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-<li>32MB RAM or higher</li>
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-<p>You can download Luxonix Ravity S 1.4.3.exe from the official website of Sonic Cat, which is the new name of Luxonix after they merged with ESI in 2015.</p>
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-<li>Double-click LUXONIX_ravity(S)_1_1_2_win.exe to execute installation.</li>
-<li>Click Next button to continue.</li>
-<li>Set the folder that install Ravity(S). Basically, your VstPlugIns Folder is default.</li>
-<li>Click Install button to start installing Ravity(S).</li>
-<li>Installing Ravity(S) is complete.</li>
-</ol>
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-<li>The preset browser, where you can select from over 1000 presets organized by categories.</li>
-<li>The edit panel, where you can adjust the parameters of each layer of sound.</li>
-<li>The back panel, where you can set up global functions such as MIDI assign, clipboard functions, hot-keys, etc.</li>
-<li>The LCD panel, where you can monitor and input the values of each parameter.</li>
-</ul>
-<p>To load a preset, simply click on its name in the preset browser or use the arrow keys on your keyboard.</p>
-<p>To edit a preset, click on the edit button on the top right corner of the main module or press F5 on your keyboard.</p>
-<p>You can edit each layer of sound by clicking on its number (1-4) on the left side of the edit panel or pressing F6-F9 on your keyboard.</p>
-<p>You can adjust the basic settings such as volume, pan, tune, polyphony mode, etc., by using the knobs on the top row of the edit panel.</p>
-<p>You can modify the sound characteristics by using the tabs below the knobs: OSC (oscillator), FILT (filter), AMP (amplifier), LFO (low frequency oscillator), ARP (arpeggiator).</p>
-<p>You can add effects by clicking on the LFX button on the bottom right corner of the main module or pressing F10 on your keyboard.</p>
-<p>You can choose from 24 types of effects by clicking on their names or using the arrow keys on your keyboard.</p>
-<p>You can adjust the parameters of each effect by using the knobs below their names or clicking on their names and entering values with your keyboard.</p>
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- <li>To copy and paste settings between layers or presets, use the clipboard functions by right-clicking on them or pressing Ctrl+C / Ctrl+V / Ctrl+X / Ctrl+Z / Ctrl+Y on your keyboard.</li>
- <li>To quickly access the manual or other information about Luxonix Ravity S 1.4.3.exe , click on the question mark button on the top left corner of the main module or press F12 on your keyboard.</li>
- </ul>
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- <p>Here are some FAQs about Luxonix Ravity S 1.4.3.exe that you may find useful:</p>
- <ul>
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- <li>A: Luxonix Ravity S 1.4.3.exe costs $49 USD on the official website of Sonic Cat. You can also get it as part of the Ravity Bundle for $99 USD, which includes Ravity R (a rhythm/drum sound module) and Ravity16 (a host application for Ravity S and R).</li>
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-<p>If you are a fan of comedy and fantasy anime, you might have heard of <strong>Hataraku Maou-sama</strong>, also known as <strong>The Devil is a Part-Timer!</strong>. This anime is about a demon lord who ends up in modern-day Tokyo and has to work at a fast-food restaurant to survive. Along with his loyal general and some other quirky characters, he faces hilarious situations and challenges in his daily life.</p>
-<p>Hataraku Maou-sama S1 is a 13-episode anime series that aired in 2013 and received positive reviews from critics and viewers alike. It is based on a light novel series by Satoshi Wagahara and illustrated by Oniku. The anime has a unique premise, witty humor, charming characters, and an engaging plot.</p>
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-<p>If you are interested in watching or rewatching this anime, you might be wondering how to download it for offline viewing. In this article, we will show you how to download Hataraku Maou-sama S1 from different sources and what are the advantages and disadvantages of each option.</p>
- <h2>What is Hataraku Maou-sama?</h2>
-<p>Hataraku Maou-sama is an anime series that follows the adventures of Sadao Maou, the demon lord of Ente Isla, a fantasy world where he was about to conquer it with his vast army. However, he was defeated by Emilia, the hero of Ente Isla, and forced to flee through a dimensional portal that brought him to Earth.</p>
-<p>On Earth, Maou finds himself in Tokyo with no magic or power. He assumes a human identity and starts working at MgRonald's, a local fast-food chain. He also lives with his loyal general Alsiel, who takes care of their household chores and finances. Meanwhile, Emilia follows Maou to Earth and also adopts a human identity as Emi Yusa, a customer service representative.</p>
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-<p>If you want to watch Hataraku Maou-sama online, you have several options to choose from. However, not all of them are legal, safe, or reliable. In this section, we will compare some of the most popular anime streaming platforms that have Hataraku Maou-sama in their catalog and see which one is the best for you.</p>
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-<p>Crunchyroll is one of the most popular and reputable anime streaming sites in the world. It has a huge library of anime titles, including Hataraku Maou-sama, that you can watch in high definition and with English subtitles or dubbing. You can also access exclusive content, such as manga, games, and merchandise.</p>
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-<p>The advantage of watching Hataraku Maou-sama on Crunchyroll is that you can support the official release and the creators of the anime. You can also watch it on multiple devices, such as your computer, smartphone, tablet, or smart TV. You can also download episodes for offline viewing if you have a premium account.</p>
-<p>You can watch Hataraku Maou-sama on Crunchyroll by clicking <a href="">here</a>.</p>
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-<p>If you decide to watch Hataraku Maou-sama on 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.</p>
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-<p>Now that you know where to watch Hataraku Maou-sama online, you might be wondering how to download it for offline viewing. Downloading anime episodes can be useful if you want to watch them without an internet connection, save your data usage, or share them with your friends.</p>
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-<p>The easiest and safest way to download Hataraku Maou-sama S1 is from Crunchyroll. As we mentioned before, Crunchyroll is a legal and reliable anime streaming platform that offers high-quality videos and subtitles. If you have a premium account or a free trial, you can download episodes from Crunchyroll for offline viewing.</p>
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-<li>Tap on the download icon at the bottom of the screen. You can choose the video quality and subtitle language before downloading.</li>
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-<li>Enjoy watching Hataraku Maou-sama S1 offline. You can access your downloaded episodes in the downloads section of the app.</li>
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-<p>Here are some screenshots or images to illustrate the steps:</p>
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-<img src="" alt="Crunchyroll app screenshot 2">
-<img src="" alt="Crunchyroll app screenshot 3">
-<p>The advantage of downloading from Crunchyroll is that you can enjoy high-quality videos and subtitles without any ads or interruptions. You can also watch them on any device that supports the Crunchyroll app. You can also support the official release and the creators of the anime by paying for a subscription.</p>
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-<p>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.</p>
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-<p>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.</p>
-<p>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.</p>
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-<li>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.</li>
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-<li>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.</li>
-<li>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.</li>
-<li>Removed ads: You can play the game without any annoying ads that interrupt your gameplay. You can also save your data and battery life.</li>
-<li>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.</li>
-<li>No root required: You can install and play the game without rooting your device. You can also update the game without any problem.</li>
-</ul>
- <h2>How to download and install Dynamons World Mod Apk?</h2>
-<p>If you want to download and install Dynamons World Mod Apk on your device, you need to follow these steps:</p>
- <h3>Steps to download and install</h3>
-<ol>
-<li>Click on this link to download the Dynamons World Mod Apk file on your device.</li>
-<li>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.</li>
-<li>Locate the downloaded file in your file manager and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to complete.</li>
-<li>Launch the game and enjoy playing Dynamons World Mod Apk with unlimited money and other benefits.</li>
-</ol>
- <h3>Tips and tricks for playing Dynamons World Mod Apk</h3>
-<p>Here are some tips and tricks that can help you play Dynamons World Mod Apk better:</p>
-<ul>
-<li>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.</li>
-<li>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.</li>
-<li>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.</li>
-<li>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.</li>
-<li>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.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>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.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Dynamons World Mod Apk:</p>
-<ol>
-<li>Is Dynamons World Mod Apk safe to use?</li>
-<p>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.</p>
-<li>Is Dynamons World Mod Apk legal to use?</li>
-<p>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.</p>
-<li>Can I play Dynamons World Mod Apk online?</li>
-<p>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.</p>
-<li>Can I update Dynamons World Mod Apk?</li>
-<p>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.</p>
-<li>Can I uninstall Dynamons World Mod Apk?</li>
-<p>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.</p>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />

spaces/AIatUIUC/CodeLATS/generators/generator_types.py

@@ -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]:
-        ...

spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/data/audio.py

@@ -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

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/shareConversation.ts

@@ -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);
-	}
-}

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

@@ -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;

spaces/AlexWortega/Kandinsky2.0/README.md

@@ -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

spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/utils/visualizer.py

@@ -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([
-      '<script type="text/javascript">',
-      'var column_idx;',
-      'var sort_by_ascending = ' + str(sort_by_ascending).lower() + ';',
-      '',
-      'function sorting(tbody, column_idx){',
-      '  this.column_idx = column_idx;',
-      '  Array.from(tbody.rows)',
-      '       .sort(compareCells)',
-      '       .forEach(function(row) { tbody.appendChild(row); })',
-      '  sort_by_ascending = !sort_by_ascending;',
-      '}',
-      '',
-      'function compareCells(row_a, row_b) {',
-      '  var val_a = row_a.cells[column_idx].innerText;',
-      '  var val_b = row_b.cells[column_idx].innerText;',
-      '  var flag = sort_by_ascending ? 1 : -1;',
-      '  return flag * (val_a > val_b ? 1 : -1);',
-      '}',
-      '</script>',
-      '',
-      '<html>',
-      '',
-      '<head>',
-      '<style>',
-      '  table {',
-      '    border-spacing: 0;',
-      '    border: 1px solid black;',
-      '  }',
-      '  th {',
-      '    cursor: pointer;',
-      '  }',
-      '  th, td {',
-      '    text-align: left;',
-      '    vertical-align: middle;',
-      '    border-collapse: collapse;',
-      '    border: 0.5px solid black;',
-      '    padding: 8px;',
-      '  }',
-      '  tr:nth-child(even) {',
-      '    background-color: #d2d2d2;',
-      '  }',
-      '</style>',
-      '</head>',
-      '',
-      '<body>',
-      '',
-      '<table>',
-      '<thead>',
-      '<tr>',
-      ''])
-  for idx, column_name in enumerate(column_name_list):
-    header += f'  <th onclick="sorting(tbody, {idx})">{column_name}</th>\n'
-  header += '</tr>\n'
-  header += '</thead>\n'
-  header += '<tbody id="tbody">\n'
-
-  return header
-
-
-def get_sortable_html_footer():
-  """Gets footer for sortable html page.
-
-  Check function `get_sortable_html_header()` for more details.
-  """
-  return '</tbody>\n</table>\n\n</body>\n</html>\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'<img src="data:image/jpeg;base64, {encoded_image_base64}"/>'
-
-  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'<tr>\n'
-      for j in range(self.num_cols):
-        text = self.cells[i][j]['text']
-        image = self.cells[i][j]['image']
-        if text:
-          html += f'  <td>{text}<br><br>{image}</td>\n'
-        else:
-          html += f'  <td>{image}</td>\n'
-      html += f'</tr>\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])

spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/models/stylegan2/op/__init__.py

@@ -1,2 +0,0 @@
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-from .upfirdn2d import upfirdn2d

spaces/AnandSoni2001/StockMarketPrediction/app.py

@@ -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('<style>div.row-widget.stRadio > div{flex-direction:row;}</style>', 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.')

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/latent_diffusion/test_latent_diffusion_superresolution.py

@@ -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

spaces/Andy1621/uniformer_image_detection/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py

@@ -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))

spaces/Andy1621/uniformer_image_detection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py

@@ -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))

spaces/Andy1621/uniformer_image_detection/configs/swin/cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py

@@ -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,
-)

spaces/Ariharasudhan/YoloV5/utils/torch_utils.py

@@ -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)

spaces/Arikkod/FoodVisionMini/model.py

@@ -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

spaces/Artrajz/vits-simple-api/vits/text/cantonese.py

@@ -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

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

@@ -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('&', '&amp;').  \
-                replace('<', '&lt;').   \
-                replace('>', '&gt;').   \
-                replace('"', '&quot;'). \
-                replace("'", '&#39;')
-
-
-class2style = {}
-
-class SvgFormatter(Formatter):
-    """
-    Format tokens as an SVG graphics file.  This formatter is still experimental.
-    Each line of code is a ``<text>`` element with explicit ``x`` and ``y``
-    coordinates containing ``<tspan>`` elements with the individual token styles.
-
-    By default, this formatter outputs a full SVG document including doctype
-    declaration and the ``<svg>`` root element.
-
-    .. versionadded:: 0.9
-
-    Additional options accepted:
-
-    `nowrap`
-        Don't wrap the SVG ``<text>`` elements in ``<svg><g>`` elements and
-        don't add a XML declaration and a doctype.  If true, the `fontfamily`
-        and `fontsize` options are ignored.  Defaults to ``False``.
-
-    `fontfamily`
-        The value to give the wrapping ``<g>`` element's ``font-family``
-        attribute, defaults to ``"monospace"``.
-
-    `fontsize`
-        The value to give the wrapping ``<g>`` element's ``font-size``
-        attribute, defaults to ``"14px"``.
-
-    `linenos`
-        If ``True``, add line numbers (default: ``False``).
-
-    `linenostart`
-        The line number for the first line (default: ``1``).
-
-    `linenostep`
-        If set to a number n > 1, only every nth line number is printed.
-        
-    `linenowidth`
-        Maximum width devoted to line numbers (default: ``3*ystep``, sufficient
-        for up to 4-digit line numbers. Increase width for longer code blocks).  
-        
-    `xoffset`
-        Starting offset in X direction, defaults to ``0``.
-
-    `yoffset`
-        Starting offset in Y direction, defaults to the font size if it is given
-        in pixels, or ``20`` else.  (This is necessary since text coordinates
-        refer to the text baseline, not the top edge.)
-
-    `ystep`
-        Offset to add to the Y coordinate for each subsequent line.  This should
-        roughly be the text size plus 5.  It defaults to that value if the text
-        size is given in pixels, or ``25`` else.
-
-    `spacehack`
-        Convert spaces in the source to ``&#160;``, which are non-breaking
-        spaces.  SVG provides the ``xml:space`` attribute to control how
-        whitespace inside tags is handled, in theory, the ``preserve`` value
-        could be used to keep all whitespace as-is.  However, many current SVG
-        viewers don't obey that rule, so this option is provided as a workaround
-        and defaults to ``True``.
-    """
-    name = 'SVG'
-    aliases = ['svg']
-    filenames = ['*.svg']
-
-    def __init__(self, **options):
-        Formatter.__init__(self, **options)
-        self.nowrap = get_bool_opt(options, 'nowrap', False)
-        self.fontfamily = options.get('fontfamily', 'monospace')
-        self.fontsize = options.get('fontsize', '14px')
-        self.xoffset = get_int_opt(options, 'xoffset', 0)
-        fs = self.fontsize.strip()
-        if fs.endswith('px'): fs = fs[:-2].strip()
-        try:
-            int_fs = int(fs)
-        except:
-            int_fs = 20
-        self.yoffset = get_int_opt(options, 'yoffset', int_fs)
-        self.ystep = get_int_opt(options, 'ystep', int_fs + 5)
-        self.spacehack = get_bool_opt(options, 'spacehack', True)
-        self.linenos = get_bool_opt(options,'linenos',False)
-        self.linenostart = get_int_opt(options,'linenostart',1)
-        self.linenostep = get_int_opt(options,'linenostep',1)
-        self.linenowidth = get_int_opt(options,'linenowidth', 3*self.ystep)
-        self._stylecache = {}
-
-    def format_unencoded(self, tokensource, outfile):
-        """
-        Format ``tokensource``, an iterable of ``(tokentype, tokenstring)``
-        tuples and write it into ``outfile``.
-
-        For our implementation we put all lines in their own 'line group'.
-        """
-        x = self.xoffset
-        y = self.yoffset
-        if not self.nowrap:
-            if self.encoding:
-                outfile.write('<?xml version="1.0" encoding="%s"?>\n' %
-                              self.encoding)
-            else:
-                outfile.write('<?xml version="1.0"?>\n')
-            outfile.write('<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.0//EN" '
-                          '"http://www.w3.org/TR/2001/REC-SVG-20010904/DTD/'
-                          'svg10.dtd">\n')
-            outfile.write('<svg xmlns="http://www.w3.org/2000/svg">\n')
-            outfile.write('<g font-family="%s" font-size="%s">\n' %
-                          (self.fontfamily, self.fontsize))
-        
-        counter = self.linenostart 
-        counter_step = self.linenostep
-        counter_style = self._get_style(Comment)
-        line_x = x
-        
-        if self.linenos:
-            if counter % counter_step == 0:
-                outfile.write('<text x="%s" y="%s" %s text-anchor="end">%s</text>' %
-                    (x+self.linenowidth,y,counter_style,counter))
-            line_x += self.linenowidth + self.ystep
-            counter += 1
-
-        outfile.write('<text x="%s" y="%s" xml:space="preserve">' % (line_x, y))
-        for ttype, value in tokensource:
-            style = self._get_style(ttype)
-            tspan = style and '<tspan' + style + '>' or ''
-            tspanend = tspan and '</tspan>' or ''
-            value = escape_html(value)
-            if self.spacehack:
-                value = value.expandtabs().replace(' ', '&#160;')
-            parts = value.split('\n')
-            for part in parts[:-1]:
-                outfile.write(tspan + part + tspanend)
-                y += self.ystep
-                outfile.write('</text>\n')
-                if self.linenos and counter % counter_step == 0:
-                    outfile.write('<text x="%s" y="%s" text-anchor="end" %s>%s</text>' %
-                        (x+self.linenowidth,y,counter_style,counter))
-                
-                counter += 1
-                outfile.write('<text x="%s" y="%s" ' 'xml:space="preserve">' % (line_x,y))
-            outfile.write(tspan + parts[-1] + tspanend)
-        outfile.write('</text>')
-
-        if not self.nowrap:
-            outfile.write('</g></svg>\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

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

@@ -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))

spaces/Audio-AGI/AudioSep/callbacks/base.py

@@ -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))

spaces/AyakuraMei/Real-CUGAN/README.md

@@ -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

spaces/Banbri/zcvzcv/CONTRIBUTORS.md

@@ -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
-

spaces/Banbri/zcvzcv/src/components/ui/badge.tsx

@@ -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<HTMLDivElement>,
-    VariantProps<typeof badgeVariants> {}
-
-function Badge({ className, variant, ...props }: BadgeProps) {
-  return (
-    <div className={cn(badgeVariants({ variant }), className)} {...props} />
-  )
-}
-
-export { Badge, badgeVariants }

spaces/Benson/text-generation/Examples/6tv Download Apk.md

@@ -1,64 +0,0 @@
-
-<h1>6TV APK: Cómo descargar y disfrutar de la transmisión de canales de televisión de Malasia e Indonesia</h1>
-<p>¿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. </p>
-<h2>6tv download apk</h2><br /><p><b><b>Download File</b> --->>> <a href="https://bltlly.com/2v6LPy">https://bltlly.com/2v6LPy</a></b></p><br /><br />
- <h2>¿Qué es 6TV APK? </h2>
-<p>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. </p>
- <h3>Características de 6TV APK</h3>
-<p>Algunas de las características de 6TV APK son:</p>
-<ul>
-<li>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. </li>
-<li> Tiene una interfaz fácil de usar que hace que sea fácil navegar y encontrar sus canales o estaciones favoritas. </li>
-<li> Soporta diferentes opciones de calidad de vídeo, de baja a alta, dependiendo de su velocidad y preferencia de Internet. </li>
-<li> No requiere ningún registro o suscripción para usar. Simplemente puede descargar la aplicación y comenzar a transmitir. </li>
-<li> Es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 4.1 o versiones posteriores. </li>
-</ul>
-
-<p>Para descargar e instalar 6TV APK en su dispositivo Android, siga estos pasos:</p>
-<p></p>
-<ol>
-<li>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. </li>
-<li>Toque en el botón de descarga y espere a que el archivo se descargue en su dispositivo. </li>
-<li>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. </li>
-<li>Localice el archivo descargado en el almacenamiento del dispositivo y toque en él para iniciar el proceso de instalación. </li>
-<li>Siga las instrucciones en la pantalla y conceda los permisos necesarios a la aplicación. </li>
-<li> Espere a que la instalación termine y luego inicie la aplicación desde el cajón de aplicaciones o la pantalla de inicio. </li>
-<li>Disfrute de la transmisión de sus canales de televisión favoritos y estaciones de radio de Malasia e Indonesia.</li>
-</ol>
- <h2>Beneficios de usar 6TV APK</h2>
-<p>Algunos de los beneficios de usar 6TV APK son:</p>
- <h3>Acceso a varios canales de televisión y estaciones de radio de Malasia e Indonesia</h3>
-<p>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.</p>
- <h3>Interfaz fácil de usar y transmisión de alta calidad</h3>
-
- <h3>Libre y seguro de usar</h3>
-<p>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. </p>
- <h2>Desventajas de usar 6TV APK</h2>
-<p>Sin embargo, 6TV APK también tiene algunos inconvenientes que usted debe ser consciente de antes de usarlo. Algunos de los inconvenientes son:</p>
- <h3>Disponibilidad y compatibilidad limitadas</h3>
-<p>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.</p>
- <h3>Posibles problemas y riesgos legales</h3>
-
- <h3>Dependencia de la conexión a Internet y el uso de datos</h3>
-<p>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. </p>
- <h2>Alternativas a 6TV APK</h2>
-<p>Si usted está buscando algunas alternativas a 6TV APK, aquí están algunos de ellos:</p>
- <h3>Mobdro</h3>
-<p>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]. </p>
- <h3>NetTV en vivo</h3>
-<p>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. </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre 6TV APK:</p>
-<ol>
-<li>¿Es 6TV APK legal? </li>
-
-<li> ¿Es seguro 6TV APK? </li>
-<p>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. </p>
-<li> Cómo actualizar 6TV APK? </li>
-<p>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. </p>
-<li> Cómo desinstalar 6TV APK? </li>
-<p>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. </p>
-<li> ¿Cómo ponerse en contacto con el soporte 6TV APK? </li>
-<p>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.</p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Camionero Camino Loco.md

@@ -1,47 +0,0 @@
-<br />
-<h1>Truck Driver Crazy Road: Cómo dominar el juego de conducción más desafiante</h1>
-<p>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. </p>
-<h2>camionero camino loco</h2><br /><p><b><b>Download</b> &#9658;&#9658;&#9658; <a href="https://bltlly.com/2v6KFr">https://bltlly.com/2v6KFr</a></b></p><br /><br />
-<h2>Introducción</h2>
-<h3>¿Qué es el conductor de camiones Crazy Road? </h3>
-<p>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. </p>
-<h3>¿Por qué es tan desafiante y divertido? </h3>
-<p>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. </p>
-
-<h2>Cómo jugar Truck Driver Crazy Road</h2>
-<h3>Elige tu modo y temporada</h3>
-<p>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. </p>
-<p></p>
-<p>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. </p>
-<h3>Conduce con cuidado y equilibra tu carga</h3>
-<p>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. </p>
-<p>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. </p>
-<h3>Usar la cámara y opciones de pausa</h3>
-
-<p>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. </p>
-<h2>Consejos y trucos para el conductor de camiones Crazy Road</h2>
-<h3>Práctica en el modo de estacionamiento</h3>
-<p>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. </p>
-<h3>Cuidado con los obstáculos y peligros</h3>
-<p>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. </p>
-<h3>Actualiza tu camión y desbloquea nuevos niveles</h3>
-<p>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. </p>
-
-<h2>Conclusión</h2>
-<h3>Resumen de los puntos principales</h3>
-<p>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. </p>
-<h3>Llamada a la acción para los lectores</h3>
-<p>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! </p>
-<h2>Preguntas frecuentes</h2>
-<h4>P: ¿Cómo puedo guardar mi progreso en Truck Driver Crazy Road? </h4>
-<p>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>
-<h4>P: ¿Cómo puedo cambiar el lenguaje de Truck Driver Crazy Road? </h4>
-<p>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>
-<h4>P: ¿Cómo silencio el sonido o la música de Truck Driver Crazy Road? </h4>
-<p>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>
-<h4>P: ¿Cómo informo de un error o un problema con Truck Driver Crazy Road? </h4>
-
-<h4>Q: ¿Cómo evalúo o reviso Truck Driver Crazy Road? </h4>
-<p>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. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Aparcamiento Gratuito Multijugador.md

@@ -1,78 +0,0 @@
-
-<h1>Descargar Aparcamiento gratuito Multijugador</h1>
-<p>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. </p>
-<h2>Características de Aparcamiento Multijugador</h2>
-<p>El multijugador de estacionamiento tiene muchas características que lo hacen destacar de otros juegos de estacionamiento. Aquí están algunas de ellas:</p>
-<h2>descargar aparcamiento gratuito multijugador</h2><br /><p><b><b>Download</b> &hArr; <a href="https://bltlly.com/2v6Mm1">https://bltlly.com/2v6Mm1</a></b></p><br /><br />
-<ul>
-<li><b>Modo de mundo abierto multijugador:</b> 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. </li>
-<li><b>Personalización del automóvil:</b> 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. </li>
-<li><b>Mundo abierto de alta calidad:</b> 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. </li>
-<li><b>Juego interesante:</b> 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. </li>
-</ul>
-<h2>Cómo descargar el multijugador de estacionamiento de coches gratis</h2>
-<p>Si quieres descargar el multijugador de estacionamiento gratis, tienes varias opciones dependiendo de tu dispositivo. Estas son algunas de ellas:</p>
-<ul>
-
-<li><b>Para dispositivos iOS:</b> 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. </li>
-<li><b>Para dispositivos PC:</b> 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.</li>
-</ul>
-<h2> Consejos y trucos para el estacionamiento de coches multijugador</h2>
-<p>Si quieres mejorar tus habilidades y disfrutar más del juego, aquí hay algunos consejos y trucos que puedes usar:</p>
-<ul>
-<li><b>Aprender a la deriva:</b> 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. </li>
-<li><b>Bloquea tus puertas:</b> 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. </li>
-<li><b>Usa el chat de voz:</b> 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. </li>
-
-</ul>
-<h2>Conclusión</h2>
-<p>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. </p>
-<p>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.</p>
-<p>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. </p>
-<p>Entonces, ¿qué estás esperando? Descarga el estacionamiento de coches multijugador hoy y experimenta la emoción de conducir y estacionar en un mundo abierto realista! </p>
-<h2>Preguntas frecuentes</h2>
-<h3>¿Cuáles son los requisitos del sistema para el aparcamiento multijugador? </h3>
-<p>Los requisitos del sistema para el multijugador de estacionamiento son los siguientes:</p>
-<tabla>
-<tr><th>Plataforma</th><th>Requisitos mínimos</th></tr>
-<tr><td>Android</td><td>Android 4.4 o superior, 1 GB de RAM, 300 MB de espacio libre</td></tr>
-<tr><td>iOS</td><td>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</td></tr>
-<tr><td>PC</td><td>Windows 7 o superior, 4 GB de RAM, 2 GB de espacio libre, DirectX 9.0c o superior, emulador de BlueStacks o similar</td></tr>
-</tabla>
-<h3>¿Cómo puedo obtener más dinero o monedas en el aparcamiento multijugador? </h3>
-<p>Puedes obtener más dinero o monedas en el multijugador de estacionamiento haciendo lo siguiente:</p>
-<p></p>
-
-<li>Completando escenarios de estacionamiento y conducción</li>
-<li>Ganar carreras multijugador</li>
-<li>Venta o intercambio de coches con otros jugadores</li>
-<li>Ver anuncios de recompensas</li>
-<li>Comprarlos con dinero real (opcional)</li>
-</ul>
-<h3>¿Cómo puedo cambiar mi nombre o avatar en el multijugador de estacionamiento? </h3>
-<p>Puedes cambiar tu nombre o avatar en el multijugador de estacionamiento haciendo lo siguiente:</p>
-<ul>
-<li>Pulsando en el icono de perfil en la esquina superior izquierda de la pantalla</li>
-<li>Pulsando en el icono de edición en la esquina superior derecha de la pantalla</li>
-<li>Introducir un nuevo nombre o elegir un nuevo avatar de la lista</li>
-<li>Pulsando en el icono de guardar en la esquina superior derecha de la pantalla</li>
-</ul>
-<h3>¿Cómo puedo reportar un error o un problema en el multijugador de estacionamiento? </h3>
-<p>Puedes reportar un error o un problema en el multijugador de estacionamiento haciendo lo siguiente:</p>
-<ul>
-<li>Pulsando en el icono de configuración en la esquina superior derecha de la pantalla</li>
-<li>Pulsando en la opción de retroalimentación</li>
-<li> Llenar el formulario con su nombre, correo electrónico, modelo de dispositivo, versión del juego, y la descripción de la cuestión</li>
-<li>Pulsando en el botón enviar</li>
-</ul>
- <h3>¿Cómo puedo contactar a los desarrolladores del multijugador de aparcamiento? </h3>
- <p>Puede ponerse en contacto con los desarrolladores de aparcamiento multijugador haciendo lo siguiente:</p>
- <ul>
- <li>Enviarlos por correo electrónico a [email protected]</li>
- <li>Visitar su sitio web en https:/olzhass.com/</li>
- <li>Siguiéndolos en Facebook en https://www.facebook.com/olzhassgames/</li>
- <li>Siguiéndolos en Instagram en https://www.instagram.com/olzhassgames/</li>
- <li>Siguiéndolos en Twitter en https://twitter.com/olzhassgames</li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Bidwill/Sanskrit-asr/app.py

@@ -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()

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

@@ -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 <Request>` object.
-
-    Used to prepare a :class:`PreparedRequest <PreparedRequest>`, which is sent to the server.
-
-    :param method: HTTP method to use.
-    :param url: URL to send.
-    :param headers: dictionary of headers to send.
-    :param files: dictionary of {filename: fileobject} files to multipart upload.
-    :param data: the body to attach to the request. If a dictionary or
-        list of tuples ``[(key, value)]`` is provided, form-encoding will
-        take place.
-    :param json: json for the body to attach to the request (if files or data is not specified).
-    :param params: URL parameters to append to the URL. If a dictionary or
-        list of tuples ``[(key, value)]`` is provided, form-encoding will
-        take place.
-    :param auth: Auth handler or (user, pass) tuple.
-    :param cookies: dictionary or CookieJar of cookies to attach to this request.
-    :param hooks: dictionary of callback hooks, for internal usage.
-
-    Usage::
-
-      >>> import requests
-      >>> req = requests.Request('GET', 'https://httpbin.org/get')
-      >>> req.prepare()
-      <PreparedRequest [GET]>
-    """
-
-    def __init__(
-        self,
-        method=None,
-        url=None,
-        headers=None,
-        files=None,
-        data=None,
-        params=None,
-        auth=None,
-        cookies=None,
-        hooks=None,
-        json=None,
-    ):
-
-        # Default empty dicts for dict params.
-        data = [] if data is None else data
-        files = [] if files is None else files
-        headers = {} if headers is None else headers
-        params = {} if params is None else params
-        hooks = {} if hooks is None else hooks
-
-        self.hooks = default_hooks()
-        for (k, v) in list(hooks.items()):
-            self.register_hook(event=k, hook=v)
-
-        self.method = method
-        self.url = url
-        self.headers = headers
-        self.files = files
-        self.data = data
-        self.json = json
-        self.params = params
-        self.auth = auth
-        self.cookies = cookies
-
-    def __repr__(self):
-        return f"<Request [{self.method}]>"
-
-    def prepare(self):
-        """Constructs a :class:`PreparedRequest <PreparedRequest>` for transmission and returns it."""
-        p = PreparedRequest()
-        p.prepare(
-            method=self.method,
-            url=self.url,
-            headers=self.headers,
-            files=self.files,
-            data=self.data,
-            json=self.json,
-            params=self.params,
-            auth=self.auth,
-            cookies=self.cookies,
-            hooks=self.hooks,
-        )
-        return p
-
-
-class PreparedRequest(RequestEncodingMixin, RequestHooksMixin):
-    """The fully mutable :class:`PreparedRequest <PreparedRequest>` object,
-    containing the exact bytes that will be sent to the server.
-
-    Instances are generated from a :class:`Request <Request>` object, and
-    should not be instantiated manually; doing so may produce undesirable
-    effects.
-
-    Usage::
-
-      >>> import requests
-      >>> req = requests.Request('GET', 'https://httpbin.org/get')
-      >>> r = req.prepare()
-      >>> r
-      <PreparedRequest [GET]>
-
-      >>> s = requests.Session()
-      >>> s.send(r)
-      <Response [200]>
-    """
-
-    def __init__(self):
-        #: HTTP verb to send to the server.
-        self.method = None
-        #: HTTP URL to send the request to.
-        self.url = None
-        #: dictionary of HTTP headers.
-        self.headers = None
-        # The `CookieJar` used to create the Cookie header will be stored here
-        # after prepare_cookies is called
-        self._cookies = None
-        #: request body to send to the server.
-        self.body = None
-        #: dictionary of callback hooks, for internal usage.
-        self.hooks = default_hooks()
-        #: integer denoting starting position of a readable file-like body.
-        self._body_position = None
-
-    def prepare(
-        self,
-        method=None,
-        url=None,
-        headers=None,
-        files=None,
-        data=None,
-        params=None,
-        auth=None,
-        cookies=None,
-        hooks=None,
-        json=None,
-    ):
-        """Prepares the entire request with the given parameters."""
-
-        self.prepare_method(method)
-        self.prepare_url(url, params)
-        self.prepare_headers(headers)
-        self.prepare_cookies(cookies)
-        self.prepare_body(data, files, json)
-        self.prepare_auth(auth, url)
-
-        # Note that prepare_auth must be last to enable authentication schemes
-        # such as OAuth to work on a fully prepared request.
-
-        # This MUST go after prepare_auth. Authenticators could add a hook
-        self.prepare_hooks(hooks)
-
-    def __repr__(self):
-        return f"<PreparedRequest [{self.method}]>"
-
-    def copy(self):
-        p = PreparedRequest()
-        p.method = self.method
-        p.url = self.url
-        p.headers = self.headers.copy() if self.headers is not None else None
-        p._cookies = _copy_cookie_jar(self._cookies)
-        p.body = self.body
-        p.hooks = self.hooks
-        p._body_position = self._body_position
-        return p
-
-    def prepare_method(self, method):
-        """Prepares the given HTTP method."""
-        self.method = method
-        if self.method is not None:
-            self.method = to_native_string(self.method.upper())
-
-    @staticmethod
-    def _get_idna_encoded_host(host):
-        from pip._vendor import idna
-
-        try:
-            host = idna.encode(host, uts46=True).decode("utf-8")
-        except idna.IDNAError:
-            raise UnicodeError
-        return host
-
-    def prepare_url(self, url, params):
-        """Prepares the given HTTP URL."""
-        #: Accept objects that have string representations.
-        #: We're unable to blindly call unicode/str functions
-        #: as this will include the bytestring indicator (b'')
-        #: on python 3.x.
-        #: https://github.com/psf/requests/pull/2238
-        if isinstance(url, bytes):
-            url = url.decode("utf8")
-        else:
-            url = str(url)
-
-        # Remove leading whitespaces from url
-        url = url.lstrip()
-
-        # Don't do any URL preparation for non-HTTP schemes like `mailto`,
-        # `data` etc to work around exceptions from `url_parse`, which
-        # handles RFC 3986 only.
-        if ":" in url and not url.lower().startswith("http"):
-            self.url = url
-            return
-
-        # Support for unicode domain names and paths.
-        try:
-            scheme, auth, host, port, path, query, fragment = parse_url(url)
-        except LocationParseError as e:
-            raise InvalidURL(*e.args)
-
-        if not scheme:
-            raise MissingSchema(
-                f"Invalid URL {url!r}: No scheme supplied. "
-                f"Perhaps you meant https://{url}?"
-            )
-
-        if not host:
-            raise InvalidURL(f"Invalid URL {url!r}: No host supplied")
-
-        # In general, we want to try IDNA encoding the hostname if the string contains
-        # non-ASCII characters. This allows users to automatically get the correct IDNA
-        # behaviour. For strings containing only ASCII characters, we need to also verify
-        # it doesn't start with a wildcard (*), before allowing the unencoded hostname.
-        if not unicode_is_ascii(host):
-            try:
-                host = self._get_idna_encoded_host(host)
-            except UnicodeError:
-                raise InvalidURL("URL has an invalid label.")
-        elif host.startswith(("*", ".")):
-            raise InvalidURL("URL has an invalid label.")
-
-        # Carefully reconstruct the network location
-        netloc = auth or ""
-        if netloc:
-            netloc += "@"
-        netloc += host
-        if port:
-            netloc += f":{port}"
-
-        # Bare domains aren't valid URLs.
-        if not path:
-            path = "/"
-
-        if isinstance(params, (str, bytes)):
-            params = to_native_string(params)
-
-        enc_params = self._encode_params(params)
-        if enc_params:
-            if query:
-                query = f"{query}&{enc_params}"
-            else:
-                query = enc_params
-
-        url = requote_uri(urlunparse([scheme, netloc, path, None, query, fragment]))
-        self.url = url
-
-    def prepare_headers(self, headers):
-        """Prepares the given HTTP headers."""
-
-        self.headers = CaseInsensitiveDict()
-        if headers:
-            for header in headers.items():
-                # Raise exception on invalid header value.
-                check_header_validity(header)
-                name, value = header
-                self.headers[to_native_string(name)] = value
-
-    def prepare_body(self, data, files, json=None):
-        """Prepares the given HTTP body data."""
-
-        # Check if file, fo, generator, iterator.
-        # If not, run through normal process.
-
-        # Nottin' on you.
-        body = None
-        content_type = None
-
-        if not data and json is not None:
-            # urllib3 requires a bytes-like body. Python 2's json.dumps
-            # provides this natively, but Python 3 gives a Unicode string.
-            content_type = "application/json"
-
-            try:
-                body = complexjson.dumps(json, allow_nan=False)
-            except ValueError as ve:
-                raise InvalidJSONError(ve, request=self)
-
-            if not isinstance(body, bytes):
-                body = body.encode("utf-8")
-
-        is_stream = all(
-            [
-                hasattr(data, "__iter__"),
-                not isinstance(data, (basestring, list, tuple, Mapping)),
-            ]
-        )
-
-        if is_stream:
-            try:
-                length = super_len(data)
-            except (TypeError, AttributeError, UnsupportedOperation):
-                length = None
-
-            body = data
-
-            if getattr(body, "tell", None) is not None:
-                # Record the current file position before reading.
-                # This will allow us to rewind a file in the event
-                # of a redirect.
-                try:
-                    self._body_position = body.tell()
-                except OSError:
-                    # This differentiates from None, allowing us to catch
-                    # a failed `tell()` later when trying to rewind the body
-                    self._body_position = object()
-
-            if files:
-                raise NotImplementedError(
-                    "Streamed bodies and files are mutually exclusive."
-                )
-
-            if length:
-                self.headers["Content-Length"] = builtin_str(length)
-            else:
-                self.headers["Transfer-Encoding"] = "chunked"
-        else:
-            # Multi-part file uploads.
-            if files:
-                (body, content_type) = self._encode_files(files, data)
-            else:
-                if data:
-                    body = self._encode_params(data)
-                    if isinstance(data, basestring) or hasattr(data, "read"):
-                        content_type = None
-                    else:
-                        content_type = "application/x-www-form-urlencoded"
-
-            self.prepare_content_length(body)
-
-            # Add content-type if it wasn't explicitly provided.
-            if content_type and ("content-type" not in self.headers):
-                self.headers["Content-Type"] = content_type
-
-        self.body = body
-
-    def prepare_content_length(self, body):
-        """Prepare Content-Length header based on request method and body"""
-        if body is not None:
-            length = super_len(body)
-            if length:
-                # If length exists, set it. Otherwise, we fallback
-                # to Transfer-Encoding: chunked.
-                self.headers["Content-Length"] = builtin_str(length)
-        elif (
-            self.method not in ("GET", "HEAD")
-            and self.headers.get("Content-Length") is None
-        ):
-            # Set Content-Length to 0 for methods that can have a body
-            # but don't provide one. (i.e. not GET or HEAD)
-            self.headers["Content-Length"] = "0"
-
-    def prepare_auth(self, auth, url=""):
-        """Prepares the given HTTP auth data."""
-
-        # If no Auth is explicitly provided, extract it from the URL first.
-        if auth is None:
-            url_auth = get_auth_from_url(self.url)
-            auth = url_auth if any(url_auth) else None
-
-        if auth:
-            if isinstance(auth, tuple) and len(auth) == 2:
-                # special-case basic HTTP auth
-                auth = HTTPBasicAuth(*auth)
-
-            # Allow auth to make its changes.
-            r = auth(self)
-
-            # Update self to reflect the auth changes.
-            self.__dict__.update(r.__dict__)
-
-            # Recompute Content-Length
-            self.prepare_content_length(self.body)
-
-    def prepare_cookies(self, cookies):
-        """Prepares the given HTTP cookie data.
-
-        This function eventually generates a ``Cookie`` header from the
-        given cookies using cookielib. Due to cookielib's design, the header
-        will not be regenerated if it already exists, meaning this function
-        can only be called once for the life of the
-        :class:`PreparedRequest <PreparedRequest>` object. Any subsequent calls
-        to ``prepare_cookies`` will have no actual effect, unless the "Cookie"
-        header is removed beforehand.
-        """
-        if isinstance(cookies, cookielib.CookieJar):
-            self._cookies = cookies
-        else:
-            self._cookies = cookiejar_from_dict(cookies)
-
-        cookie_header = get_cookie_header(self._cookies, self)
-        if cookie_header is not None:
-            self.headers["Cookie"] = cookie_header
-
-    def prepare_hooks(self, hooks):
-        """Prepares the given hooks."""
-        # hooks can be passed as None to the prepare method and to this
-        # method. To prevent iterating over None, simply use an empty list
-        # if hooks is False-y
-        hooks = hooks or []
-        for event in hooks:
-            self.register_hook(event, hooks[event])
-
-
-class Response:
-    """The :class:`Response <Response>` object, which contains a
-    server's response to an HTTP request.
-    """
-
-    __attrs__ = [
-        "_content",
-        "status_code",
-        "headers",
-        "url",
-        "history",
-        "encoding",
-        "reason",
-        "cookies",
-        "elapsed",
-        "request",
-    ]
-
-    def __init__(self):
-        self._content = False
-        self._content_consumed = False
-        self._next = None
-
-        #: Integer Code of responded HTTP Status, e.g. 404 or 200.
-        self.status_code = None
-
-        #: Case-insensitive Dictionary of Response Headers.
-        #: For example, ``headers['content-encoding']`` will return the
-        #: value of a ``'Content-Encoding'`` response header.
-        self.headers = CaseInsensitiveDict()
-
-        #: File-like object representation of response (for advanced usage).
-        #: Use of ``raw`` requires that ``stream=True`` be set on the request.
-        #: This requirement does not apply for use internally to Requests.
-        self.raw = None
-
-        #: Final URL location of Response.
-        self.url = None
-
-        #: Encoding to decode with when accessing r.text.
-        self.encoding = None
-
-        #: A list of :class:`Response <Response>` objects from
-        #: the history of the Request. Any redirect responses will end
-        #: up here. The list is sorted from the oldest to the most recent request.
-        self.history = []
-
-        #: Textual reason of responded HTTP Status, e.g. "Not Found" or "OK".
-        self.reason = None
-
-        #: A CookieJar of Cookies the server sent back.
-        self.cookies = cookiejar_from_dict({})
-
-        #: The amount of time elapsed between sending the request
-        #: and the arrival of the response (as a timedelta).
-        #: This property specifically measures the time taken between sending
-        #: the first byte of the request and finishing parsing the headers. It
-        #: is therefore unaffected by consuming the response content or the
-        #: value of the ``stream`` keyword argument.
-        self.elapsed = datetime.timedelta(0)
-
-        #: The :class:`PreparedRequest <PreparedRequest>` object to which this
-        #: is a response.
-        self.request = None
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, *args):
-        self.close()
-
-    def __getstate__(self):
-        # Consume everything; accessing the content attribute makes
-        # sure the content has been fully read.
-        if not self._content_consumed:
-            self.content
-
-        return {attr: getattr(self, attr, None) for attr in self.__attrs__}
-
-    def __setstate__(self, state):
-        for name, value in state.items():
-            setattr(self, name, value)
-
-        # pickled objects do not have .raw
-        setattr(self, "_content_consumed", True)
-        setattr(self, "raw", None)
-
-    def __repr__(self):
-        return f"<Response [{self.status_code}]>"
-
-    def __bool__(self):
-        """Returns True if :attr:`status_code` is less than 400.
-
-        This attribute checks if the status code of the response is between
-        400 and 600 to see if there was a client error or a server error. If
-        the status code, is between 200 and 400, this will return True. This
-        is **not** a check to see if the response code is ``200 OK``.
-        """
-        return self.ok
-
-    def __nonzero__(self):
-        """Returns True if :attr:`status_code` is less than 400.
-
-        This attribute checks if the status code of the response is between
-        400 and 600 to see if there was a client error or a server error. If
-        the status code, is between 200 and 400, this will return True. This
-        is **not** a check to see if the response code is ``200 OK``.
-        """
-        return self.ok
-
-    def __iter__(self):
-        """Allows you to use a response as an iterator."""
-        return self.iter_content(128)
-
-    @property
-    def ok(self):
-        """Returns True if :attr:`status_code` is less than 400, False if not.
-
-        This attribute checks if the status code of the response is between
-        400 and 600 to see if there was a client error or a server error. If
-        the status code is between 200 and 400, this will return True. This
-        is **not** a check to see if the response code is ``200 OK``.
-        """
-        try:
-            self.raise_for_status()
-        except HTTPError:
-            return False
-        return True
-
-    @property
-    def is_redirect(self):
-        """True if this Response is a well-formed HTTP redirect that could have
-        been processed automatically (by :meth:`Session.resolve_redirects`).
-        """
-        return "location" in self.headers and self.status_code in REDIRECT_STATI
-
-    @property
-    def is_permanent_redirect(self):
-        """True if this Response one of the permanent versions of redirect."""
-        return "location" in self.headers and self.status_code in (
-            codes.moved_permanently,
-            codes.permanent_redirect,
-        )
-
-    @property
-    def next(self):
-        """Returns a PreparedRequest for the next request in a redirect chain, if there is one."""
-        return self._next
-
-    @property
-    def apparent_encoding(self):
-        """The apparent encoding, provided by the charset_normalizer or chardet libraries."""
-        return chardet.detect(self.content)["encoding"]
-
-    def iter_content(self, chunk_size=1, decode_unicode=False):
-        """Iterates over the response data.  When stream=True is set on the
-        request, this avoids reading the content at once into memory for
-        large responses.  The chunk size is the number of bytes it should
-        read into memory.  This is not necessarily the length of each item
-        returned as decoding can take place.
-
-        chunk_size must be of type int or None. A value of None will
-        function differently depending on the value of `stream`.
-        stream=True will read data as it arrives in whatever size the
-        chunks are received. If stream=False, data is returned as
-        a single chunk.
-
-        If decode_unicode is True, content will be decoded using the best
-        available encoding based on the response.
-        """
-
-        def generate():
-            # Special case for urllib3.
-            if hasattr(self.raw, "stream"):
-                try:
-                    yield from self.raw.stream(chunk_size, decode_content=True)
-                except ProtocolError as e:
-                    raise ChunkedEncodingError(e)
-                except DecodeError as e:
-                    raise ContentDecodingError(e)
-                except ReadTimeoutError as e:
-                    raise ConnectionError(e)
-                except SSLError as e:
-                    raise RequestsSSLError(e)
-            else:
-                # Standard file-like object.
-                while True:
-                    chunk = self.raw.read(chunk_size)
-                    if not chunk:
-                        break
-                    yield chunk
-
-            self._content_consumed = True
-
-        if self._content_consumed and isinstance(self._content, bool):
-            raise StreamConsumedError()
-        elif chunk_size is not None and not isinstance(chunk_size, int):
-            raise TypeError(
-                f"chunk_size must be an int, it is instead a {type(chunk_size)}."
-            )
-        # simulate reading small chunks of the content
-        reused_chunks = iter_slices(self._content, chunk_size)
-
-        stream_chunks = generate()
-
-        chunks = reused_chunks if self._content_consumed else stream_chunks
-
-        if decode_unicode:
-            chunks = stream_decode_response_unicode(chunks, self)
-
-        return chunks
-
-    def iter_lines(
-        self, chunk_size=ITER_CHUNK_SIZE, decode_unicode=False, delimiter=None
-    ):
-        """Iterates over the response data, one line at a time.  When
-        stream=True is set on the request, this avoids reading the
-        content at once into memory for large responses.
-
-        .. note:: This method is not reentrant safe.
-        """
-
-        pending = None
-
-        for chunk in self.iter_content(
-            chunk_size=chunk_size, decode_unicode=decode_unicode
-        ):
-
-            if pending is not None:
-                chunk = pending + chunk
-
-            if delimiter:
-                lines = chunk.split(delimiter)
-            else:
-                lines = chunk.splitlines()
-
-            if lines and lines[-1] and chunk and lines[-1][-1] == chunk[-1]:
-                pending = lines.pop()
-            else:
-                pending = None
-
-            yield from lines
-
-        if pending is not None:
-            yield pending
-
-    @property
-    def content(self):
-        """Content of the response, in bytes."""
-
-        if self._content is False:
-            # Read the contents.
-            if self._content_consumed:
-                raise RuntimeError("The content for this response was already consumed")
-
-            if self.status_code == 0 or self.raw is None:
-                self._content = None
-            else:
-                self._content = b"".join(self.iter_content(CONTENT_CHUNK_SIZE)) or b""
-
-        self._content_consumed = True
-        # don't need to release the connection; that's been handled by urllib3
-        # since we exhausted the data.
-        return self._content
-
-    @property
-    def text(self):
-        """Content of the response, in unicode.
-
-        If Response.encoding is None, encoding will be guessed using
-        ``charset_normalizer`` or ``chardet``.
-
-        The encoding of the response content is determined based solely on HTTP
-        headers, following RFC 2616 to the letter. If you can take advantage of
-        non-HTTP knowledge to make a better guess at the encoding, you should
-        set ``r.encoding`` appropriately before accessing this property.
-        """
-
-        # Try charset from content-type
-        content = None
-        encoding = self.encoding
-
-        if not self.content:
-            return ""
-
-        # Fallback to auto-detected encoding.
-        if self.encoding is None:
-            encoding = self.apparent_encoding
-
-        # Decode unicode from given encoding.
-        try:
-            content = str(self.content, encoding, errors="replace")
-        except (LookupError, TypeError):
-            # A LookupError is raised if the encoding was not found which could
-            # indicate a misspelling or similar mistake.
-            #
-            # A TypeError can be raised if encoding is None
-            #
-            # So we try blindly encoding.
-            content = str(self.content, errors="replace")
-
-        return content
-
-    def json(self, **kwargs):
-        r"""Returns the json-encoded content of a response, if any.
-
-        :param \*\*kwargs: Optional arguments that ``json.loads`` takes.
-        :raises requests.exceptions.JSONDecodeError: If the response body does not
-            contain valid json.
-        """
-
-        if not self.encoding and self.content and len(self.content) > 3:
-            # No encoding set. JSON RFC 4627 section 3 states we should expect
-            # UTF-8, -16 or -32. Detect which one to use; If the detection or
-            # decoding fails, fall back to `self.text` (using charset_normalizer to make
-            # a best guess).
-            encoding = guess_json_utf(self.content)
-            if encoding is not None:
-                try:
-                    return complexjson.loads(self.content.decode(encoding), **kwargs)
-                except UnicodeDecodeError:
-                    # Wrong UTF codec detected; usually because it's not UTF-8
-                    # but some other 8-bit codec.  This is an RFC violation,
-                    # and the server didn't bother to tell us what codec *was*
-                    # used.
-                    pass
-                except JSONDecodeError as e:
-                    raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
-
-        try:
-            return complexjson.loads(self.text, **kwargs)
-        except JSONDecodeError as e:
-            # Catch JSON-related errors and raise as requests.JSONDecodeError
-            # This aliases json.JSONDecodeError and simplejson.JSONDecodeError
-            raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
-
-    @property
-    def links(self):
-        """Returns the parsed header links of the response, if any."""
-
-        header = self.headers.get("link")
-
-        resolved_links = {}
-
-        if header:
-            links = parse_header_links(header)
-
-            for link in links:
-                key = link.get("rel") or link.get("url")
-                resolved_links[key] = link
-
-        return resolved_links
-
-    def raise_for_status(self):
-        """Raises :class:`HTTPError`, if one occurred."""
-
-        http_error_msg = ""
-        if isinstance(self.reason, bytes):
-            # We attempt to decode utf-8 first because some servers
-            # choose to localize their reason strings. If the string
-            # isn't utf-8, we fall back to iso-8859-1 for all other
-            # encodings. (See PR #3538)
-            try:
-                reason = self.reason.decode("utf-8")
-            except UnicodeDecodeError:
-                reason = self.reason.decode("iso-8859-1")
-        else:
-            reason = self.reason
-
-        if 400 <= self.status_code < 500:
-            http_error_msg = (
-                f"{self.status_code} Client Error: {reason} for url: {self.url}"
-            )
-
-        elif 500 <= self.status_code < 600:
-            http_error_msg = (
-                f"{self.status_code} Server Error: {reason} for url: {self.url}"
-            )
-
-        if http_error_msg:
-            raise HTTPError(http_error_msg, response=self)
-
-    def close(self):
-        """Releases the connection back to the pool. Once this method has been
-        called the underlying ``raw`` object must not be accessed again.
-
-        *Note: Should not normally need to be called explicitly.*
-        """
-        if not self._content_consumed:
-            self.raw.close()
-
-        release_conn = getattr(self.raw, "release_conn", None)
-        if release_conn is not None:
-            release_conn()

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/packaging/markers.py

@@ -1,304 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import operator
-import os
-import platform
-import sys
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-
-from setuptools.extern.pyparsing import (  # noqa: N817
-    Forward,
-    Group,
-    Literal as L,
-    ParseException,
-    ParseResults,
-    QuotedString,
-    ZeroOrMore,
-    stringEnd,
-    stringStart,
-)
-
-from .specifiers import InvalidSpecifier, Specifier
-
-__all__ = [
-    "InvalidMarker",
-    "UndefinedComparison",
-    "UndefinedEnvironmentName",
-    "Marker",
-    "default_environment",
-]
-
-Operator = Callable[[str, str], bool]
-
-
-class InvalidMarker(ValueError):
-    """
-    An invalid marker was found, users should refer to PEP 508.
-    """
-
-
-class UndefinedComparison(ValueError):
-    """
-    An invalid operation was attempted on a value that doesn't support it.
-    """
-
-
-class UndefinedEnvironmentName(ValueError):
-    """
-    A name was attempted to be used that does not exist inside of the
-    environment.
-    """
-
-
-class Node:
-    def __init__(self, value: Any) -> None:
-        self.value = value
-
-    def __str__(self) -> str:
-        return str(self.value)
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__}('{self}')>"
-
-    def serialize(self) -> str:
-        raise NotImplementedError
-
-
-class Variable(Node):
-    def serialize(self) -> str:
-        return str(self)
-
-
-class Value(Node):
-    def serialize(self) -> str:
-        return f'"{self}"'
-
-
-class Op(Node):
-    def serialize(self) -> str:
-        return str(self)
-
-
-VARIABLE = (
-    L("implementation_version")
-    | L("platform_python_implementation")
-    | L("implementation_name")
-    | L("python_full_version")
-    | L("platform_release")
-    | L("platform_version")
-    | L("platform_machine")
-    | L("platform_system")
-    | L("python_version")
-    | L("sys_platform")
-    | L("os_name")
-    | L("os.name")  # PEP-345
-    | L("sys.platform")  # PEP-345
-    | L("platform.version")  # PEP-345
-    | L("platform.machine")  # PEP-345
-    | L("platform.python_implementation")  # PEP-345
-    | L("python_implementation")  # undocumented setuptools legacy
-    | L("extra")  # PEP-508
-)
-ALIASES = {
-    "os.name": "os_name",
-    "sys.platform": "sys_platform",
-    "platform.version": "platform_version",
-    "platform.machine": "platform_machine",
-    "platform.python_implementation": "platform_python_implementation",
-    "python_implementation": "platform_python_implementation",
-}
-VARIABLE.setParseAction(lambda s, l, t: Variable(ALIASES.get(t[0], t[0])))
-
-VERSION_CMP = (
-    L("===") | L("==") | L(">=") | L("<=") | L("!=") | L("~=") | L(">") | L("<")
-)
-
-MARKER_OP = VERSION_CMP | L("not in") | L("in")
-MARKER_OP.setParseAction(lambda s, l, t: Op(t[0]))
-
-MARKER_VALUE = QuotedString("'") | QuotedString('"')
-MARKER_VALUE.setParseAction(lambda s, l, t: Value(t[0]))
-
-BOOLOP = L("and") | L("or")
-
-MARKER_VAR = VARIABLE | MARKER_VALUE
-
-MARKER_ITEM = Group(MARKER_VAR + MARKER_OP + MARKER_VAR)
-MARKER_ITEM.setParseAction(lambda s, l, t: tuple(t[0]))
-
-LPAREN = L("(").suppress()
-RPAREN = L(")").suppress()
-
-MARKER_EXPR = Forward()
-MARKER_ATOM = MARKER_ITEM | Group(LPAREN + MARKER_EXPR + RPAREN)
-MARKER_EXPR << MARKER_ATOM + ZeroOrMore(BOOLOP + MARKER_EXPR)
-
-MARKER = stringStart + MARKER_EXPR + stringEnd
-
-
-def _coerce_parse_result(results: Union[ParseResults, List[Any]]) -> List[Any]:
-    if isinstance(results, ParseResults):
-        return [_coerce_parse_result(i) for i in results]
-    else:
-        return results
-
-
-def _format_marker(
-    marker: Union[List[str], Tuple[Node, ...], str], first: Optional[bool] = True
-) -> str:
-
-    assert isinstance(marker, (list, tuple, str))
-
-    # Sometimes we have a structure like [[...]] which is a single item list
-    # where the single item is itself it's own list. In that case we want skip
-    # the rest of this function so that we don't get extraneous () on the
-    # outside.
-    if (
-        isinstance(marker, list)
-        and len(marker) == 1
-        and isinstance(marker[0], (list, tuple))
-    ):
-        return _format_marker(marker[0])
-
-    if isinstance(marker, list):
-        inner = (_format_marker(m, first=False) for m in marker)
-        if first:
-            return " ".join(inner)
-        else:
-            return "(" + " ".join(inner) + ")"
-    elif isinstance(marker, tuple):
-        return " ".join([m.serialize() for m in marker])
-    else:
-        return marker
-
-
-_operators: Dict[str, Operator] = {
-    "in": lambda lhs, rhs: lhs in rhs,
-    "not in": lambda lhs, rhs: lhs not in rhs,
-    "<": operator.lt,
-    "<=": operator.le,
-    "==": operator.eq,
-    "!=": operator.ne,
-    ">=": operator.ge,
-    ">": operator.gt,
-}
-
-
-def _eval_op(lhs: str, op: Op, rhs: str) -> bool:
-    try:
-        spec = Specifier("".join([op.serialize(), rhs]))
-    except InvalidSpecifier:
-        pass
-    else:
-        return spec.contains(lhs)
-
-    oper: Optional[Operator] = _operators.get(op.serialize())
-    if oper is None:
-        raise UndefinedComparison(f"Undefined {op!r} on {lhs!r} and {rhs!r}.")
-
-    return oper(lhs, rhs)
-
-
-class Undefined:
-    pass
-
-
-_undefined = Undefined()
-
-
-def _get_env(environment: Dict[str, str], name: str) -> str:
-    value: Union[str, Undefined] = environment.get(name, _undefined)
-
-    if isinstance(value, Undefined):
-        raise UndefinedEnvironmentName(
-            f"{name!r} does not exist in evaluation environment."
-        )
-
-    return value
-
-
-def _evaluate_markers(markers: List[Any], environment: Dict[str, str]) -> bool:
-    groups: List[List[bool]] = [[]]
-
-    for marker in markers:
-        assert isinstance(marker, (list, tuple, str))
-
-        if isinstance(marker, list):
-            groups[-1].append(_evaluate_markers(marker, environment))
-        elif isinstance(marker, tuple):
-            lhs, op, rhs = marker
-
-            if isinstance(lhs, Variable):
-                lhs_value = _get_env(environment, lhs.value)
-                rhs_value = rhs.value
-            else:
-                lhs_value = lhs.value
-                rhs_value = _get_env(environment, rhs.value)
-
-            groups[-1].append(_eval_op(lhs_value, op, rhs_value))
-        else:
-            assert marker in ["and", "or"]
-            if marker == "or":
-                groups.append([])
-
-    return any(all(item) for item in groups)
-
-
-def format_full_version(info: "sys._version_info") -> str:
-    version = "{0.major}.{0.minor}.{0.micro}".format(info)
-    kind = info.releaselevel
-    if kind != "final":
-        version += kind[0] + str(info.serial)
-    return version
-
-
-def default_environment() -> Dict[str, str]:
-    iver = format_full_version(sys.implementation.version)
-    implementation_name = sys.implementation.name
-    return {
-        "implementation_name": implementation_name,
-        "implementation_version": iver,
-        "os_name": os.name,
-        "platform_machine": platform.machine(),
-        "platform_release": platform.release(),
-        "platform_system": platform.system(),
-        "platform_version": platform.version(),
-        "python_full_version": platform.python_version(),
-        "platform_python_implementation": platform.python_implementation(),
-        "python_version": ".".join(platform.python_version_tuple()[:2]),
-        "sys_platform": sys.platform,
-    }
-
-
-class Marker:
-    def __init__(self, marker: str) -> None:
-        try:
-            self._markers = _coerce_parse_result(MARKER.parseString(marker))
-        except ParseException as e:
-            raise InvalidMarker(
-                f"Invalid marker: {marker!r}, parse error at "
-                f"{marker[e.loc : e.loc + 8]!r}"
-            )
-
-    def __str__(self) -> str:
-        return _format_marker(self._markers)
-
-    def __repr__(self) -> str:
-        return f"<Marker('{self}')>"
-
-    def evaluate(self, environment: Optional[Dict[str, str]] = None) -> bool:
-        """Evaluate a marker.
-
-        Return the boolean from evaluating the given marker against the
-        environment. environment is an optional argument to override all or
-        part of the determined environment.
-
-        The environment is determined from the current Python process.
-        """
-        current_environment = default_environment()
-        if environment is not None:
-            current_environment.update(environment)
-
-        return _evaluate_markers(self._markers, current_environment)

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/launch.py

@@ -1,36 +0,0 @@
-"""
-Launch the Python script on the command line after
-setuptools is bootstrapped via import.
-"""
-
-# Note that setuptools gets imported implicitly by the
-# invocation of this script using python -m setuptools.launch
-
-import tokenize
-import sys
-
-
-def run():
-    """
-    Run the script in sys.argv[1] as if it had
-    been invoked naturally.
-    """
-    __builtins__
-    script_name = sys.argv[1]
-    namespace = dict(
-        __file__=script_name,
-        __name__='__main__',
-        __doc__=None,
-    )
-    sys.argv[:] = sys.argv[1:]
-
-    open_ = getattr(tokenize, 'open', open)
-    with open_(script_name) as fid:
-        script = fid.read()
-    norm_script = script.replace('\\r\\n', '\\n')
-    code = compile(norm_script, script_name, 'exec')
-    exec(code, namespace)
-
-
-if __name__ == '__main__':
-    run()

spaces/BongoCaat/ArtGenerator/stable_diffusion_2_0.py

@@ -1,611 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/qunash/stable-diffusion-2-gui/blob/main/stable_diffusion_2_0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "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",
-        "<br>\n",
-        "\n",
-        "Colab by [anzorq](https://twitter.com/hahahahohohe). If you like it, please consider supporting me:\n",
-        "\n",
-        "[<a href=\"https://www.buymeacoffee.com/anzorq\" target=\"_blank\"><img src=\"https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png\" height=\"32px\" width=\"108px\" alt=\"Buy Me A Coffee\"></a>](https://www.buymeacoffee.com/anzorq)\n",
-        "<br>\n",
-        "[![GitHub Repo stars](https://img.shields.io/github/stars/qunash/stable-diffusion-2-gui?style=social)](https://github.com/qunash/stable-diffusion-2-gui)\n",
-        "\n",
-        "![visitors](https://visitor-badge.glitch.me/badge?page_id=anzorq.sd-2-colab-header)"
-      ]
-    },
-    {
-      "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.<br>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",
-        "          <div class=\"main-div\">\n",
-        "            <div>\n",
-        "              <h1>Stable Diffusion 2.1</h1>\n",
-        "            </div><br>\n",
-        "            <p> Model used: <a href=\"https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.ckpt\" target=\"_blank\">v2-1_768-ema-pruned.ckpt</a></p>\n",
-        "            Running on <b>{\"GPU 🔥\" if torch.cuda.is_available() else \"CPU 🥶\"}</b>\n",
-        "          </div>\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).<br>\n",
-        "                                        Bigger images will be sliced into 128x128 tiles which will be upscaled individually.<br>\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",
-        "    <div style=\"border-top: 1px solid #303030;\">\n",
-        "      <br>\n",
-        "      <p>Space by: <a href=\"https://twitter.com/hahahahohohe\"><img src=\"https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social\" alt=\"Twitter Follow\"></a></p><br>\n",
-        "      <p>Enjoying this app? Please consider <a href=\"https://www.buymeacoffee.com/anzorq\">supporting me</a></p>\n",
-        "      <a href=\"https://www.buymeacoffee.com/anzorq\" target=\"_blank\"><img src=\"https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png\" alt=\"Buy Me A Coffee\" style=\"height: 45px !important;width: 162px !important;\" ></a><br><br>\n",
-        "      <a href=\"https://github.com/qunash/stable-diffusion-2-gui\" target=\"_blank\"><img alt=\"GitHub Repo stars\" src=\"https://img.shields.io/github/stars/qunash/stable-diffusion-2-gui?style=social\"></a>\n",
-        "      <p><img src=\"https://visitor-badge.glitch.me/badge?page_id=anzorq.sd-2-colab\" alt=\"visitors\"></p>\n",
-        "    </div>\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
-}

spaces/CVPR/DualStyleGAN/style.css

@@ -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;
-}

spaces/Chris4K/llms_compare/CabelasDangerousHunts2013-SKIDROW-REPACK-Crack-Fix-Torrent-Download.md

@@ -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
-
- 
-
- 
-

spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/model/tool.js

@@ -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
-}

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

@@ -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"],
-)

spaces/CjangCjengh/Sanskrit-TTS/commons.py

@@ -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

spaces/CofAI/chat/g4f/Provider/Provider.py

@@ -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]])

spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/models/diffusion/sampling_util.py

@@ -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)

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/middleware/trustedhost.py

@@ -1,3 +0,0 @@
-from starlette.middleware.trustedhost import (  # noqa
-    TrustedHostMiddleware as TrustedHostMiddleware,
-)

spaces/Deci/DeciDiffusion-v1-0/app.py

@@ -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(
-        """
-            <div class="footer">
-                <p>Model by <a href="https://deci.ai" style="text-decoration: underline;" target="_blank">Deci.ai</a> - Gradio Demo by 🤗 Hugging Face
-                </p>
-            </div>
-            <div class="acknowledgments">
-                    <p><h4>LICENSE</h4>
-The model is licensed with a <a href="https://huggingface.co/Deci/DeciDiffusion-v1-0/blob/main/LICENSE-WEIGHTS.md" style="text-decoration: underline;" target="_blank">CreativeML Open RAIL-M</a> 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 <a href="https://huggingface.co/Deci/DeciDiffusion-v1-0/blob/main/LICENSE-WEIGHTS.md" target="_blank" style="text-decoration: underline;" target="_blank">read the license</a></p>
-                    <p><h4>Biases and content acknowledgment</h4>
-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 <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B dataset</a>, 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 <a href="https://huggingface.co/Deci/DeciDiffusion-v1-0" style="text-decoration: underline;" target="_blank">model card</a></p>
-               </div>
-        """
-    )
-
-demo.queue(max_size=50).launch()

spaces/ElainaFanBoy/MusicGen/tests/common_utils/temp_utils.py

@@ -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

spaces/EngAbod/Liveness_Detection/README.md

@@ -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

spaces/FL33TW00D/whisper-turbo/README.md

@@ -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

spaces/Fadil369/docker/README.md

@@ -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).

spaces/Feifei315/flax-midjourney-v4-diffusion/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/flax/midjourney-v4-diffusion").launch()