=2, dim=1) - - f0 = np.load(filename + ".f0.npy") - f0 = torch.FloatTensor(f0) - lmin = min(c.size(-1), spec.size(-1), f0.shape[0]) - assert abs(c.size(-1) - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape) - assert abs(f0.shape[0] - spec.shape[-1]) < 4, (c.size(-1), spec.size(-1), f0.shape) - spec, c, f0 = spec[:, :lmin], c[:, :lmin], f0[:lmin] - audio_norm = audio_norm[:, :lmin * self.hop_length] - - return c, f0, spec, audio_norm, spk - - def __getitem__(self, index): - return self.get_audio(self.audiopaths[index][0]) - - def __len__(self): - return len(self.audiopaths) - diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/templating.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/templating.py deleted file mode 100644 index 0cb868486edd9dda38f90c65f314597813128cf8..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/templating.py +++ /dev/null @@ -1 +0,0 @@ -from starlette.templating import Jinja2Templates as Jinja2Templates # noqa diff --git a/spaces/colakin/video-generater/public/assets/css/main.css b/spaces/colakin/video-generater/public/assets/css/main.css deleted file mode 100644 index c3ca70ed83511cad907bb961e59bd3da58215cdc..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/assets/css/main.css +++ /dev/null @@ -1,3512 +0,0 @@ -@import url("fontawesome-all.min.css"); -@import url("https://fonts.googleapis.com/css?family=Source+Sans+Pro:300,300italic,400,400italic"); - -/* - Photon by HTML5 UP - html5up.net | @ajlkn - Free for personal and commercial use under the CCA 3.0 license (html5up.net/license) -*/ - -html, body, div, span, applet, object, -iframe, h1, h2, h3, h4, h5, h6, p, blockquote, -pre, a, abbr, acronym, address, big, cite, -code, del, dfn, em, img, ins, kbd, q, s, samp, -small, strike, strong, sub, sup, tt, var, b, -u, i, center, dl, dt, dd, ol, ul, li, fieldset, -form, label, legend, table, caption, tbody, -tfoot, thead, tr, th, td, article, aside, -canvas, details, embed, figure, figcaption, -footer, header, hgroup, menu, nav, output, ruby, -section, summary, time, mark, audio, video { - margin: 0; - padding: 0; - border: 0; - font-size: 100%; - font: inherit; - vertical-align: baseline;} - -article, aside, details, figcaption, figure, -footer, header, hgroup, menu, nav, section { - display: block;} - -body { - line-height: 1; -} - -ol, ul { - list-style: none; -} - -blockquote, q { - quotes: none; -} - - blockquote:before, blockquote:after, q:before, q:after { - content: ''; - content: none; - } - -table { - border-collapse: collapse; - border-spacing: 0; -} - -body { - -webkit-text-size-adjust: none; -} - -mark { - background-color: transparent; - color: inherit; -} - -input::-moz-focus-inner { - border: 0; - padding: 0; -} - -input, select, textarea { - -moz-appearance: none; - -webkit-appearance: none; - -ms-appearance: none; - appearance: none; -} - -/* Basic */ - - html { - box-sizing: border-box; - } - - *, *:before, *:after { - box-sizing: inherit; - } - - body { - background: #fff; - } - - body.is-preload *, body.is-preload *:before, body.is-preload *:after { - -moz-animation: none !important; - -webkit-animation: none !important; - -ms-animation: none !important; - animation: none !important; - -moz-transition: none !important; - -webkit-transition: none !important; - -ms-transition: none !important; - transition: none !important; - } - - body, input, select, textarea { - color: #666; - font-family: "Source Sans Pro", Helvetica, sans-serif; - font-size: 16pt; - font-weight: 300; - line-height: 1.65em; - } - - a { - -moz-transition: color 0.2s ease-in-out, border-color 0.2s ease-in-out; - -webkit-transition: color 0.2s ease-in-out, border-color 0.2s ease-in-out; - -ms-transition: color 0.2s ease-in-out, border-color 0.2s ease-in-out; - transition: color 0.2s ease-in-out, border-color 0.2s ease-in-out; - border-bottom: dotted 1px #666; - color: inherit; - text-decoration: none; - } - - a:hover { - border-bottom-color: transparent !important; - color: #6bd4c8; - } - - strong, b { - color: #555; - font-weight: 400; - } - - em, i { - font-style: italic; - } - - p { - margin: 0 0 2em 0; - } - - h1, h2, h3, h4, h5, h6 { - color: #555; - line-height: 1em; - margin: 0 0 1em 0; - } - - h1 a, h2 a, h3 a, h4 a, h5 a, h6 a { - color: inherit; - text-decoration: none; - } - - h1 { - font-size: 2.25em; - line-height: 1.35em; - } - - h2 { - font-size: 2em; - line-height: 1.35em; - } - - h3 { - font-size: 1.35em; - line-height: 1.5em; - } - - h4 { - font-size: 1.25em; - line-height: 1.5em; - } - - h5 { - font-size: 0.9em; - line-height: 1.5em; - } - - h6 { - font-size: 0.7em; - line-height: 1.5em; - } - - sub { - font-size: 0.8em; - position: relative; - top: 0.5em; - } - - sup { - font-size: 0.8em; - position: relative; - top: -0.5em; - } - - hr { - border: 0; - border-bottom: solid 1px rgba(144, 144, 144, 0.5); - margin: 2em 0; - } - - hr.major { - margin: 3em 0; - } - - blockquote { - border-left: solid 4px rgba(144, 144, 144, 0.5); - font-style: italic; - margin: 0 0 2em 0; - padding: 0.5em 0 0.5em 2em; - } - - code { - background: rgba(144, 144, 144, 0.075); - border-radius: 4px; - border: solid 1px rgba(144, 144, 144, 0.5); - font-family: "Courier New", monospace; - font-size: 0.9em; - margin: 0 0.25em; - padding: 0.25em 0.65em; - } - - pre { - -webkit-overflow-scrolling: touch; - font-family: "Courier New", monospace; - font-size: 0.9em; - margin: 0 0 2em 0; - } - - pre code { - display: block; - line-height: 1.75em; - padding: 1em 1.5em; - overflow-x: auto; - } - - .align-left { - text-align: left; - } - - .align-center { - text-align: center; - } - - .align-right { - text-align: right; - } - -/* Container */ - - .container { - margin: 0 auto; - max-width: calc(100% - 4em); - width: 60em; - } - - .container.xsmall { - width: 15em; - } - - .container.small { - width: 30em; - } - - .container.medium { - width: 45em; - } - - .container.large { - width: 75em; - } - - .container.xlarge { - width: 90em; - } - - .container.max { - width: 100%; - } - - @media screen and (max-width: 980px) { - - .container { - max-width: 100% !important; - width: 100% !important; - } - - } - - @media screen and (max-width: 480px) { - - .container { - max-width: calc(100% - 3em); - } - - } - -/* Row */ - - .row { - display: flex; - flex-wrap: wrap; - box-sizing: border-box; - align-items: stretch; - } - - .row > * { - box-sizing: border-box; - } - - .row.gtr-uniform > * > :last-child { - margin-bottom: 0; - } - - .row.aln-left { - justify-content: flex-start; - } - - .row.aln-center { - justify-content: center; - } - - .row.aln-right { - justify-content: flex-end; - } - - .row.aln-top { - align-items: flex-start; - } - - .row.aln-middle { - align-items: center; - } - - .row.aln-bottom { - align-items: flex-end; - } - - .row > .imp { - order: -1; - } - - .row > .col-1 { - width: 8.33333%; - } - - .row > .off-1 { - margin-left: 8.33333%; - } - - .row > .col-2 { - width: 16.66667%; - } - - .row > .off-2 { - margin-left: 16.66667%; - } - - .row > .col-3 { - width: 25%; - } - - .row > .off-3 { - margin-left: 25%; - } - - .row > .col-4 { - width: 33.33333%; - } - - .row > .off-4 { - margin-left: 33.33333%; - } - - .row > .col-5 { - width: 41.66667%; - } - - .row > .off-5 { - margin-left: 41.66667%; - } - - .row > .col-6 { - width: 50%; - } - - .row > .off-6 { - margin-left: 50%; - } - - .row > .col-7 { - width: 58.33333%; - } - - .row > .off-7 { - margin-left: 58.33333%; - } - - .row > .col-8 { - width: 66.66667%; - } - - .row > .off-8 { - margin-left: 66.66667%; - } - - .row > .col-9 { - width: 75%; - } - - .row > .off-9 { - margin-left: 75%; - } - - .row > .col-10 { - width: 83.33333%; - } - - .row > .off-10 { - margin-left: 83.33333%; - } - - .row > .col-11 { - width: 91.66667%; - } - - .row > .off-11 { - margin-left: 91.66667%; - } - - .row > .col-12 { - width: 100%; - } - - .row > .off-12 { - margin-left: 100%; - } - - .row.gtr-0 { - margin-top: 0; - margin-left: 0em; - } - - .row.gtr-0 > * { - padding: 0 0 0 0em; - } - - .row.gtr-0.gtr-uniform { - margin-top: 0em; - } - - .row.gtr-0.gtr-uniform > * { - padding-top: 0em; - } - - .row.gtr-25 { - margin-top: 0; - margin-left: -0.5em; - } - - .row.gtr-25 > * { - padding: 0 0 0 0.5em; - } - - .row.gtr-25.gtr-uniform { - margin-top: -0.5em; - } - - .row.gtr-25.gtr-uniform > * { - padding-top: 0.5em; - } - - .row.gtr-50 { - margin-top: 0; - margin-left: -1em; - } - - .row.gtr-50 > * { - padding: 0 0 0 1em; - } - - .row.gtr-50.gtr-uniform { - margin-top: -1em; - } - - .row.gtr-50.gtr-uniform > * { - padding-top: 1em; - } - - .row { - margin-top: 0; - margin-left: -2em; - } - - .row > * { - padding: 0 0 0 2em; - } - - .row.gtr-uniform { - margin-top: -2em; - } - - .row.gtr-uniform > * { - padding-top: 2em; - } - - .row.gtr-150 { - margin-top: 0; - margin-left: -3em; - } - - .row.gtr-150 > * { - padding: 0 0 0 3em; - } - - .row.gtr-150.gtr-uniform { - margin-top: -3em; - } - - .row.gtr-150.gtr-uniform > * { - padding-top: 3em; - } - - .row.gtr-200 { - margin-top: 0; - margin-left: -4em; - } - - .row.gtr-200 > * { - padding: 0 0 0 4em; - } - - .row.gtr-200.gtr-uniform { - margin-top: -4em; - } - - .row.gtr-200.gtr-uniform > * { - padding-top: 4em; - } - - @media screen and (max-width: 1680px) { - - .row { - display: flex; - flex-wrap: wrap; - box-sizing: border-box; - align-items: stretch; - } - - .row > * { - box-sizing: border-box; - } - - .row.gtr-uniform > * > :last-child { - margin-bottom: 0; - } - - .row.aln-left { - justify-content: flex-start; - } - - .row.aln-center { - justify-content: center; - } - - .row.aln-right { - justify-content: flex-end; - } - - .row.aln-top { - align-items: flex-start; - } - - .row.aln-middle { - align-items: center; - } - - .row.aln-bottom { - align-items: flex-end; - } - - .row > .imp-xlarge { - order: -1; - } - - .row > .col-1-xlarge { - width: 8.33333%; - } - - .row > .off-1-xlarge { - margin-left: 8.33333%; - } - - .row > .col-2-xlarge { - width: 16.66667%; - } - - .row > .off-2-xlarge { - margin-left: 16.66667%; - } - - .row > .col-3-xlarge { - width: 25%; - } - - .row > .off-3-xlarge { - margin-left: 25%; - } - - .row > .col-4-xlarge { - width: 33.33333%; - } - - .row > .off-4-xlarge { - margin-left: 33.33333%; - } - - .row > .col-5-xlarge { - width: 41.66667%; - } - - .row > .off-5-xlarge { - margin-left: 41.66667%; - } - - .row > .col-6-xlarge { - width: 50%; - } - - .row > .off-6-xlarge { - margin-left: 50%; - } - - .row > .col-7-xlarge { - width: 58.33333%; - } - - .row > .off-7-xlarge { - margin-left: 58.33333%; - } - - .row > .col-8-xlarge { - width: 66.66667%; - } - - .row > .off-8-xlarge { - margin-left: 66.66667%; - } - - .row > .col-9-xlarge { - width: 75%; - } - - .row > .off-9-xlarge { - margin-left: 75%; - } - - .row > .col-10-xlarge { - width: 83.33333%; - } - - .row > .off-10-xlarge { - margin-left: 83.33333%; - } - - .row > .col-11-xlarge { - width: 91.66667%; - } - - .row > .off-11-xlarge { - margin-left: 91.66667%; - } - - .row > .col-12-xlarge { - width: 100%; - } - - .row > .off-12-xlarge { - margin-left: 100%; - } - - .row.gtr-0 { - margin-top: 0; - margin-left: 0em; - } - - .row.gtr-0 > * { - padding: 0 0 0 0em; - } - - .row.gtr-0.gtr-uniform { - margin-top: 0em; - } - - .row.gtr-0.gtr-uniform > * { - padding-top: 0em; - } - - .row.gtr-25 { - margin-top: 0; - margin-left: -0.5em; - } - - .row.gtr-25 > * { - padding: 0 0 0 0.5em; - } - - .row.gtr-25.gtr-uniform { - margin-top: -0.5em; - } - - .row.gtr-25.gtr-uniform > * { - padding-top: 0.5em; - } - - .row.gtr-50 { - margin-top: 0; - margin-left: -1em; - } - - .row.gtr-50 > * { - padding: 0 0 0 1em; - } - - .row.gtr-50.gtr-uniform { - margin-top: -1em; - } - - .row.gtr-50.gtr-uniform > * { - padding-top: 1em; - } - - .row { - margin-top: 0; - margin-left: -2em; - } - - .row > * { - padding: 0 0 0 2em; - } - - .row.gtr-uniform { - margin-top: -2em; - } - - .row.gtr-uniform > * { - padding-top: 2em; - } - - .row.gtr-150 { - margin-top: 0; - margin-left: -3em; - } - - .row.gtr-150 > * { - padding: 0 0 0 3em; - } - - .row.gtr-150.gtr-uniform { - margin-top: -3em; - } - - .row.gtr-150.gtr-uniform > * { - padding-top: 3em; - } - - .row.gtr-200 { - margin-top: 0; - margin-left: -4em; - } - - .row.gtr-200 > * { - padding: 0 0 0 4em; - } - - .row.gtr-200.gtr-uniform { - margin-top: -4em; - } - - .row.gtr-200.gtr-uniform > * { - padding-top: 4em; - } - - } - - @media screen and (max-width: 1140px) { - - .row { - display: flex; - flex-wrap: wrap; - box-sizing: border-box; - align-items: stretch; - } - - .row > * { - box-sizing: border-box; - } - - .row.gtr-uniform > * > :last-child { - margin-bottom: 0; - } - - .row.aln-left { - justify-content: flex-start; - } - - .row.aln-center { - justify-content: center; - } - - .row.aln-right { - justify-content: flex-end; - } - - .row.aln-top { - align-items: flex-start; - } - - .row.aln-middle { - align-items: center; - } - - .row.aln-bottom { - align-items: flex-end; - } - - .row > .imp-large { - order: -1; - } - - .row > .col-1-large { - width: 8.33333%; - } - - .row > .off-1-large { - margin-left: 8.33333%; - } - - .row > .col-2-large { - width: 16.66667%; - } - - .row > .off-2-large { - margin-left: 16.66667%; - } - - .row > .col-3-large { - width: 25%; - } - - .row > .off-3-large { - margin-left: 25%; - } - - .row > .col-4-large { - width: 33.33333%; - } - - .row > .off-4-large { - margin-left: 33.33333%; - } - - .row > .col-5-large { - width: 41.66667%; - } - - .row > .off-5-large { - margin-left: 41.66667%; - } - - .row > .col-6-large { - width: 50%; - } - - .row > .off-6-large { - margin-left: 50%; - } - - .row > .col-7-large { - width: 58.33333%; - } - - .row > .off-7-large { - margin-left: 58.33333%; - } - - .row > .col-8-large { - width: 66.66667%; - } - - .row > .off-8-large { - margin-left: 66.66667%; - } - - .row > .col-9-large { - width: 75%; - } - - .row > .off-9-large { - margin-left: 75%; - } - - .row > .col-10-large { - width: 83.33333%; - } - - .row > .off-10-large { - margin-left: 83.33333%; - } - - .row > .col-11-large { - width: 91.66667%; - } - - .row > .off-11-large { - margin-left: 91.66667%; - } - - .row > .col-12-large { - width: 100%; - } - - .row > .off-12-large { - margin-left: 100%; - } - - .row.gtr-0 { - margin-top: 0; - margin-left: 0em; - } - - .row.gtr-0 > * { - padding: 0 0 0 0em; - } - - .row.gtr-0.gtr-uniform { - margin-top: 0em; - } - - .row.gtr-0.gtr-uniform > * { - padding-top: 0em; - } - - .row.gtr-25 { - margin-top: 0; - margin-left: -0.5em; - } - - .row.gtr-25 > * { - padding: 0 0 0 0.5em; - } - 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} - - header.major:after { - background: rgba(144, 144, 144, 0.5); - content: ''; - display: inline-block; - height: 1px; - margin-top: 1.5em; - width: 6em; - } - - header.major h2, header.major h3, header.major h4, header.major h5, header.major h6 { - margin: 0; - } - - header.major p { - margin: 0.7em 0 0 0; - } - - header.special { - text-align: center; - } - -/* Form */ - - form { - margin: 0 0 2em 0; - } - - label { - color: #555; - display: block; - font-size: 0.9em; - font-weight: 400; - margin: 0 0 1em 0; - } - - input[type="text"], - input[type="password"], - input[type="email"], - select, - textarea { - -moz-appearance: none; - -webkit-appearance: none; - -ms-appearance: none; - appearance: none; - background: rgba(144, 144, 144, 0.075); - border-radius: 4px; - border: none; - border: solid 1px rgba(144, 144, 144, 0.5); - color: inherit; - display: block; - outline: 0; - padding: 0 1em; - text-decoration: none; - width: 100%; - } - - input[type="text"]:invalid, - input[type="password"]:invalid, - input[type="email"]:invalid, - select:invalid, - textarea:invalid { - box-shadow: none; - } - - input[type="text"]:focus, - input[type="password"]:focus, - input[type="email"]:focus, - select:focus, - textarea:focus { - border-color: #6bd4c8; - } - - select { - background-image: url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' width='40' height='40' preserveAspectRatio='none' viewBox='0 0 40 40'%3E%3Cpath d='M9.4,12.3l10.4,10.4l10.4-10.4c0.2-0.2,0.5-0.4,0.9-0.4c0.3,0,0.6,0.1,0.9,0.4l3.3,3.3c0.2,0.2,0.4,0.5,0.4,0.9 c0,0.4-0.1,0.6-0.4,0.9L20.7,31.9c-0.2,0.2-0.5,0.4-0.9,0.4c-0.3,0-0.6-0.1-0.9-0.4L4.3,17.3c-0.2-0.2-0.4-0.5-0.4-0.9 c0-0.4,0.1-0.6,0.4-0.9l3.3-3.3c0.2-0.2,0.5-0.4,0.9-0.4S9.1,12.1,9.4,12.3z' fill='rgba(144, 144, 144, 0.5)' /%3E%3C/svg%3E"); - background-size: 1.25rem; - background-repeat: no-repeat; - background-position: calc(100% - 1rem) center; - height: 2.75em; - padding-right: 2.75em; - text-overflow: ellipsis; - } - - select option { - color: #555; - background: #fff; - } - - select:focus::-ms-value { - background-color: transparent; - } - - select::-ms-expand { - display: none; - } - - input[type="text"], - input[type="password"], - input[type="email"], - select { - height: 2.75em; - } - - textarea { - padding: 0.75em 1em; - } - - input[type="checkbox"], - input[type="radio"] { - -moz-appearance: none; - -webkit-appearance: none; - -ms-appearance: none; - appearance: none; - display: block; - float: left; - margin-right: -2em; - opacity: 0; - width: 1em; - z-index: -1; - } - - input[type="checkbox"] + label, - input[type="radio"] + label { - text-decoration: none; - color: #666; - cursor: pointer; - display: inline-block; - font-size: 1em; - font-weight: 300; - padding-left: 2.4em; - padding-right: 0.75em; - position: relative; - } - - input[type="checkbox"] + label:before, - input[type="radio"] + label:before { - -moz-osx-font-smoothing: grayscale; - -webkit-font-smoothing: antialiased; - display: inline-block; - font-style: normal; - font-variant: normal; - text-rendering: auto; - line-height: 1; - text-transform: none !important; - font-family: 'Font Awesome 5 Free'; - font-weight: 900; - } - - input[type="checkbox"] + label:before, - input[type="radio"] + label:before { - background: rgba(144, 144, 144, 0.075); - border-radius: 4px; - border: solid 1px rgba(144, 144, 144, 0.5); - content: ''; - display: inline-block; - font-size: 0.8em; - height: 2.0625em; - left: 0; - line-height: 2.0625em; - position: absolute; - text-align: center; - top: 0; - width: 2.0625em; - } - - input[type="checkbox"]:checked + label:before, - input[type="radio"]:checked + label:before { - background: #666666; - border-color: #666666; - color: #ffffff; - content: '\f00c'; - } - - input[type="checkbox"]:focus + label:before, - input[type="radio"]:focus + label:before { - border-color: #6bd4c8; - } - - input[type="checkbox"] + label:before { - border-radius: 4px; - } - - input[type="radio"] + label:before { - border-radius: 100%; - } - - ::-webkit-input-placeholder { - color: #999 !important; - opacity: 1.0; - } - - :-moz-placeholder { - color: #999 !important; - opacity: 1.0; - } - - ::-moz-placeholder { - color: #999 !important; - opacity: 1.0; - } - - :-ms-input-placeholder { - color: #999 !important; - opacity: 1.0; - } - -/* Box */ - - .box { - border-radius: 4px; - border: solid 1px rgba(144, 144, 144, 0.5); - margin-bottom: 2em; - padding: 1.5em; - } - - .box > :last-child, - .box > :last-child > :last-child, - .box > :last-child > :last-child > :last-child { - margin-bottom: 0; - } - - .box.alt { - border: 0; - border-radius: 0; - padding: 0; - } - -/* Icon */ - - .icon { - text-decoration: none; - border-bottom: none; - position: relative; - } - - .icon:before { - -moz-osx-font-smoothing: grayscale; - -webkit-font-smoothing: antialiased; - display: inline-block; - font-style: normal; - font-variant: normal; - text-rendering: auto; - line-height: 1; - text-transform: none !important; - font-family: 'Font Awesome 5 Free'; - font-weight: 400; - } - - .icon > .label { - display: none; - } - - .icon:before { - line-height: inherit; - } - - .icon.solid:before { - font-weight: 900 !important; - } - - .icon.brands:before { - font-family: 'Font Awesome 5 Brands'; - } - - .icon.style1:before { - color: #c3e895; - } - - .icon.style2:before { - color: #8addaa; - } - - .icon.style3:before { - color: #6bd4c8; - } - - .icon.style4:before { - color: #57aed3; - } - - .icon.style5:before { - color: #4a87d3; - } - - .icon.style6:before { - color: #6b88e6; - } - - .icon.major { - text-decoration: none; - color: #555; - cursor: default; - display: inline-block; - margin: 0 0 1.5em 0; - } - - .icon.major:before { - -moz-osx-font-smoothing: grayscale; - -webkit-font-smoothing: antialiased; - display: inline-block; - font-style: normal; - font-variant: normal; - text-rendering: auto; - line-height: 1; - text-transform: none !important; - font-family: 'Font Awesome 5 Free'; - font-weight: 400; - } - - .icon.major:before { - font-size: 4em; - line-height: 1em; - } - -/* Image */ - - .image { - border-radius: 4px; - border: 0; - display: inline-block; - position: relative; - } - - .image img { - border-radius: 4px; - display: block; - } - - .image.left { - float: left; - padding: 0 1.5em 1em 0; - top: 0.25em; - } - - .image.right { - float: right; - padding: 0 0 1em 1.5em; - top: 0.25em; - } - - .image.left, .image.right { - max-width: 40%; - } - - .image.left img, .image.right img { - width: 100%; - } - - .image.fit { - display: block; - margin: 0 0 2em 0; - width: 100%; - } - - .image.fit img { - width: 100%; - } - -/* List */ - - ol { - list-style: decimal; - margin: 0 0 2em 0; - padding-left: 1.25em; - } - - ol li { - padding-left: 0.25em; - } - - ul { - list-style: disc; - margin: 0 0 2em 0; - padding-left: 1em; - } - - ul li { - padding-left: 0.5em; - } - - ul.alt { - list-style: none; - padding-left: 0; - } - - ul.alt li { - border-top: solid 1px rgba(144, 144, 144, 0.5); - padding: 0.5em 0; - } - - ul.alt li:first-child { - border-top: 0; - padding-top: 0; - } - - dl { - margin: 0 0 2em 0; - } - -/* Actions */ - - ul.actions { - display: -moz-flex; - display: -webkit-flex; - display: -ms-flex; - display: flex; - cursor: default; - list-style: none; - margin-left: -1em; - padding-left: 0; - } - - ul.actions li { - padding: 0 0 0 1em; - vertical-align: middle; - } - - ul.actions.special { - -moz-justify-content: center; - -webkit-justify-content: center; - -ms-justify-content: center; - justify-content: center; - width: 100%; - margin-left: 0; - } - - ul.actions.special li:first-child { - padding-left: 0; - } - - ul.actions.stacked { - -moz-flex-direction: column; - -webkit-flex-direction: column; - -ms-flex-direction: column; - flex-direction: column; - margin-left: 0; - } - - ul.actions.stacked li { - padding: 1.3em 0 0 0; - } - - ul.actions.stacked li:first-child { - padding-top: 0; - } - - ul.actions.fit { - width: calc(100% + 1em); - } - - ul.actions.fit li { - -moz-flex-grow: 1; - -webkit-flex-grow: 1; - -ms-flex-grow: 1; - flex-grow: 1; - -moz-flex-shrink: 1; - -webkit-flex-shrink: 1; - -ms-flex-shrink: 1; - flex-shrink: 1; - width: 100%; - } - - ul.actions.fit li > * { - width: 100%; - } - - ul.actions.fit.stacked { - width: 100%; - } - - @media screen and (max-width: 480px) { - - ul.actions:not(.fixed) { - -moz-flex-direction: column; - -webkit-flex-direction: column; - -ms-flex-direction: column; - flex-direction: column; - margin-left: 0; - width: 100% !important; - } - - ul.actions:not(.fixed) li { - -moz-flex-grow: 1; - -webkit-flex-grow: 1; - -ms-flex-grow: 1; - flex-grow: 1; - -moz-flex-shrink: 1; - -webkit-flex-shrink: 1; - -ms-flex-shrink: 1; - flex-shrink: 1; - padding: 1em 0 0 0; - text-align: center; - width: 100%; - } - - ul.actions:not(.fixed) li > * { - width: 100%; - } - - ul.actions:not(.fixed) li:first-child { - padding-top: 0; - } - - ul.actions:not(.fixed) li input[type="submit"], - ul.actions:not(.fixed) li input[type="reset"], - ul.actions:not(.fixed) li input[type="button"], - ul.actions:not(.fixed) li button, - ul.actions:not(.fixed) li .button { - width: 100%; - } - - ul.actions:not(.fixed) li input[type="submit"].icon:before, - ul.actions:not(.fixed) li input[type="reset"].icon:before, - ul.actions:not(.fixed) li input[type="button"].icon:before, - ul.actions:not(.fixed) li button.icon:before, - ul.actions:not(.fixed) li .button.icon:before { - margin-left: -0.5em; - } - - } - -/* Icons */ - - ul.icons { - cursor: default; - list-style: none; - padding-left: 0; - } - - ul.icons li { - display: inline-block; - padding: 0 1.25em 0 0; - } - - ul.icons li:last-child { - padding-right: 0; - } - - ul.icons li .icon:before { - font-size: 1.5em; - } - -/* Major Icons */ - - ul.major-icons { - list-style: none; - padding-left: 0; - } - - ul.major-icons li { - display: inline-block; - padding: 2.5em; - text-align: center; - } - - ul.major-icons li .icon { - -moz-transform: rotate(45deg); - -webkit-transform: rotate(45deg); - -ms-transform: rotate(45deg); - transform: rotate(45deg); - border-radius: 4px; - border: solid 1px rgba(144, 144, 144, 0.5); - display: inline-block; - height: 8em; - line-height: 8em; - margin: 0; - text-align: center; - width: 8em; - } - - ul.major-icons li .icon:before { - -moz-transform: rotate(-45deg); - -webkit-transform: rotate(-45deg); - -ms-transform: rotate(-45deg); - transform: rotate(-45deg); - display: inline-block; - line-height: inherit; - } - -/* Table */ - - .table-wrapper { - -webkit-overflow-scrolling: touch; - overflow-x: auto; - } - - table { - margin: 0 0 2em 0; - width: 100%; - } - - table tbody tr { - border-left: 0; - border-right: 0; - } - - table tbody tr:nth-child(2n + 1) { - background-color: rgba(144, 144, 144, 0.075); - } - - table td { - padding: 0.75em 0.75em; - } - - table th { - color: #555; - font-size: 0.9em; - font-weight: 400; - padding: 0 0.75em 0.75em 0.75em; - text-align: left; - } - - table thead { - border-bottom: solid 1px rgba(144, 144, 144, 0.5); - } - - table tfoot { - border-top: solid 1px rgba(144, 144, 144, 0.5); - } - - table.alt { - border-collapse: separate; - } - - table.alt tbody tr td { - border: solid 1px rgba(144, 144, 144, 0.5); - border-left-width: 0; - border-top-width: 0; - } - - table.alt tbody tr td:first-child { - border-left-width: 1px; - } - - table.alt tbody tr:first-child td { - border-top-width: 1px; - } - - table.alt thead { - border-bottom: 0; - } - - table.alt tfoot { - border-top: 0; - } - -/* Button */ - - input[type="submit"], - input[type="reset"], - input[type="button"], - button, - .button { - -moz-appearance: none; - -webkit-appearance: none; - -ms-appearance: none; - appearance: none; - -moz-transition: background-color 0.2s ease-in-out, color 0.2s ease-in-out; - -webkit-transition: background-color 0.2s ease-in-out, color 0.2s ease-in-out; - -ms-transition: background-color 0.2s ease-in-out, color 0.2s ease-in-out; - transition: background-color 0.2s ease-in-out, color 0.2s ease-in-out; - background-color: transparent; - border-radius: 4px; - border: 0; - box-shadow: inset 0 0 0 1px rgba(144, 144, 144, 0.5); - color: #555 !important; - cursor: pointer; - display: inline-block; - font-size: 0.9em; - font-weight: 400; - height: 2.75em; - letter-spacing: 0.125em; - line-height: 2.75em; - padding: 0 2em; - text-align: center; - text-decoration: none; - text-indent: 0.125em; - text-transform: uppercase; - white-space: nowrap; - } - - input[type="submit"]:hover, - input[type="reset"]:hover, - input[type="button"]:hover, - button:hover, - .button:hover { - background-color: rgba(144, 144, 144, 0.075); - } - - input[type="submit"]:active, - input[type="reset"]:active, - input[type="button"]:active, - button:active, - .button:active { - background-color: rgba(144, 144, 144, 0.2); - } - - input[type="submit"].icon:before, - input[type="reset"].icon:before, - input[type="button"].icon:before, - button.icon:before, - .button.icon:before { - margin-right: 0.5em; - } - - input[type="submit"].fit, - input[type="reset"].fit, - input[type="button"].fit, - button.fit, - .button.fit { - width: 100%; - } - - input[type="submit"].small, - input[type="reset"].small, - input[type="button"].small, - button.small, - .button.small { - font-size: 0.7em; - } - - input[type="submit"].large, - input[type="reset"].large, - input[type="button"].large, - button.large, - .button.large { - font-size: 1.25em; - } - - input[type="submit"].wide, - input[type="reset"].wide, - input[type="button"].wide, - button.wide, - .button.wide { - min-width: 12em; - } - - input[type="submit"].primary, - input[type="reset"].primary, - input[type="button"].primary, - button.primary, - .button.primary { - background-color: #555; - box-shadow: none !important; - color: #fff !important; - } - - input[type="submit"].primary:hover, - input[type="reset"].primary:hover, - input[type="button"].primary:hover, - button.primary:hover, - .button.primary:hover { - background-color: #626262; - } - - input[type="submit"].primary:active, - input[type="reset"].primary:active, - input[type="button"].primary:active, - button.primary:active, - .button.primary:active { - background-color: #484848; - } - - input[type="submit"].disabled, input[type="submit"]:disabled, - input[type="reset"].disabled, - input[type="reset"]:disabled, - input[type="button"].disabled, - input[type="button"]:disabled, - button.disabled, - button:disabled, - .button.disabled, - .button:disabled { - cursor: default; - opacity: 0.25; - } - -/* Main */ - - .main { - padding: 6em 0 4em 0 ; - } - - .main.style1 { - background: #fff; - } - - .main.style2 { - background-color: #333; - color: rgba(255, 255, 255, 0.75); - background-attachment: fixed, fixed; - background-image: url("images/overlay1.png"), url("../../images/header.jpg"); - background-size: auto, cover; - } - - .main.style2 a { - border-bottom-color: rgba(255, 255, 255, 0.5); - } - - .main.style2 a:hover { - color: #ffffff; - } - - .main.style2 h1, .main.style2 h2, .main.style2 h3, .main.style2 h4, .main.style2 h5, .main.style2 h6, .main.style2 strong, .main.style2 b { - color: #ffffff; - } - - .main.style2 header p { - color: #ffffff; - } - - .main.style2 header.major:after { - background: #ffffff; - } - - .main.style2 input[type="submit"], - .main.style2 input[type="reset"], - .main.style2 input[type="button"], - .main.style2 button, - .main.style2 .button { - box-shadow: inset 0 0 0 1px #ffffff; - color: #ffffff !important; - } - - .main.style2 input[type="submit"]:hover, - .main.style2 input[type="reset"]:hover, - .main.style2 input[type="button"]:hover, - .main.style2 button:hover, - .main.style2 .button:hover { - background-color: rgba(255, 255, 255, 0.125); - } - - .main.style2 input[type="submit"]:active, - .main.style2 input[type="reset"]:active, - .main.style2 input[type="button"]:active, - .main.style2 button:active, - .main.style2 .button:active { - background-color: rgba(255, 255, 255, 0.25); - } - - .main.style2 input[type="submit"].primary, - .main.style2 input[type="reset"].primary, - .main.style2 input[type="button"].primary, - .main.style2 button.primary, - .main.style2 .button.primary { - background-color: #ffffff; - box-shadow: inset 0 0 0 1px #ffffff !important; - color: #333 !important; - } - - .main.style2 input[type="submit"].primary:hover, - .main.style2 input[type="reset"].primary:hover, - .main.style2 input[type="button"].primary:hover, - .main.style2 button.primary:hover, - .main.style2 .button.primary:hover { - background-color: rgba(255, 255, 255, 0.125) !important; - color: #ffffff !important; - } - - .main.style2 input[type="submit"].primary:active, - .main.style2 input[type="reset"].primary:active, - .main.style2 input[type="button"].primary:active, - .main.style2 button.primary:active, - .main.style2 .button.primary:active { - background-color: rgba(255, 255, 255, 0.25) !important; - } - - .main.style2 ul.major-icons li .icon { - border-color: #ffffff; - } - - .main.style2 .icon.major { - color: #ffffff; - } - -/* Header */ - - #header { - padding: 9em 0 9em 0 ; - background-color: #4686a0; - color: rgba(255, 255, 255, 0.75); - background-attachment: fixed, fixed, fixed; - background-image: url("images/overlay2.png"), url("images/overlay3.svg"), linear-gradient(45deg, #9dc66b 5%, #4fa49a 30%, #4361c2); - background-position: top left, center center, center center; - background-size: auto, cover, cover; - overflow: hidden; - position: relative; - text-align: center; - } - - #header a { - border-bottom-color: rgba(255, 255, 255, 0.5); - } - - #header a:hover { - color: #ffffff; - } - - #header h1, #header h2, #header h3, #header h4, #header h5, #header h6, #header strong, #header b { - color: #ffffff; - } - - #header header p { - color: #ffffff; - } - - #header header.major:after { - background: #ffffff; - } - - #header input[type="submit"], - #header input[type="reset"], - #header input[type="button"], - #header button, - #header .button { - box-shadow: inset 0 0 0 1px #ffffff; - color: #ffffff !important; - } - - #header input[type="submit"]:hover, - #header input[type="reset"]:hover, - #header input[type="button"]:hover, - #header button:hover, - #header .button:hover { - background-color: rgba(255, 255, 255, 0.125); - } - - #header input[type="submit"]:active, - #header input[type="reset"]:active, - #header input[type="button"]:active, - #header button:active, - #header .button:active { - background-color: rgba(255, 255, 255, 0.25); - } - - #header input[type="submit"].primary, - #header input[type="reset"].primary, - #header input[type="button"].primary, - #header button.primary, - #header .button.primary { - background-color: #ffffff; - box-shadow: inset 0 0 0 1px #ffffff !important; - color: #4686a0 !important; - } - - #header input[type="submit"].primary:hover, - #header input[type="reset"].primary:hover, - #header input[type="button"].primary:hover, - #header button.primary:hover, - #header .button.primary:hover { - background-color: rgba(255, 255, 255, 0.125) !important; - color: #ffffff !important; - } - - #header input[type="submit"].primary:active, - #header input[type="reset"].primary:active, - #header input[type="button"].primary:active, - #header button.primary:active, - #header .button.primary:active { - background-color: rgba(255, 255, 255, 0.25) !important; - } - - #header ul.major-icons li .icon { - border-color: #ffffff; - } - - #header .icon.major { - color: #ffffff; - } - - #header h1 { - margin: 0; - } - - #header p { - margin: 1em 0 0 0; - } - - #header .actions { - -moz-justify-content: center; - -webkit-justify-content: center; - -ms-justify-content: center; - justify-content: center; - margin-top: 2.5em; - margin-left: 0; - position: relative; - } - - #header .actions li:first-child { - padding-left: 0; - } - - #header .actions:after { - background: #ffffff; - content: ''; - height: 100vh; - left: 50%; - position: absolute; - top: 100%; - width: 1px; - } - - #header .inner { - -moz-transition: -moz-transform 1.5s ease, opacity 2s ease; - -webkit-transition: -webkit-transform 1.5s ease, opacity 2s ease; - -ms-transition: -ms-transform 1.5s ease, opacity 2s ease; - transition: transform 1.5s ease, opacity 2s ease; - -moz-transition-delay: 0.25s; - -webkit-transition-delay: 0.25s; - -ms-transition-delay: 0.25s; - transition-delay: 0.25s; - -moz-transform: scale(1); - -webkit-transform: scale(1); - -ms-transform: scale(1); - transform: scale(1); - opacity: 1; - position: relative; - z-index: 1; - } - - #header .inner .actions { - -moz-transition: -moz-transform 1.25s ease; - -webkit-transition: -webkit-transform 1.25s ease; - -ms-transition: -ms-transform 1.25s ease; - transition: transform 1.25s ease; - -moz-transition-delay: 1s; - -webkit-transition-delay: 1s; - -ms-transition-delay: 1s; - transition-delay: 1s; - -moz-transform: translateY(0); - -webkit-transform: translateY(0); - -ms-transform: translateY(0); - transform: translateY(0); - opacity: 1; - } - - #header:after { - -moz-transition: opacity 1s ease; - -webkit-transition: opacity 1s ease; - -ms-transition: opacity 1s ease; - transition: opacity 1s ease; - background-image: linear-gradient(45deg, #4fa49a, #4361c2); - content: ''; - height: 100%; - left: 0; - opacity: 0; - position: absolute; - top: 0; - width: 100%; - } - - body.is-preload #header .inner { - -moz-transform: scale(1.05); - -webkit-transform: scale(1.05); - -ms-transform: scale(1.05); - transform: scale(1.05); - opacity: 0; - } - - body.is-preload #header .inner .actions { - -moz-transform: translateY(30em); - -webkit-transform: translateY(30em); - -ms-transform: translateY(30em); - transform: translateY(30em); - opacity: 0; - } - - body.is-preload #header:after { - opacity: 1; - } - -/* Footer */ - - #footer { - padding: 6em 0 6em 0 ; - background-color: #4686a0; - color: rgba(255, 255, 255, 0.75); - background-attachment: fixed, fixed, fixed; - background-image: url("images/overlay2.png"), url("images/overlay4.svg"), linear-gradient(45deg, #4361c2, #4fa49a 50%, #9dc66b 95%); - background-position: top left, center center, center center; - background-size: auto, cover, cover; - text-align: center; - } - - #footer a { - border-bottom-color: rgba(255, 255, 255, 0.5); - } - - #footer a:hover { - color: #ffffff; - } - - #footer h1, #footer h2, #footer h3, #footer h4, #footer h5, #footer h6, #footer strong, #footer b { - color: #ffffff; - } - - #footer header p { - color: #ffffff; - } - - #footer header.major:after { - background: #ffffff; - } - - #footer input[type="submit"], - #footer input[type="reset"], - #footer input[type="button"], - #footer button, - #footer .button { - box-shadow: inset 0 0 0 1px #ffffff; - color: #ffffff !important; - } - - #footer input[type="submit"]:hover, - #footer input[type="reset"]:hover, - #footer input[type="button"]:hover, - #footer button:hover, - #footer .button:hover { - background-color: rgba(255, 255, 255, 0.125); - } - - #footer input[type="submit"]:active, - #footer input[type="reset"]:active, - #footer input[type="button"]:active, - #footer button:active, - #footer .button:active { - background-color: rgba(255, 255, 255, 0.25); - } - - #footer input[type="submit"].primary, - #footer input[type="reset"].primary, - #footer input[type="button"].primary, - #footer button.primary, - #footer .button.primary { - background-color: #ffffff; - box-shadow: inset 0 0 0 1px #ffffff !important; - color: #4686a0 !important; - } - - #footer input[type="submit"].primary:hover, - #footer input[type="reset"].primary:hover, - #footer input[type="button"].primary:hover, - #footer button.primary:hover, - #footer .button.primary:hover { - background-color: rgba(255, 255, 255, 0.125) !important; - color: #ffffff !important; - } - - #footer input[type="submit"].primary:active, - #footer input[type="reset"].primary:active, - #footer input[type="button"].primary:active, - #footer button.primary:active, - #footer .button.primary:active { - background-color: rgba(255, 255, 255, 0.25) !important; - } - - #footer ul.major-icons li .icon { - border-color: #ffffff; - } - - #footer .icon.major { - color: #ffffff; - } - - #footer .icons { - margin: 0; - } - - #footer .copyright { - font-size: 0.8em; - list-style: none; - margin: 2em 0 0 0; - padding: 0; - } - - #footer .copyright li { - border-left: solid 1px; - display: inline-block; - line-height: 1em; - margin-left: 1em; - padding: 0 0 0 1em; - } - - #footer .copyright li:first-child { - border-left: 0; - margin-left: 0; - padding: 0; - } - -/* XLarge */ - - @media screen and (max-width: 1680px) { - - /* Basic */ - - body, input, select, textarea { - font-size: 14pt; - } - - /* Header */ - - #header { - padding: 6em 0 6em 0 ; - } - - } - -/* Large */ - - @media screen and (max-width: 1140px) { - - /* Basic */ - - body, input, select, textarea { - font-size: 13pt; - } - - h1 br, h2 br, h3 br, h4 br, h5 br, h6 br { - display: none; - } - - /* List */ - - ul.major-icons li { - padding: 2em; - } - - ul.major-icons li .icon { - height: 8em; - line-height: 8em; - width: 8em; - } - - /* Main */ - - .main { - padding: 4em 0 2em 0 ; - } - - .main.style2 { - background-attachment: scroll; - } - - /* Header */ - - #header { - padding: 5em 0 5em 0 ; - background-attachment: scroll; - } - - #header br { - display: inline; - } - - /* Footer */ - - #footer { - padding: 4em 0 4em 0 ; - background-attachment: scroll; - } - - } - -/* Medium */ - - @media screen and (max-width: 980px) { - - /* List */ - - ul.major-icons li { - padding: 2em; - } - - ul.major-icons li .icon { - height: 7em; - line-height: 7em; - width: 7em; - } - - /* Main */ - - .main { - padding: 5em 3em 3em 3em ; - } - - /* Header */ - - #header { - padding: 8em 3em 8em 3em ; - } - - /* Footer */ - - #footer { - padding: 5em 3em 5em 3em ; - } - - /* One */ - - #one { - text-align: center; - } - - /* Two */ - - #two { - text-align: center; - } - - } - -/* Small */ - - @media screen and (max-width: 736px) { - - /* Basic */ - - body, input, select, textarea { - font-size: 12pt; - } - - h1 { - font-size: 1.75em; - } - - h2 { - font-size: 1.5em; - } - - h3 { - font-size: 1.1em; - } - - h4 { - font-size: 1em; - } - - /* List */ - - ul.major-icons li { - padding: 1.5em; - } - - ul.major-icons li .icon { - height: 5em; - line-height: 5em; - width: 5em; - } - - ul.major-icons li .icon:before { - font-size: 42px; - } - - /* Icon */ - - .icon.major { - margin: 0 0 1em 0; - } - - /* Button */ - - input[type="submit"], - input[type="reset"], - input[type="button"], - button, - .button { - height: 3em; - line-height: 3em; - } - - /* Main */ - - .main { - padding: 3em 1.5em 1em 1.5em ; - } - - /* Header */ - - #header { - padding: 4em 3em 4em 3em ; - } - - #header .actions { - margin: 2em 0 0 0; - } - - /* Footer */ - - #footer { - padding: 3em 1.5em 3em 1.5em ; - } - - } - -/* XSmall */ - - @media screen and (max-width: 480px) { - - /* Button */ - - input[type="submit"], - input[type="reset"], - input[type="button"], - .button { - padding: 0; - } - - /* Main */ - - .main { - padding: 2em 1.5em 0.1em 1.5em ; - } - - /* Header */ - - #header { - padding: 4em 2em 4em 2em ; - } - - #header br { - display: none; - } - - /* Footer */ - - #footer { - padding: 2em 1.5em 2em 1.5em ; - } - - #footer .copyright { - margin: 1.5em 0 0 0; - } - - #footer .copyright li { - border: 0; - display: block; - margin: 1em 0 0 0; - padding: 0; - } - - #footer .copyright li:first-child { - margin-top: 0; - } - - } - -/* XXSmall */ - - @media screen and (max-width: 320px) { - - /* Basic */ - - html, body { - min-width: 320px; - } - - /* Main */ - - .main { - padding: 2em 1em 0.1em 1em ; - } - - /* Header */ - - #header { - padding: 3em 1em 3em 1em ; - } - - /* Footer */ - - #footer { - padding: 2em 1em 2em 1em ; - } - - } \ No newline at end of file diff --git a/spaces/colakin/video-generater/public/ffmpeg/doc/examples/encode_audio.c b/spaces/colakin/video-generater/public/ffmpeg/doc/examples/encode_audio.c deleted file mode 100644 index bb16683d946b055f371e66840e75c71f03c7d782..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/doc/examples/encode_audio.c +++ /dev/null @@ -1,240 +0,0 @@ -/* - * Copyright (c) 2001 Fabrice Bellard - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to deal - * in the Software without restriction, including without limitation the rights - * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell - * copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in - * all copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL - * THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN - * THE SOFTWARE. - */ - -/** - * @file libavcodec encoding audio API usage examples - * @example encode_audio.c - * - * Generate a synthetic audio signal and encode it to an output MP2 file. - */ - -#include