Update parquet files (step 98 of 249)

spaces/0xSynapse/Image_captioner/README.md

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----
-title: Image Captioner
-emoji: ⚡
-colorFrom: indigo
-colorTo: green
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Bartender 2022 Full Crack.md

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-<h1>Basketball Grand Slam APK: A Real-Time Competitive Basketball Game</h1>
- <h2>Introduction</h2>
- <p>If you are a fan of basketball and want to experience the thrill of real-time competition on your mobile device, then you should check out Basketball Grand Slam APK. This is a game that lets you play with or against other players from around the world in various modes and events. You can also unlock and use legendary players with different skills and characteristics to form your own super lineup.</p>
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- <h3>3v3 Qualifying Mode</h3>
- <p>This is the mode where you can compete with other players in a 3v3 format. You can either join a random team or invite your friends to form your own team. The goal is to win as many matches as possible and climb up the ranking ladder. You can also earn rewards such as coins, gems, chests, and tickets by playing this mode.</p>
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- <p>This is the mode where you can participate in various events and tournaments that have different rules and rewards. You can choose from different difficulty levels and modes such as knockout, round robin, and ladder. You can also customize your own event and invite other players to join. The goal is to win as many matches as possible and earn trophies and prizes.</p>
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- <h3>How to unlock legendary players?</h3>
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- <h2>Tips and Tricks</h2>
- <h3>How to improve your operation and hand feel?</h3>
- <p>To improve your operation and hand feel, you need to practice and familiarize yourself with the game controls and mechanics. You can use the training mode to learn the basic moves and skills of each player. You can also adjust the sensitivity and feedback settings to suit your preference. You can also watch some tutorials and guides online to learn some tips and tricks from other players.</p>
- <h3>How to use different skills and tactics?</h3>
- <p>To use different skills and tactics, you need to know the strengths and weaknesses of each player and team. You can check the stats and attributes of each player in your lineup and choose the ones that match your style and strategy. You can also use the skill buttons to activate different skills such as crossover, dunk, block, etc. You can also use the tactic buttons to switch between different tactics such as man-to-man, zone, pick-and-roll, etc.</p>
- <h3>How to cooperate with your teammates?</h3>
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- <h2>Conclusion</h2>
- <p>Basketball Grand Slam APK is a game that allows you to enjoy the excitement and fun of basketball on your mobile device. You can play with or against other players from around the world in various modes and events. You can also unlock and use legendary players with different skills and characteristics to form your own super lineup. If you are a basketball fan, you should not miss this game.</p>
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-<li>Q4: What are some of the best basketball trivia and facts?<br>
-A4: Here are some of the best basketball trivia and facts:</p>
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-<tr><th>Trivia</th><th>Fact</th></tr>
-<tr><td>The inventor of basketball was James Naismith.</td><td>He created the game in 1891 as a physical education instructor at Springfield College in Massachusetts.</td></tr>
-<tr><td>The first official basketball game was played on January 20, 1892.</td><td>The game was played between two teams of nine players each at Springfield College. The final score was 1-0.</td></tr>
-<tr><td>The first NBA game was played on November 1, 1946.</td><td>The game was played between the New York Knicks and the Toronto Huskies at Maple Leaf Gardens in Toronto. The final score was 68-66 in favor of the Knicks.</td></tr>
-<tr><td>The shortest NBA player ever was Muggsy Bogues.</td><td>He was only 5 feet 3 inches tall and played as a point guard for 14 seasons in the NBA.</td></tr>
-<tr><td>The tallest NBA player ever was Manute Bol.</td><td>He was 7 feet 7 inches tall and played as a center for 10 seasons in the NBA. He also holds the record for the most blocks per game in a season with 5.0.</td></tr>
-</table>
-<li>Q5: What are some of the basic basketball rules and terms?<br>
-A5: Here are some of the basic basketball rules and terms:</p>
-<ul>
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-<h3>Install and Play the Game</h3>
-<p>After you have added the game to your library, you are ready to download and install it on your PC. To do that, you need to go to the "Library" tab at the top of the Epic Games Launcher window. There, you will see a list of all the games you own or have access to.</p>
-<p>Find hair challenge game in your library and click on it. This will open a new window where you can see more details about the game, such as its size, version, and update status. You will also see a button that says "Install". Click on it to start downloading and installing the game on your PC.</p>
-<p>The download and installation process may take some time depending on your internet speed and PC performance. You can check the progress of the download and installation in the same window, or in the "Downloads" section of the launcher. You can also pause or resume the download at any time.</p>
-<p>Once the download and installation are complete, you can start playing hair challenge game on your PC. To do that, just click on the "Launch" button in the same window where you installed the game, or in your library. This will open the game and let you enjoy it.</p>
-<h2>Tips and Tricks for Playing Hair Challenge Game</h2>
-<p>Hair challenge game is a fun and easy game to play, but it can also be challenging and addictive. Here are some tips and tricks to help you get better at it and have more fun:</p>
-<h3>Avoid Obstacles</h3>
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-<p>To move left or right, you can use the arrow keys on your keyboard, or drag your mouse left or right. To jump or duck, you can use the spacebar on your keyboard, or click with your mouse. Be careful not to hit any obstacles or walls, as this will reduce your hair length and score.</p>
-<h3>Collect Hair Extensions</h3>
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-<p>Hair challenge game is a fun and addictive game that lets you grow and style your hair as you run through different levels. It is a great way to relax and have fun, as well as to unleash your creativity and personality.</p>
-<p>If you want to enjoy hair challenge game on your PC, you can easily download it for free from the Epic Games Launcher. All you need to do is install the launcher, create an account, find the game, and install it. Then, you can start playing and customizing your character.</p>
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-<li>A: The game will update automatically when you launch it from the Epic Games Launcher. You can also check for updates manually by clicking on the "Update" button in the library section of the launcher.</li>
-<li><b>Q: How do I uninstall hair challenge game from my PC?</b></li>
-<li>A: To uninstall the game, you need to go to the library section of the Epic Games Launcher, find the game, and click on the "Uninstall" button. You can also uninstall the game from the control panel of your PC.</li>
-<li><b>Q: Is hair challenge game safe to download and play on my PC?</b></li>
-<li>A: Yes, hair challenge game is safe and secure to download and play on your PC. The game is verified by Epic Games and does not contain any viruses or malware.</li>
-<li><b>Q: Can I play hair challenge game offline on my PC?</b></li>
-<li>A: Yes, you can play hair challenge game offline on your PC. However, you will not be able to access some features that require an internet connection, such as watching ads or connecting with friends.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />

spaces/2ndelement/voicevox/voicevox_engine/full_context_label.py

@@ -1,525 +0,0 @@
-import re
-from dataclasses import dataclass
-from itertools import chain
-from typing import Dict, List, Optional
-
-import pyopenjtalk
-
-
-@dataclass
-class Phoneme:
-    """
-    音素(母音・子音)クラス、音素の元となるcontextを保持する
-    音素には、母音や子音以外にも無音(silent/pause)も含まれる
-
-    Attributes
-    ----------
-    contexts: Dict[str, str]
-        音素の元
-    """
-
-    contexts: Dict[str, str]
-
-    @classmethod
-    def from_label(cls, label: str):
-        """
-        pyopenjtalk.extract_fullcontextで得られる音素の元(ラベル)から、Phonemeクラスを作成する
-        Parameters
-        ----------
-        label : str
-            pyopenjtalk.extract_fullcontextで得られるラベルを渡す
-
-        Returns
-        -------
-        phoneme: Phoneme
-            Phonemeクラスを返す
-        """
-
-        # フルコンテキストラベルの仕様は、
-        # http://hts.sp.nitech.ac.jp/?Download の HTS-2.3のJapanese tar.bz2 (126 MB)をダウンロードして、data/lab_format.pdfを見るとリストが見つかります。 # noqa
-        contexts = re.search(
-            r"^(?P<p1>.+?)\^(?P<p2>.+?)\-(?P<p3>.+?)\+(?P<p4>.+?)\=(?P<p5>.+?)"
-            r"/A\:(?P<a1>.+?)\+(?P<a2>.+?)\+(?P<a3>.+?)"
-            r"/B\:(?P<b1>.+?)\-(?P<b2>.+?)\_(?P<b3>.+?)"
-            r"/C\:(?P<c1>.+?)\_(?P<c2>.+?)\+(?P<c3>.+?)"
-            r"/D\:(?P<d1>.+?)\+(?P<d2>.+?)\_(?P<d3>.+?)"
-            r"/E\:(?P<e1>.+?)\_(?P<e2>.+?)\!(?P<e3>.+?)\_(?P<e4>.+?)\-(?P<e5>.+?)"
-            r"/F\:(?P<f1>.+?)\_(?P<f2>.+?)\#(?P<f3>.+?)\_(?P<f4>.+?)\@(?P<f5>.+?)\_(?P<f6>.+?)\|(?P<f7>.+?)\_(?P<f8>.+?)"  # noqa
-            r"/G\:(?P<g1>.+?)\_(?P<g2>.+?)\%(?P<g3>.+?)\_(?P<g4>.+?)\_(?P<g5>.+?)"
-            r"/H\:(?P<h1>.+?)\_(?P<h2>.+?)"
-            r"/I\:(?P<i1>.+?)\-(?P<i2>.+?)\@(?P<i3>.+?)\+(?P<i4>.+?)\&(?P<i5>.+?)\-(?P<i6>.+?)\|(?P<i7>.+?)\+(?P<i8>.+?)"  # noqa
-            r"/J\:(?P<j1>.+?)\_(?P<j2>.+?)"
-            r"/K\:(?P<k1>.+?)\+(?P<k2>.+?)\-(?P<k3>.+?)$",
-            label,
-        ).groupdict()
-        return cls(contexts=contexts)
-
-    @property
-    def label(self):
-        """
-        pyopenjtalk.extract_fullcontextで得られるラベルと等しい
-        Returns
-        -------
-        lebel: str
-            ラベルを返す
-        """
-        return (
-            "{p1}^{p2}-{p3}+{p4}={p5}"
-            "/A:{a1}+{a2}+{a3}"
-            "/B:{b1}-{b2}_{b3}"
-            "/C:{c1}_{c2}+{c3}"
-            "/D:{d1}+{d2}_{d3}"
-            "/E:{e1}_{e2}!{e3}_{e4}-{e5}"
-            "/F:{f1}_{f2}#{f3}_{f4}@{f5}_{f6}|{f7}_{f8}"
-            "/G:{g1}_{g2}%{g3}_{g4}_{g5}"
-            "/H:{h1}_{h2}"
-            "/I:{i1}-{i2}@{i3}+{i4}&{i5}-{i6}|{i7}+{i8}"
-            "/J:{j1}_{j2}"
-            "/K:{k1}+{k2}-{k3}"
-        ).format(**self.contexts)
-
-    @property
-    def phoneme(self):
-        """
-        音素クラスの中で、発声に必要な要素を返す
-        Returns
-        -------
-        phoneme : str
-            発声に必要な要素を返す
-        """
-        return self.contexts["p3"]
-
-    def is_pause(self):
-        """
-        音素がポーズ(無音、silent/pause)であるかを返す
-        Returns
-        -------
-        is_pose : bool
-            音素がポーズ(無音、silent/pause)であるか(True)否か(False)
-        """
-        return self.contexts["f1"] == "xx"
-
-    def __repr__(self):
-        return f"<Phoneme phoneme='{self.phoneme}'>"
-
-
-@dataclass
-class Mora:
-    """
-    モーラクラス
-    モーラは1音素(母音や促音「っ」、撥音「ん」など)か、2音素(母音と子音の組み合わせ)で成り立つ
-
-    Attributes
-    ----------
-    consonant : Optional[Phoneme]
-        子音
-    vowel : Phoneme
-        母音
-    """
-
-    consonant: Optional[Phoneme]
-    vowel: Phoneme
-
-    def set_context(self, key: str, value: str):
-        """
-        Moraクラス内に含まれるPhonemeのcontextのうち、指定されたキーの値を変更する
-        consonantが存在する場合は、vowelと同じようにcontextを変更する
-        Parameters
-        ----------
-        key : str
-            変更したいcontextのキー
-        value : str
-            変更したいcontextの値
-        """
-        self.vowel.contexts[key] = value
-        if self.consonant is not None:
-            self.consonant.contexts[key] = value
-
-    @property
-    def phonemes(self):
-        """
-        音素群を返す
-        Returns
-        -------
-        phonemes : List[Phoneme]
-            母音しかない場合は母音のみ、子音もある場合は子音、母音の順番でPhonemeのリストを返す
-        """
-        if self.consonant is not None:
-            return [self.consonant, self.vowel]
-        else:
-            return [self.vowel]
-
-    @property
-    def labels(self):
-        """
-        ラベル群を返す
-        Returns
-        -------
-        labels : List[str]
-            Moraに含まれるすべてのラベルを返す
-        """
-        return [p.label for p in self.phonemes]
-
-
-@dataclass
-class AccentPhrase:
-    """
-    アクセント句クラス
-    同じアクセントのMoraを複数保持する
-    Attributes
-    ----------
-    moras : List[Mora]
-        音韻のリスト
-    accent : int
-        アクセント
-    """
-
-    moras: List[Mora]
-    accent: int
-    is_interrogative: bool
-
-    @classmethod
-    def from_phonemes(cls, phonemes: List[Phoneme]):
-        """
-        PhonemeのリストからAccentPhraseクラスを作成する
-        Parameters
-        ----------
-        phonemes : List[Phoneme]
-            phonemeのリストを渡す
-
-        Returns
-        -------
-        accent_phrase : AccentPhrase
-            AccentPhraseクラスを返す
-        """
-        moras: List[Mora] = []
-
-        mora_phonemes: List[Phoneme] = []
-        for phoneme, next_phoneme in zip(phonemes, phonemes[1:] + [None]):
-            # workaround for Hihosiba/voicevox_engine#57
-            # (py)openjtalk によるアクセント句内のモーラへの附番は 49 番目まで
-            # 49 番目のモーラについて、続く音素のモーラ番号を単一モーラの特定に使えない
-            if int(phoneme.contexts["a2"]) == 49:
-                break
-
-            mora_phonemes.append(phoneme)
-
-            if (
-                next_phoneme is None
-                or phoneme.contexts["a2"] != next_phoneme.contexts["a2"]
-            ):
-                if len(mora_phonemes) == 1:
-                    consonant, vowel = None, mora_phonemes[0]
-                elif len(mora_phonemes) == 2:
-                    consonant, vowel = mora_phonemes[0], mora_phonemes[1]
-                else:
-                    raise ValueError(mora_phonemes)
-                mora = Mora(consonant=consonant, vowel=vowel)
-                moras.append(mora)
-                mora_phonemes = []
-
-        accent = int(moras[0].vowel.contexts["f2"])
-        # workaround for Hihosiba/voicevox_engine#55
-        # アクセント位置とするキー f2 の値がアクセント句内のモーラ数を超える場合がある
-        accent = accent if accent <= len(moras) else len(moras)
-        is_interrogative = moras[-1].vowel.contexts["f3"] == "1"
-        return cls(moras=moras, accent=accent, is_interrogative=is_interrogative)
-
-    def set_context(self, key: str, value: str):
-        """
-        AccentPhraseに間接的に含まれる全てのPhonemeのcontextの、指定されたキーの値を変更する
-        Parameters
-        ----------
-        key : str
-            変更したいcontextのキー
-        value : str
-            変更したいcontextの値
-        """
-        for mora in self.moras:
-            mora.set_context(key, value)
-
-    @property
-    def phonemes(self):
-        """
-        音素群を返す
-        Returns
-        -------
-        phonemes : List[Phoneme]
-            AccentPhraseに間接的に含まれる全てのPhonemeを返す
-        """
-        return list(chain.from_iterable(m.phonemes for m in self.moras))
-
-    @property
-    def labels(self):
-        """
-        ラベル群を返す
-        Returns
-        -------
-        labels : List[str]
-            AccentPhraseに間接的に含まれる全てのラベルを返す
-        """
-        return [p.label for p in self.phonemes]
-
-    def merge(self, accent_phrase: "AccentPhrase"):
-        """
-        AccentPhraseを合成する
-        (このクラスが保持するmorasの後ろに、引数として渡されたAccentPhraseのmorasを合成する)
-        Parameters
-        ----------
-        accent_phrase : AccentPhrase
-            合成したいAccentPhraseを渡す
-
-        Returns
-        -------
-        accent_phrase : AccentPhrase
-            合成されたAccentPhraseを返す
-        """
-        return AccentPhrase(
-            moras=self.moras + accent_phrase.moras,
-            accent=self.accent,
-            is_interrogative=accent_phrase.is_interrogative,
-        )
-
-
-@dataclass
-class BreathGroup:
-    """
-    発声の区切りクラス
-    アクセントの異なるアクセント句を複数保持する
-    Attributes
-    ----------
-    accent_phrases : List[AccentPhrase]
-        アクセント句のリスト
-    """
-
-    accent_phrases: List[AccentPhrase]
-
-    @classmethod
-    def from_phonemes(cls, phonemes: List[Phoneme]):
-        """
-        PhonemeのリストからBreathGroupクラスを作成する
-        Parameters
-        ----------
-        phonemes : List[Phoneme]
-            phonemeのリストを渡す
-
-        Returns
-        -------
-        breath_group : BreathGroup
-            BreathGroupクラスを返す
-        """
-        accent_phrases: List[AccentPhrase] = []
-        accent_phonemes: List[Phoneme] = []
-        for phoneme, next_phoneme in zip(phonemes, phonemes[1:] + [None]):
-            accent_phonemes.append(phoneme)
-
-            if (
-                next_phoneme is None
-                or phoneme.contexts["i3"] != next_phoneme.contexts["i3"]
-                or phoneme.contexts["f5"] != next_phoneme.contexts["f5"]
-            ):
-                accent_phrase = AccentPhrase.from_phonemes(accent_phonemes)
-                accent_phrases.append(accent_phrase)
-                accent_phonemes = []
-
-        return cls(accent_phrases=accent_phrases)
-
-    def set_context(self, key: str, value: str):
-        """
-        BreathGroupに間接的に含まれる全てのPhonemeのcontextの、指定されたキーの値を変更する
-        Parameters
-        ----------
-        key : str
-            変更したいcontextのキー
-        value : str
-            変更したいcontextの値
-        """
-        for accent_phrase in self.accent_phrases:
-            accent_phrase.set_context(key, value)
-
-    @property
-    def phonemes(self):
-        """
-        音素群を返す
-        Returns
-        -------
-        phonemes : List[Phoneme]
-            BreathGroupに間接的に含まれる全てのPhonemeを返す
-        """
-        return list(
-            chain.from_iterable(
-                accent_phrase.phonemes for accent_phrase in self.accent_phrases
-            )
-        )
-
-    @property
-    def labels(self):
-        """
-        ラベル群を返す
-        Returns
-        -------
-        labels : List[str]
-            BreathGroupに間接的に含まれる全てのラベルを返す
-        """
-        return [p.label for p in self.phonemes]
-
-
-@dataclass
-class Utterance:
-    """
-    発声クラス
-    発声の区切りと無音を複数保持する
-    Attributes
-    ----------
-    breath_groups : List[BreathGroup]
-        発声の区切りのリスト
-    pauses : List[Phoneme]
-        無音のリスト
-    """
-
-    breath_groups: List[BreathGroup]
-    pauses: List[Phoneme]
-
-    @classmethod
-    def from_phonemes(cls, phonemes: List[Phoneme]):
-        """
-        Phonemeの完全なリストからUtteranceクラスを作成する
-        Parameters
-        ----------
-        phonemes : List[Phoneme]
-            phonemeのリストを渡す
-
-        Returns
-        -------
-        utterance : Utterance
-            Utteranceクラスを返す
-        """
-        pauses: List[Phoneme] = []
-
-        breath_groups: List[BreathGroup] = []
-        group_phonemes: List[Phoneme] = []
-        for phoneme in phonemes:
-            if not phoneme.is_pause():
-                group_phonemes.append(phoneme)
-
-            else:
-                pauses.append(phoneme)
-
-                if len(group_phonemes) > 0:
-                    breath_group = BreathGroup.from_phonemes(group_phonemes)
-                    breath_groups.append(breath_group)
-                    group_phonemes = []
-
-        return cls(breath_groups=breath_groups, pauses=pauses)
-
-    def set_context(self, key: str, value: str):
-        """
-        Utteranceに間接的に含まれる全てのPhonemeのcontextの、指定されたキーの値を変更する
-        Parameters
-        ----------
-        key : str
-            変更したいcontextのキー
-        value : str
-            変更したいcontextの値
-        """
-        for breath_group in self.breath_groups:
-            breath_group.set_context(key, value)
-
-    @property
-    def phonemes(self):
-        """
-        音素群を返す
-        Returns
-        -------
-        phonemes : List[Phoneme]
-            Utteranceクラスに直接的・間接的に含まれる、全てのPhonemeを返す
-        """
-        accent_phrases = list(
-            chain.from_iterable(
-                breath_group.accent_phrases for breath_group in self.breath_groups
-            )
-        )
-        for prev, cent, post in zip(
-            [None] + accent_phrases[:-1],
-            accent_phrases,
-            accent_phrases[1:] + [None],
-        ):
-            mora_num = len(cent.moras)
-            accent = cent.accent
-
-            if prev is not None:
-                prev.set_context("g1", str(mora_num))
-                prev.set_context("g2", str(accent))
-
-            if post is not None:
-                post.set_context("e1", str(mora_num))
-                post.set_context("e2", str(accent))
-
-            cent.set_context("f1", str(mora_num))
-            cent.set_context("f2", str(accent))
-            for i_mora, mora in enumerate(cent.moras):
-                mora.set_context("a1", str(i_mora - accent + 1))
-                mora.set_context("a2", str(i_mora + 1))
-                mora.set_context("a3", str(mora_num - i_mora))
-
-        for prev, cent, post in zip(
-            [None] + self.breath_groups[:-1],
-            self.breath_groups,
-            self.breath_groups[1:] + [None],
-        ):
-            accent_phrase_num = len(cent.accent_phrases)
-
-            if prev is not None:
-                prev.set_context("j1", str(accent_phrase_num))
-
-            if post is not None:
-                post.set_context("h1", str(accent_phrase_num))
-
-            cent.set_context("i1", str(accent_phrase_num))
-            cent.set_context(
-                "i5", str(accent_phrases.index(cent.accent_phrases[0]) + 1)
-            )
-            cent.set_context(
-                "i6",
-                str(len(accent_phrases) - accent_phrases.index(cent.accent_phrases[0])),
-            )
-
-        self.set_context(
-            "k2",
-            str(
-                sum(
-                    [
-                        len(breath_group.accent_phrases)
-                        for breath_group in self.breath_groups
-                    ]
-                )
-            ),
-        )
-
-        phonemes: List[Phoneme] = []
-        for i in range(len(self.pauses)):
-            if self.pauses[i] is not None:
-                phonemes += [self.pauses[i]]
-
-            if i < len(self.pauses) - 1:
-                phonemes += self.breath_groups[i].phonemes
-
-        return phonemes
-
-    @property
-    def labels(self):
-        """
-        ラベル群を返す
-        Returns
-        -------
-        labels : List[str]
-            Utteranceクラスに直接的・間接的に含まれる全てのラベルを返す
-        """
-        return [p.label for p in self.phonemes]
-
-
-def extract_full_context_label(text: str):
-    labels = pyopenjtalk.extract_fullcontext(text)
-    phonemes = [Phoneme.from_label(label=label) for label in labels]
-    utterance = Utterance.from_phonemes(phonemes)
-    return utterance

spaces/A00001/bingothoo/src/components/toaster.tsx

@@ -1,3 +0,0 @@
-'use client'
-
-export { Toaster } from 'react-hot-toast'

spaces/A666sxr/Genshin_TTS/mel_processing.py

@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec

spaces/AHzizi/WaifuVoiceGen/text/cleaners.py

@@ -1,475 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-'''
-
-import re
-from unidecode import unidecode
-import pyopenjtalk
-from jamo import h2j, j2hcj
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba, cn2an
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r'\s+')
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('mrs', 'misess'),
-  ('mr', 'mister'),
-  ('dr', 'doctor'),
-  ('st', 'saint'),
-  ('co', 'company'),
-  ('jr', 'junior'),
-  ('maj', 'major'),
-  ('gen', 'general'),
-  ('drs', 'doctors'),
-  ('rev', 'reverend'),
-  ('lt', 'lieutenant'),
-  ('hon', 'honorable'),
-  ('sgt', 'sergeant'),
-  ('capt', 'captain'),
-  ('esq', 'esquire'),
-  ('ltd', 'limited'),
-  ('col', 'colonel'),
-  ('ft', 'fort'),
-]]
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
-  ('ㄳ', 'ㄱㅅ'),
-  ('ㄵ', 'ㄴㅈ'),
-  ('ㄶ', 'ㄴㅎ'),
-  ('ㄺ', 'ㄹㄱ'),
-  ('ㄻ', 'ㄹㅁ'),
-  ('ㄼ', 'ㄹㅂ'),
-  ('ㄽ', 'ㄹㅅ'),
-  ('ㄾ', 'ㄹㅌ'),
-  ('ㄿ', 'ㄹㅍ'),
-  ('ㅀ', 'ㄹㅎ'),
-  ('ㅄ', 'ㅂㅅ'),
-  ('ㅘ', 'ㅗㅏ'),
-  ('ㅙ', 'ㅗㅐ'),
-  ('ㅚ', 'ㅗㅣ'),
-  ('ㅝ', 'ㅜㅓ'),
-  ('ㅞ', 'ㅜㅔ'),
-  ('ㅟ', 'ㅜㅣ'),
-  ('ㅢ', 'ㅡㅣ'),
-  ('ㅑ', 'ㅣㅏ'),
-  ('ㅒ', 'ㅣㅐ'),
-  ('ㅕ', 'ㅣㅓ'),
-  ('ㅖ', 'ㅣㅔ'),
-  ('ㅛ', 'ㅣㅗ'),
-  ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('a', '에이'),
-  ('b', '비'),
-  ('c', '시'),
-  ('d', '디'),
-  ('e', '이'),
-  ('f', '에프'),
-  ('g', '지'),
-  ('h', '에이치'),
-  ('i', '아이'),
-  ('j', '제이'),
-  ('k', '케이'),
-  ('l', '엘'),
-  ('m', '엠'),
-  ('n', '엔'),
-  ('o', '오'),
-  ('p', '피'),
-  ('q', '큐'),
-  ('r', '아르'),
-  ('s', '에스'),
-  ('t', '티'),
-  ('u', '유'),
-  ('v', '브이'),
-  ('w', '더블유'),
-  ('x', '엑스'),
-  ('y', '와이'),
-  ('z', '제트')
-]]
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('a', 'ㄟˉ'),
-  ('b', 'ㄅㄧˋ'),
-  ('c', 'ㄙㄧˉ'),
-  ('d', 'ㄉㄧˋ'),
-  ('e', 'ㄧˋ'),
-  ('f', 'ㄝˊㄈㄨˋ'),
-  ('g', 'ㄐㄧˋ'),
-  ('h', 'ㄝˇㄑㄩˋ'),
-  ('i', 'ㄞˋ'),
-  ('j', 'ㄐㄟˋ'),
-  ('k', 'ㄎㄟˋ'),
-  ('l', 'ㄝˊㄛˋ'),
-  ('m', 'ㄝˊㄇㄨˋ'),
-  ('n', 'ㄣˉ'),
-  ('o', 'ㄡˉ'),
-  ('p', 'ㄆㄧˉ'),
-  ('q', 'ㄎㄧㄡˉ'),
-  ('r', 'ㄚˋ'),
-  ('s', 'ㄝˊㄙˋ'),
-  ('t', 'ㄊㄧˋ'),
-  ('u', 'ㄧㄡˉ'),
-  ('v', 'ㄨㄧˉ'),
-  ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
-  ('x', 'ㄝˉㄎㄨˋㄙˋ'),
-  ('y', 'ㄨㄞˋ'),
-  ('z', 'ㄗㄟˋ')
-]]
-
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('ㄅㄛ', 'p⁼wo'),
-  ('ㄆㄛ', 'pʰwo'),
-  ('ㄇㄛ', 'mwo'),
-  ('ㄈㄛ', 'fwo'),
-  ('ㄅ', 'p⁼'),
-  ('ㄆ', 'pʰ'),
-  ('ㄇ', 'm'),
-  ('ㄈ', 'f'),
-  ('ㄉ', 't⁼'),
-  ('ㄊ', 'tʰ'),
-  ('ㄋ', 'n'),
-  ('ㄌ', 'l'),
-  ('ㄍ', 'k⁼'),
-  ('ㄎ', 'kʰ'),
-  ('ㄏ', 'h'),
-  ('ㄐ', 'ʧ⁼'),
-  ('ㄑ', 'ʧʰ'),
-  ('ㄒ', 'ʃ'),
-  ('ㄓ', 'ʦ`⁼'),
-  ('ㄔ', 'ʦ`ʰ'),
-  ('ㄕ', 's`'),
-  ('ㄖ', 'ɹ`'),
-  ('ㄗ', 'ʦ⁼'),
-  ('ㄘ', 'ʦʰ'),
-  ('ㄙ', 's'),
-  ('ㄚ', 'a'),
-  ('ㄛ', 'o'),
-  ('ㄜ', 'ə'),
-  ('ㄝ', 'e'),
-  ('ㄞ', 'ai'),
-  ('ㄟ', 'ei'),
-  ('ㄠ', 'au'),
-  ('ㄡ', 'ou'),
-  ('ㄧㄢ', 'yeNN'),
-  ('ㄢ', 'aNN'),
-  ('ㄧㄣ', 'iNN'),
-  ('ㄣ', 'əNN'),
-  ('ㄤ', 'aNg'),
-  ('ㄧㄥ', 'iNg'),
-  ('ㄨㄥ', 'uNg'),
-  ('ㄩㄥ', 'yuNg'),
-  ('ㄥ', 'əNg'),
-  ('ㄦ', 'əɻ'),
-  ('ㄧ', 'i'),
-  ('ㄨ', 'u'),
-  ('ㄩ', 'ɥ'),
-  ('ˉ', '→'),
-  ('ˊ', '↑'),
-  ('ˇ', '↓↑'),
-  ('ˋ', '↓'),
-  ('˙', ''),
-  ('，', ','),
-  ('。', '.'),
-  ('！', '!'),
-  ('？', '?'),
-  ('—', '-')
-]]
-
-
-def expand_abbreviations(text):
-  for regex, replacement in _abbreviations:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def lowercase(text):
-  return text.lower()
-
-
-def collapse_whitespace(text):
-  return re.sub(_whitespace_re, ' ', text)
-
-
-def convert_to_ascii(text):
-  return unidecode(text)
-
-
-def japanese_to_romaji_with_accent(text):
-  '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-  sentences = re.split(_japanese_marks, text)
-  marks = re.findall(_japanese_marks, text)
-  text = ''
-  for i, sentence in enumerate(sentences):
-    if re.match(_japanese_characters, sentence):
-      if text!='':
-        text+=' '
-      labels = pyopenjtalk.extract_fullcontext(sentence)
-      for n, label in enumerate(labels):
-        phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
-        if phoneme not in ['sil','pau']:
-          text += phoneme.replace('ch','ʧ').replace('sh','ʃ').replace('cl','Q')
-        else:
-          continue
-        n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
-        a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
-        a2 = int(re.search(r"\+(\d+)\+", label).group(1))
-        a3 = int(re.search(r"\+(\d+)/", label).group(1))
-        if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil','pau']:
-          a2_next=-1
-        else:
-          a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
-        # Accent phrase boundary
-        if a3 == 1 and a2_next == 1:
-          text += ' '
-        # Falling
-        elif a1 == 0 and a2_next == a2 + 1 and a2 != n_moras:
-          text += '↓'
-        # Rising
-        elif a2 == 1 and a2_next == 2:
-          text += '↑'
-    if i<len(marks):
-      text += unidecode(marks[i]).replace(' ','')
-  return text
-
-
-def latin_to_hangul(text):
-  for regex, replacement in _latin_to_hangul:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def divide_hangul(text):
-  for regex, replacement in _hangul_divided:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def hangul_number(num, sino=True):
-  '''Reference https://github.com/Kyubyong/g2pK'''
-  num = re.sub(',', '', num)
-
-  if num == '0':
-      return '영'
-  if not sino and num == '20':
-      return '스무'
-
-  digits = '123456789'
-  names = '일이삼사오육칠팔구'
-  digit2name = {d: n for d, n in zip(digits, names)}
-  
-  modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
-  decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
-  digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
-  digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
-
-  spelledout = []
-  for i, digit in enumerate(num):
-    i = len(num) - i - 1
-    if sino:
-      if i == 0:
-        name = digit2name.get(digit, '')
-      elif i == 1:
-        name = digit2name.get(digit, '') + '십'
-        name = name.replace('일십', '십')
-    else:
-      if i == 0:
-        name = digit2mod.get(digit, '')
-      elif i == 1:
-        name = digit2dec.get(digit, '')
-    if digit == '0':
-      if i % 4 == 0:
-        last_three = spelledout[-min(3, len(spelledout)):]
-        if ''.join(last_three) == '':
-          spelledout.append('')
-          continue
-      else:
-        spelledout.append('')
-        continue
-    if i == 2:
-      name = digit2name.get(digit, '') + '백'
-      name = name.replace('일백', '백')
-    elif i == 3:
-      name = digit2name.get(digit, '') + '천'
-      name = name.replace('일천', '천')
-    elif i == 4:
-      name = digit2name.get(digit, '') + '만'
-      name = name.replace('일만', '만')
-    elif i == 5:
-      name = digit2name.get(digit, '') + '십'
-      name = name.replace('일십', '십')
-    elif i == 6:
-      name = digit2name.get(digit, '') + '백'
-      name = name.replace('일백', '백')
-    elif i == 7:
-      name = digit2name.get(digit, '') + '천'
-      name = name.replace('일천', '천')
-    elif i == 8:
-      name = digit2name.get(digit, '') + '억'
-    elif i == 9:
-      name = digit2name.get(digit, '') + '십'
-    elif i == 10:
-      name = digit2name.get(digit, '') + '백'
-    elif i == 11:
-      name = digit2name.get(digit, '') + '천'
-    elif i == 12:
-      name = digit2name.get(digit, '') + '조'
-    elif i == 13:
-      name = digit2name.get(digit, '') + '십'
-    elif i == 14:
-      name = digit2name.get(digit, '') + '백'
-    elif i == 15:
-      name = digit2name.get(digit, '') + '천'
-    spelledout.append(name)
-  return ''.join(elem for elem in spelledout)
-
-
-def number_to_hangul(text):
-  '''Reference https://github.com/Kyubyong/g2pK'''
-  tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
-  for token in tokens:
-    num, classifier = token
-    if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
-      spelledout = hangul_number(num, sino=False)
-    else:
-      spelledout = hangul_number(num, sino=True)
-    text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
-  # digit by digit for remaining digits
-  digits = '0123456789'
-  names = '영일이삼사오육칠팔구'
-  for d, n in zip(digits, names):
-    text = text.replace(d, n)
-  return text
-
-
-def number_to_chinese(text):
-  numbers = re.findall(r'\d+(?:\.?\d+)?', text)
-  for number in numbers:
-    text = text.replace(number, cn2an.an2cn(number),1)
-  return text
-
-
-def chinese_to_bopomofo(text):
-  text=text.replace('、','，').replace('；','，').replace('：','，')
-  words=jieba.lcut(text,cut_all=False)
-  text=''
-  for word in words:
-    bopomofos=lazy_pinyin(word,BOPOMOFO)
-    if not re.search('[\u4e00-\u9fff]',word):
-      text+=word
-      continue
-    for i in range(len(bopomofos)):
-      if re.match('[\u3105-\u3129]',bopomofos[i][-1]):
-        bopomofos[i]+='ˉ'
-    if text!='':
-      text+=' '
-    text+=''.join(bopomofos)
-  return text
-
-
-def latin_to_bopomofo(text):
-  for regex, replacement in _latin_to_bopomofo:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def bopomofo_to_romaji(text):
-  for regex, replacement in _bopomofo_to_romaji:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def basic_cleaners(text):
-  '''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def transliteration_cleaners(text):
-  '''Pipeline for non-English text that transliterates to ASCII.'''
-  text = convert_to_ascii(text)
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def japanese_cleaners(text):
-  text=japanese_to_romaji_with_accent(text)
-  if re.match('[A-Za-z]',text[-1]):
-    text += '.'
-  return text
-
-
-def japanese_cleaners2(text):
-  return japanese_cleaners(text).replace('ts','ʦ').replace('...','…')
-
-
-def korean_cleaners(text):
-  '''Pipeline for Korean text'''
-  text = latin_to_hangul(text)
-  text = number_to_hangul(text)
-  text = j2hcj(h2j(text))
-  text = divide_hangul(text)
-  if re.match('[\u3131-\u3163]',text[-1]):
-    text += '.'
-  return text
-
-
-def chinese_cleaners(text):
-  '''Pipeline for Chinese text'''
-  text=number_to_chinese(text)
-  text=chinese_to_bopomofo(text)
-  text=latin_to_bopomofo(text)
-  if re.match('[ˉˊˇˋ˙]',text[-1]):
-    text += '。'
-  return text
-
-
-def zh_ja_mixture_cleaners(text):
-  chinese_texts=re.findall(r'\[ZH\].*?\[ZH\]',text)
-  japanese_texts=re.findall(r'\[JA\].*?\[JA\]',text)
-  for chinese_text in chinese_texts:
-    cleaned_text=number_to_chinese(chinese_text[4:-4])
-    cleaned_text=chinese_to_bopomofo(cleaned_text)
-    cleaned_text=latin_to_bopomofo(cleaned_text)
-    cleaned_text=bopomofo_to_romaji(cleaned_text)
-    cleaned_text=re.sub('i[aoe]',lambda x:'y'+x.group(0)[1:],cleaned_text)
-    cleaned_text=re.sub('u[aoəe]',lambda x:'w'+x.group(0)[1:],cleaned_text)
-    cleaned_text=re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑]+)',lambda x:x.group(1)+'ɹ`'+x.group(2),cleaned_text).replace('ɻ','ɹ`')
-    cleaned_text=re.sub('([ʦs][⁼ʰ]?)([→↓↑]+)',lambda x:x.group(1)+'ɹ'+x.group(2),cleaned_text)
-    text = text.replace(chinese_text,cleaned_text+' ',1)
-  for japanese_text in japanese_texts:
-    cleaned_text=japanese_to_romaji_with_accent(japanese_text[4:-4]).replace('ts','ʦ').replace('u','ɯ').replace('...','…')
-    text = text.replace(japanese_text,cleaned_text+' ',1)
-  text=text[:-1]
-  if len(text) and re.match('[A-Za-zɯɹəɥ→↓↑]',text[-1]):
-    text += '.'
-  return text

spaces/AI-Hobbyist/Hoyo-RVC/config.py

@@ -1,123 +0,0 @@
-import argparse
-import torch
-from multiprocessing import cpu_count
-
-
-def config_file_change_fp32():
-    for config_file in ["32k.json", "40k.json", "48k.json"]:
-        with open(f"configs/{config_file}", "r") as f:
-            strr = f.read().replace("true", "false")
-        with open(f"configs/{config_file}", "w") as f:
-            f.write(strr)
-    with open("trainset_preprocess_pipeline_print.py", "r") as f:
-        strr = f.read().replace("3.7", "3.0")
-    with open("trainset_preprocess_pipeline_print.py", "w") as f:
-        f.write(strr)
-
-
-class Config:
-    def __init__(self):
-        self.device = "cuda:0"
-        self.is_half = True
-        self.n_cpu = 0
-        self.gpu_name = None
-        self.gpu_mem = None
-        (
-            self.python_cmd,
-            self.listen_port,
-            self.iscolab,
-            self.noparallel,
-            self.noautoopen,
-        ) = self.arg_parse()
-        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-
-    @staticmethod
-    def arg_parse() -> tuple:
-        parser = argparse.ArgumentParser()
-        parser.add_argument("--port", type=int, default=7865, help="Listen port")
-        parser.add_argument(
-            "--pycmd", type=str, default="python", help="Python command"
-        )
-        parser.add_argument("--colab", action="store_true", help="Launch in colab")
-        parser.add_argument(
-            "--noparallel", action="store_true", help="Disable parallel processing"
-        )
-        parser.add_argument(
-            "--noautoopen",
-            action="store_true",
-            help="Do not open in browser automatically",
-        )
-        cmd_opts = parser.parse_args()
-
-        cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
-
-        return (
-            cmd_opts.pycmd,
-            cmd_opts.port,
-            cmd_opts.colab,
-            cmd_opts.noparallel,
-            cmd_opts.noautoopen,
-        )
-
-    def device_config(self) -> tuple:
-        if torch.cuda.is_available():
-            i_device = int(self.device.split(":")[-1])
-            self.gpu_name = torch.cuda.get_device_name(i_device)
-            if (
-                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
-                or "P40" in self.gpu_name.upper()
-                or "1060" in self.gpu_name
-                or "1070" in self.gpu_name
-                or "1080" in self.gpu_name
-            ):
-                print("16系/10系显卡和P40强制单精度")
-                self.is_half = False
-                config_file_change_fp32()
-            else:
-                self.gpu_name = None
-            self.gpu_mem = int(
-                torch.cuda.get_device_properties(i_device).total_memory
-                / 1024
-                / 1024
-                / 1024
-                + 0.4
-            )
-            if self.gpu_mem <= 4:
-                with open("trainset_preprocess_pipeline_print.py", "r") as f:
-                    strr = f.read().replace("3.7", "3.0")
-                with open("trainset_preprocess_pipeline_print.py", "w") as f:
-                    f.write(strr)
-        elif torch.backends.mps.is_available():
-            print("没有发现支持的N卡, 使用MPS进行推理")
-            self.device = "mps"
-            self.is_half = False
-            config_file_change_fp32()
-        else:
-            print("没有发现支持的N卡, 使用CPU进行推理")
-            self.device = "cpu"
-            self.is_half = False
-            config_file_change_fp32()
-
-        if self.n_cpu == 0:
-            self.n_cpu = cpu_count()
-
-        if self.is_half:
-            # 6G显存配置
-            x_pad = 3
-            x_query = 10
-            x_center = 60
-            x_max = 65
-        else:
-            # 5G显存配置
-            x_pad = 1
-            x_query = 6
-            x_center = 38
-            x_max = 41
-
-        if self.gpu_mem != None and self.gpu_mem <= 4:
-            x_pad = 1
-            x_query = 5
-            x_center = 30
-            x_max = 32
-
-        return x_pad, x_query, x_center, x_max

spaces/AIConsultant/MusicGen/tests/losses/test_losses.py

@@ -1,78 +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 random
-
-import torch
-
-from audiocraft.losses import (
-    MelSpectrogramL1Loss,
-    MultiScaleMelSpectrogramLoss,
-    MRSTFTLoss,
-    SISNR,
-    STFTLoss,
-)
-
-
-def test_mel_l1_loss():
-    N, C, T = 2, 2, random.randrange(1000, 100_000)
-    t1 = torch.randn(N, C, T)
-    t2 = torch.randn(N, C, T)
-
-    mel_l1 = MelSpectrogramL1Loss(sample_rate=22_050)
-    loss = mel_l1(t1, t2)
-    loss_same = mel_l1(t1, t1)
-
-    assert isinstance(loss, torch.Tensor)
-    assert isinstance(loss_same, torch.Tensor)
-    assert loss_same.item() == 0.0
-
-
-def test_msspec_loss():
-    N, C, T = 2, 2, random.randrange(1000, 100_000)
-    t1 = torch.randn(N, C, T)
-    t2 = torch.randn(N, C, T)
-
-    msspec = MultiScaleMelSpectrogramLoss(sample_rate=22_050)
-    loss = msspec(t1, t2)
-    loss_same = msspec(t1, t1)
-
-    assert isinstance(loss, torch.Tensor)
-    assert isinstance(loss_same, torch.Tensor)
-    assert loss_same.item() == 0.0
-
-
-def test_mrstft_loss():
-    N, C, T = 2, 2, random.randrange(1000, 100_000)
-    t1 = torch.randn(N, C, T)
-    t2 = torch.randn(N, C, T)
-
-    mrstft = MRSTFTLoss()
-    loss = mrstft(t1, t2)
-
-    assert isinstance(loss, torch.Tensor)
-
-
-def test_sisnr_loss():
-    N, C, T = 2, 2, random.randrange(1000, 100_000)
-    t1 = torch.randn(N, C, T)
-    t2 = torch.randn(N, C, T)
-
-    sisnr = SISNR()
-    loss = sisnr(t1, t2)
-
-    assert isinstance(loss, torch.Tensor)
-
-
-def test_stft_loss():
-    N, C, T = 2, 2, random.randrange(1000, 100_000)
-    t1 = torch.randn(N, C, T)
-    t2 = torch.randn(N, C, T)
-
-    mrstft = STFTLoss()
-    loss = mrstft(t1, t2)
-
-    assert isinstance(loss, torch.Tensor)

spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/latent_diffusion/ddim.py

@@ -1,377 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-import numpy as np
-from tqdm import tqdm
-
-from audioldm.latent_diffusion.util import (
-    make_ddim_sampling_parameters,
-    make_ddim_timesteps,
-    noise_like,
-    extract_into_tensor,
-)
-import gradio as gr
-
-class DDIMSampler(object):
-    def __init__(self, model, schedule="linear", **kwargs):
-        super().__init__()
-        self.model = model
-        self.ddpm_num_timesteps = model.num_timesteps
-        self.schedule = schedule
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            if attr.device != torch.device("cuda"):
-                attr = attr.to(torch.device("cuda"))
-        setattr(self, name, attr)
-
-    def make_schedule(
-        self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True
-    ):
-        self.ddim_timesteps = make_ddim_timesteps(
-            ddim_discr_method=ddim_discretize,
-            num_ddim_timesteps=ddim_num_steps,
-            num_ddpm_timesteps=self.ddpm_num_timesteps,
-            verbose=verbose,
-        )
-        alphas_cumprod = self.model.alphas_cumprod
-        assert (
-            alphas_cumprod.shape[0] == self.ddpm_num_timesteps
-        ), "alphas have to be defined for each timestep"
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
-
-        self.register_buffer("betas", to_torch(self.model.betas))
-        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
-        self.register_buffer(
-            "alphas_cumprod_prev", to_torch(self.model.alphas_cumprod_prev)
-        )
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer(
-            "sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod.cpu()))
-        )
-        self.register_buffer(
-            "sqrt_one_minus_alphas_cumprod",
-            to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),
-        )
-        self.register_buffer(
-            "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod.cpu()))
-        )
-        self.register_buffer(
-            "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu()))
-        )
-        self.register_buffer(
-            "sqrt_recipm1_alphas_cumprod",
-            to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),
-        )
-
-        # ddim sampling parameters
-        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(
-            alphacums=alphas_cumprod.cpu(),
-            ddim_timesteps=self.ddim_timesteps,
-            eta=ddim_eta,
-            verbose=verbose,
-        )
-        self.register_buffer("ddim_sigmas", ddim_sigmas)
-        self.register_buffer("ddim_alphas", ddim_alphas)
-        self.register_buffer("ddim_alphas_prev", ddim_alphas_prev)
-        self.register_buffer("ddim_sqrt_one_minus_alphas", np.sqrt(1.0 - ddim_alphas))
-        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
-            (1 - self.alphas_cumprod_prev)
-            / (1 - self.alphas_cumprod)
-            * (1 - self.alphas_cumprod / self.alphas_cumprod_prev)
-        )
-        self.register_buffer(
-            "ddim_sigmas_for_original_num_steps", sigmas_for_original_sampling_steps
-        )
-
-    @torch.no_grad()
-    def sample(
-        self,
-        S,
-        batch_size,
-        shape,
-        conditioning=None,
-        callback=None,
-        normals_sequence=None,
-        img_callback=None,
-        quantize_x0=False,
-        eta=0.0,
-        mask=None,
-        x0=None,
-        temperature=1.0,
-        noise_dropout=0.0,
-        score_corrector=None,
-        corrector_kwargs=None,
-        verbose=True,
-        x_T=None,
-        log_every_t=100,
-        unconditional_guidance_scale=1.0,
-        unconditional_conditioning=None,
-        # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-        **kwargs,
-    ):
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
-                if cbs != batch_size:
-                    print(
-                        f"Warning: Got {cbs} conditionings but batch-size is {batch_size}"
-                    )
-            else:
-                if conditioning.shape[0] != batch_size:
-                    print(
-                        f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}"
-                    )
-
-        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-        # sampling
-        C, H, W = shape
-        size = (batch_size, C, H, W)
-        samples, intermediates = self.ddim_sampling(
-            conditioning,
-            size,
-            callback=callback,
-            img_callback=img_callback,
-            quantize_denoised=quantize_x0,
-            mask=mask,
-            x0=x0,
-            ddim_use_original_steps=False,
-            noise_dropout=noise_dropout,
-            temperature=temperature,
-            score_corrector=score_corrector,
-            corrector_kwargs=corrector_kwargs,
-            x_T=x_T,
-            log_every_t=log_every_t,
-            unconditional_guidance_scale=unconditional_guidance_scale,
-            unconditional_conditioning=unconditional_conditioning,
-        )
-        return samples, intermediates
-
-    @torch.no_grad()
-    def ddim_sampling(
-        self,
-        cond,
-        shape,
-        x_T=None,
-        ddim_use_original_steps=False,
-        callback=None,
-        timesteps=None,
-        quantize_denoised=False,
-        mask=None,
-        x0=None,
-        img_callback=None,
-        log_every_t=100,
-        temperature=1.0,
-        noise_dropout=0.0,
-        score_corrector=None,
-        corrector_kwargs=None,
-        unconditional_guidance_scale=1.0,
-        unconditional_conditioning=None,
-    ):
-        device = self.model.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        if timesteps is None:
-            timesteps = (
-                self.ddpm_num_timesteps
-                if ddim_use_original_steps
-                else self.ddim_timesteps
-            )
-        elif timesteps is not None and not ddim_use_original_steps:
-            subset_end = (
-                int(
-                    min(timesteps / self.ddim_timesteps.shape[0], 1)
-                    * self.ddim_timesteps.shape[0]
-                )
-                - 1
-            )
-            timesteps = self.ddim_timesteps[:subset_end]
-
-        intermediates = {"x_inter": [img], "pred_x0": [img]}
-        time_range = (
-            reversed(range(0, timesteps))
-            if ddim_use_original_steps
-            else np.flip(timesteps)
-        )
-        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-        # print(f"Running DDIM Sampling with {total_steps} timesteps")
-
-        # iterator = gr.Progress().tqdm(time_range, desc="DDIM Sampler", total=total_steps)
-        iterator = tqdm(time_range, desc="DDIM Sampler", total=total_steps)
-
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full((b,), step, device=device, dtype=torch.long)
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.model.q_sample(
-                    x0, ts
-                )  # TODO deterministic forward pass?
-                img = (
-                    img_orig * mask + (1.0 - mask) * img
-                )  # In the first sampling step, img is pure gaussian noise
-
-            outs = self.p_sample_ddim(
-                img,
-                cond,
-                ts,
-                index=index,
-                use_original_steps=ddim_use_original_steps,
-                quantize_denoised=quantize_denoised,
-                temperature=temperature,
-                noise_dropout=noise_dropout,
-                score_corrector=score_corrector,
-                corrector_kwargs=corrector_kwargs,
-                unconditional_guidance_scale=unconditional_guidance_scale,
-                unconditional_conditioning=unconditional_conditioning,
-            )
-            img, pred_x0 = outs
-            if callback:
-                callback(i)
-            if img_callback:
-                img_callback(pred_x0, i)
-
-            if index % log_every_t == 0 or index == total_steps - 1:
-                intermediates["x_inter"].append(img)
-                intermediates["pred_x0"].append(pred_x0)
-
-        return img, intermediates
-
-    @torch.no_grad()
-    def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
-        # fast, but does not allow for exact reconstruction
-        # t serves as an index to gather the correct alphas
-        if use_original_steps:
-            sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
-            sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
-        else:
-            sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
-            sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
-
-        if noise is None:
-            noise = torch.randn_like(x0)
-
-        return (
-            extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0
-            + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise
-        )
-
-    @torch.no_grad()
-    def decode(
-        self,
-        x_latent,
-        cond,
-        t_start,
-        unconditional_guidance_scale=1.0,
-        unconditional_conditioning=None,
-        use_original_steps=False,
-    ):
-
-        timesteps = (
-            np.arange(self.ddpm_num_timesteps)
-            if use_original_steps
-            else self.ddim_timesteps
-        )
-        timesteps = timesteps[:t_start]
-
-        time_range = np.flip(timesteps)
-        total_steps = timesteps.shape[0]
-        # print(f"Running DDIM Sampling with {total_steps} timesteps")
-
-        # iterator = gr.Progress().tqdm(time_range, desc="Decoding image", total=total_steps)
-        iterator = tqdm(time_range, desc="Decoding image", total=total_steps)
-        x_dec = x_latent
-
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full(
-                (x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long
-            )
-            x_dec, _ = self.p_sample_ddim(
-                x_dec,
-                cond,
-                ts,
-                index=index,
-                use_original_steps=use_original_steps,
-                unconditional_guidance_scale=unconditional_guidance_scale,
-                unconditional_conditioning=unconditional_conditioning,
-            )
-        return x_dec
-
-    @torch.no_grad()
-    def p_sample_ddim(
-        self,
-        x,
-        c,
-        t,
-        index,
-        repeat_noise=False,
-        use_original_steps=False,
-        quantize_denoised=False,
-        temperature=1.0,
-        noise_dropout=0.0,
-        score_corrector=None,
-        corrector_kwargs=None,
-        unconditional_guidance_scale=1.0,
-        unconditional_conditioning=None,
-    ):
-        b, *_, device = *x.shape, x.device
-
-        if unconditional_conditioning is None or unconditional_guidance_scale == 1.0:
-            e_t = self.model.apply_model(x, t, c)
-        else:
-            x_in = torch.cat([x] * 2)
-            t_in = torch.cat([t] * 2)
-            c_in = torch.cat([unconditional_conditioning, c])
-            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-            # When unconditional_guidance_scale == 1: only e_t
-            # When unconditional_guidance_scale == 0: only unconditional
-            # When unconditional_guidance_scale > 1: add more unconditional guidance
-            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
-        if score_corrector is not None:
-            assert self.model.parameterization == "eps"
-            e_t = score_corrector.modify_score(
-                self.model, e_t, x, t, c, **corrector_kwargs
-            )
-
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-        alphas_prev = (
-            self.model.alphas_cumprod_prev
-            if use_original_steps
-            else self.ddim_alphas_prev
-        )
-        sqrt_one_minus_alphas = (
-            self.model.sqrt_one_minus_alphas_cumprod
-            if use_original_steps
-            else self.ddim_sqrt_one_minus_alphas
-        )
-        sigmas = (
-            self.model.ddim_sigmas_for_original_num_steps
-            if use_original_steps
-            else self.ddim_sigmas
-        )
-        # select parameters corresponding to the currently considered timestep
-        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-        sqrt_one_minus_at = torch.full(
-            (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
-        )
-
-        # current prediction for x_0
-        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-        if quantize_denoised:
-            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-        # direction pointing to x_t
-        dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
-        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.0:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise  # TODO
-        return x_prev, pred_x0

spaces/AIGC-Audio/AudioGPT/NeuralSeq/utils/cwt.py

@@ -1,146 +0,0 @@
-import librosa
-import numpy as np
-from pycwt import wavelet
-from scipy.interpolate import interp1d
-
-
-def load_wav(wav_file, sr):
-    wav, _ = librosa.load(wav_file, sr=sr, mono=True)
-    return wav
-
-
-def convert_continuos_f0(f0):
-    '''CONVERT F0 TO CONTINUOUS F0
-    Args:
-        f0 (ndarray): original f0 sequence with the shape (T)
-    Return:
-        (ndarray): continuous f0 with the shape (T)
-    '''
-    # get uv information as binary
-    f0 = np.copy(f0)
-    uv = np.float32(f0 != 0)
-
-    # get start and end of f0
-    if (f0 == 0).all():
-        print("| all of the f0 values are 0.")
-        return uv, f0
-    start_f0 = f0[f0 != 0][0]
-    end_f0 = f0[f0 != 0][-1]
-
-    # padding start and end of f0 sequence
-    start_idx = np.where(f0 == start_f0)[0][0]
-    end_idx = np.where(f0 == end_f0)[0][-1]
-    f0[:start_idx] = start_f0
-    f0[end_idx:] = end_f0
-
-    # get non-zero frame index
-    nz_frames = np.where(f0 != 0)[0]
-
-    # perform linear interpolation
-    f = interp1d(nz_frames, f0[nz_frames])
-    cont_f0 = f(np.arange(0, f0.shape[0]))
-
-    return uv, cont_f0
-
-
-def get_cont_lf0(f0, frame_period=5.0):
-    uv, cont_f0_lpf = convert_continuos_f0(f0)
-    # cont_f0_lpf = low_pass_filter(cont_f0_lpf, int(1.0 / (frame_period * 0.001)), cutoff=20)
-    cont_lf0_lpf = np.log(cont_f0_lpf)
-    return uv, cont_lf0_lpf
-
-
-def get_lf0_cwt(lf0):
-    '''
-    input:
-        signal of shape (N)
-    output:
-        Wavelet_lf0 of shape(10, N), scales of shape(10)
-    '''
-    mother = wavelet.MexicanHat()
-    dt = 0.005
-    dj = 1
-    s0 = dt * 2
-    J = 9
-
-    Wavelet_lf0, scales, _, _, _, _ = wavelet.cwt(np.squeeze(lf0), dt, dj, s0, J, mother)
-    # Wavelet.shape => (J + 1, len(lf0))
-    Wavelet_lf0 = np.real(Wavelet_lf0).T
-    return Wavelet_lf0, scales
-
-
-def norm_scale(Wavelet_lf0):
-    Wavelet_lf0_norm = np.zeros((Wavelet_lf0.shape[0], Wavelet_lf0.shape[1]))
-    mean = Wavelet_lf0.mean(0)[None, :]
-    std = Wavelet_lf0.std(0)[None, :]
-    Wavelet_lf0_norm = (Wavelet_lf0 - mean) / std
-    return Wavelet_lf0_norm, mean, std
-
-
-def normalize_cwt_lf0(f0, mean, std):
-    uv, cont_lf0_lpf = get_cont_lf0(f0)
-    cont_lf0_norm = (cont_lf0_lpf - mean) / std
-    Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_norm)
-    Wavelet_lf0_norm, _, _ = norm_scale(Wavelet_lf0)
-
-    return Wavelet_lf0_norm
-
-
-def get_lf0_cwt_norm(f0s, mean, std):
-    uvs = list()
-    cont_lf0_lpfs = list()
-    cont_lf0_lpf_norms = list()
-    Wavelet_lf0s = list()
-    Wavelet_lf0s_norm = list()
-    scaless = list()
-
-    means = list()
-    stds = list()
-    for f0 in f0s:
-        uv, cont_lf0_lpf = get_cont_lf0(f0)
-        cont_lf0_lpf_norm = (cont_lf0_lpf - mean) / std
-
-        Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_lpf_norm)  # [560,10]
-        Wavelet_lf0_norm, mean_scale, std_scale = norm_scale(Wavelet_lf0)  # [560,10],[1,10],[1,10]
-
-        Wavelet_lf0s_norm.append(Wavelet_lf0_norm)
-        uvs.append(uv)
-        cont_lf0_lpfs.append(cont_lf0_lpf)
-        cont_lf0_lpf_norms.append(cont_lf0_lpf_norm)
-        Wavelet_lf0s.append(Wavelet_lf0)
-        scaless.append(scales)
-        means.append(mean_scale)
-        stds.append(std_scale)
-
-    return Wavelet_lf0s_norm, scaless, means, stds
-
-
-def inverse_cwt_torch(Wavelet_lf0, scales):
-    import torch
-    b = ((torch.arange(0, len(scales)).float().to(Wavelet_lf0.device)[None, None, :] + 1 + 2.5) ** (-2.5))
-    lf0_rec = Wavelet_lf0 * b
-    lf0_rec_sum = lf0_rec.sum(-1)
-    lf0_rec_sum = (lf0_rec_sum - lf0_rec_sum.mean(-1, keepdim=True)) / lf0_rec_sum.std(-1, keepdim=True)
-    return lf0_rec_sum
-
-
-def inverse_cwt(Wavelet_lf0, scales):
-    b = ((np.arange(0, len(scales))[None, None, :] + 1 + 2.5) ** (-2.5))
-    lf0_rec = Wavelet_lf0 * b
-    lf0_rec_sum = lf0_rec.sum(-1)
-    lf0_rec_sum = (lf0_rec_sum - lf0_rec_sum.mean(-1, keepdims=True)) / lf0_rec_sum.std(-1, keepdims=True)
-    return lf0_rec_sum
-
-
-def cwt2f0(cwt_spec, mean, std, cwt_scales):
-    assert len(mean.shape) == 1 and len(std.shape) == 1 and len(cwt_spec.shape) == 3
-    import torch
-    if isinstance(cwt_spec, torch.Tensor):
-        f0 = inverse_cwt_torch(cwt_spec, cwt_scales)
-        f0 = f0 * std[:, None] + mean[:, None]
-        f0 = f0.exp()  # [B, T]
-    else:
-        f0 = inverse_cwt(cwt_spec, cwt_scales)
-        f0 = f0 * std[:, None] + mean[:, None]
-        f0 = np.exp(f0)  # [B, T]
-    return f0

spaces/AIGC-Audio/AudioGPT/text_to_speech/data_gen/tts/runs/binarize.py

@@ -1,17 +0,0 @@
-import utils.commons.single_thread_env  # NOQA
-from text_to_speech.utils.commons.hparams import hparams, set_hparams
-import importlib
-
-
-def binarize():
-    binarizer_cls = hparams.get("binarizer_cls", 'data_gen.tts.base_binarizer.BaseBinarizer')
-    pkg = ".".join(binarizer_cls.split(".")[:-1])
-    cls_name = binarizer_cls.split(".")[-1]
-    binarizer_cls = getattr(importlib.import_module(pkg), cls_name)
-    print("| Binarizer: ", binarizer_cls)
-    binarizer_cls().process()
-
-
-if __name__ == '__main__':
-    set_hparams()
-    binarize()

spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/commons/hparams.py

@@ -1,131 +0,0 @@
-import argparse
-import os
-import yaml
-
-from text_to_speech.utils.os_utils import remove_file
-
-global_print_hparams = True
-hparams = {}
-
-
-class Args:
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            self.__setattr__(k, v)
-
-
-def override_config(old_config: dict, new_config: dict):
-    for k, v in new_config.items():
-        if isinstance(v, dict) and k in old_config:
-            override_config(old_config[k], new_config[k])
-        else:
-            old_config[k] = v
-
-
-def set_hparams(config='', exp_name='', hparams_str='', print_hparams=True, global_hparams=True):
-    if config == '' and exp_name == '':
-        parser = argparse.ArgumentParser(description='')
-        parser.add_argument('--config', type=str, default='',
-                            help='location of the data corpus')
-        parser.add_argument('--exp_name', type=str, default='', help='exp_name')
-        parser.add_argument('-hp', '--hparams', type=str, default='',
-                            help='location of the data corpus')
-        parser.add_argument('--infer', action='store_true', help='infer')
-        parser.add_argument('--validate', action='store_true', help='validate')
-        parser.add_argument('--reset', action='store_true', help='reset hparams')
-        parser.add_argument('--remove', action='store_true', help='remove old ckpt')
-        parser.add_argument('--debug', action='store_true', help='debug')
-        args, unknown = parser.parse_known_args()
-        print("| Unknow hparams: ", unknown)
-    else:
-        args = Args(config=config, exp_name=exp_name, hparams=hparams_str,
-                    infer=False, validate=False, reset=False, debug=False, remove=False)
-    global hparams
-    assert args.config != '' or args.exp_name != ''
-    if args.config != '':
-        assert os.path.exists(args.config)
-
-    config_chains = []
-    loaded_config = set()
-
-    def load_config(config_fn):
-        # deep first inheritance and avoid the second visit of one node
-        if not os.path.exists(config_fn):
-            return {}
-        with open(config_fn) as f:
-            hparams_ = yaml.safe_load(f)
-        loaded_config.add(config_fn)
-        if 'base_config' in hparams_:
-            ret_hparams = {}
-            if not isinstance(hparams_['base_config'], list):
-                hparams_['base_config'] = [hparams_['base_config']]
-            for c in hparams_['base_config']:
-                if c.startswith('.'):
-                    c = f'{os.path.dirname(config_fn)}/{c}'
-                    c = os.path.normpath(c)
-                if c not in loaded_config:
-                    override_config(ret_hparams, load_config(c))
-            override_config(ret_hparams, hparams_)
-        else:
-            ret_hparams = hparams_
-        config_chains.append(config_fn)
-        return ret_hparams
-
-    saved_hparams = {}
-    args_work_dir = ''
-    if args.exp_name != '':
-        args_work_dir = f'checkpoints/{args.exp_name}'
-        ckpt_config_path = f'{args_work_dir}/config.yaml'
-        if os.path.exists(ckpt_config_path):
-            with open(ckpt_config_path) as f:
-                saved_hparams_ = yaml.safe_load(f)
-                if saved_hparams_ is not None:
-                    saved_hparams.update(saved_hparams_)
-    hparams_ = {}
-    if args.config != '':
-        hparams_.update(load_config(args.config))
-    if not args.reset:
-        hparams_.update(saved_hparams)
-    hparams_['work_dir'] = args_work_dir
-
-    # Support config overriding in command line. Support list type config overriding.
-    # Examples: --hparams="a=1,b.c=2,d=[1 1 1]"
-    if args.hparams != "":
-        for new_hparam in args.hparams.split(","):
-            k, v = new_hparam.split("=")
-            v = v.strip("\'\" ")
-            config_node = hparams_
-            for k_ in k.split(".")[:-1]:
-                config_node = config_node[k_]
-            k = k.split(".")[-1]
-            if v in ['True', 'False'] or type(config_node[k]) in [bool, list, dict]:
-                if type(config_node[k]) == list:
-                    v = v.replace(" ", ",")
-                config_node[k] = eval(v)
-            else:
-                config_node[k] = type(config_node[k])(v)
-    if args_work_dir != '' and args.remove:
-        answer = input("REMOVE old checkpoint? Y/N [Default: N]: ")
-        if answer.lower() == "y":
-            remove_file(args_work_dir)
-    if args_work_dir != '' and (not os.path.exists(ckpt_config_path) or args.reset) and not args.infer:
-        os.makedirs(hparams_['work_dir'], exist_ok=True)
-        with open(ckpt_config_path, 'w') as f:
-            yaml.safe_dump(hparams_, f)
-
-    hparams_['infer'] = args.infer
-    hparams_['debug'] = args.debug
-    hparams_['validate'] = args.validate
-    hparams_['exp_name'] = args.exp_name
-    global global_print_hparams
-    if global_hparams:
-        hparams.clear()
-        hparams.update(hparams_)
-    if print_hparams and global_print_hparams and global_hparams:
-        print('| Hparams chains: ', config_chains)
-        print('| Hparams: ')
-        for i, (k, v) in enumerate(sorted(hparams_.items())):
-            print(f"\033[;33;m{k}\033[0m: {v}, ", end="\n" if i % 5 == 4 else "")
-        print("")
-        global_print_hparams = False
-    return hparams_

spaces/AIGC-Audio/Make_An_Audio/ldm/modules/image_degradation/__init__.py

@@ -1,2 +0,0 @@
-from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
-from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light

spaces/AISuperheroes/09SL-AI-Image-Music-Video-AIUIUX/Article.md

@@ -1,51 +0,0 @@
-
-# Image Generation for Art, Marketing, Ideation, Design, and Use in Business
-
-A number of multiple AI pipeline element strategies have evolved on the open market which allow you to generate images using a combination of image prompts and word prompts.  This brief analysis gives an idea of the prompting capabilities as well as image rendering techniques that are used in the strategy to generate art from human understanding of images and text used to describe a scene.
-
-First a top five list on state of the art generators both free and paid is worth consideration.
-
-1) Midjourney - a Discord server based chatboat AI that allows /imagine prompts which can generate multiple images at a time.  This is best at parallel creation, high accuracy even photo real creations.
-2) Artbreeder - A multiple capability tool which now features a Collager which assists in starting image composition.  By far the most innovative approach which does great to combine the right partial elements in a scene.
-3) Dreamstudio - A Huggingface derived art program in beta which uses stable diffusion to create highly accurate art and images.
-4) Nightcafe - A credit based creation AI app that can do generation of video dives into an AI art piece which can produce some of the best experiences in Video.
-5) RunwayML - a quintessential tool in processing morph audio and video tracks which rival most high end video edit tools.
-
-These 5 tools make up some of the best AI pipeline programs that are cloud based that allow anyone to begin easily building their portfolio of art.
-
-The prompting capabilities often involve having a set of text based prompts to get started.  Most also feature a starter image which could be an example of what you would like to create.
-
-URL Links:
-1) Collager:  https://www.artbreeder.com/beta/collage
-2) NightCafe: https://creator.nightcafe.studio/explore
-3) Midjourney: https://www.midjourney.com/app/users/779773261440614430/
-4) Dreamstudio: https://beta.dreamstudio.ai/dream
-5) RunwayML: https://app.runwayml.com/
-
-## Getting Started and Organizing Your AI Pipeline and Process
-
-Any great strategy has a number of steps that combine all capabilities at your disposal.  It is useful to note how you can easily fir these together into a process that works for you.
-
-The techniques worth noted are listed below.  Consider how you will use them will make your pipeline easier and more automated to allow you to spend the majority of your time curating what you have made, and ideating what you want to create next.
-
-1) Source materials: Since prompting requires text and text examples can quickly help you compose good input, its worth considering and documenting some effective prompts.  Nightcafe with its integration into email, sends you a copy of your creation plus the prompting text so one option is to use your email account to keep a record of which prompts work for which outputs.
-2) Source materials:  Discord since its a public chat format allows you to easily see what others are using for prompts in bulk.  There are a number of chat channels designed for people new to the platform and often you can copy and paste if you see very effective prompts with material you are looking for.
-3) Source materials: Collager is unique in its ability to add additive parts and then dial in the percent of AI you would like with that.  This allows you to add a few image elements which help start out your generation.  
-4) Source materials: Since images and prompts are going to be your mainstay for inputs its worth considering an open standard for storing and retrieving these from anywhere.  Github is a good place since markdown language can involve text in table or list format and includes a capability to reference uploaded images within markdown.  This is also a good form for portability since you can later fork and download your repository with a few clicks from anywhere.
-5) Source materials: Google drive is integrated into the Artbreeder Collager workflow which allows you easily expand your work and even compose albums of the ones you like to place in Google photo albums.  The portfolio you save on different sites have different degrees of ease when aggregating your collections.  Collager for instance allows right click save for instant saving of your creation.  Dreamstudio features a history.  Midjourney features a profile site for you to store and review creations even triggering Upscales which important to use to get the highest resolution output for your creations.
-
-##  Social Media integration
-
-Depending on your target "safe for work" exports of your work, it is sometimes important to know your accepted social media outlets that you can integrate.  Cloud based interactions are the key to successful audiences if you want to scale and share your process with others.
-
-The key social media outlets supported for these tools are here in a sorted link list which start with public open source first:
-
-1) Github - Github is open at most companies and allow creation of a free space to share your content.
-2) LinkedIn - LinkedIn is acceptable use at nearly every company.
-3) Twitter - Twitter is supported as a social media outlet at most companies yet can also be used with security restrictions which might limit posting but allow read access.
-4) Facebook - Meta's Facebook is a good outlet since it allows creation of large folios of your images along with stories.  This venue however is locked down at many organizations.
-5) Instagram - Instagram is supported as an output channel for many tools yet has decreased in popularity due to high frequency of ads and pay for likes models.  While it can still be one of the best places for domain specific arrangements of images it is likely locked down in most secure organizations.
-6) Youtube - For video uploads with automated captioning and long term storage of short and long form video this is an essential for any creation you compose as video.  It is also useful to review and compose playlists of videos here for yourself that speed up your learning - Spend some time at Youtube university and keep a record of keyword searches there sometimes along with your playlists to accelerate learning.
-7) Gmail - With the baility to move email in and out its useful to create and wrap up details within email.  Most email policies come with a content limitation (for example no files larger than 25MB.  For this reason get used to creating pproject wrap up archives with winzip or compression software.  With the convenience of keyword searching you can usually use this as a base.
-8) Last a worth mention is Huggingface.com.  Like github as you become more sophisticated in your public open source capabilities, HuggingFace can allow you to wrap up using one of three software development kits which are gadio, streamlit, and HTML5 each with unique AI and UI integration components and features.  If you want to create your own AI pipelines this one also has the open source code and models ready to go to help you on your journey.
-

spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/quantization/vq.py

@@ -1,116 +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 math
-import typing as tp
-
-import torch
-
-from .base import BaseQuantizer, QuantizedResult
-from .core_vq import ResidualVectorQuantization
-
-
-class ResidualVectorQuantizer(BaseQuantizer):
-    """Residual Vector Quantizer.
-
-    Args:
-        dimension (int): Dimension of the codebooks.
-        n_q (int): Number of residual vector quantizers used.
-        q_dropout (bool): Random quantizer drop out at train time.
-        bins (int): Codebook size.
-        decay (float): Decay for exponential moving average over the codebooks.
-        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
-        kmeans_iters (int): Number of iterations used for kmeans initialization.
-        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
-            that have an exponential moving average cluster size less than the specified threshold with
-            randomly selected vector from the current batch.
-        orthogonal_reg_weight (float): Orthogonal regularization weights.
-        orthogonal_reg_active_codes_only (bool): Apply orthogonal regularization only on active codes.
-        orthogonal_reg_max_codes (optional int): Maximum number of codes to consider.
-            for orthogonal regulariation.
-    """
-    def __init__(
-        self,
-        dimension: int = 256,
-        n_q: int = 8,
-        q_dropout: bool = False,
-        bins: int = 1024,
-        decay: float = 0.99,
-        kmeans_init: bool = True,
-        kmeans_iters: int = 10,
-        threshold_ema_dead_code: int = 2,
-        orthogonal_reg_weight: float = 0.0,
-        orthogonal_reg_active_codes_only: bool = False,
-        orthogonal_reg_max_codes: tp.Optional[int] = None,
-    ):
-        super().__init__()
-        self.max_n_q = n_q
-        self.n_q = n_q
-        self.q_dropout = q_dropout
-        self.dimension = dimension
-        self.bins = bins
-        self.decay = decay
-        self.kmeans_init = kmeans_init
-        self.kmeans_iters = kmeans_iters
-        self.threshold_ema_dead_code = threshold_ema_dead_code
-        self.orthogonal_reg_weight = orthogonal_reg_weight
-        self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
-        self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
-        self.vq = ResidualVectorQuantization(
-            dim=self.dimension,
-            codebook_size=self.bins,
-            num_quantizers=self.n_q,
-            decay=self.decay,
-            kmeans_init=self.kmeans_init,
-            kmeans_iters=self.kmeans_iters,
-            threshold_ema_dead_code=self.threshold_ema_dead_code,
-            orthogonal_reg_weight=self.orthogonal_reg_weight,
-            orthogonal_reg_active_codes_only=self.orthogonal_reg_active_codes_only,
-            orthogonal_reg_max_codes=self.orthogonal_reg_max_codes,
-            channels_last=False
-        )
-
-    def forward(self, x: torch.Tensor, frame_rate: int):
-        n_q = self.n_q
-        if self.training and self.q_dropout:
-            n_q = int(torch.randint(1, self.n_q + 1, (1,)).item())
-        bw_per_q = math.log2(self.bins) * frame_rate / 1000
-        quantized, codes, commit_loss = self.vq(x, n_q=n_q)
-        codes = codes.transpose(0, 1)
-        # codes is [B, K, T], with T frames, K nb of codebooks.
-        bw = torch.tensor(n_q * bw_per_q).to(x)
-        return QuantizedResult(quantized, codes, bw, penalty=torch.mean(commit_loss))
-
-    def encode(self, x: torch.Tensor) -> torch.Tensor:
-        """Encode a given input tensor with the specified frame rate at the given bandwidth.
-        The RVQ encode method sets the appropriate number of quantizer to use
-        and returns indices for each quantizer.
-        """
-        n_q = self.n_q
-        codes = self.vq.encode(x, n_q=n_q)
-        codes = codes.transpose(0, 1)
-        # codes is [B, K, T], with T frames, K nb of codebooks.
-        return codes
-
-    def decode(self, codes: torch.Tensor) -> torch.Tensor:
-        """Decode the given codes to the quantized representation.
-        """
-        # codes is [B, K, T], with T frames, K nb of codebooks, vq.decode expects [K, B, T].
-        codes = codes.transpose(0, 1)
-        quantized = self.vq.decode(codes)
-        return quantized
-
-    @property
-    def total_codebooks(self):
-        return self.max_n_q
-
-    @property
-    def num_codebooks(self):
-        return self.n_q
-
-    def set_num_codebooks(self, n: int):
-        assert n > 0 and n <= self.max_n_q
-        self.n_q = n

spaces/Acapellas/Extract_Vocals_Instrumentals/README.md

@@ -1,38 +0,0 @@
----
-title: Extract Acapellas & Instrumentals
-emoji: null
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-app_file: app.py
-pinned: False
-duplicated_from: Thafx/Demucs_v4_2s_HT
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Liaobots.py

@@ -1,106 +0,0 @@
-from __future__ import annotations
-
-import uuid
-
-from aiohttp import ClientSession
-
-from ..typing import AsyncGenerator
-from .base_provider import AsyncGeneratorProvider
-
-models = {
-    "gpt-4": {
-        "id": "gpt-4",
-        "name": "GPT-4",
-        "maxLength": 24000,
-        "tokenLimit": 8000,
-    },
-    "gpt-3.5-turbo": {
-        "id": "gpt-3.5-turbo",
-        "name": "GPT-3.5",
-        "maxLength": 12000,
-        "tokenLimit": 4000,
-    },
-    "gpt-3.5-turbo-16k": {
-        "id": "gpt-3.5-turbo-16k",
-        "name": "GPT-3.5-16k",
-        "maxLength": 48000,
-        "tokenLimit": 16000,
-    },
-}
-
-class Liaobots(AsyncGeneratorProvider):
-    url = "https://liaobots.site"
-    working = True
-    supports_gpt_35_turbo = True
-    supports_gpt_4 = True
-    _auth_code = None
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        auth: str = None,
-        proxy: str = None,
-        **kwargs
-    ) -> AsyncGenerator:
-        model = model if model in models else "gpt-3.5-turbo"
-        headers = {
-            "authority": "liaobots.com",
-            "content-type": "application/json",
-            "origin": cls.url,
-            "referer": cls.url + "/",
-            "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36",
-        }
-        async with ClientSession(
-            headers=headers
-        ) as session:
-            cls._auth_code = auth if isinstance(auth, str) else cls._auth_code
-            if not cls._auth_code:
-                async with session.post(
-                    "https://liaobots.work/recaptcha/api/login",
-                    proxy=proxy,
-                    data={"token": "abcdefghijklmnopqrst"},
-                    verify_ssl=False
-                ) as response:
-                    response.raise_for_status()
-                async with session.post(
-                    "https://liaobots.work/api/user",
-                    proxy=proxy,
-                    json={"authcode": ""},
-                    verify_ssl=False
-                ) as response:
-                    response.raise_for_status()
-                    cls._auth_code = (await response.json(content_type=None))["authCode"]
-            data = {
-                "conversationId": str(uuid.uuid4()),
-                "model": models[model],
-                "messages": messages,
-                "key": "",
-                "prompt": "You are ChatGPT, a large language model trained by OpenAI. Follow the user's instructions carefully.",
-            }
-            async with session.post(
-                "https://liaobots.work/api/chat",
-                proxy=proxy,
-                json=data,
-                headers={"x-auth-code": cls._auth_code},
-                verify_ssl=False
-            ) as response:
-                response.raise_for_status()
-                async for stream in response.content.iter_any():
-                    if stream:
-                        yield stream.decode()
-
-
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-            ("proxy", "str"),
-            ("auth", "str"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"

spaces/AchyuthGamer/OpenGPT/g4f/Provider/deprecated/V50.py

@@ -1,67 +0,0 @@
-from __future__ import annotations
-
-import uuid
-
-import requests
-
-from ...typing import Any, CreateResult
-from ..base_provider import BaseProvider
-
-
-class V50(BaseProvider):
-    url                     = 'https://p5.v50.ltd'
-    supports_gpt_35_turbo   = True
-    supports_stream         = False
-    needs_auth              = False
-    working                 = False
-
-    @staticmethod
-    def create_completion(
-        model: str,
-        messages: list[dict[str, str]],
-        stream: bool, **kwargs: Any) -> CreateResult:
-        
-        conversation = "\n".join(f"{message['role']}: {message['content']}" for message in messages)
-        conversation += "\nassistant: "
-
-        payload = {
-            "prompt"        : conversation,
-            "options"       : {},
-            "systemMessage" : ".",
-            "temperature"   : kwargs.get("temperature", 0.4),
-            "top_p"         : kwargs.get("top_p", 0.4),
-            "model"         : model,
-            "user"          : str(uuid.uuid4())
-        }
-        
-        headers = {
-            'authority'         : 'p5.v50.ltd',
-            'accept'            : 'application/json, text/plain, */*',
-            'accept-language'   : 'id-ID,id;q=0.9,en-US;q=0.8,en;q=0.7',
-            'content-type'      : 'application/json',
-            'origin'            : 'https://p5.v50.ltd',
-            'referer'           : 'https://p5.v50.ltd/',
-            'sec-ch-ua-platform': '"Windows"',
-            'sec-fetch-dest'    : 'empty',
-            'sec-fetch-mode'    : 'cors',
-            'sec-fetch-site'    : 'same-origin',
-            'user-agent'        : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36'
-        }
-        response = requests.post("https://p5.v50.ltd/api/chat-process", 
-                                json=payload, headers=headers, proxies=kwargs['proxy'] if 'proxy' in kwargs else {})
-        
-        if "https://fk1.v50.ltd" not in response.text:
-            yield response.text
-
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-            ("temperature", "float"),
-            ("top_p", "int"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"

spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/order/__init__.py

@@ -1,11 +0,0 @@
-from agentverse.registry import Registry
-order_registry = Registry(name="OrderRegistry")
-
-from .base import BaseOrder
-from .sequential import SequentialOrder
-from .random import RandomOrder
-from .concurrent import ConcurrentOrder
-from .classroom import ClassroomOrder
-from .prisoner import PrisonerOrder
-from .sde_team import SdeTeamOrder
-from .sde_team_given_tests import SdeTeamGivenTestsOrder

spaces/AlexZou/Deploy_Restoration/utils/utils_image.py

@@ -1,778 +0,0 @@
-import os
-import math
-import random
-import numpy as np
-import torch
-import cv2
-from torchvision.utils import make_grid
-from datetime import datetime
-# import torchvision.transforms as transforms
-import matplotlib.pyplot as plt
-
-'''
-modified by Kai Zhang (github: https://github.com/cszn)
-03/03/2019
-https://github.com/twhui/SRGAN-pyTorch
-https://github.com/xinntao/BasicSR
-'''
-
-IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP']
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def get_timestamp():
-    return datetime.now().strftime('%y%m%d-%H%M%S')
-
-
-def imshow(x, title=None, cbar=False, figsize=None):
-    plt.figure(figsize=figsize)
-    plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
-    if title:
-        plt.title(title)
-    if cbar:
-        plt.colorbar()
-    plt.show()
-
-
-'''
-# =======================================
-# get image pathes of files
-# =======================================
-'''
-
-
-def get_image_paths(dataroot):
-    paths = None  # return None if dataroot is None
-    if dataroot is not None:
-        paths = sorted(_get_paths_from_images(dataroot))
-    return paths
-
-
-def _get_paths_from_images(path):
-    assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
-    images = []
-    for dirpath, _, fnames in sorted(os.walk(path)):
-        for fname in sorted(fnames):
-            if is_image_file(fname):
-                img_path = os.path.join(dirpath, fname)
-                images.append(img_path)
-    assert images, '{:s} has no valid image file'.format(path)
-    return images
-
-
-'''
-# =======================================
-# makedir
-# =======================================
-'''
-
-
-def mkdir(path):
-    if not os.path.exists(path):
-        os.makedirs(path)
-
-
-def mkdirs(paths):
-    if isinstance(paths, str):
-        mkdir(paths)
-    else:
-        for path in paths:
-            mkdir(path)
-
-
-def mkdir_and_rename(path):
-    if os.path.exists(path):
-        new_name = path + '_archived_' + get_timestamp()
-        print('Path already exists. Rename it to [{:s}]'.format(new_name))
-        os.rename(path, new_name)
-    os.makedirs(path)
-
-
-'''
-# =======================================
-# read image from path
-# Note: opencv is fast
-# but read BGR numpy image
-# =======================================
-'''
-
-
-# ----------------------------------------
-# get single image of size HxWxn_channles (BGR)
-# ----------------------------------------
-def read_img(path):
-    # read image by cv2
-    # return: Numpy float32, HWC, BGR, [0,1]
-    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # cv2.IMREAD_GRAYSCALE
-    img = img.astype(np.float32) / 255.
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    # some images have 4 channels
-    if img.shape[2] > 3:
-        img = img[:, :, :3]
-    return img
-
-
-# ----------------------------------------
-# get uint8 image of size HxWxn_channles (RGB)
-# ----------------------------------------
-def imread_uint(path, n_channels=3):
-    #  input: path
-    # output: HxWx3(RGB or GGG), or HxWx1 (G)
-    if n_channels == 1:
-        img = cv2.imread(path, 0)  # cv2.IMREAD_GRAYSCALE
-        img = np.expand_dims(img, axis=2)  # HxWx1
-    elif n_channels == 3:
-        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # BGR or G
-        if img.ndim == 2:
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # GGG
-        else:
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # RGB
-    return img
-
-
-def imsave(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-
-'''
-# =======================================
-# numpy(single) <--->  numpy(uint)
-# numpy(single) <--->  tensor
-# numpy(uint)   <--->  tensor
-# =======================================
-'''
-
-
-# --------------------------------
-# numpy(single) <--->  numpy(uint)
-# --------------------------------
-
-
-def uint2single(img):
-
-    return np.float32(img/255.)
-
-
-def uint2single1(img):
-
-    return np.float32(np.squeeze(img)/255.)
-
-
-def single2uint(img):
-
-    return np.uint8((img.clip(0, 1)*255.).round())
-
-
-def uint162single(img):
-
-    return np.float32(img/65535.)
-
-
-def single2uint16(img):
-
-    return np.uint8((img.clip(0, 1)*65535.).round())
-
-
-# --------------------------------
-# numpy(uint) <--->  tensor
-# uint (HxWxn_channels (RGB) or G)
-# --------------------------------
-
-
-# convert uint (HxWxn_channels) to 4-dimensional torch tensor
-def uint2tensor4(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
-
-
-# convert uint (HxWxn_channels) to 3-dimensional torch tensor
-def uint2tensor3(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
-
-
-# convert torch tensor to uint
-def tensor2uint(img):
-    img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    return np.uint8((img*255.0).round())
-
-
-# --------------------------------
-# numpy(single) <--->  tensor
-# single (HxWxn_channels (RGB) or G)
-# --------------------------------
-
-
-# convert single (HxWxn_channels) to 4-dimensional torch tensor
-def single2tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
-
-
-# convert single (HxWxn_channels) to 3-dimensional torch tensor
-def single2tensor3(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
-
-
-# convert torch tensor to single
-def tensor2single(img):
-    img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-
-    return img
-
-def tensor2single3(img):
-    img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    elif img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return img
-
-
-# from skimage.io import imread, imsave
-def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
-    '''
-    Converts a torch Tensor into an image Numpy array of BGR channel order
-    Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
-    Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
-    '''
-    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # squeeze first, then clamp
-    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
-    n_dim = tensor.dim()
-    if n_dim == 4:
-        n_img = len(tensor)
-        img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 3:
-        img_np = tensor.numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 2:
-        img_np = tensor.numpy()
-    else:
-        raise TypeError(
-            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
-    if out_type == np.uint8:
-        img_np = (img_np * 255.0).round()
-        # Important. Unlike matlab, numpy.uint8() WILL NOT round by default.
-    return img_np.astype(out_type)
-
-
-'''
-# =======================================
-# image processing process on numpy image
-# augment(img_list, hflip=True, rot=True):
-# =======================================
-'''
-
-
-def augment_img(img, mode=0):
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return np.flipud(np.rot90(img))
-    elif mode == 2:
-        return np.flipud(img)
-    elif mode == 3:
-        return np.rot90(img, k=3)
-    elif mode == 4:
-        return np.flipud(np.rot90(img, k=2))
-    elif mode == 5:
-        return np.rot90(img)
-    elif mode == 6:
-        return np.rot90(img, k=2)
-    elif mode == 7:
-        return np.flipud(np.rot90(img, k=3))
-
-
-def augment_img_np3(img, mode=0):
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.transpose(1, 0, 2)
-    elif mode == 2:
-        return img[::-1, :, :]
-    elif mode == 3:
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 4:
-        return img[:, ::-1, :]
-    elif mode == 5:
-        img = img[:, ::-1, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 6:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        return img
-    elif mode == 7:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-
-
-def augment_img_tensor(img, mode=0):
-    img_size = img.size()
-    img_np = img.data.cpu().numpy()
-    if len(img_size) == 3:
-        img_np = np.transpose(img_np, (1, 2, 0))
-    elif len(img_size) == 4:
-        img_np = np.transpose(img_np, (2, 3, 1, 0))
-    img_np = augment_img(img_np, mode=mode)
-    img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
-    if len(img_size) == 3:
-        img_tensor = img_tensor.permute(2, 0, 1)
-    elif len(img_size) == 4:
-        img_tensor = img_tensor.permute(3, 2, 0, 1)
-
-    return img_tensor.type_as(img)
-
-
-def augment_imgs(img_list, hflip=True, rot=True):
-    # horizontal flip OR rotate
-    hflip = hflip and random.random() < 0.5
-    vflip = rot and random.random() < 0.5
-    rot90 = rot and random.random() < 0.5
-
-    def _augment(img):
-        if hflip:
-            img = img[:, ::-1, :]
-        if vflip:
-            img = img[::-1, :, :]
-        if rot90:
-            img = img.transpose(1, 0, 2)
-        return img
-
-    return [_augment(img) for img in img_list]
-
-
-'''
-# =======================================
-# image processing process on numpy image
-# channel_convert(in_c, tar_type, img_list):
-# rgb2ycbcr(img, only_y=True):
-# bgr2ycbcr(img, only_y=True):
-# ycbcr2rgb(img):
-# modcrop(img_in, scale):
-# =======================================
-'''
-
-
-def rgb2ycbcr(img, only_y=True):
-    '''same as matlab rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
-                              [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def ycbcr2rgb(img):
-    '''same as matlab ycbcr2rgb
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
-                          [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def bgr2ycbcr(img, only_y=True):
-    '''bgr version of rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
-                              [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def modcrop(img_in, scale):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    if img.ndim == 2:
-        H, W = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r]
-    elif img.ndim == 3:
-        H, W, C = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r, :]
-    else:
-        raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
-    return img
-
-
-def shave(img_in, border=0):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    h, w = img.shape[:2]
-    img = img[border:h-border, border:w-border]
-    return img
-
-
-def channel_convert(in_c, tar_type, img_list):
-    # conversion among BGR, gray and y
-    if in_c == 3 and tar_type == 'gray':  # BGR to gray
-        gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in gray_list]
-    elif in_c == 3 and tar_type == 'y':  # BGR to y
-        y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in y_list]
-    elif in_c == 1 and tar_type == 'RGB':  # gray/y to BGR
-        return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
-    else:
-        return img_list
-
-
-'''
-# =======================================
-# metric, PSNR and SSIM
-# =======================================
-'''
-
-
-# ----------
-# PSNR
-# ----------
-def calculate_psnr(img1, img2, border=0):
-    # img1 and img2 have range [0, 255]
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    mse = np.mean((img1 - img2)**2)
-    if mse == 0:
-        return float('inf')
-    return 20 * math.log10(255.0 / math.sqrt(mse))
-
-
-# ----------
-# SSIM
-# ----------
-def calculate_ssim(img1, img2, border=0):
-    '''calculate SSIM
-    the same outputs as MATLAB's
-    img1, img2: [0, 255]
-    '''
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    if img1.ndim == 2:
-        return ssim(img1, img2)
-    elif img1.ndim == 3:
-        if img1.shape[2] == 3:
-            ssims = []
-            for i in range(3):
-                ssims.append(ssim(img1, img2))
-            return np.array(ssims).mean()
-        elif img1.shape[2] == 1:
-            return ssim(np.squeeze(img1), np.squeeze(img2))
-    else:
-        raise ValueError('Wrong input image dimensions.')
-
-
-def ssim(img1, img2):
-    C1 = (0.01 * 255)**2
-    C2 = (0.03 * 255)**2
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    kernel = cv2.getGaussianKernel(11, 1.5)
-    window = np.outer(kernel, kernel.transpose())
-
-    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
-    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
-    mu1_sq = mu1**2
-    mu2_sq = mu2**2
-    mu1_mu2 = mu1 * mu2
-    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
-    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
-    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
-
-    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
-                                                            (sigma1_sq + sigma2_sq + C2))
-    return ssim_map.mean()
-
-
-'''
-# =======================================
-# pytorch version of matlab imresize
-# =======================================
-'''
-
-
-# matlab 'imresize' function, now only support 'bicubic'
-def cubic(x):
-    absx = torch.abs(x)
-    absx2 = absx**2
-    absx3 = absx**3
-    return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
-        (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
-
-
-def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
-    if (scale < 1) and (antialiasing):
-        # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
-        kernel_width = kernel_width / scale
-
-    # Output-space coordinates
-    x = torch.linspace(1, out_length, out_length)
-
-    # Input-space coordinates. Calculate the inverse mapping such that 0.5
-    # in output space maps to 0.5 in input space, and 0.5+scale in output
-    # space maps to 1.5 in input space.
-    u = x / scale + 0.5 * (1 - 1 / scale)
-
-    # What is the left-most pixel that can be involved in the computation?
-    left = torch.floor(u - kernel_width / 2)
-
-    # What is the maximum number of pixels that can be involved in the
-    # computation?  Note: it's OK to use an extra pixel here; if the
-    # corresponding weights are all zero, it will be eliminated at the end
-    # of this function.
-    P = math.ceil(kernel_width) + 2
-
-    # The indices of the input pixels involved in computing the k-th output
-    # pixel are in row k of the indices matrix.
-    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
-        1, P).expand(out_length, P)
-
-    # The weights used to compute the k-th output pixel are in row k of the
-    # weights matrix.
-    distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
-    # apply cubic kernel
-    if (scale < 1) and (antialiasing):
-        weights = scale * cubic(distance_to_center * scale)
-    else:
-        weights = cubic(distance_to_center)
-    # Normalize the weights matrix so that each row sums to 1.
-    weights_sum = torch.sum(weights, 1).view(out_length, 1)
-    weights = weights / weights_sum.expand(out_length, P)
-
-    # If a column in weights is all zero, get rid of it. only consider the first and last column.
-    weights_zero_tmp = torch.sum((weights == 0), 0)
-    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 1, P - 2)
-        weights = weights.narrow(1, 1, P - 2)
-    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 0, P - 2)
-        weights = weights.narrow(1, 0, P - 2)
-    weights = weights.contiguous()
-    indices = indices.contiguous()
-    sym_len_s = -indices.min() + 1
-    sym_len_e = indices.max() - in_length
-    indices = indices + sym_len_s - 1
-    return weights, indices, int(sym_len_s), int(sym_len_e)
-
-
-# --------------------------------
-# imresize for tensor image
-# --------------------------------
-def imresize(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: pytorch tensor, CHW or HW [0,1]
-    # output: CHW or HW [0,1] w/o round
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(0)
-    in_C, in_H, in_W = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
-    img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:, :sym_len_Hs, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[:, -sym_len_He:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(in_C, out_H, in_W)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
-    out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :, :sym_len_Ws]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, :, -sym_len_We:]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(in_C, out_H, out_W)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-    return out_2
-
-
-# --------------------------------
-# imresize for numpy image
-# --------------------------------
-def imresize_np(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: Numpy, HWC or HW [0,1]
-    # output: HWC or HW [0,1] w/o round
-    img = torch.from_numpy(img)
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(2)
-
-    in_H, in_W, in_C = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
-    img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:sym_len_Hs, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[-sym_len_He:, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(out_H, in_W, in_C)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
-    out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :sym_len_Ws, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, -sym_len_We:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(out_H, out_W, in_C)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-
-    return out_2.numpy()
-
-
-if __name__ == '__main__':
-    img = imread_uint('test.bmp',3)

spaces/AllAideas/SegmentacionVideo/utils/constants.py

@@ -1,4 +0,0 @@
-MAX_SEQ_LENGTH = 20
-NUM_FEATURES = 1024
-IMG_SIZE = 128
-CLASS_VOCAB = ['CricketShot', 'PlayingCello', 'Punch', 'ShavingBeard', 'TennisSwing']

spaces/Amrrs/openai-whisper-live-transcribe/README.md

@@ -1,13 +0,0 @@
----
-title: Openai Whisper Live Transcribe
-emoji: 🎙
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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

@@ -1,55 +0,0 @@
-dataset_type = 'CityscapesDataset'
-data_root = 'data/cityscapes/'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(2048, 1024),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=1,
-    workers_per_gpu=2,
-    train=dict(
-        type='RepeatDataset',
-        times=8,
-        dataset=dict(
-            type=dataset_type,
-            ann_file=data_root +
-            'annotations/instancesonly_filtered_gtFine_train.json',
-            img_prefix=data_root + 'leftImg8bit/train/',
-            pipeline=train_pipeline)),
-    val=dict(
-        type=dataset_type,
-        ann_file=data_root +
-        'annotations/instancesonly_filtered_gtFine_val.json',
-        img_prefix=data_root + 'leftImg8bit/val/',
-        pipeline=test_pipeline),
-    test=dict(
-        type=dataset_type,
-        ann_file=data_root +
-        'annotations/instancesonly_filtered_gtFine_test.json',
-        img_prefix=data_root + 'leftImg8bit/test/',
-        pipeline=test_pipeline))
-evaluation = dict(interval=1, metric='bbox')

spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py

@@ -1,2 +0,0 @@
-_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_segmentation/configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py

@@ -1,7 +0,0 @@
-_base_ = [
-    '../_base_/models/ccnet_r50-d8.py',
-    '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_20k.py'
-]
-model = dict(
-    decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))

spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py

@@ -1,11 +0,0 @@
-_base_ = './deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py'
-model = dict(
-    pretrained='open-mmlab://resnet101_v1c',
-    backbone=dict(
-        depth=101,
-        dilations=(1, 1, 1, 2),
-        strides=(1, 2, 2, 1),
-        multi_grid=(1, 2, 4)),
-    decode_head=dict(
-        dilations=(1, 6, 12, 18),
-        sampler=dict(type='OHEMPixelSampler', min_kept=100000)))

spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/CLIP/model-card.md

@@ -1,120 +0,0 @@
-# Model Card: CLIP
-
-Inspired by [Model Cards for Model Reporting (Mitchell et al.)](https://arxiv.org/abs/1810.03993) and [Lessons from Archives (Jo & Gebru)](https://arxiv.org/pdf/1912.10389.pdf), we’re providing some accompanying information about the multimodal model.
-
-## Model Details
-
-The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.
-
-### Model Date
-
-January 2021
-
-### Model Type
-
-The base model uses a ResNet50 with several modifications as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. There is also a variant of the model where the ResNet image encoder is replaced with a Vision Transformer.
-
-### Model Versions
-
-Initially, we’ve released one CLIP model based on the Vision Transformer architecture equivalent to ViT-B/32, along with the RN50 model, using the architecture equivalent to ResNet-50.
-
-As part of the staged release process, we have also released the RN101 model, as well as RN50x4, a RN50 scaled up 4x according to the [EfficientNet](https://arxiv.org/abs/1905.11946) scaling rule. In July 2021, we additionally released the RN50x16 and ViT-B/16 models.
-
-Please see the paper linked below for further details about their specification.
-
-### Documents
-
-- [Blog Post](https://openai.com/blog/clip/)
-- [CLIP Paper](https://arxiv.org/abs/2103.00020)
-
-
-
-## Model Use
-
-### Intended Use
-
-The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
-
-#### Primary intended uses
-
-The primary intended users of these models are AI researchers.
-
-We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.
-
-### Out-of-Scope Use Cases
-
-**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. 
-
-Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
-
-Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
-
-
-
-## Data
-
-The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users.
-
-### Data Mission Statement
-
-Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset.
-
-
-
-## Performance and Limitations
-
-### Performance
-
-We have evaluated the performance of CLIP on a wide range of benchmarks across a variety of computer vision datasets such as OCR to texture recognition to fine-grained classification. The paper describes model performance on the following datasets:
-
-- Food101
-- CIFAR10   
-- CIFAR100   
-- Birdsnap
-- SUN397
-- Stanford Cars
-- FGVC Aircraft
-- VOC2007
-- DTD
-- Oxford-IIIT Pet dataset
-- Caltech101
-- Flowers102
-- MNIST   
-- SVHN 
-- IIIT5K   
-- Hateful Memes   
-- SST-2
-- UCF101
-- Kinetics700
-- Country211
-- CLEVR Counting
-- KITTI Distance
-- STL-10
-- RareAct
-- Flickr30
-- MSCOCO
-- ImageNet
-- ImageNet-A
-- ImageNet-R
-- ImageNet Sketch
-- ObjectNet (ImageNet Overlap)
-- Youtube-BB
-- ImageNet-Vid
-
-## Limitations
-
-CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance.
-
-### Bias and Fairness
-
-We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper).
-
-We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with ‘Middle Eastern’ having the highest accuracy (98.4%) and ‘White’ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks.
-
-
-
-## Feedback
-
-### Where to send questions or comments about the model
-
-Please use [this Google Form](https://forms.gle/Uv7afRH5dvY34ZEs9)

spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/evaluations/evaluation.py

@@ -1,98 +0,0 @@
-import os
-import glob
-import shutil
-import lpips
-import numpy as np
-import argparse
-from PIL import Image
-from skimage.metrics import structural_similarity as ssim
-from skimage.metrics import peak_signal_noise_ratio as psnr
-from dataloader.image_folder import make_dataset
-from util import util
-import torch
-
-parser = argparse.ArgumentParser(description='Image quality evaluations on the dataset')
-parser.add_argument('--gt_path', type=str, default='../results/', help='path to original gt data')
-parser.add_argument('--g_path', type=str, default='../results.', help='path to the generated data')
-parser.add_argument('--save_path', type=str, default=None, help='path to save the best results')
-parser.add_argument('--center', action='store_true', help='only calculate the center masked regions for the image quality')
-parser.add_argument('--num_test', type=int, default=0, help='how many examples to load for testing')
-
-args = parser.parse_args()
-lpips_alex = lpips.LPIPS(net='alex')
-
-
-def calculate_score(img_gt, img_test):
-    """
-    function to calculate the image quality score
-    :param img_gt: original image
-    :param img_test: generated image
-    :return: mae, ssim, psnr
-    """
-
-    l1loss = np.mean(np.abs(img_gt-img_test))
-
-    psnr_score = psnr(img_gt, img_test, data_range=1)
-
-    ssim_score = ssim(img_gt, img_test, multichannel=True, data_range=1, win_size=11)
-
-    lpips_dis = lpips_alex(torch.from_numpy(img_gt).permute(2, 0, 1), torch.from_numpy(img_test).permute(2, 0, 1), normalize=True)
-
-    return l1loss, ssim_score, psnr_score, lpips_dis.data.numpy().item()
-
-
-if __name__ == '__main__':
-    gt_paths, gt_size = make_dataset(args.gt_path)
-    g_paths, g_size = make_dataset(args.g_path)
-
-    l1losses = []
-    ssims = []
-    psnrs = []
-    lpipses = []
-
-    size = args.num_test if args.num_test > 0 else gt_size
-
-    for i in range(size):
-        gt_img = Image.open(gt_paths[i + 0*2000]).resize([256, 256]).convert('RGB')
-        gt_numpy = np.array(gt_img).astype(np.float32) / 255.0
-        if args.center:
-            gt_numpy = gt_numpy[64:192, 64:192, :]
-
-        l1loss_sample = 1000
-        ssim_sample = 0
-        psnr_sample = 0
-        lpips_sample = 1000
-
-        name = gt_paths[i + 0*2000].split('/')[-1].split(".")[0] + "*"
-        g_paths = sorted(glob.glob(os.path.join(args.g_path, name)))
-        num_files = len(g_paths)
-
-        for j in range(num_files):
-            index = j
-            try:
-                g_img = Image.open(g_paths[j]).resize([256, 256]).convert('RGB')
-                g_numpy = np.array(g_img).astype(np.float32) / 255.0
-                if args.center:
-                    g_numpy = g_numpy[64:192, 64:192, :]
-                l1loss, ssim_score, psnr_score, lpips_score = calculate_score(gt_numpy, g_numpy)
-                if l1loss - ssim_score - psnr_score + lpips_score < l1loss_sample - ssim_sample - psnr_sample + lpips_sample:
-                    l1loss_sample, ssim_sample, psnr_sample, lpips_sample = l1loss, ssim_score, psnr_score, lpips_score
-                    best_index = index
-            except:
-                print(g_paths[index])
-
-        if l1loss_sample != 1000 and ssim_sample !=0 and psnr_sample != 0:
-            print(g_paths[best_index])
-            print(l1loss_sample, ssim_sample, psnr_sample, lpips_sample)
-            l1losses.append(l1loss_sample)
-            ssims.append(ssim_sample)
-            psnrs.append(psnr_sample)
-            lpipses.append(lpips_sample)
-
-            if args.save_path is not None:
-                util.mkdir(args.save_path)
-                shutil.copy(g_paths[best_index], args.save_path)
-
-    print('{:>10},{:>10},{:>10},{:>10}'.format('l1loss', 'SSIM', 'PSNR', 'LPIPS'))
-    print('{:10.4f},{:10.4f},{:10.4f},{:10.4f}'.format(np.mean(l1losses), np.mean(ssims), np.mean(psnrs), np.mean(lpipses)))
-    print('{:10.4f},{:10.4f},{:10.4f},{:10.4f}'.format(np.var(l1losses), np.var(ssims), np.var(psnrs), np.var(lpipses)))

spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/model/stylegan_ops/fused_bias_act.cpp

@@ -1,21 +0,0 @@
-#include <torch/extension.h>
-
-
-torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
-    int act, int grad, float alpha, float scale);
-
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
-    int act, int grad, float alpha, float scale) {
-    CHECK_CUDA(input);
-    CHECK_CUDA(bias);
-
-    return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
-}

spaces/AnthonyTruchetPoC/persistent-docker/start_streamlit.sh

@@ -1,9 +0,0 @@
-#!/bin/bash
-set -e
-
-mkdir -p $APP_DATA $HF_HOME
-
-streamlit run                       \
-    --server.enableXsrfProtection=true  \
-    --server.fileWatcherType=auto       \
-    $APP_CODE/apps/streamlit_demo.py

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/resolution/legacy/resolver.py

@@ -1,600 +0,0 @@
-"""Dependency Resolution
-
-The dependency resolution in pip is performed as follows:
-
-for top-level requirements:
-    a. only one spec allowed per project, regardless of conflicts or not.
-       otherwise a "double requirement" exception is raised
-    b. they override sub-dependency requirements.
-for sub-dependencies
-    a. "first found, wins" (where the order is breadth first)
-"""
-
-# The following comment should be removed at some point in the future.
-# mypy: strict-optional=False
-
-import logging
-import sys
-from collections import defaultdict
-from itertools import chain
-from typing import DefaultDict, Iterable, List, Optional, Set, Tuple
-
-from pip._vendor.packaging import specifiers
-from pip._vendor.packaging.requirements import Requirement
-
-from pip._internal.cache import WheelCache
-from pip._internal.exceptions import (
-    BestVersionAlreadyInstalled,
-    DistributionNotFound,
-    HashError,
-    HashErrors,
-    InstallationError,
-    NoneMetadataError,
-    UnsupportedPythonVersion,
-)
-from pip._internal.index.package_finder import PackageFinder
-from pip._internal.metadata import BaseDistribution
-from pip._internal.models.link import Link
-from pip._internal.models.wheel import Wheel
-from pip._internal.operations.prepare import RequirementPreparer
-from pip._internal.req.req_install import (
-    InstallRequirement,
-    check_invalid_constraint_type,
-)
-from pip._internal.req.req_set import RequirementSet
-from pip._internal.resolution.base import BaseResolver, InstallRequirementProvider
-from pip._internal.utils import compatibility_tags
-from pip._internal.utils.compatibility_tags import get_supported
-from pip._internal.utils.direct_url_helpers import direct_url_from_link
-from pip._internal.utils.logging import indent_log
-from pip._internal.utils.misc import normalize_version_info
-from pip._internal.utils.packaging import check_requires_python
-
-logger = logging.getLogger(__name__)
-
-DiscoveredDependencies = DefaultDict[str, List[InstallRequirement]]
-
-
-def _check_dist_requires_python(
-    dist: BaseDistribution,
-    version_info: Tuple[int, int, int],
-    ignore_requires_python: bool = False,
-) -> None:
-    """
-    Check whether the given Python version is compatible with a distribution's
-    "Requires-Python" value.
-
-    :param version_info: A 3-tuple of ints representing the Python
-        major-minor-micro version to check.
-    :param ignore_requires_python: Whether to ignore the "Requires-Python"
-        value if the given Python version isn't compatible.
-
-    :raises UnsupportedPythonVersion: When the given Python version isn't
-        compatible.
-    """
-    # This idiosyncratically converts the SpecifierSet to str and let
-    # check_requires_python then parse it again into SpecifierSet. But this
-    # is the legacy resolver so I'm just not going to bother refactoring.
-    try:
-        requires_python = str(dist.requires_python)
-    except FileNotFoundError as e:
-        raise NoneMetadataError(dist, str(e))
-    try:
-        is_compatible = check_requires_python(
-            requires_python,
-            version_info=version_info,
-        )
-    except specifiers.InvalidSpecifier as exc:
-        logger.warning(
-            "Package %r has an invalid Requires-Python: %s", dist.raw_name, exc
-        )
-        return
-
-    if is_compatible:
-        return
-
-    version = ".".join(map(str, version_info))
-    if ignore_requires_python:
-        logger.debug(
-            "Ignoring failed Requires-Python check for package %r: %s not in %r",
-            dist.raw_name,
-            version,
-            requires_python,
-        )
-        return
-
-    raise UnsupportedPythonVersion(
-        "Package {!r} requires a different Python: {} not in {!r}".format(
-            dist.raw_name, version, requires_python
-        )
-    )
-
-
-class Resolver(BaseResolver):
-    """Resolves which packages need to be installed/uninstalled to perform \
-    the requested operation without breaking the requirements of any package.
-    """
-
-    _allowed_strategies = {"eager", "only-if-needed", "to-satisfy-only"}
-
-    def __init__(
-        self,
-        preparer: RequirementPreparer,
-        finder: PackageFinder,
-        wheel_cache: Optional[WheelCache],
-        make_install_req: InstallRequirementProvider,
-        use_user_site: bool,
-        ignore_dependencies: bool,
-        ignore_installed: bool,
-        ignore_requires_python: bool,
-        force_reinstall: bool,
-        upgrade_strategy: str,
-        py_version_info: Optional[Tuple[int, ...]] = None,
-    ) -> None:
-        super().__init__()
-        assert upgrade_strategy in self._allowed_strategies
-
-        if py_version_info is None:
-            py_version_info = sys.version_info[:3]
-        else:
-            py_version_info = normalize_version_info(py_version_info)
-
-        self._py_version_info = py_version_info
-
-        self.preparer = preparer
-        self.finder = finder
-        self.wheel_cache = wheel_cache
-
-        self.upgrade_strategy = upgrade_strategy
-        self.force_reinstall = force_reinstall
-        self.ignore_dependencies = ignore_dependencies
-        self.ignore_installed = ignore_installed
-        self.ignore_requires_python = ignore_requires_python
-        self.use_user_site = use_user_site
-        self._make_install_req = make_install_req
-
-        self._discovered_dependencies: DiscoveredDependencies = defaultdict(list)
-
-    def resolve(
-        self, root_reqs: List[InstallRequirement], check_supported_wheels: bool
-    ) -> RequirementSet:
-        """Resolve what operations need to be done
-
-        As a side-effect of this method, the packages (and their dependencies)
-        are downloaded, unpacked and prepared for installation. This
-        preparation is done by ``pip.operations.prepare``.
-
-        Once PyPI has static dependency metadata available, it would be
-        possible to move the preparation to become a step separated from
-        dependency resolution.
-        """
-        requirement_set = RequirementSet(check_supported_wheels=check_supported_wheels)
-        for req in root_reqs:
-            if req.constraint:
-                check_invalid_constraint_type(req)
-            self._add_requirement_to_set(requirement_set, req)
-
-        # Actually prepare the files, and collect any exceptions. Most hash
-        # exceptions cannot be checked ahead of time, because
-        # _populate_link() needs to be called before we can make decisions
-        # based on link type.
-        discovered_reqs: List[InstallRequirement] = []
-        hash_errors = HashErrors()
-        for req in chain(requirement_set.all_requirements, discovered_reqs):
-            try:
-                discovered_reqs.extend(self._resolve_one(requirement_set, req))
-            except HashError as exc:
-                exc.req = req
-                hash_errors.append(exc)
-
-        if hash_errors:
-            raise hash_errors
-
-        return requirement_set
-
-    def _add_requirement_to_set(
-        self,
-        requirement_set: RequirementSet,
-        install_req: InstallRequirement,
-        parent_req_name: Optional[str] = None,
-        extras_requested: Optional[Iterable[str]] = None,
-    ) -> Tuple[List[InstallRequirement], Optional[InstallRequirement]]:
-        """Add install_req as a requirement to install.
-
-        :param parent_req_name: The name of the requirement that needed this
-            added. The name is used because when multiple unnamed requirements
-            resolve to the same name, we could otherwise end up with dependency
-            links that point outside the Requirements set. parent_req must
-            already be added. Note that None implies that this is a user
-            supplied requirement, vs an inferred one.
-        :param extras_requested: an iterable of extras used to evaluate the
-            environment markers.
-        :return: Additional requirements to scan. That is either [] if
-            the requirement is not applicable, or [install_req] if the
-            requirement is applicable and has just been added.
-        """
-        # If the markers do not match, ignore this requirement.
-        if not install_req.match_markers(extras_requested):
-            logger.info(
-                "Ignoring %s: markers '%s' don't match your environment",
-                install_req.name,
-                install_req.markers,
-            )
-            return [], None
-
-        # If the wheel is not supported, raise an error.
-        # Should check this after filtering out based on environment markers to
-        # allow specifying different wheels based on the environment/OS, in a
-        # single requirements file.
-        if install_req.link and install_req.link.is_wheel:
-            wheel = Wheel(install_req.link.filename)
-            tags = compatibility_tags.get_supported()
-            if requirement_set.check_supported_wheels and not wheel.supported(tags):
-                raise InstallationError(
-                    "{} is not a supported wheel on this platform.".format(
-                        wheel.filename
-                    )
-                )
-
-        # This next bit is really a sanity check.
-        assert (
-            not install_req.user_supplied or parent_req_name is None
-        ), "a user supplied req shouldn't have a parent"
-
-        # Unnamed requirements are scanned again and the requirement won't be
-        # added as a dependency until after scanning.
-        if not install_req.name:
-            requirement_set.add_unnamed_requirement(install_req)
-            return [install_req], None
-
-        try:
-            existing_req: Optional[
-                InstallRequirement
-            ] = requirement_set.get_requirement(install_req.name)
-        except KeyError:
-            existing_req = None
-
-        has_conflicting_requirement = (
-            parent_req_name is None
-            and existing_req
-            and not existing_req.constraint
-            and existing_req.extras == install_req.extras
-            and existing_req.req
-            and install_req.req
-            and existing_req.req.specifier != install_req.req.specifier
-        )
-        if has_conflicting_requirement:
-            raise InstallationError(
-                "Double requirement given: {} (already in {}, name={!r})".format(
-                    install_req, existing_req, install_req.name
-                )
-            )
-
-        # When no existing requirement exists, add the requirement as a
-        # dependency and it will be scanned again after.
-        if not existing_req:
-            requirement_set.add_named_requirement(install_req)
-            # We'd want to rescan this requirement later
-            return [install_req], install_req
-
-        # Assume there's no need to scan, and that we've already
-        # encountered this for scanning.
-        if install_req.constraint or not existing_req.constraint:
-            return [], existing_req
-
-        does_not_satisfy_constraint = install_req.link and not (
-            existing_req.link and install_req.link.path == existing_req.link.path
-        )
-        if does_not_satisfy_constraint:
-            raise InstallationError(
-                "Could not satisfy constraints for '{}': "
-                "installation from path or url cannot be "
-                "constrained to a version".format(install_req.name)
-            )
-        # If we're now installing a constraint, mark the existing
-        # object for real installation.
-        existing_req.constraint = False
-        # If we're now installing a user supplied requirement,
-        # mark the existing object as such.
-        if install_req.user_supplied:
-            existing_req.user_supplied = True
-        existing_req.extras = tuple(
-            sorted(set(existing_req.extras) | set(install_req.extras))
-        )
-        logger.debug(
-            "Setting %s extras to: %s",
-            existing_req,
-            existing_req.extras,
-        )
-        # Return the existing requirement for addition to the parent and
-        # scanning again.
-        return [existing_req], existing_req
-
-    def _is_upgrade_allowed(self, req: InstallRequirement) -> bool:
-        if self.upgrade_strategy == "to-satisfy-only":
-            return False
-        elif self.upgrade_strategy == "eager":
-            return True
-        else:
-            assert self.upgrade_strategy == "only-if-needed"
-            return req.user_supplied or req.constraint
-
-    def _set_req_to_reinstall(self, req: InstallRequirement) -> None:
-        """
-        Set a requirement to be installed.
-        """
-        # Don't uninstall the conflict if doing a user install and the
-        # conflict is not a user install.
-        if not self.use_user_site or req.satisfied_by.in_usersite:
-            req.should_reinstall = True
-        req.satisfied_by = None
-
-    def _check_skip_installed(
-        self, req_to_install: InstallRequirement
-    ) -> Optional[str]:
-        """Check if req_to_install should be skipped.
-
-        This will check if the req is installed, and whether we should upgrade
-        or reinstall it, taking into account all the relevant user options.
-
-        After calling this req_to_install will only have satisfied_by set to
-        None if the req_to_install is to be upgraded/reinstalled etc. Any
-        other value will be a dist recording the current thing installed that
-        satisfies the requirement.
-
-        Note that for vcs urls and the like we can't assess skipping in this
-        routine - we simply identify that we need to pull the thing down,
-        then later on it is pulled down and introspected to assess upgrade/
-        reinstalls etc.
-
-        :return: A text reason for why it was skipped, or None.
-        """
-        if self.ignore_installed:
-            return None
-
-        req_to_install.check_if_exists(self.use_user_site)
-        if not req_to_install.satisfied_by:
-            return None
-
-        if self.force_reinstall:
-            self._set_req_to_reinstall(req_to_install)
-            return None
-
-        if not self._is_upgrade_allowed(req_to_install):
-            if self.upgrade_strategy == "only-if-needed":
-                return "already satisfied, skipping upgrade"
-            return "already satisfied"
-
-        # Check for the possibility of an upgrade.  For link-based
-        # requirements we have to pull the tree down and inspect to assess
-        # the version #, so it's handled way down.
-        if not req_to_install.link:
-            try:
-                self.finder.find_requirement(req_to_install, upgrade=True)
-            except BestVersionAlreadyInstalled:
-                # Then the best version is installed.
-                return "already up-to-date"
-            except DistributionNotFound:
-                # No distribution found, so we squash the error.  It will
-                # be raised later when we re-try later to do the install.
-                # Why don't we just raise here?
-                pass
-
-        self._set_req_to_reinstall(req_to_install)
-        return None
-
-    def _find_requirement_link(self, req: InstallRequirement) -> Optional[Link]:
-        upgrade = self._is_upgrade_allowed(req)
-        best_candidate = self.finder.find_requirement(req, upgrade)
-        if not best_candidate:
-            return None
-
-        # Log a warning per PEP 592 if necessary before returning.
-        link = best_candidate.link
-        if link.is_yanked:
-            reason = link.yanked_reason or "<none given>"
-            msg = (
-                # Mark this as a unicode string to prevent
-                # "UnicodeEncodeError: 'ascii' codec can't encode character"
-                # in Python 2 when the reason contains non-ascii characters.
-                "The candidate selected for download or install is a "
-                "yanked version: {candidate}\n"
-                "Reason for being yanked: {reason}"
-            ).format(candidate=best_candidate, reason=reason)
-            logger.warning(msg)
-
-        return link
-
-    def _populate_link(self, req: InstallRequirement) -> None:
-        """Ensure that if a link can be found for this, that it is found.
-
-        Note that req.link may still be None - if the requirement is already
-        installed and not needed to be upgraded based on the return value of
-        _is_upgrade_allowed().
-
-        If preparer.require_hashes is True, don't use the wheel cache, because
-        cached wheels, always built locally, have different hashes than the
-        files downloaded from the index server and thus throw false hash
-        mismatches. Furthermore, cached wheels at present have undeterministic
-        contents due to file modification times.
-        """
-        if req.link is None:
-            req.link = self._find_requirement_link(req)
-
-        if self.wheel_cache is None or self.preparer.require_hashes:
-            return
-        cache_entry = self.wheel_cache.get_cache_entry(
-            link=req.link,
-            package_name=req.name,
-            supported_tags=get_supported(),
-        )
-        if cache_entry is not None:
-            logger.debug("Using cached wheel link: %s", cache_entry.link)
-            if req.link is req.original_link and cache_entry.persistent:
-                req.cached_wheel_source_link = req.link
-            if cache_entry.origin is not None:
-                req.download_info = cache_entry.origin
-            else:
-                # Legacy cache entry that does not have origin.json.
-                # download_info may miss the archive_info.hashes field.
-                req.download_info = direct_url_from_link(
-                    req.link, link_is_in_wheel_cache=cache_entry.persistent
-                )
-            req.link = cache_entry.link
-
-    def _get_dist_for(self, req: InstallRequirement) -> BaseDistribution:
-        """Takes a InstallRequirement and returns a single AbstractDist \
-        representing a prepared variant of the same.
-        """
-        if req.editable:
-            return self.preparer.prepare_editable_requirement(req)
-
-        # satisfied_by is only evaluated by calling _check_skip_installed,
-        # so it must be None here.
-        assert req.satisfied_by is None
-        skip_reason = self._check_skip_installed(req)
-
-        if req.satisfied_by:
-            return self.preparer.prepare_installed_requirement(req, skip_reason)
-
-        # We eagerly populate the link, since that's our "legacy" behavior.
-        self._populate_link(req)
-        dist = self.preparer.prepare_linked_requirement(req)
-
-        # NOTE
-        # The following portion is for determining if a certain package is
-        # going to be re-installed/upgraded or not and reporting to the user.
-        # This should probably get cleaned up in a future refactor.
-
-        # req.req is only avail after unpack for URL
-        # pkgs repeat check_if_exists to uninstall-on-upgrade
-        # (#14)
-        if not self.ignore_installed:
-            req.check_if_exists(self.use_user_site)
-
-        if req.satisfied_by:
-            should_modify = (
-                self.upgrade_strategy != "to-satisfy-only"
-                or self.force_reinstall
-                or self.ignore_installed
-                or req.link.scheme == "file"
-            )
-            if should_modify:
-                self._set_req_to_reinstall(req)
-            else:
-                logger.info(
-                    "Requirement already satisfied (use --upgrade to upgrade): %s",
-                    req,
-                )
-        return dist
-
-    def _resolve_one(
-        self,
-        requirement_set: RequirementSet,
-        req_to_install: InstallRequirement,
-    ) -> List[InstallRequirement]:
-        """Prepare a single requirements file.
-
-        :return: A list of additional InstallRequirements to also install.
-        """
-        # Tell user what we are doing for this requirement:
-        # obtain (editable), skipping, processing (local url), collecting
-        # (remote url or package name)
-        if req_to_install.constraint or req_to_install.prepared:
-            return []
-
-        req_to_install.prepared = True
-
-        # Parse and return dependencies
-        dist = self._get_dist_for(req_to_install)
-        # This will raise UnsupportedPythonVersion if the given Python
-        # version isn't compatible with the distribution's Requires-Python.
-        _check_dist_requires_python(
-            dist,
-            version_info=self._py_version_info,
-            ignore_requires_python=self.ignore_requires_python,
-        )
-
-        more_reqs: List[InstallRequirement] = []
-
-        def add_req(subreq: Requirement, extras_requested: Iterable[str]) -> None:
-            # This idiosyncratically converts the Requirement to str and let
-            # make_install_req then parse it again into Requirement. But this is
-            # the legacy resolver so I'm just not going to bother refactoring.
-            sub_install_req = self._make_install_req(str(subreq), req_to_install)
-            parent_req_name = req_to_install.name
-            to_scan_again, add_to_parent = self._add_requirement_to_set(
-                requirement_set,
-                sub_install_req,
-                parent_req_name=parent_req_name,
-                extras_requested=extras_requested,
-            )
-            if parent_req_name and add_to_parent:
-                self._discovered_dependencies[parent_req_name].append(add_to_parent)
-            more_reqs.extend(to_scan_again)
-
-        with indent_log():
-            # We add req_to_install before its dependencies, so that we
-            # can refer to it when adding dependencies.
-            if not requirement_set.has_requirement(req_to_install.name):
-                # 'unnamed' requirements will get added here
-                # 'unnamed' requirements can only come from being directly
-                # provided by the user.
-                assert req_to_install.user_supplied
-                self._add_requirement_to_set(
-                    requirement_set, req_to_install, parent_req_name=None
-                )
-
-            if not self.ignore_dependencies:
-                if req_to_install.extras:
-                    logger.debug(
-                        "Installing extra requirements: %r",
-                        ",".join(req_to_install.extras),
-                    )
-                missing_requested = sorted(
-                    set(req_to_install.extras) - set(dist.iter_provided_extras())
-                )
-                for missing in missing_requested:
-                    logger.warning(
-                        "%s %s does not provide the extra '%s'",
-                        dist.raw_name,
-                        dist.version,
-                        missing,
-                    )
-
-                available_requested = sorted(
-                    set(dist.iter_provided_extras()) & set(req_to_install.extras)
-                )
-                for subreq in dist.iter_dependencies(available_requested):
-                    add_req(subreq, extras_requested=available_requested)
-
-        return more_reqs
-
-    def get_installation_order(
-        self, req_set: RequirementSet
-    ) -> List[InstallRequirement]:
-        """Create the installation order.
-
-        The installation order is topological - requirements are installed
-        before the requiring thing. We break cycles at an arbitrary point,
-        and make no other guarantees.
-        """
-        # The current implementation, which we may change at any point
-        # installs the user specified things in the order given, except when
-        # dependencies must come earlier to achieve topological order.
-        order = []
-        ordered_reqs: Set[InstallRequirement] = set()
-
-        def schedule(req: InstallRequirement) -> None:
-            if req.satisfied_by or req in ordered_reqs:
-                return
-            if req.constraint:
-                return
-            ordered_reqs.add(req)
-            for dep in self._discovered_dependencies[req.name]:
-                schedule(dep)
-            order.append(req)
-
-        for install_req in req_set.requirements.values():
-            schedule(install_req)
-        return order

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.py

@@ -1,8 +0,0 @@
-from ..common.optim import SGD as optimizer
-from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
-from ..common.data.coco_panoptic_separated import dataloader
-from ..common.models.panoptic_fpn import model
-from ..common.train import train
-
-model.backbone.bottom_up.freeze_at = 2
-train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl"

spaces/Banbri/zcvzcv/src/app/page.tsx

@@ -1,43 +0,0 @@
-"use server"
-
-import Head from "next/head"
-
-import Main from "./main"
-import { TooltipProvider } from "@/components/ui/tooltip"
-import Script from "next/script"
-// import { Maintenance } from "./interface/maintenance"
-
-// https://nextjs.org/docs/pages/building-your-application/optimizing/fonts 
-
-export default async function IndexPage({ params: { ownerId } }: { params: { ownerId: string }}) {
-  return (
-    <>
-      <Head>
-        <link rel="preconnect" href="https://fonts.gstatic.com" crossOrigin="anonymous" />
-        <link rel="preconnect" href="https://fonts.googleapis.com" crossOrigin="anonymous" />
-        <meta name="viewport" content="width=device-width, initial-scale=0.86, maximum-scale=5.0, minimum-scale=0.86" />
-      </Head>
-      <main className={
-        `light fixed inset-0 w-screen h-screen flex flex-col items-center
-         bg-zinc-50 text-stone-900 overflow-y-scroll
-        `}>
-        <TooltipProvider delayDuration={100}>
-          
-          <Main />
-         {/* <Maintenance /> */}
-
-        </TooltipProvider>
-        <Script src="https://www.googletagmanager.com/gtag/js?id=GTM-WH4MGSHS" />
-        <Script id="google-analytics">
-          {`
-            window.dataLayer = window.dataLayer || [];
-            function gtag(){dataLayer.push(arguments);}
-            gtag('js', new Date());
-    
-            gtag('config', 'GTM-WH4MGSHS');
-          `}
-        </Script>
-      </main>
-    </>
-  )
-}

spaces/Bart92/RVC_HF/gui_v0.py

@@ -1,786 +0,0 @@
-import os, sys, traceback, re
-
-import json
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from configs.config import Config
-
-Config = Config()
-import PySimpleGUI as sg
-import sounddevice as sd
-import noisereduce as nr
-import numpy as np
-from fairseq import checkpoint_utils
-import librosa, torch, pyworld, faiss, time, threading
-import torch.nn.functional as F
-import torchaudio.transforms as tat
-import scipy.signal as signal
-import torchcrepe
-
-# import matplotlib.pyplot as plt
-from lib.infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from i18n import I18nAuto
-
-i18n = I18nAuto()
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-current_dir = os.getcwd()
-
-
-class RVC:
-    def __init__(
-        self, key, f0_method, hubert_path, pth_path, index_path, npy_path, index_rate
-    ) -> None:
-        """
-        初始化
-        """
-        try:
-            self.f0_up_key = key
-            self.time_step = 160 / 16000 * 1000
-            self.f0_min = 50
-            self.f0_max = 1100
-            self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
-            self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
-            self.f0_method = f0_method
-            self.sr = 16000
-            self.window = 160
-
-            # Get Torch Device
-            if torch.cuda.is_available():
-                self.torch_device = torch.device(
-                    f"cuda:{0 % torch.cuda.device_count()}"
-                )
-            elif torch.backends.mps.is_available():
-                self.torch_device = torch.device("mps")
-            else:
-                self.torch_device = torch.device("cpu")
-
-            if index_rate != 0:
-                self.index = faiss.read_index(index_path)
-                # self.big_npy = np.load(npy_path)
-                self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
-                print("index search enabled")
-            self.index_rate = index_rate
-            model_path = hubert_path
-            print("load model(s) from {}".format(model_path))
-            models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-                [model_path],
-                suffix="",
-            )
-            self.model = models[0]
-            self.model = self.model.to(device)
-            if Config.is_half:
-                self.model = self.model.half()
-            else:
-                self.model = self.model.float()
-            self.model.eval()
-            cpt = torch.load(pth_path, map_location="cpu")
-            self.tgt_sr = cpt["config"][-1]
-            cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-            self.if_f0 = cpt.get("f0", 1)
-            self.version = cpt.get("version", "v1")
-            if self.version == "v1":
-                if self.if_f0 == 1:
-                    self.net_g = SynthesizerTrnMs256NSFsid(
-                        *cpt["config"], is_half=Config.is_half
-                    )
-                else:
-                    self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-            elif self.version == "v2":
-                if self.if_f0 == 1:
-                    self.net_g = SynthesizerTrnMs768NSFsid(
-                        *cpt["config"], is_half=Config.is_half
-                    )
-                else:
-                    self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-            del self.net_g.enc_q
-            print(self.net_g.load_state_dict(cpt["weight"], strict=False))
-            self.net_g.eval().to(device)
-            if Config.is_half:
-                self.net_g = self.net_g.half()
-            else:
-                self.net_g = self.net_g.float()
-        except:
-            print(traceback.format_exc())
-
-    def get_regular_crepe_computation(self, x, f0_min, f0_max, model="full"):
-        batch_size = 512
-        # Compute pitch using first gpu
-        audio = torch.tensor(np.copy(x))[None].float()
-        f0, pd = torchcrepe.predict(
-            audio,
-            self.sr,
-            self.window,
-            f0_min,
-            f0_max,
-            model,
-            batch_size=batch_size,
-            device=self.torch_device,
-            return_periodicity=True,
-        )
-        pd = torchcrepe.filter.median(pd, 3)
-        f0 = torchcrepe.filter.mean(f0, 3)
-        f0[pd < 0.1] = 0
-        f0 = f0[0].cpu().numpy()
-        return f0
-
-    def get_harvest_computation(self, x, f0_min, f0_max):
-        f0, t = pyworld.harvest(
-            x.astype(np.double),
-            fs=self.sr,
-            f0_ceil=f0_max,
-            f0_floor=f0_min,
-            frame_period=10,
-        )
-        f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
-        f0 = signal.medfilt(f0, 3)
-        return f0
-
-    def get_f0(self, x, f0_up_key, inp_f0=None):
-        # Calculate Padding and f0 details here
-        p_len = x.shape[0] // 512  # For Now This probs doesn't work
-        x_pad = 1
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
-        f0 = 0
-        # Here, check f0_methods and get their computations
-        if self.f0_method == "harvest":
-            f0 = self.get_harvest_computation(x, f0_min, f0_max)
-        elif self.f0_method == "reg-crepe":
-            f0 = self.get_regular_crepe_computation(x, f0_min, f0_max)
-        elif self.f0_method == "reg-crepe-tiny":
-            f0 = self.get_regular_crepe_computation(x, f0_min, f0_max, "tiny")
-
-        # Calculate f0_course and f0_bak here
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-        return f0_coarse, f0bak  # 1-0
-
-    def infer(self, feats: torch.Tensor) -> np.ndarray:
-        """
-        推理函数
-        """
-        audio = feats.clone().cpu().numpy()
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).fill_(False)
-        if Config.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        inputs = {
-            "source": feats.to(device),
-            "padding_mask": padding_mask.to(device),
-            "output_layer": 9 if self.version == "v1" else 12,
-        }
-        torch.cuda.synchronize()
-        with torch.no_grad():
-            logits = self.model.extract_features(**inputs)
-            feats = (
-                self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
-            )
-
-        ####索引优化
-        try:
-            if (
-                hasattr(self, "index")
-                and hasattr(self, "big_npy")
-                and self.index_rate != 0
-            ):
-                npy = feats[0].cpu().numpy().astype("float32")
-                score, ix = self.index.search(npy, k=8)
-                weight = np.square(1 / score)
-                weight /= weight.sum(axis=1, keepdims=True)
-                npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-                if Config.is_half:
-                    npy = npy.astype("float16")
-                feats = (
-                    torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
-                    + (1 - self.index_rate) * feats
-                )
-            else:
-                print("index search FAIL or disabled")
-        except:
-            traceback.print_exc()
-            print("index search FAIL")
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        torch.cuda.synchronize()
-        print(feats.shape)
-        if self.if_f0 == 1:
-            pitch, pitchf = self.get_f0(audio, self.f0_up_key)
-            p_len = min(feats.shape[1], 13000, pitch.shape[0])  # 太大了爆显存
-        else:
-            pitch, pitchf = None, None
-            p_len = min(feats.shape[1], 13000)  # 太大了爆显存
-        torch.cuda.synchronize()
-        # print(feats.shape,pitch.shape)
-        feats = feats[:, :p_len, :]
-        if self.if_f0 == 1:
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
-            pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
-        p_len = torch.LongTensor([p_len]).to(device)
-        ii = 0  # sid
-        sid = torch.LongTensor([ii]).to(device)
-        with torch.no_grad():
-            if self.if_f0 == 1:
-                infered_audio = (
-                    self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
-                    .data.cpu()
-                    .float()
-                )
-            else:
-                infered_audio = (
-                    self.net_g.infer(feats, p_len, sid)[0][0, 0].data.cpu().float()
-                )
-        torch.cuda.synchronize()
-        return infered_audio
-
-
-class GUIConfig:
-    def __init__(self) -> None:
-        self.hubert_path: str = ""
-        self.pth_path: str = ""
-        self.index_path: str = ""
-        self.npy_path: str = ""
-        self.f0_method: str = ""
-        self.pitch: int = 12
-        self.samplerate: int = 44100
-        self.block_time: float = 1.0  # s
-        self.buffer_num: int = 1
-        self.threhold: int = -30
-        self.crossfade_time: float = 0.08
-        self.extra_time: float = 0.04
-        self.I_noise_reduce = False
-        self.O_noise_reduce = False
-        self.index_rate = 0.3
-
-
-class GUI:
-    def __init__(self) -> None:
-        self.config = GUIConfig()
-        self.flag_vc = False
-
-        self.launcher()
-
-    def load(self):
-        (
-            input_devices,
-            output_devices,
-            input_devices_indices,
-            output_devices_indices,
-        ) = self.get_devices()
-        try:
-            with open("values1.json", "r") as j:
-                data = json.load(j)
-        except:
-            # Injecting f0_method into the json data
-            with open("values1.json", "w") as j:
-                data = {
-                    "pth_path": "",
-                    "index_path": "",
-                    "sg_input_device": input_devices[
-                        input_devices_indices.index(sd.default.device[0])
-                    ],
-                    "sg_output_device": output_devices[
-                        output_devices_indices.index(sd.default.device[1])
-                    ],
-                    "threhold": "-45",
-                    "pitch": "0",
-                    "index_rate": "0",
-                    "block_time": "1",
-                    "crossfade_length": "0.04",
-                    "extra_time": "1",
-                }
-        return data
-
-    def launcher(self):
-        data = self.load()
-        sg.theme("DarkTeal12")
-        input_devices, output_devices, _, _ = self.get_devices()
-        layout = [
-            [
-                sg.Frame(
-                    title="Proudly forked by Mangio621",
-                ),
-                sg.Frame(
-                    title=i18n("Load model"),
-                    layout=[
-                        [
-                            sg.Input(
-                                default_text="hubert_base.pt",
-                                key="hubert_path",
-                                disabled=True,
-                            ),
-                            sg.FileBrowse(
-                                i18n("Hubert Model"),
-                                initial_folder=os.path.join(os.getcwd()),
-                                file_types=(("pt files", "*.pt"),),
-                            ),
-                        ],
-                        [
-                            sg.Input(
-                                default_text=data.get("pth_path", ""),
-                                key="pth_path",
-                            ),
-                            sg.FileBrowse(
-                                i18n("Select the .pth file"),
-                                initial_folder=os.path.join(os.getcwd(), "weights"),
-                                file_types=(("weight files", "*.pth"),),
-                            ),
-                        ],
-                        [
-                            sg.Input(
-                                default_text=data.get("index_path", ""),
-                                key="index_path",
-                            ),
-                            sg.FileBrowse(
-                                i18n("Select the .index file"),
-                                initial_folder=os.path.join(os.getcwd(), "logs"),
-                                file_types=(("index files", "*.index"),),
-                            ),
-                        ],
-                        [
-                            sg.Input(
-                                default_text="你不需要填写这个You don't need write this.",
-                                key="npy_path",
-                                disabled=True,
-                            ),
-                            sg.FileBrowse(
-                                i18n("Select the .npy file"),
-                                initial_folder=os.path.join(os.getcwd(), "logs"),
-                                file_types=(("feature files", "*.npy"),),
-                            ),
-                        ],
-                    ],
-                ),
-            ],
-            [
-                # Mangio f0 Selection frame Here
-                sg.Frame(
-                    layout=[
-                        [
-                            sg.Radio(
-                                "Harvest", "f0_method", key="harvest", default=True
-                            ),
-                            sg.Radio("Crepe", "f0_method", key="reg-crepe"),
-                            sg.Radio("Crepe Tiny", "f0_method", key="reg-crepe-tiny"),
-                        ]
-                    ],
-                    title="Select an f0 Method",
-                )
-            ],
-            [
-                sg.Frame(
-                    layout=[
-                        [
-                            sg.Text(i18n("Input device")),
-                            sg.Combo(
-                                input_devices,
-                                key="sg_input_device",
-                                default_value=data.get("sg_input_device", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("Output device")),
-                            sg.Combo(
-                                output_devices,
-                                key="sg_output_device",
-                                default_value=data.get("sg_output_device", ""),
-                            ),
-                        ],
-                    ],
-                    title=i18n("Audio device (please use the same type of driver)"),
-                )
-            ],
-            [
-                sg.Frame(
-                    layout=[
-                        [
-                            sg.Text(i18n("Response threshold")),
-                            sg.Slider(
-                                range=(-60, 0),
-                                key="threhold",
-                                resolution=1,
-                                orientation="h",
-                                default_value=data.get("threhold", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("Pitch settings")),
-                            sg.Slider(
-                                range=(-24, 24),
-                                key="pitch",
-                                resolution=1,
-                                orientation="h",
-                                default_value=data.get("pitch", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("Index Rate")),
-                            sg.Slider(
-                                range=(0.0, 1.0),
-                                key="index_rate",
-                                resolution=0.01,
-                                orientation="h",
-                                default_value=data.get("index_rate", ""),
-                            ),
-                        ],
-                    ],
-                    title=i18n("General settings"),
-                ),
-                sg.Frame(
-                    layout=[
-                        [
-                            sg.Text(i18n("Sample length")),
-                            sg.Slider(
-                                range=(0.1, 3.0),
-                                key="block_time",
-                                resolution=0.1,
-                                orientation="h",
-                                default_value=data.get("block_time", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("Fade length")),
-                            sg.Slider(
-                                range=(0.01, 0.15),
-                                key="crossfade_length",
-                                resolution=0.01,
-                                orientation="h",
-                                default_value=data.get("crossfade_length", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("Extra推理时长")),
-                            sg.Slider(
-                                range=(0.05, 3.00),
-                                key="extra_time",
-                                resolution=0.01,
-                                orientation="h",
-                                default_value=data.get("extra_time", ""),
-                            ),
-                        ],
-                        [
-                            sg.Checkbox(i18n("Input noise reduction"), key="I_noise_reduce"),
-                            sg.Checkbox(i18n("Output noise reduction"), key="O_noise_reduce"),
-                        ],
-                    ],
-                    title=i18n("Performance settings"),
-                ),
-            ],
-            [
-                sg.Button(i18n("开始音频Convert"), key="start_vc"),
-                sg.Button(i18n("停止音频Convert"), key="stop_vc"),
-                sg.Text(i18n("Inference time (ms):")),
-                sg.Text("0", key="infer_time"),
-            ],
-        ]
-        self.window = sg.Window("RVC - GUI", layout=layout)
-        self.event_handler()
-
-    def event_handler(self):
-        while True:
-            event, values = self.window.read()
-            if event == sg.WINDOW_CLOSED:
-                self.flag_vc = False
-                exit()
-            if event == "start_vc" and self.flag_vc == False:
-                if self.set_values(values) == True:
-                    print("using_cuda:" + str(torch.cuda.is_available()))
-                    self.start_vc()
-                    settings = {
-                        "pth_path": values["pth_path"],
-                        "index_path": values["index_path"],
-                        "f0_method": self.get_f0_method_from_radios(values),
-                        "sg_input_device": values["sg_input_device"],
-                        "sg_output_device": values["sg_output_device"],
-                        "threhold": values["threhold"],
-                        "pitch": values["pitch"],
-                        "index_rate": values["index_rate"],
-                        "block_time": values["block_time"],
-                        "crossfade_length": values["crossfade_length"],
-                        "extra_time": values["extra_time"],
-                    }
-                    with open("values1.json", "w") as j:
-                        json.dump(settings, j)
-            if event == "stop_vc" and self.flag_vc == True:
-                self.flag_vc = False
-
-    # Function that returns the used f0 method in string format "harvest"
-    def get_f0_method_from_radios(self, values):
-        f0_array = [
-            {"name": "harvest", "val": values["harvest"]},
-            {"name": "reg-crepe", "val": values["reg-crepe"]},
-            {"name": "reg-crepe-tiny", "val": values["reg-crepe-tiny"]},
-        ]
-        # Filter through to find a true value
-        used_f0 = ""
-        for f0 in f0_array:
-            if f0["val"] == True:
-                used_f0 = f0["name"]
-                break
-        if used_f0 == "":
-            used_f0 = "harvest"  # Default Harvest if used_f0 is empty somehow
-        return used_f0
-
-    def set_values(self, values):
-        if len(values["pth_path"].strip()) == 0:
-            sg.popup(i18n("Select the pth file"))
-            return False
-        if len(values["index_path"].strip()) == 0:
-            sg.popup(i18n("Select the index file"))
-            return False
-        pattern = re.compile("[^\x00-\x7F]+")
-        if pattern.findall(values["hubert_path"]):
-            sg.popup(i18n("The hubert model path must not contain Chinese characters"))
-            return False
-        if pattern.findall(values["pth_path"]):
-            sg.popup(i18n("The pth file path must not contain Chinese characters."))
-            return False
-        if pattern.findall(values["index_path"]):
-            sg.popup(i18n("The index file path must not contain Chinese characters."))
-            return False
-        self.set_devices(values["sg_input_device"], values["sg_output_device"])
-        self.config.hubert_path = os.path.join(current_dir, "hubert_base.pt")
-        self.config.pth_path = values["pth_path"]
-        self.config.index_path = values["index_path"]
-        self.config.npy_path = values["npy_path"]
-        self.config.f0_method = self.get_f0_method_from_radios(values)
-        self.config.threhold = values["threhold"]
-        self.config.pitch = values["pitch"]
-        self.config.block_time = values["block_time"]
-        self.config.crossfade_time = values["crossfade_length"]
-        self.config.extra_time = values["extra_time"]
-        self.config.I_noise_reduce = values["I_noise_reduce"]
-        self.config.O_noise_reduce = values["O_noise_reduce"]
-        self.config.index_rate = values["index_rate"]
-        return True
-
-    def start_vc(self):
-        torch.cuda.empty_cache()
-        self.flag_vc = True
-        self.block_frame = int(self.config.block_time * self.config.samplerate)
-        self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate)
-        self.sola_search_frame = int(0.012 * self.config.samplerate)
-        self.delay_frame = int(0.01 * self.config.samplerate)  # 往前预留0.02s
-        self.extra_frame = int(self.config.extra_time * self.config.samplerate)
-        self.rvc = None
-        self.rvc = RVC(
-            self.config.pitch,
-            self.config.f0_method,
-            self.config.hubert_path,
-            self.config.pth_path,
-            self.config.index_path,
-            self.config.npy_path,
-            self.config.index_rate,
-        )
-        self.input_wav: np.ndarray = np.zeros(
-            self.extra_frame
-            + self.crossfade_frame
-            + self.sola_search_frame
-            + self.block_frame,
-            dtype="float32",
-        )
-        self.output_wav: torch.Tensor = torch.zeros(
-            self.block_frame, device=device, dtype=torch.float32
-        )
-        self.sola_buffer: torch.Tensor = torch.zeros(
-            self.crossfade_frame, device=device, dtype=torch.float32
-        )
-        self.fade_in_window: torch.Tensor = torch.linspace(
-            0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32
-        )
-        self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
-        self.resampler1 = tat.Resample(
-            orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
-        )
-        self.resampler2 = tat.Resample(
-            orig_freq=self.rvc.tgt_sr,
-            new_freq=self.config.samplerate,
-            dtype=torch.float32,
-        )
-        thread_vc = threading.Thread(target=self.soundinput)
-        thread_vc.start()
-
-    def soundinput(self):
-        """
-        接受音频输入
-        """
-        with sd.Stream(
-            channels=2,
-            callback=self.audio_callback,
-            blocksize=self.block_frame,
-            samplerate=self.config.samplerate,
-            dtype="float32",
-        ):
-            while self.flag_vc:
-                time.sleep(self.config.block_time)
-                print("Audio block passed.")
-        print("ENDing VC")
-
-    def audio_callback(
-        self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
-    ):
-        """
-        音频处理
-        """
-        start_time = time.perf_counter()
-        indata = librosa.to_mono(indata.T)
-        if self.config.I_noise_reduce:
-            indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate)
-
-        """noise gate"""
-        frame_length = 2048
-        hop_length = 1024
-        rms = librosa.feature.rms(
-            y=indata, frame_length=frame_length, hop_length=hop_length
-        )
-        db_threhold = librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold
-        # print(rms.shape,db.shape,db)
-        for i in range(db_threhold.shape[0]):
-            if db_threhold[i]:
-                indata[i * hop_length : (i + 1) * hop_length] = 0
-        self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata)
-
-        # infer
-        print("input_wav:" + str(self.input_wav.shape))
-        # print('infered_wav:'+str(infer_wav.shape))
-        infer_wav: torch.Tensor = self.resampler2(
-            self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav)))
-        )[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to(
-            device
-        )
-        print("infer_wav:" + str(infer_wav.shape))
-
-        # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
-        cor_nom = F.conv1d(
-            infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
-            self.sola_buffer[None, None, :],
-        )
-        cor_den = torch.sqrt(
-            F.conv1d(
-                infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
-                ** 2,
-                torch.ones(1, 1, self.crossfade_frame, device=device),
-            )
-            + 1e-8
-        )
-        sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
-        print("sola offset: " + str(int(sola_offset)))
-
-        # crossfade
-        self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
-        self.output_wav[: self.crossfade_frame] *= self.fade_in_window
-        self.output_wav[: self.crossfade_frame] += self.sola_buffer[:]
-        if sola_offset < self.sola_search_frame:
-            self.sola_buffer[:] = (
-                infer_wav[
-                    -self.sola_search_frame
-                    - self.crossfade_frame
-                    + sola_offset : -self.sola_search_frame
-                    + sola_offset
-                ]
-                * self.fade_out_window
-            )
-        else:
-            self.sola_buffer[:] = (
-                infer_wav[-self.crossfade_frame :] * self.fade_out_window
-            )
-
-        if self.config.O_noise_reduce:
-            outdata[:] = np.tile(
-                nr.reduce_noise(
-                    y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
-                ),
-                (2, 1),
-            ).T
-        else:
-            outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
-        total_time = time.perf_counter() - start_time
-        self.window["infer_time"].update(int(total_time * 1000))
-        print("infer time:" + str(total_time))
-        print("f0_method: " + str(self.config.f0_method))
-
-    def get_devices(self, update: bool = True):
-        """获取设备列表"""
-        if update:
-            sd._terminate()
-            sd._initialize()
-        devices = sd.query_devices()
-        hostapis = sd.query_hostapis()
-        for hostapi in hostapis:
-            for device_idx in hostapi["devices"]:
-                devices[device_idx]["hostapi_name"] = hostapi["name"]
-        input_devices = [
-            f"{d['name']} ({d['hostapi_name']})"
-            for d in devices
-            if d["max_input_channels"] > 0
-        ]
-        output_devices = [
-            f"{d['name']} ({d['hostapi_name']})"
-            for d in devices
-            if d["max_output_channels"] > 0
-        ]
-        input_devices_indices = [
-            d["index"] if "index" in d else d["name"]
-            for d in devices
-            if d["max_input_channels"] > 0
-        ]
-        output_devices_indices = [
-            d["index"] if "index" in d else d["name"]
-            for d in devices
-            if d["max_output_channels"] > 0
-        ]
-        return (
-            input_devices,
-            output_devices,
-            input_devices_indices,
-            output_devices_indices,
-        )
-
-    def set_devices(self, input_device, output_device):
-        """设置输出设备"""
-        (
-            input_devices,
-            output_devices,
-            input_device_indices,
-            output_device_indices,
-        ) = self.get_devices()
-        sd.default.device[0] = input_device_indices[input_devices.index(input_device)]
-        sd.default.device[1] = output_device_indices[
-            output_devices.index(output_device)
-        ]
-        print("input device:" + str(sd.default.device[0]) + ":" + str(input_device))
-        print("output device:" + str(sd.default.device[1]) + ":" + str(output_device))
-
-
-gui = GUI()

spaces/Benson/text-generation/Examples/8 Ball Pool Soldi Infiniti Apk.md

@@ -1,86 +0,0 @@
-
-<h1>8 piscina de bolas Soldi Infiniti APK: Cómo jugar piscina ilimitada en su dispositivo Android</h1>
-<p>Si eres un fan de los juegos de billar, es posible que hayas oído hablar de <strong>8 Ball Pool</strong>, uno de los juegos de billar más populares y adictivos en dispositivos móviles. Le permite jugar en línea con millones de otros jugadores de todo el mundo, competir en torneos, ganar trofeos y monedas, y personalizar su señal y mesa. Sin embargo, si desea disfrutar del juego sin limitaciones ni restricciones, es posible que esté interesado en <strong>8 Ball Pool Soldi Infiniti APK</strong>, una versión modificada del juego que le da monedas ilimitadas y dinero en efectivo, así como otras características que mejoran su experiencia de juego. En este artículo, le diremos qué es 8 Ball Pool Soldi Infiniti APK es, cómo descargarlo e instalarlo en su dispositivo Android, cómo jugarlo, cuáles son los beneficios y riesgos de jugarlo, y responder a algunas preguntas frecuentes. </p>
-<h2>8 ball pool soldi infiniti apk</h2><br /><p><b><b>DOWNLOAD</b> &#10004;&#10004;&#10004; <a href="https://bltlly.com/2v6K1c">https://bltlly.com/2v6K1c</a></b></p><br /><br />
- <h2>¿Qué es 8 piscina de bolas Soldi Infiniti APK? </h2>
-<h3>Una versión modificada del popular juego de billar</h3>
-<p>8 Ball Pool Soldi Infiniti APK es una versión modificada o hackeada del juego original 8 Ball Pool desarrollado por Miniclip. No es una aplicación oficial de Miniclip, sino una aplicación de terceros creada por algunos desarrolladores desconocidos que han ajustado el código del juego para darte monedas y efectivo ilimitados, así como otras características que no están disponibles en el juego original. La palabra "soldi infiniti" significa "dinero infinito" en italiano, lo que indica la característica principal de esta aplicación modded. </p>
-<h3> Características de 8 bola piscina Soldi Infiniti APK</h3>
-<p>Algunas de las características que se pueden disfrutar al jugar 8 Ball Pool Soldi Infiniti APK son:</p>
-<ul>
-<li><strong>Monedas ilimitadas y dinero en efectivo:</strong> Puedes obtener monedas ilimitadas y efectivo en tu cuenta, que puedes usar para comprar nuevas pistas, mesas, paquetes de chat, minijuegos y más. También puedes participar en partidas y torneos de apuestas más altas sin preocuparte por perder dinero. </li>
-
-<li><strong>Función anti-van:</strong> Puedes jugar online con otros jugadores sin ser detectado o prohibido por Miniclip. La aplicación modded tiene una función anti-van que protege su cuenta de ser marcado o suspendido. </li>
-<li><strong>No hay anuncios:</strong> Puedes jugar el juego sin ningún anuncio molesto o pop-ups que interrumpan tu juego o consuman tus datos. </li>
-<li><strong>No se requiere raíz:</strong> Puedes instalar la aplicación sin rootear tu dispositivo o comprometer su seguridad. Solo necesita habilitar la instalación de fuentes desconocidas en su configuración. </li>
-</ul>
- <h2>Cómo descargar e instalar 8 piscina de bolas Soldi Infiniti APK? </h2>
-<h3>Pasos para descargar el archivo APK</h3>
-<p>Para descargar el 8 Ball Pool Soldi Infiniti APK archivo, es necesario seguir estos pasos:</p>
-<ol>
-<li>Ir a un sitio web confiable y de confianza que ofrece el enlace de descarga para la aplicación modded. Puede buscar "8 Ball Pool Soldi Infiniti APK" en Google o Bing y elegir entre los resultados. Asegúrese de que el sitio web esté seguro y protegido, y evite cualquier enlace sospechoso o malicioso. </li>
-<li>Haga clic en el botón de descarga o enlace y espere a que el archivo se descargue en su dispositivo. El tamaño del archivo puede variar dependiendo de la versión de la aplicación, pero no debería tardar mucho en descargarse. </li>
-<li>Una vez que el archivo se descarga, localizarlo en el almacenamiento de su dispositivo y toque en él para abrirlo. Es posible que necesite conceder algunos permisos o acceso a su dispositivo para que el archivo se ejecute. </li>
-</ol>
-<h3>Pasos para instalar el archivo APK</h3>
-<p>Para instalar el 8 Ball Pool Soldi Infiniti APK archivo, es necesario seguir estos pasos:</p>
-<p></p>
-<ol>
-<li>Antes de instalar la aplicación, asegúrese de haber habilitado la instalación de fuentes desconocidas en la configuración de su dispositivo. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. Esto le permitirá instalar aplicaciones que no son de Google Play Store.</li>
-
-<li>Espere a que la aplicación se instale en su dispositivo. Puede tardar unos segundos o minutos dependiendo del rendimiento y la velocidad del dispositivo. </li>
-<li>Una vez que la aplicación está instalada, puede iniciarla desde el cajón de aplicaciones o la pantalla de inicio. Es posible que necesites iniciar sesión con tu cuenta de Facebook o Google para acceder a tu perfil y progresar en el juego. </li>
-</ol>
- <h2>Cómo jugar 8 piscina de bolas Soldi Infiniti APK? </h2>
-<h3>Elige el modo de juego y la tabla</h3>
-<p>Al abrir la aplicación, verá diferentes modos de juego y mesas que puede elegir. Puedes jugar partidas 1 a 1 con jugadores aleatorios o tus amigos, unirte a torneos con diferentes apuestas y premios o practicar sin límite de tiempo. También puede seleccionar entre varias tablas con diferentes temas, tamaños y reglas. Algunas de las tablas son exclusivas de 8 Ball Pool Soldi Infiniti APK, tales como la tabla de Halloween, la tabla de la galaxia, y la tabla de oro. </p>
-<h3>Apunta y dispara con precisión y potencia</h3>
-<p>Para jugar el juego, es necesario apuntar y disparar la bola blanca a las bolas de colores en la mesa. Puede ajustar su objetivo arrastrando el dedo en la pantalla, y ajustar su poder tirando hacia atrás la barra de potencia en la parte inferior. También puedes usar el giro tocando el icono de la bola blanca en la esquina superior derecha y moviéndolo. El objetivo es meter todas tus bolas (ya sea sólidos o rayas) antes que tu oponente, y luego meter la bola 8 negro pasado. También puedes ganar por defecto si tu oponente comete faltas tres veces seguidas, mete la bola 8 antes de limpiar sus bolas, o mete la bola blanca junto con la bola 8. </p>
-<h3>Disfruta de monedas ilimitadas y dinero en efectivo</h3>
-
- <h2>¿Cuáles son los beneficios de jugar 8 piscina de bolas Soldi Infiniti APK? </h2>
-<h3>Mejora tus habilidades y sube de rango</h3>
-<p>Jugando 8 Ball Pool Soldi Infiniti APK puede ayudar a mejorar sus habilidades y rango en el juego. Puedes practicar tu puntería, poder, efectos y estrategia en diferentes mesas y contra diferentes oponentes. También puede aprender de sus errores y mejorar su rendimiento. A medida que ganes más partidos y torneos, puedes clasificarte de Bronce a Gran Maestro, y desbloquear más logros y recompensas. </p>
-<h3>Desafía a otros jugadores online</h3>
-<p>Jugar 8 Ball Pool Soldi Infiniti APK también puede ayudar a desafiar a otros jugadores en línea y divertirse. Puedes jugar con millones de jugadores en todo el mundo, chatear con ellos, enviarles regalos y agregarlos como amigos. También puedes unirte a clubes y ligas, donde puedes hacer equipo con otros jugadores, competir en torneos de clubes y ganar puntos de clubes. También puedes retar a tus amigos a un partido amistoso o una revancha en cualquier momento. </p>
-<h3>Personaliza tu señal y tabla</h3>
-<p>Jugar al billar de 8 bolas Soldi Infiniti APK también puede ayudarlo a personalizar su señal y tabla para adaptarse a su estilo y preferencia. Puede elegir entre una variedad de pistas y tablas con diferentes diseños, estadísticas y efectos. También puedes actualizar tus señales para aumentar sus atributos, como fuerza, objetivo, efectos y tiempo. También puede cambiar el color y el patrón de su mantel, la forma y el tamaño de sus bolsillos, y el tipo de bolas que utiliza. </p>
- <h2>¿Cuáles son los riesgos de jugar 8 Ball Pool Soldi Infiniti APK? </h2>
-<h3>Posible infección de malware o virus</h3>
-
-<h3>Posible prohibición o suspensión de cuentas</h3>
-<p>Otro riesgo de jugar 8 Ball Pool Soldi Infiniti APK es que usted puede conseguir prohibido o suspendido por Miniclip para violar sus términos de servicio o la política de juego limpio. A pesar de que la aplicación tiene una función anti-van que protege su cuenta de ser detectado o marcado, todavía hay una posibilidad de que Miniclip puede descubrir que está utilizando una aplicación modificada y tomar medidas contra usted. Para evitar este riesgo, no debes usar la aplicación con demasiada frecuencia o en exceso, evitar jugar con jugadores que te denuncien o sospechen que haces trampa, y evitar presumir de tus monedas y dinero ilimitados o mostrar tus pistas y mesas modificadas. </p>
-<h3>Posibles problemas legales o violaciones</h3>
-<p>Un tercer riesgo de jugar 8 Ball Pool Soldi Infiniti APK es que usted puede enfrentar problemas legales o violaciones por infringir los derechos de propiedad intelectual de Miniclip u otras partes involucradas en el desarrollo y distribución del juego original. Mediante el uso de una aplicación modificada que altera el código del juego o el contenido sin permiso o autorización, puede estar infringiendo la ley o violando el contrato entre usted y Miniclip. Para evitar este riesgo, debe respetar los derechos de Miniclip y otras partes, reconocer que son los legítimos propietarios del juego y sus elementos, y abstenerse de distribuir o compartir la aplicación modificada con otros. </p>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre 8 Piscina de bolas Soldi Infiniti APK:</p>
-<tabla>
-<tr>
-<th>Pregunta</th>
-<th>Respuesta</th>
-</tr>
-<tr>
-<td>Es 8 bola de piscina Soldi Infiniti APK seguro de usar? </td>
-<td>La aplicación no es 100% segura de usar, ya que puede contener malware o virus que pueden dañar su dispositivo o robar sus datos. También puede hacer que Miniclip lo prohíba o suspenda por violar sus términos de servicio o su política de juego limpio. También puede causar problemas legales o violaciones por infringir los derechos de propiedad intelectual de Miniclip u otras partes. Por lo tanto, solo debe descargar la aplicación desde un sitio web confiable y confiable, escanear el archivo con un software antivirus antes de instalarlo y evitar conceder permisos innecesarios o acceso a la aplicación. </td>
-</tr>
-<tr>
-<td>Es 8 Ball Pool Soldi Infiniti APK descarga gratuita? </td>
-<td>La aplicación es gratuita para descargar desde varios sitios web que ofrecen el enlace de descarga para la aplicación modded. Sin embargo, debe tener cuidado y evitar cualquier enlace sospechoso o malicioso que pueda dañar su dispositivo o datos. También debe comprobar el tamaño del archivo y la versión antes de descargarlo, y asegúrese de que coincide con la descripción y los requisitos de la aplicación. </td>
-</tr>
-<tr>
-<td>¿Puedo jugar 8 bola piscina Soldi Infiniti APK offline? </td>
-<td>La aplicación te permite jugar sin conexión sin límite de tiempo, pero no podrás acceder a algunas de las funciones que requieren una conexión a Internet, como jugar en línea con otros jugadores, unirse a torneos, comprar artículos de la tienda, jugar minijuegos, o conseguir monedas gratis y dinero en efectivo. Tampoco podrás guardar tu progreso o sincronizar tu cuenta con Facebook o Google.</td>
-</tr>
-<tr>
-<td>¿Puedo jugar 8 bola piscina Soldi Infiniti APK en dispositivos iOS? </td>
-
-</tr>
-<tr>
-<td>¿Puedo actualizar 8 bola piscina Soldi Infiniti APK? </td>
-<td>Es posible que la aplicación no se actualice automáticamente, ya que no es de Google Play Store. Tendrá que comprobar las actualizaciones manualmente desde el sitio web donde descargó la aplicación, y descargar e instalar la última versión de la aplicación. Sin embargo, debe tener cuidado y evitar cualquier versión falsa o desactualizada de la aplicación que pueda dañar su dispositivo o datos. </td>
-</tr>
-</tabla></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Amor De Mi Vida Tono De Llamada.md

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-<h1>Cómo descargar y establecer "Amor de mi vida" como su tono de llamada</h1>
- <p>Si usted está buscando una manera de personalizar su teléfono y expresar su amor por la música, es posible que desee considerar la descarga y la configuración de "Love of My Life" como su tono de llamada. Esta es una canción clásica de la legendaria banda de rock Queen, que ha sido cubierta y adaptada por muchos otros artistas a lo largo de los años. En este artículo, te contaremos más sobre la canción, su origen, significado y popularidad, así como cómo descargarla gratis y configurarla como tu tono de llamada en dispositivos Android o iPhone. </p>
- <h2>¿Qué es "Love of My Life" y por qué es una opción de tono de llamada popular? </h2>
- <p>"Love of My Life" es una canción de la banda de rock británica Queen de su álbum de 1975 A Night at the Opera. La balada fue escrita por Freddie Mercury, el cantante y pianista de la banda, dedicándola a Mary Austin, su ex-novia. Según John Reid, Freddie Mercury escribió "Love of My Life" sobre David Minns, un hombre con el que Freddie tenía una aventura a espaldas de Mary Austin entre 1975 y 1978. </p>
-<h2>amor de mi vida tono de llamada</h2><br /><p><b><b>Download</b> &#10040; <a href="https://bltlly.com/2v6LRG">https://bltlly.com/2v6LRG</a></b></p><br /><br />
- <h3>El origen y significado de la canción de Queen</h3>
- <p>La canción fue interpretada por primera vez en vivo en 1975 en el odeón de Hammersmith en Londres. Rápidamente se convirtió en un favorito de los fans y se tocó en casi todos los conciertos de Queen. Fue especialmente popular en América del Sur, donde las multitudes cantaban junto con Mercury, a veces tomando el control de toda la canción. Mercurio a menudo dejaba de cantar y permitía al público seguir la melodía, como se puede ver en su compilación Live in Rio. </p>
- <p>La canción detalla la difícil situación de un hombre que ha sido abandonado por su amante. Siente que el amor que le han quitado le afecta mucho más que a su amante, y le suplica, "Tráemelo a casa porque no sabes lo que significa para mí." Las letras son simples pero sentidas, expresando el dolor y el anhelo de perder a alguien que amas. </p>
- <h3>Las versiones de portada y adaptaciones de otros artistas</h3>
-
- <ul>
-<li>Extreme, una banda de rock estadounidense, interpretó una mezcla de canciones de Queen en el Freddie Mercury Tribute Concert en 1992, incluyendo "Love of My Life". Su versión contó con guitarras acústicas y armonías, y fue bien recibida por la audiencia y los miembros sobrevivientes de Queen.</li>
-<li>Scorpions, una banda alemana de hard rock, grabó una versión de "Love of My Life" para su álbum de 2000 Moment of Glory, que contó con la Orquesta Filarmónica de Berlín. Su versión agregó algunos solos de guitarra eléctrica y arreglos orquestales. </li>
-<li>Shirley Bassey, una cantante galesa, grabó una versión de "Love of My Life" para su álbum de 2007 Get the Party Started. Su versión era un remix dance-pop con ritmos electrónicos y sintetizadores. </li>
-<li>Vince Gill y Ann Wilson (de Corazón), dos cantantes estadounidenses, se unieron para grabar una versión de "Love of My Life" para el álbum en solitario de Wilson Fierce Bliss. Su versión era un dúo reducido con guitarras acústicas y voces. </li>
-<li>Harry Styles, un cantautor inglés, lanzó una canción llamada "Love Of My Life" en su álbum de 2023 Fine Line. Su canción no es una versión de la canción de Queen, sino una nueva canción original con el mismo título. Su canción es una balada pop-rock con acompañamiento de piano y guitarra, y letras sobre encontrar el amor de su vida. </li>
-</ul>
- <h3>El atractivo emocional y el factor nostalgia de la canción</h3>
- <p>Una de las razones por las que "Love of My Life" es una opción de tono de llamada popular es debido a su atractivo emocional y factor de nostalgia. La canción es una expresión atemporal de amor y pérdida, que resuena con muchas personas que han experimentado sentimientos similares. La canción también recuerda a Freddie Mercury, quien murió en 1991 debido a complicaciones relacionadas con el sida, y su legado como uno de los mejores cantantes e intérpretes de todos los tiempos. La canción también evoca recuerdos de los años 1970 y 1980, cuando Queen estaba en el pico de su popularidad e influencia. </p>
- <h2>Cómo descargar los tonos de llamada "Love of My Life" gratis</h2>
-
- <h3>Los mejores sitios para descargar tonos de llamada gratis</h3>
- <h4>iRingPro</h4>
- <p>iRingPro es un sitio web que ofrece tonos de llamada de alta calidad para dispositivos iPhone y Android. Puede navegar por su catálogo de más de 500 tonos de llamada, que se clasifican por género, estado de ánimo y estilo. También puede obtener una vista previa de los tonos de llamada antes de descargarlos. Para descargar un tono de llamada, solo tienes que introducir tu dirección de correo electrónico y te enviarán un enlace para descargar el archivo. A continuación, puede transferir el archivo a su teléfono utilizando iTunes o un cable USB. </p>
- <h4>NASA Audio y tonos de llamada</h4>
- <p>Si usted es un fan de la exploración espacial y la ciencia, es posible que desee echa un vistazo a NASA Audio y tonos de llamada. Este es un sitio web que ofrece clips de audio gratuitos y tonos de llamada de las misiones y programas de la NASA. Puede escuchar sonidos de cohetes, satélites, planetas, astronautas y más. También puede descargar los archivos en formato MP3 o M4R para su teléfono. Para descargar un tono de llamada, solo tiene que hacer clic derecho en el nombre del archivo y seleccione "Guardar enlace como". A continuación, puede transferir el archivo a su teléfono utilizando iTunes o un cable USB. </p>
- <h4>Tonos de llamada de Mob.org</h4>
- <p>Mob.org Ringtones es un sitio web que ofrece tonos de llamada gratuitos para dispositivos Android. Puede buscar tonos de llamada por palabras clave, géneros o popularidad. También puedes encontrar tonos de llamada basados en artistas o canciones específicas, como "Love of My Life" de Queen. Puede obtener una vista previa de los tonos de llamada antes de descargarlos. Para descargar un tono de llamada, solo tiene que hacer clic en el botón "Descargar" y seleccione el formato de archivo (MP3 o M4R). A continuación, puede transferir el archivo a su teléfono mediante un cable USB o Bluetooth.</p>
- <h3>Las mejores aplicaciones para descargar tonos de llamada gratis</h3>
- <h4>Zedge</h4>
-
- <p>RingDroid es una aplicación que te permite crear tus propios tonos de llamada desde cualquier archivo de audio de tu teléfono. También puede grabar su propia voz o sonido y convertirlo en un tono de llamada. Puede editar el archivo de audio cortando, recortando, desvaneciendo y ajustando el volumen. También puedes asignar diferentes tonos de llamada a diferentes contactos o notificaciones. Para crear un tono de llamada, solo necesita seleccionar un archivo de audio de su teléfono o grabar uno nuevo. A continuación, puede utilizar la interfaz de la aplicación para editar el archivo y guardarlo como un tono de llamada. La aplicación establecerá automáticamente el tono de llamada para su teléfono. </p>
-<p></p>
- <h4>Audiko</h4>
- <p>Audiko es otra aplicación que te permite crear tus propios tonos de llamada desde cualquier archivo de audio en tu teléfono o desde su biblioteca en línea de millones de tonos de llamada. También puedes encontrar tonos de llamada basados en artistas o canciones específicas, como "Love of My Life" de Queen. Puede editar el archivo de audio cortando, recortando, desvaneciendo y ajustando el volumen. También puedes añadir efectos, como eco, reverb, flanger, etc. Para crear un tono, solo tienes que seleccionar un archivo de audio de tu teléfono o de su biblioteca. A continuación, puede utilizar la interfaz de la aplicación para editar el archivo y guardarlo como un tono de llamada. La aplicación establecerá automáticamente el tono de llamada para su teléfono. </p>
- <h2>Cómo establecer "Amor de mi vida" como su tono de llamada en Android o iPhone</h2>
- <p>Una vez que haya descargado o creado su tono de llamada "Love of My Life", debe configurarlo como su tono de llamada en su teléfono. Los pasos pueden variar dependiendo del tipo de dispositivo que tenga, pero aquí hay algunas pautas generales:</p>
- <h3>Los pasos para dispositivos Android</h3>
- <ol>
-<li>Ir a Configuración > Sonido > Tono de llamada del teléfono. </li>
-<li>Toque en el icono "Añadir" o "Más" y busque el archivo de tono de llamada "Amor de mi vida" en su teléfono. </li>
-<li>Seleccione el archivo y toque en "OK" o "Hecho". </li>
-<li> Deberías ver el tono de llamada "Love of My Life" como tu tono de llamada predeterminado. </li>
-</ol>
- <h3>Los pasos para dispositivos iPhone</h3>
- <ol>
-
-<li>Seleccione su iPhone de la lista de dispositivos y haga clic en la pestaña "Tonos". </li>
-<li> Arrastre y suelte el archivo de tono de llamada "Love of My Life" desde su computadora a la lista de tonos en iTunes. </li>
-<li>Sincroniza tu iPhone con iTunes y desconéctalo de tu ordenador. </li>
-<li>Ir a Configuración > Sonidos > Tono de llamada.</li>
-<li> Desplácese hacia abajo y seleccione el tono de llamada "Amor de mi vida" de la lista personalizada. </li>
-<li> Deberías ver el tono de llamada "Love of My Life" como tu tono de llamada predeterminado. </li>
-</ol>
- <p>Felicidades! Usted ha descargado con éxito y establecer "Love of My Life" como su tono de llamada. Ahora puedes disfrutar de esta hermosa canción cada vez que alguien te llame o recibas una notificación. </p>
- <h1>Conclusión</h1>
- <p>En este artículo, te hemos mostrado cómo descargar y configurar "Love of My Life" como tu tono de llamada. Hemos explicado de qué trata la canción, su origen, significado y popularidad, así como cómo descargarla de forma gratuita desde varios sitios y aplicaciones. También le hemos dado los pasos para configurarlo como su tono de llamada en dispositivos Android o iPhone. Esperamos que este artículo le resulte útil e informativo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. </p>
- <h2>Preguntas frecuentes</h2>
- <h3>Q: ¿Puedo usar cualquier canción como mi tono de llamada? </h3>
- <p>A: Sí, puedes usar cualquier canción como tu tono de llamada, siempre y cuando tengas el permiso del artista o propietario original de la canción. También puede utilizar música libre de derechos o música de dominio público como su tono de llamada sin ningún problema legal. </p>
- <h3>Q: ¿Cómo puedo hacer mis propios tonos de llamada? </h3>
- <p>A: Puedes hacer tus propios tonos de llamada usando aplicaciones como RingDroid o Audiko, que te permiten editar cualquier archivo de audio en tu teléfono y guardarlo como un tono de llamada. También puede grabar su propia voz o sonido y convertirlo en un tono de llamada usando estas aplicaciones. </p>
- <h3>Q: ¿Cómo puedo cambiar mi tono de llamada para diferentes contactos o notificaciones? </h3> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Annas Merge Adventure Mod Apk.md

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-<br />
-<h1>Fusión de Anna Aventura Mod Apk: Un divertido y relajante juego de puzzle</h1>
- <p>¿Te gustan los juegos de puzzle que son fáciles de jugar pero difíciles de dominar? ¿Te gusta explorar nuevos mundos y descubrir nuevos objetos? ¿Quieres experimentar un juego divertido y relajante? Si usted respondió sí a cualquiera de estas preguntas, entonces usted debe tratar de Anna Merge Adventure mod apk, un juego que combina la fusión, aventura, y elementos casuales en uno. </p>
-<h2>anna’s merge adventure mod apk</h2><br /><p><b><b>DOWNLOAD</b> >>>>> <a href="https://bltlly.com/2v6Lou">https://bltlly.com/2v6Lou</a></b></p><br /><br />
- <h2>¿Qué es la aventura de Anna? </h2>
- <p>Anna’s Merge Adventure es un juego desarrollado por ZYMobile, una compañía que se especializa en crear juegos casuales y rompecabezas para dispositivos móviles. El juego fue lanzado en junio de 2023 y ha recibido críticas positivas de jugadores y críticos por igual. Estos son algunos de los aspectos que hacen este juego tan atractivo:</p>
- <h3>La historia de Anna y su familia</h3>
- <p>El juego sigue la historia de Anna, una joven a la que le encanta explorar nuevos lugares con su familia. Un día, decidieron hacer un crucero alrededor del mundo, pero su barco fue golpeado por un tsunami y terminaron en una isla misteriosa. Allí, descubrieron que la isla estaba llena de objetos mágicos que podían fusionarse para crear otros nuevos. Anna decidió utilizar sus habilidades de fusión para ayudar a su familia a sobrevivir y encontrar un camino de vuelta a casa. </p>
- <h3>El juego de fusión y exploración</h3>
- <p>El juego se basa en el concepto simple de combinar tres o más elementos idénticos para obtener un elemento avanzado. Por ejemplo, puede combinar tres hierbas para obtener una flor, o combinar tres flores para obtener un árbol. También puede combinar cinco elementos idénticos para obtener dos elementos avanzados en lugar de uno. Cuantos más elementos fusiones, más elementos desbloquearás y más espacio despejarás en la isla. </p>
-<p></p>
-
- <h3>Las características de la versión apk mod</h3>
- <p>Si quieres disfrutar de este juego aún más, usted debe descargar la versión apk mod de Anna’s Merge Adventure. Esta versión tiene algunas características adicionales que harán tu juego más fácil y divertido. Algunas de estas características son:</p>
- <ul>
-<li>Gemas ilimitadas: Las gemas son la moneda premium en este juego que se puede utilizar para comprar varios artículos, como cofres, refuerzos, decoraciones y más. Con gemas ilimitadas, puedes comprar lo que quieras sin preocuparte por quedarte sin nada. </li>
-<li>Monedas ilimitadas: Monedas son la moneda regular en este juego que se puede utilizar para actualizar sus artículos, tales como casas, granjas, fábricas y más. Con monedas ilimitadas, puede actualizar cualquier cosa que desee sin esperar mucho tiempo. </li>
-<li>Sin anuncios: Los anuncios son molestas interrupciones que pueden arruinar su experiencia de juego. Sin anuncios, puedes jugar a este juego sin distracciones ni retrasos. </li>
-</ul>
- <h2>Cómo descargar e instalar apk de Anna Merge Adventure mod? </h2>
- <p>Si usted está interesado en la descarga e instalación de Anna Merge Adventure mod apk, es necesario seguir estos sencillos pasos:</p>
- <h3>Los requisitos para el mod apk</h3 <p>Antes de descargar e instalar el mod apk, debe asegurarse de que su dispositivo cumple con los siguientes requisitos:</p>
- <ul>
-<li>Versión para Android: 4.4 o superior</li>
-<li>Espacio de almacenamiento: 100 MB o más</li>
-<li>Conexión a Internet: Necesaria para algunas funciones</li>
-<li>Permiso: Permitir la instalación desde fuentes desconocidas</li>
-</ul>
- <h3>Los pasos para descargar e instalar el mod apk</h3>
- <p>Una vez que haya comprobado los requisitos, puede proceder a descargar e instalar el apk mod siguiendo estos pasos:</p>
- <ol>
-<li>Haga clic en este enlace para descargar el archivo apk mod: [Anna’s Merge Adventure Mod Apk Download]</li>
-<li>Espere a que la descarga termine y localice el archivo en el administrador de archivos de su dispositivo. </li>
-<li>Toque en el archivo y seleccione "Instalar". </li>
-
-<li>Disfruta jugando Anna’s Merge Adventure con gemas ilimitadas, monedas y sin anuncios. </li>
-</ol>
- <h3>Las precauciones a tomar antes de instalar el mod apk</h3>
- <p>Si bien instalar el apk mod es fácil y seguro, todavía debe tomar algunas precauciones antes de hacerlo. Aquí están algunos de ellos:</p>
- <ul>
-<li>Copia de seguridad de sus datos: Si usted ha jugado la versión original de Anna’s Merge Adventure antes, es posible que desee copia de seguridad de sus datos antes de instalar el apk mod. De esta manera, puede restaurar su progreso si algo sale mal. </li>
-<li>Deshabilitar antivirus: Algunos programas antivirus pueden detectar el apk mod como un virus o malware y bloquear su instalación. Para evitar esto, debe desactivar su antivirus temporalmente antes de instalar el apk mod. </li>
-<li>Usar una VPN: Algunas regiones pueden restringir el acceso al enlace de descarga mod apk o al juego en sí. Para evitar esto, debe usar un servicio VPN que pueda cambiar su dirección IP y ubicación. </li>
-</ul>
- <h2>¿Cómo se juega Anna’s Merge Adventure mod apk? </h2>
- <p>Jugar apk Merge Adventure mod de Anna es fácil y divertido. Solo tienes que seguir estos pasos básicos:</p>
- <h3>Los conceptos básicos de la fusión y recolección de elementos</h3>
- <p>El objetivo principal de este juego es combinar objetos y recogerlos. Puedes hacerlo arrastrando y soltando objetos en la isla. Cuando fusionas tres o más elementos idénticos, se transformarán en un elemento avanzado. Por ejemplo, si fusiona tres hierbas, obtendrá una flor. Si fusiona cinco hierbas, obtendrá dos flores. </p>
- <p>También puedes recoger objetos tocando en ellos. Cuando recojas artículos, se almacenarán en tu inventario, que se encuentra en la parte inferior de la pantalla. Puedes usar tu inventario para almacenar artículos que no necesitas o quieres usar más tarde. También puedes vender artículos de tu inventario para monedas. </p>
- <h3>Los consejos y trucos para progresar más rápido</h3>
- <p>Si quieres progresar más rápido en este juego, debes seguir estos consejos y trucos:</p>
- <ul>
-
-<li>Use boosters: Los boosters son artículos especiales que pueden ayudarlo de varias maneras, como acelerar la fusión, eliminar obstáculos, encontrar objetos ocultos y más. Puedes comprar boosters con gemas o obtenerlos gratis viendo anuncios o completando misiones. </li>
-<li>Misiones completas: Las misiones son tareas que puedes completar para obtener recompensas, como gemas, monedas, cofres, boosters y más. Puedes encontrar misiones tocando el icono de la misión en la esquina superior izquierda de la pantalla. También puedes obtener misiones de otros personajes de la isla. </li>
-<li>Decora tu isla: Decorar tu isla no solo es divertido sino también beneficioso. Al colocar decoraciones en su isla, como casas, granjas, fábricas y más, puede aumentar sus ingresos y productividad. También puede personalizar su isla con diferentes temas y estilos. </li>
-</ul>
- <h3>Los desafíos y recompensas para disfrutar</h3 <p>Este juego no solo es relajante sino también desafiante. Puedes disfrutar de varios desafíos y recompensas en este juego, como:</p>
- <ul>
-<li>Logros: Los logros son objetivos que puedes alcanzar jugando a este juego, como combinar un cierto número de elementos, explorar un cierto número de áreas, ayudar a un cierto número de personajes y más. Puedes comprobar tus logros tocando el icono del trofeo en la esquina superior derecha de la pantalla. También puedes obtener recompensas por completar logros, como gemas, monedas, cofres, boosters y más. </li>
-<li>Tareas diarias: Las tareas diarias son tareas que puedes completar todos los días para obtener recompensas, como gemas, monedas, cofres, boosters y más . Puede encontrar las tareas diarias pulsando en el icono del calendario en la esquina superior izquierda de la pantalla. También puede obtener recompensas adicionales por completar un cierto número de tareas diarias en una fila. </li>
-
-<li>Tablas de clasificación: Las tablas de clasificación son clasificaciones que muestran cómo se compara con otros jugadores en este juego, como su nivel, sus ingresos, sus artículos y más. Usted puede comprobar sus tablas de clasificación tocando en el icono de la tabla de clasificación en la esquina superior derecha de la pantalla. También puedes obtener premios por posicionarte alto en las tablas de clasificación, como gemas, monedas, cofres, boosters y más. </li>
-</ul>
- <h2>¿Por qué deberías jugar Anna’s Merge Adventure mod apk? </h2>
- <p>Si todavía no está convencido de jugar apk Fusión Aventura mod de Anna, aquí hay algunas razones por las que debe:</p>
- <h3>Los beneficios de jugar un juego de puzzle</h3>
- <p>Jugar un juego de puzzle como Anna’s Merge Adventure mod apk puede tener muchos beneficios para su cerebro y su estado de ánimo. Algunos de estos beneficios son:</p>
- <ul>
-<li>Mejorar su memoria: La fusión y la recopilación de elementos puede ayudarle a mejorar su memoria a corto y largo plazo mediante la estimulación de las células cerebrales y la creación de nuevas conexiones neuronales. </li>
-<li>Mejorar tu creatividad: Explorar y decorar tu isla puede ayudarte a mejorar tu creatividad permitiéndote expresarte y usar tu imaginación. </li>
-<li>Reducir su estrés: Jugar un juego de puzzle puede ayudarle a reducir su estrés al desviar su atención de sus preocupaciones y problemas y darle una sensación de logro y satisfacción. </li>
-</ul>
- <h3>Las ventajas de jugar una versión apk mod</h3>
- <p>Jugar una versión mod apk de Anna’s Merge Adventure puede tener muchas ventajas sobre jugar la versión original. Algunas de estas ventajas son:</p>
- <ul>
-<li>Ahorrando su tiempo: Tener gemas y monedas ilimitadas puede ayudarle a ahorrar su tiempo al permitirle comprar y actualizar cualquier cosa que desee sin esperar o moler. </li>
-<li>Aumentar su diversión: Tener gemas y monedas ilimitadas puede ayudarle a aumentar su diversión al permitirle probar diferentes artículos, potenciadores, decoraciones y más sin limitaciones. </li>
-
-</ul>
- <h3>Los testimonios de otros jugadores</h3>
- <p>Si quieres saber lo que otros jugadores piensan acerca de Anna Merge Adventure mod apk, aquí están algunos de sus testimonios:</p>
- <tabla>
-<tr>
-<th>Nombre</th>
-<th>Valoración</th>
-<th>Comentario</th>
-</tr>
-<tr>
-<td>Alice</td>
-<td>5 estrellas</td>
-<td>Este juego es tan adictivo y relajante. Me encanta combinar elementos y descubrir nuevos. Los gráficos son lindos y coloridos. El apk mod es impresionante. Puedo comprar cualquier cosa que quiera con gemas y monedas ilimitadas. También me gusta que no haya anuncios. Este es el mejor juego de puzzle de la historia. </td>
-</tr>
-<tr>
-<td>Bob</td>
-<td>4 estrellas</td>
-<td>Este juego es divertido y desafiante. Me gusta explorar la isla y ayudar a los personajes. El juego es suave y fácil de entender. El apk mod es genial. Puedo actualizar mis artículos más rápido con monedas ilimitadas. También aprecio que no hay anuncios. Este es un buen juego de puzzle. </td>
-</tr>
-<tr>
-<td>Carol</td>
-<td>3 estrellas</td <td>Este juego está bien pero no es increíble. Me gusta combinar elementos pero se vuelve aburrido después de un tiempo. Los gráficos son decentes, pero no impresionante. El apk mod es agradable, pero no es necesario. Realmente no necesito gemas o monedas ilimitadas. No me importan los anuncios tampoco. Este es un juego de puzzle promedio. </td>
-</tr>
-</tabla>
- <h2>Conclusión</h2>
- <p>En conclusión, Anna’s Merge Adventure mod apk es un divertido y relajante juego de puzzle que combina la fusión, aventura, y elementos casuales en uno. Puedes combinar objetos y recogerlos, explorar la isla y encontrar nuevas áreas, interactuar con diferentes personajes y ayudarlos con sus misiones, decorar tu isla con varios temas y estilos, participar en eventos y desafíos, y disfrutar de gemas ilimitadas, monedas y sin anuncios. Si usted está buscando un juego que puede estimular su cerebro, mejorar su creatividad, reducir el estrés, ahorrar tiempo, aumentar su diversión, y eliminar su molestia, entonces usted debe descargar e instalar Anna’s Merge Adventure mod apk hoy. </p>
- <h2>Preguntas frecuentes</h2>
-
- <ol <li>¿Cuál es la diferencia entre Anna’s Merge Adventure y Anna’s Merge Adventure mod apk? </li>
-<p>Anna’s Merge Adventure es la versión original del juego que puedes descargar desde la Google Play Store o la App Store. Anna’s Merge Adventure mod apk es la versión modificada del juego que se puede descargar desde este enlace: [Anna’s Merge Adventure Mod Apk Download]. La versión apk mod tiene gemas ilimitadas, monedas, y no hay anuncios, mientras que la versión original no. </p>
- <li> ¿Es seguro usar apk mod Merge Adventure de Anna? </li>
-<p>Sí, Anna Merge Adventure mod apk es seguro de usar, siempre y cuando se descarga de una fuente de confianza y siga las instrucciones cuidadosamente. Sin embargo, usted todavía debe tomar algunas precauciones antes de instalar el apk mod, tales como copias de seguridad de sus datos, desactivar el antivirus, y el uso de una VPN.</p>
- <li> ¿Cómo puedo actualizar apk de Anna Merge Adventure mod? </li>
-<p>Para actualizar apk Merge Adventure mod de Anna, es necesario descargar la última versión del archivo apk mod de este enlace: [Anna’s Merge Adventure Mod Apk Download]. A continuación, es necesario desinstalar la versión anterior de la apk mod e instalar el nuevo. También puede buscar actualizaciones pulsando en el icono de configuración en la esquina superior derecha de la pantalla y seleccionando "Buscar actualizaciones". </p>
- <li>¿Cómo puedo contactar con el desarrollador de Anna’s Merge Adventure mod apk? </li>
-<p>Si tiene alguna pregunta, sugerencia, o retroalimentación acerca de Anna Merge Adventure mod apk, puede ponerse en contacto con el desarrollador enviando un correo electrónico a esta dirección: [[email protected]]. También puede visitar su sitio web: [annasmergeadventuremodapk.com] o seguirlos en las redes sociales: [Facebook], [Twitter], [Instagram]. </p>
- <li>¿Cómo puedo apoyar al desarrollador de Anna’s Merge Adventure mod apk? </li> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Bike Race.md

@@ -1,134 +0,0 @@
-<br />
-<h1>Carreras de bicicletas: un deporte divertido y saludable para todos</h1>
- <p>Las carreras de bicicletas son una actividad física competitiva que implica montar bicicletas a altas velocidades en diferentes terrenos y cursos. Es uno de los deportes más populares y diversos del mundo, con millones de participantes y aficionados. Las carreras de bicicletas pueden ser disfrutadas por personas de todas las edades, géneros y habilidades, ya que hay muchas categorías y niveles de dificultad para satisfacer las preferencias y objetivos de todos. </p>
-<h2>Bike Race</h2><br /><p><b><b>Download File</b> ->->->-> <a href="https://bltlly.com/2v6KRw">https://bltlly.com/2v6KRw</a></b></p><br /><br />
- <p>En este artículo, exploraremos los diferentes tipos de carreras de bicicletas, los beneficios de las carreras de bicicletas para su salud y bienestar, y algunos consejos sobre cómo comenzar o mejorar su rendimiento en este emocionante deporte. </p>
- <h2>Tipos de carreras de bicicletas</h2>
- <p>Hay muchos tipos de carreras de bicicletas, cada una con sus propias reglas, formatos, equipos y habilidades. Estos son algunos de los más comunes:</p>
- <h3>Carreras de bicicletas de carretera</h3>
- <p>Carreras de bicicletas de carretera es el tipo más popular y prestigioso de carreras de bicicletas. Se trata de montar en carreteras pavimentadas o caminos con bicicletas de carretera que tienen neumáticos delgados, manillar y marcos ligeros. Las carreras de bicicleta de carretera pueden ser eventos de un día o eventos de varias etapas que duran varios días o semanas. Algunos ejemplos de famosas carreras de bicicleta de carretera son el Tour de Francia, el Giro de Italia y el París-Roubaix.</p>
- <h3>Carreras de bicicleta de montaña</h3>
- <p>Las carreras de bicicleta de montaña son un tipo de carreras de bicicleta que se lleva a cabo fuera de la carretera en senderos de tierra, caminos de grava u otros terrenos naturales. Las bicicletas de montaña tienen neumáticos más anchos, manillar plano o vertical, y sistemas de suspensión para absorber choques y golpes. Las carreras de bicicleta de montaña pueden ser de fondo (XC), descenso (DH), enduro (EN), o de cuatro cruces (4X), dependiendo del diseño del campo y el estilo de conducción. </p>
- <h3>Carreras de bicicleta de pista</h3>
-
- <h3>Carreras de bicicletas BMX</h3>
- <p>Las carreras de bicicletas BMX son un tipo de carreras de bicicletas que involucran montar en una pista de tierra con saltos, bermas y obstáculos. Las bicicletas BMX tienen ruedas pequeñas, cuadros bajos y marchas individuales. Las carreras de BMX suelen ser cortas y rápidas, con ocho ciclistas compitiendo en cada calor. El ciclismo BMX es un deporte olímpico desde 2008. </p>
- <h3>Carreras de ciclocross</h3>
- <p>Ciclocross es un tipo de bicicleta de carreras que combina ciclismo de carretera y ciclismo de montaña. Se trata de montar en un campo de terreno mixto que incluye pavimento, hierba, barro, arena y obstáculos como escaleras o barreras. Las bicicletas de ciclocross tienen manillares, neumáticos y frenos de disco. Las carreras de ciclocross se suelen realizar en otoño o invierno y duran aproximadamente una hora. </p>
-<p></p>
- <h2>Beneficios de las carreras de bicicletas</h2>
- <p>Las carreras de bicicletas no solo son divertidas, sino también beneficiosas para su salud física y mental. Estos son algunos de los beneficios de las carreras de bicicletas:</p>
- <h3>Beneficios físicos</h3>
- <ul>
-<li>Las carreras de bicicletas mejoran tu condición cardiovascular al fortalecer tu corazón y pulmones. </li>
-<li>Carreras de bicicletas quema calorías y grasa mediante el aumento de su tasa metabólica. </li>
-<li>Las carreras de bicicletas construyen masa muscular y o sea estimulando su sistema esquelético. </li>
-<li>Las carreras de bicicletas mejoran el equilibrio y la coordinación al desafiar el sistema nervioso. </li>
-<li>Las carreras de bicicletas reducen el riesgo de enfermedades crónicas como la diabetes, la hipertensión y el cáncer al regular sus niveles de azúcar en la sangre y colesterol. </li>
-</ul>
-
- <p>Las carreras de bicicletas son una actividad física competitiva que implica montar bicicletas a altas velocidades en diferentes terrenos y cursos. Es uno de los deportes más populares y diversos del mundo, con millones de participantes y aficionados. Las carreras de bicicletas pueden ser disfrutadas por personas de todas las edades, géneros y habilidades, ya que hay muchas categorías y niveles de dificultad para satisfacer las preferencias y objetivos de todos. </p>
- <p>En este artículo, exploraremos los diferentes tipos de carreras de bicicletas, los beneficios de las carreras de bicicletas para su salud y bienestar, y algunos consejos sobre cómo comenzar o mejorar su rendimiento en este emocionante deporte. </p>
- <h2>Tipos de carreras de bicicletas</h2>
- <p>Hay muchos tipos de carreras de bicicletas, cada una con sus propias reglas, formatos, equipos y habilidades. Estos son algunos de los más comunes:</p>
- <h3>Carreras de bicicletas de carretera</h3>
- <p>Carreras de bicicletas de carretera es el tipo más popular y prestigioso de carreras de bicicletas. Se trata de montar en carreteras pavimentadas o caminos con bicicletas de carretera que tienen neumáticos delgados, manillar y marcos ligeros. Las carreras de bicicleta de carretera pueden ser eventos de un día o eventos de varias etapas que duran varios días o semanas. Algunos ejemplos de famosas carreras de bicicleta de carretera son el Tour de Francia, el Giro de Italia y el París-Roubaix.</p>
- <h3>Carreras de bicicleta de montaña</h3>
- <p>Las carreras de bicicleta de montaña son un tipo de carreras de bicicleta que se lleva a cabo fuera de la carretera en senderos de tierra, caminos de grava u otros terrenos naturales. Las bicicletas de montaña tienen neumáticos más anchos, manillar plano o vertical, y sistemas de suspensión para absorber choques y golpes. Las carreras de bicicleta de montaña pueden ser de fondo (XC), descenso (DH), enduro (EN), o de cuatro cruces (4X), dependiendo del diseño del campo y el estilo de conducción. </p>
- <h3>Carreras de bicicleta de pista</h3>
-
- <h3>Carreras de bicicletas BMX</h3>
- <p>Las carreras de bicicletas BMX son un tipo de carreras de bicicletas que involucran montar en una pista de tierra con saltos, bermas y obstáculos. Las bicicletas BMX tienen ruedas pequeñas, cuadros bajos y marchas individuales. Las carreras de BMX suelen ser cortas y rápidas, con ocho ciclistas compitiendo en cada calor. El ciclismo BMX es un deporte olímpico desde 2008. </p>
- <h3>Carreras de ciclocross</h3>
- <p>Ciclocross es un tipo de bicicleta de carreras que combina ciclismo de carretera y ciclismo de montaña. Se trata de montar en un campo de terreno mixto que incluye pavimento, hierba, barro, arena y obstáculos como escaleras o barreras. Las bicicletas de ciclocross tienen manillares, neumáticos y frenos de disco. Las carreras de ciclocross se suelen realizar en otoño o invierno y duran aproximadamente una hora. </p>
- <h2>Beneficios de las carreras de bicicletas</h2>
- <p>Las carreras de bicicletas no solo son divertidas, sino también beneficiosas para su salud física y mental. Estos son algunos de los beneficios de las carreras de bicicletas:</p>
- <h3>Beneficios físicos</h3>
- <ul>
-<li>Las carreras de bicicletas mejoran tu condición cardiovascular al fortalecer tu corazón y pulmones. </li>
-<li>Carreras de bicicletas quema calorías y grasa mediante el aumento de su tasa metabólica. </li>
-<li>Las carreras de bicicletas construyen masa muscular y o sea estimulando su sistema esquelético. </li>
-<li>Las carreras de bicicletas mejoran el equilibrio y la coordinación al desafiar el sistema nervioso. </li>
-<li>Las carreras de bicicletas reducen el riesgo de enfermedades crónicas como la diabetes, la hipertensión y el cáncer al regular sus niveles de azúcar en la sangre y colesterol. </li>
-</ul>
- <h3>Beneficios mentales</h3. <ul>
-<li>Las carreras de bicicletas aumentan tu estado de ánimo y autoestima al liberar endorfinas y serotonina. </li>
-<li>Las carreras de bicicletas alivian el estrés y la ansiedad al reducir los niveles de cortisol y adrenalina. </li>
-<li>Las carreras de bicicletas mejoran la función cognitiva y la memoria al aumentar el flujo sanguíneo y el oxígeno al cerebro. </li>
-
-<li>Las carreras de bicicletas mejoran tu resistencia mental y disciplina al enseñarte cómo lidiar con el fracaso y la adversidad. </li>
-</ul>
- <h3>Beneficios sociales</h3>
- <ul>
-<li>Las carreras de bicicletas fomentan la interacción social y la comunicación al permitirle conocer y vincularse con otros corredores. </li>
-<li>Las carreras de bicicletas desarrollan sus habilidades de trabajo en equipo y liderazgo al requerir que coopere y se coordine con sus compañeros de equipo o oponentes. </li>
-<li>Las carreras de bicicletas promueven tu espíritu deportivo y el juego limpio alentándote a respetar las reglas y a los demás participantes. </li>
-<li>Las carreras de bicicletas expanden tu conciencia cultural y diversidad al exponerte a diferentes personas y lugares. </li>
-<li>Las carreras de bicicletas apoyan a su comunidad y causas participando en eventos de caridad o como voluntarios para organizaciones relacionadas con la bicicleta. </li>
-</ul>
- <h3>Beneficios ambientales</h3>
- <ul>
-<li>Las carreras de bicicletas reducen su huella de carbono y la contaminación mediante el uso de una fuente de energía limpia y renovable. </li>
-<li>Las carreras de bicicletas conservan sus recursos naturales y la vida silvestre al minimizar su impacto en el medio ambiente. </li>
-<li>Las carreras de bicicletas mejoran su conciencia y responsabilidad ambiental al educarlo sobre los problemas y las soluciones relacionados con las carreras de bicicletas. </li>
-<li>Las carreras de bicicletas inspiran su activismo y defensa del medio ambiente al motivarlo a apoyar o unirse a iniciativas o movimientos favorables a las bicicletas. </li>
-<li>Las carreras de bicicletas mejoran su apreciación y disfrute del medio ambiente al permitirle experimentar la belleza y diversidad de la naturaleza. </li>
-</ul>
- <h2>Consejos para carreras de bicicletas</h2>
- <p>Si estás interesado en las carreras de motos, aquí hay algunos consejos sobre cómo empezar o mejorar tu rendimiento:</p>
- <h3>Entrenamiento</h3>
- <ul>
-<li>Establezca objetivos realistas y específicos para usted basado en su nivel de condición física actual, experiencia e intereses. </li>
-<li>Sigue un plan de entrenamiento estructurado y progresivo que incluye una variedad de entrenamientos, como intervalos, tempo, resistencia, recuperación, etc.</li>
-
-<li>Realice un seguimiento de su progreso y ajuste su plan de capacitación en consecuencia utilizando un registro de capacitación, un diario o una plataforma en línea. </li>
-<li>Busca orientación profesional o únete a un club o grupo si necesitas más apoyo, comentarios o motivación. </li>
-</ul>
- <h3>Nutrición</h3>
- <ul>
-<li>Consuma una dieta equilibrada y nutritiva que proporcione suficientes calorías, carbohidratos, proteínas, grasas, vitaminas, minerales y líquidos para sus necesidades de energía y recuperación. </li>
-<li>Consuma una comida previa a la carrera que sea alta en carbohidratos, moderada en proteínas, baja en grasas y fácil de digerir al menos 2-3 horas antes de la carrera. </li>
-<li> Beba mucha agua o bebidas deportivas antes, durante y después de la carrera para mantenerse hidratado y reponer sus electrolitos. </li>
-<li>Refrigerio en barras energéticas, geles, masticables o frutas durante la carrera para mantener sus niveles de azúcar en la sangre y prevenir la fatiga. </li>
-<li>Tener una comida post-carrera que es alta en proteínas, moderada en carbohidratos, baja en grasa, y rica en antioxidantes dentro de los 30 minutos después de la carrera para reparar los músculos y reducir la inflamación. </li>
-</ul>
- <h3>Equipo</h3>
- <ul>
-<li>Elija una bicicleta que se adapte a su tipo de carreras de bicicletas, se adapte a su tamaño y forma corporal, y cumple con su presupuesto y preferencias. </li>
-<li>Mantenga su bicicleta regularmente comprobando los neumáticos, frenos, engranajes, cadenas y otros componentes para detectar cualquier desgaste o daño. </li>
-<li>Use ropa adecuada que sea cómoda, transpirable y resistente a la intemperie. No olvide usar un casco, guantes y gafas de sol para protegerse. </li>
-<li>Utilice accesorios que pueden mejorar su rendimiento o seguridad, como zapatos, pedales, tacos, silla de montar, manillar, computadora, luces, etc. </li>
-<li>Empaque un kit de reparación que incluye un tubo de repuesto, una bomba, un kit de parches, una herramienta múltiple y algo de efectivo o tarjeta de crédito en caso de emergencia. </li>
-</ul>
- <h3>Seguridad</h3>
- <ul>
-<li>Calentar correctamente antes de la carrera haciendo algunos ejercicios ligeros de cardio y estiramiento para preparar los músculos y las articulaciones. </li>
-
-<li>Evite el entrenamiento excesivo o insuficiente escuchando su cuerpo y descansando cuando sea necesario. </li>
-<li>Pre <li>Prevenga lesiones o enfermedades usando equipo adecuado, siguiendo las reglas, manteniéndose alerta y buscando atención médica si es necesario. </li>
-<li>Respete el medio ambiente siguiendo los principios de no dejar rastro, como la eliminación de sus residuos correctamente, minimizando su impacto y dejando lo que encuentra. </li>
-</ul>
- <h3>Técnica</h3>
- <ul>
-<li>Mejore su eficiencia de pedaleo mediante el uso de una cadencia suave y consistente, aplicando una presión uniforme en ambos pedales y cambiando de marcha adecuadamente. </li>
-<li>Mejore su eficiencia de frenado utilizando ambos frenos simultáneamente, aplicando presión gradual y controlada, y evitando patinar o bloquear sus ruedas. </li>
-<li>Mejore su eficiencia en las curvas inclinando su bicicleta y cuerpo en la curva, mirando hacia adelante a donde desea ir y saliendo de la curva con velocidad y equilibrio. </li>
-<li>Mejore su eficiencia de escalada cambiando a una velocidad más baja, de pie o sentado dependiendo del gradiente, y manteniendo un ritmo constante y la respiración. </li>
-<li>Mejore su eficiencia descendente cambiando a una velocidad más alta, bajando su centro de gravedad y usando sus frenos con moderación y sin problemas. </li>
-</ul>
- <h2>Conclusión</h2>
- <p>Las carreras de bicicletas son un deporte divertido y saludable que puede ofrecerle muchos beneficios para su bienestar físico, mental, social y ambiental. También puede desafiarle a mejorar sus habilidades y rendimiento en varios tipos de carreras de bicicletas, como carretera, montaña, pista, BMX o ciclocross. Si usted es un principiante o un experto, las carreras de bicicletas pueden ser una experiencia gratificante y agradable para usted. </p>
- <p>Si usted está interesado en las carreras de bicicletas, esperamos que este artículo le ha dado información útil y consejos sobre cómo empezar o mejorar su rendimiento. Recuerda entrenar inteligentemente, comer saludablemente, equiparte apropiadamente, mantenerte seguro y divertirte. ¡Feliz carrera de bicicletas! </p>
-
- <h3>¿Cuáles son las mejores bicicletas para las carreras de bicicletas? </h3>
- <p>Las mejores bicicletas para las carreras de bicicletas dependen del tipo de carreras de bicicletas que quieras hacer. Para las carreras de bicicleta de carretera, necesita una bicicleta de carretera que sea ligera, aerodinámica y rápida. Para las carreras de bicicleta de montaña, necesita una bicicleta de montaña que sea resistente, estable y versátil. Para las carreras de bicicleta de pista, necesita una bicicleta de pista que sea simple, rígida y ágil. Para las carreras de BMX, necesitas una bicicleta BMX pequeña, duradera y maniobrable. Para las carreras de ciclocross, necesitas una bicicleta ciclocross que sea similar a una bicicleta de carretera pero con neumáticos más anchos, marchas más bajas y mejores frenos. </p>
- <h3>¿Cómo entreno para las carreras de bicicletas? </h3>
- <p>Para entrenar en las carreras de bicicletas, necesitas seguir un plan de entrenamiento estructurado y progresivo que incluya una variedad de entrenamientos, como intervalos, tempo, resistencia, recuperación, etc. También necesitas controlar la intensidad de tu entrenamiento, duración, frecuencia y recuperación utilizando un monitor de frecuencia cardíaca, un medidor de potencia, un dispositivo GPS o una aplicación. También debe realizar un seguimiento de su progreso y ajustar su plan de capacitación en consecuencia utilizando un registro de capacitación, un diario o una plataforma en línea. También necesitas buscar orientación profesional o unirte a un club o grupo si necesitas más apoyo, comentarios o motivación. </p>
- <h3> ¿Qué debo comer antes, durante y después de una carrera de bicicletas? </h3>
- <p>Antes de una carrera en bicicleta, debe comer una comida previa a la carrera que sea alta en carbohidratos, moderada en proteínas, baja en grasas y fácil de digerir al menos 2-3 horas antes de la carrera. Durante una carrera en bicicleta, debes comer barritas energéticas, geles, masticables o frutas para mantener tus niveles de azúcar en la sangre y prevenir la fatiga. También debe beber mucha agua o bebidas deportivas para mantenerse hidratado y reponer sus electrolitos. Después de una carrera en bicicleta, debe tener una comida post-carrera que es alta en proteínas, moderada en carbohidratos, baja en grasa y rica en antioxidantes dentro de los 30 minutos después de la carrera para reparar sus músculos y reducir la inflamación. </p>
-
- <p>Para prevenir lesiones o enfermedades de las carreras de bicicletas, es necesario calentar correctamente antes de la carrera haciendo algunos ejercicios de cardio y estiramiento ligeros para preparar los músculos y las articulaciones. También necesitas refrescarte adecuadamente después de la carrera haciendo ejercicios de cardio y estiramiento suaves para relajar tus músculos y articulaciones. También es necesario evitar el entrenamiento excesivo o insuficiente al escuchar a su cuerpo y descansar cuando sea necesario. También necesita usar el equipo adecuado, seguir las reglas, mantenerse alerta y buscar atención médica si es necesario. </p>
- <h3>¿Cómo respeto el medio ambiente cuando corro en bicicleta? </h3>
- <p>Respetar Para respetar el medio ambiente en las carreras de bicicletas, es necesario seguir los principios de no dejar rastro, tales como la eliminación de sus residuos correctamente, minimizando su impacto y dejando lo que encuentra. También necesita conservar sus recursos naturales y vida silvestre evitando áreas sensibles, permaneciendo en senderos designados y no perturbar o dañar ninguna planta o animal. También necesita mejorar su conciencia y responsabilidad ambiental al educarse a sí mismo y a otros sobre los problemas y soluciones relacionados con las carreras de bicicletas. También necesita apoyar a su comunidad y causas participando en eventos de caridad o como voluntario para organizaciones relacionadas con la bicicleta. También necesitas inspirar tu activismo y activismo ambiental apoyando o uniéndote a iniciativas o movimientos amigables con la bicicleta. También necesitas mejorar tu apreciación y disfrute ambiental al experimentar la belleza y diversidad de la naturaleza. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Apk De Choque Royale Lite.md

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-<br />
-<h1>Choque Royale Lite APK Descargar: Cómo jugar el juego de estrategia popular en dispositivos de gama baja</h1>
-<p>Clash Royale es uno de los juegos móviles más populares del mundo, con millones de jugadores disfrutando de su juego rápido y adictivo. Sin embargo, no todo el mundo tiene un dispositivo de alta gama que puede ejecutar el juego sin problemas y sin retraso. Si usted es una de esas personas que aman Clash Royale pero tienen un dispositivo de gama baja, no se preocupe. Hay una solución para usted: Clash Royale Lite.</p>
-<p>Clash Royale Lite es una versión modificada de Clash Royale que está diseñada para funcionar en dispositivos de gama baja con menos RAM y espacio de almacenamiento. Tiene todas las características y la diversión del juego original, pero con gráficos reducidos y tamaño de archivo. En este artículo, te mostraremos cómo descargar e instalar Clash Royale Lite en tu dispositivo Android, cómo jugar y disfrutar del juego, y cómo evitar posibles riesgos o problemas. ¡Vamos a empezar! </p>
-<h2>descargar apk de choque royale lite</h2><br /><p><b><b>Download</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://bltlly.com/2v6L2w">https://bltlly.com/2v6L2w</a></b></p><br /><br />
- <h2>¿Qué es Clash Royale Lite? </h2>
-<h3>Una breve introducción a Clash Royale y sus características</h3>
-<p>Clash Royale es un juego de estrategia en tiempo real desarrollado por Supercell, los creadores de Clash of Clans, Brawl Stars, Hay Day y más. Fue lanzado en 2016 y desde entonces se ha convertido en uno de los juegos móviles más exitosos de la historia. En Clash Royale, coleccionas y mejoras cartas con personajes, hechizos y edificios del universo Clash. Utiliza estas cartas para construir tu mazo de batalla y luchar contra otros jugadores en línea en duelos de ritmo rápido. El objetivo es destruir las torres de tu oponente mientras defiendes las tuyas. También puedes unirte o crear clanes, participar en torneos, eventos, desafíos y más. </p>
-<h3>La diferencia entre Clash Royale y Clash Royale Lite</h3>
-<p>Clash Royale Lite es una versión modificada de Clash Royale que está optimizada para dispositivos de gama baja. Tiene la misma jugabilidad y características que el juego original, pero con algunas diferencias:</p>
-<ul>
-<li>Los gráficos son de menor calidad y menos detallados. </li>
-
-<li>El tiempo de carga es más rápido. </li>
-<li> El rendimiento es más suave y estable. </li>
-<li>El consumo de batería es menor. </li>
-</ul>
-<p>Estas diferencias hacen Clash Royale Lite más accesible y agradable para los jugadores que tienen dispositivos de gama baja o conexión a Internet limitada. </p>
-<h3>Los beneficios de jugar Clash Royale Lite</h3>
-<p>Jugar a Clash Royale Lite tiene varios beneficios para los jugadores que aman el juego pero tienen dispositivos de gama baja. Algunos de estos beneficios son:</p>
-<ul>
-<li> Puede jugar el juego sin retraso o se bloquea. </li>
-<li> Puede ahorrar espacio de almacenamiento en su dispositivo. </li>
-<li> Puede guardar datos en su dispositivo. </li>
-<li>Puedes jugar el juego incluso con una conexión a Internet débil o inestable. </li>
-<li>Puedes disfrutar de las mismas características y contenido que el juego original. </li>
-</ul>
-<p>Jugar a Clash Royale Lite no significa que te estés perdiendo nada. Todavía puedes divertirte y competir con otros jugadores de todo el mundo. </p>
-<p></p>
- <h2>Cómo descargar e instalar Clash Royale Lite en su dispositivo Android</h2>
-<h3>Los requisitos y la compatibilidad de Clash Royale Lite</h3>
-<p>Clash Royale Lite es compatible con la mayoría de los dispositivos Android que tienen al menos 1 GB de RAM y Android 4.4 o superior. Sin embargo, algunos dispositivos pueden no ser capaces de ejecutar el juego correctamente debido a limitaciones de hardware o problemas de software. Para comprobar si su dispositivo es compatible, puede visitar el sitio web oficial de Clash Royale Lite y ver la lista de dispositivos compatibles. También puede ponerse en contacto con los desarrolladores si tiene alguna pregunta o problema con respecto a la compatibilidad de su dispositivo. </p>
-<h3>Los pasos para descargar e instalar Clash Royale Lite desde una fuente de confianza</h3>
-<p>Clash Royale Lite no está disponible en Google Play Store, por lo que tendrá que descargar e instalar desde una fuente de confianza. Estos son los pasos para hacerlo:</p>
-<ol>
-
-<li>Antes de descargar el archivo APK, asegúrese de que ha habilitado la opción de instalar aplicaciones de fuentes desconocidas en su dispositivo. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </li>
-<li>Una vez que haya descargado el archivo APK, localizarlo en su dispositivo y toque en él para iniciar el proceso de instalación. Siga las instrucciones de la pantalla y espere a que termine la instalación. </li>
-<li>Después de que la instalación se haya completado, puede iniciar Clash Royale Lite desde el cajón de la aplicación o la pantalla de inicio y disfrutar del juego. </li>
-</ol>
-<h3>Los consejos para evitar malware y virus al descargar archivos APK</h3>
-<p>Descargar archivos APK de fuentes desconocidas puede ser arriesgado, ya que pueden contener malware o virus que pueden dañar su dispositivo o robar su información personal. Para evitar esto, debes seguir estos consejos:</p>
-<ul>
-<li>Solo descargar archivos APK de fuentes confiables y verificadas, como el sitio web oficial de Clash Royale Lite u otros sitios web de renombre. </li>
-<li>Escanear el archivo APK con una aplicación antivirus antes de instalarlo en su dispositivo. </li>
-<li>No haga clic en ningún enlace sospechoso o ventanas emergentes que puedan aparecer durante el proceso de descarga o instalación. </li>
-<li>No conceda ningún permiso innecesario o acceso a la aplicación que pueda comprometer su privacidad o seguridad. </li>
-</ul>
-<p>Siguiendo estos consejos, puede asegurarse de que está descargando e instalando Clash Royale Lite de forma segura. </p>
- <h2>Cómo jugar y disfrutar de Clash Royale Lite</h2>
-<h3>El juego básico y las reglas de Clash Royale Lite</h3>
-
-<h3>Las mejores estrategias y consejos para ganar batallas en Clash Royale Lite</h3>
-<p>Para ganar batallas en Clash Royale Lite, necesitas tener una buena estrategia y algunos consejos en mente. Estos son algunos de ellos:</p>
-<ul>
-<li>Elige un mazo de batalla equilibrado que se adapte a tu estilo de juego y tenga cartas que puedan contrarrestar diferentes tipos de amenazas. Por ejemplo, debes tener algunas tarjetas que puedan causar daño por salpicaduras, algunas tarjetas que puedan apuntar a unidades de aire, algunas tarjetas que puedan dañar tanques, etc.</li>
-<li>Conozca las fortalezas y debilidades de cada tarjeta y cómo usarlas de manera efectiva. Por ejemplo, debes saber cuándo usar hechizos, cuándo colocar edificios, cuándo empujar o defender, etc.</li>
-<li>Preste atención a su manejo de elixir y no lo desperdicie en movimientos innecesarios. Trata de obtener una ventaja de elixir sobre tu oponente haciendo operaciones de elixir positivas (usando menos elixir que tu oponente para tratar con sus cartas). </li>
-<li>Analiza la baraja de batalla y la estrategia de tu oponente y adáptala en consecuencia. Intenta predecir sus movimientos y contrarrestarlos con jugadas inteligentes. </li>
-<li>Usa combos y sinergias entre tus cartas para crear poderosos ataques o defensas. </li>
-<li>Sea consciente de la hora y la puntuación y ajustar su estrategia en consecuencia. Por ejemplo, puede que quieras jugar de forma más agresiva o defensiva dependiendo de la situación. </li>
-<li>Diviértete y disfruta del juego. No te frustres ni te enojes si pierdes. Aprende de tus errores y mejora tus habilidades. </li>
-</ul>
-<h3>Las formas de recoger y actualizar tarjetas, unirse a clanes, y participar en eventos en Clash Royale Lite</h3>
-<p>Clash Royale Lite tiene las mismas formas de recoger y actualizar tarjetas, unirse a clanes, y participar en eventos como Clash Royale. Puedes hacer lo siguiente:</p>
-<ul>
-<li>Recoge cartas abriendo los cofres que obtienes al ganar batallas, completar misiones o comprar en la tienda. También puedes solicitar o donar tarjetas a los miembros de tu clan. </li>
-
-<li>Únete a los clanes buscando uno o creando el tuyo. Los clanes son grupos de jugadores que pueden chatear, compartir cartas y jugar juntos. También puedes participar en guerras de clanes, que son competiciones por equipos que te recompensan con cofres y medallas. </li>
-<li>Participe en eventos introduciéndolos desde la pestaña de eventos. Los eventos son modos especiales o desafíos que tienen diferentes reglas y recompensas. Puedes jugar eventos gratis o con gemas, dependiendo del evento. </li>
-</ul>
-<p>Al hacer estas cosas, puedes mejorar tu experiencia de juego y divertirte más con Clash Royale Lite.</p>
- <h2>Conclusión</h2>
-<h3>Un resumen de los puntos principales del artículo</h3>
-<p>En conclusión, Clash Royale Lite es una gran alternativa para los jugadores que aman Clash Royale pero tienen dispositivos de gama baja. Tiene todas las características y la diversión del juego original, pero con gráficos reducidos y tamaño de archivo. Es fácil de descargar e instalar desde una fuente de confianza, y es seguro jugar si sigues algunos consejos. También tiene la misma jugabilidad y reglas que Clash Royale, pero con algunos consejos y estrategias para ayudarte a ganar batallas. También puedes recoger y actualizar cartas, unirte a clanes y participar en eventos en Clash Royale Lite.</p>
-<h3>Un llamado a la acción para que los lectores prueben Clash Royale Lite</h3>
-<p>Si usted está buscando una manera de jugar Clash Royale en su dispositivo de gama baja sin ningún problema, definitivamente debe probar Clash Royale Lite. Es un juego divertido y emocionante que te mantendrá entretenido durante horas. Puede descargarlo desde el sitio web oficial de Clash Royale Lite o desde otros sitios web de renombre. También puedes compartirlo con tus amigos que tienen dispositivos de gama baja y juegan juntos. ¿Qué estás esperando? Descargar Clash Royale Lite hoy y disfrutar del juego! </p>
- <h2>Preguntas frecuentes</h2>
-<h3>¿Es seguro descargar y jugar Clash Royale Lite? </h3>
-
-<h3>¿Puedo jugar Clash Royale Lite con mis amigos que tienen Clash Royale? </h3>
-<p>No, Clash Royale Lite no es compatible con Clash Royale, por lo que no puedes jugar con tus amigos que tienen Clash Royale. Sin embargo, puedes jugar con tus amigos que tienen Clash Royale Lite agregándolos como amigos en el juego o uniéndote al mismo clan que ellos. </p>
-<h3>¿Cuánto espacio de almacenamiento ocupa Clash Royale Lite en mi dispositivo? </h3>
-<p>Clash Royale Lite ocupa alrededor de 150 MB de espacio de almacenamiento en su dispositivo, en comparación con 445 MB para Clash Royale. Esto significa que puede ahorrar mucho espacio de almacenamiento en su dispositivo jugando Clash Royale Lite en lugar de Clash Royale.</p>
-<h3>¿Con qué frecuencia se actualiza Clash Royale Lite con nuevas características y contenido? </h3>
-<p>Clash Royale Lite se actualiza regularmente con nuevas características y contenido, al igual que Clash Royale. Puede esperar ver nuevas tarjetas, arenas, modos, eventos, cambios de equilibrio, correcciones de errores y más en cada actualización. Puede consultar el sitio web oficial de Clash Royale Lite o seguir sus cuentas de redes sociales para mantenerse al día sobre las últimas noticias y actualizaciones. </p>
-<h3>¿Cuáles son algunos otros juegos como Clash Royale que puedo jugar en mi dispositivo? </h3>
-<p>Si te gusta jugar juegos de estrategia como Clash Royale, es posible que también te guste jugar otros juegos similares o relacionados con él. Algunos de estos juegos son:</p>
-<ul>
-<li>Brawl Stars: Un juego de disparos multijugador de ritmo rápido donde se puede elegir entre diferentes personajes con diferentes habilidades y modos de juego. </li>
-<li>Choque de clanes: un juego de estrategia donde puedes construir tu propia aldea, entrenar a tus tropas y asaltar las bases de otros jugadores. </li>
-<li>Boom Beach: un juego de estrategia donde puedes explorar un archipiélago tropical, luchar contra el malvado Blackguard y formar equipo con otros jugadores. </li>
-<li>plantas vs zombies 2: un juego de torre de defensa donde se puede utilizar las plantas para defender el cerebro de los zombies. </li>
-
-</ul>
-<p>Estos son algunos de los juegos que puedes jugar en tu dispositivo si te gusta Clash Royale. Puedes encontrarlos en Google Play Store u otras fuentes. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BernardoOlisan/vqganclip/CLIP/model-card.md

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-# Model Card: CLIP
-
-Inspired by [Model Cards for Model Reporting (Mitchell et al.)](https://arxiv.org/abs/1810.03993) and [Lessons from Archives (Jo & Gebru)](https://arxiv.org/pdf/1912.10389.pdf), we’re providing some accompanying information about the multimodal model.
-
-## Model Details
-
-The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.
-
-### Model Date
-
-January 2021
-
-### Model Type
-
-The base model uses a ResNet50 with several modifications as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. There is also a variant of the model where the ResNet image encoder is replaced with a Vision Transformer.
-
-### Model Versions
-
-Initially, we’ve released one CLIP model based on the Vision Transformer architecture equivalent to ViT-B/32, along with the RN50 model, using the architecture equivalent to ResNet-50.
-
-As part of the staged release process, we have also released the RN101 model, as well as RN50x4, a RN50 scaled up 4x according to the [EfficientNet](https://arxiv.org/abs/1905.11946) scaling rule. In July 2021, we additionally released the RN50x16 and ViT-B/16 models.
-
-Please see the paper linked below for further details about their specification.
-
-### Documents
-
-- [Blog Post](https://openai.com/blog/clip/)
-- [CLIP Paper](https://arxiv.org/abs/2103.00020)
-
-
-
-## Model Use
-
-### Intended Use
-
-The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
-
-#### Primary intended uses
-
-The primary intended users of these models are AI researchers.
-
-We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.
-
-### Out-of-Scope Use Cases
-
-**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. 
-
-Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
-
-Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
-
-
-
-## Data
-
-The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users.
-
-### Data Mission Statement
-
-Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset.
-
-
-
-## Performance and Limitations
-
-### Performance
-
-We have evaluated the performance of CLIP on a wide range of benchmarks across a variety of computer vision datasets such as OCR to texture recognition to fine-grained classification. The paper describes model performance on the following datasets:
-
-- Food101
-- CIFAR10   
-- CIFAR100   
-- Birdsnap
-- SUN397
-- Stanford Cars
-- FGVC Aircraft
-- VOC2007
-- DTD
-- Oxford-IIIT Pet dataset
-- Caltech101
-- Flowers102
-- MNIST   
-- SVHN 
-- IIIT5K   
-- Hateful Memes   
-- SST-2
-- UCF101
-- Kinetics700
-- Country211
-- CLEVR Counting
-- KITTI Distance
-- STL-10
-- RareAct
-- Flickr30
-- MSCOCO
-- ImageNet
-- ImageNet-A
-- ImageNet-R
-- ImageNet Sketch
-- ObjectNet (ImageNet Overlap)
-- Youtube-BB
-- ImageNet-Vid
-
-## Limitations
-
-CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance.
-
-### Bias and Fairness
-
-We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper).
-
-We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with ‘Middle Eastern’ having the highest accuracy (98.4%) and ‘White’ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks.
-
-
-
-## Feedback
-
-### Where to send questions or comments about the model
-
-Please use [this Google Form](https://forms.gle/Uv7afRH5dvY34ZEs9)

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-            Closely-Opened System [__T_P_CO__.Ss]' based Data-Architectural Model for developing various
-            Software/Hardware Applications/Protocol using Computational Compiler/Interpretor based devices (such as
-            Computer).The 'BiTransSciencia' was inspired from Properties of general living organims in which, where and
-            how the Data is stored and accessed in these organims.
-        </p>
-        <p>
-            The 'BiTransSciencia' mainly focusses on two ascpects i.e. 'Data Storage' and 'Transmission of Data'
-            with/without manipulation of data. On the basis of these ascpects the Architecture was developed such that
-            the data transmits from one to another using a transmission medium; where we generally name the two
-            data-points(from where the data is stored) as 'Server[0]' and 'Sensor(s)[1]' where the 'Server' is basically
-            inspired from the functioning of 'Brain'/'CPU' and 'Sensor(s)' have been inspired from functioning of 'Sense
-            Organs in a living organisms & the external components of a Computer
-            (Mouse,Keyboard,Speakers,Monitor,etc...)' and the 'transmission-media[8]' was inspired from functionality of
-            'Nerves in living organisms & wires/wireless functionality in Computer Devices'.
-            <br>
-            Here the role of 'Server[0]/Sensors[1]' is to store data by itself or from 'Sensors[1]/Server[0]'
-            respectively via 'Transmission[8]' with/without manipulation of data. The data can only be manipulated at
-            'Transmission[8]' after acessing and before storing the data (if required).
-        </p>
-        <p>
-            'BiTransSciencia' is a cyclic process where the data gets transmitted to and from 'Server&harr;Sensors'
-        </p>
-    </fieldset>
-    <br>
-    <fieldset id="fs__origin">
-        <legend id="l__origin">Origin (Derivation of the term 'BiTransSciencia')</legend>
-        <p>
-            The term 'BiTransSciencia' is the composition of three terms: Bi-Trans-Sciencia; where 'Bi' means 'Two'(Two
-            Data Points), 'Trans' means 'across-Transmission(here)' and 'Sciencia' means 'Science(Knowledge)'.
-            <br>
-            So, the term 'BiTransSciencia' provides the 'Knowledge about where and how the data to be stored &
-            manipulated and gets transmited across the points (Server and Sensor(s))'.
-        </p>
-        <p>
-            The logo of 'BiTransSciencia' consists of the composition of numerics '0,8,1' which indicates
-            'Data-Point-0(Server),Transmission Medium,Data-Point-1(Sensor)'[from left-right] and a schematic
-            transmission of data from 0&harr;1 via 8 along a line (irrespective of Size,Color and alignments)
-            <br>
-            In the logo '0 & 1' were represented inside '8' for indication of cyclic transmission of data across 0 and 1
-        </p>
-    </fieldset>
-    <br>
-    <fieldset id="fs__terminology">
-        <legend id="l__terminology">Terminology in 'BiTransSciencia'</legend>
-        <div class="div__terminology">
-            <ul>
-                <li>
-                    <b><i><u>UniPolySciencia</u></i></b>: Study of the Universe discovered by 'K.V.N.Aditya'(not yet
-                    published 'E.T.: __0_0_0__'),The 'UniPolySciencia' is the composition of 'Uni:Mono Atomic
-                    Particle','Poly: Complex Molecules' (definition is unstable* due to E.T.: __0_0_0__).
-                </li>
-                <li>
-                    <b><i><u>BiTransSciencia [081]</u></i></b>: A Data-Architectural Model used for designing Hardware
-                    Models and developing Software Applications which was discovered by 'K.V.N.Aditya'.
-                </li>
-                <li>
-                    <b><i><u>System</u></i></b>: The root Molecule/Folder which consists of all the Molecules/Folders
-                    &(or)/ Particles/Files which aids in storing and transmiting data across Data-Points with some
-                    unique characteristics (from 'UniPolySciencia')
-                </li>
-                <li>
-                    <b><i><u>Surrounding(s)</u></i></b>: Except the System Molecule/Folder, all other Molecules/Folders
-                    and Particles/Files in the Universe/a Device and where the Molecules & Particles can be inherited
-                    from and to the System. (from 'UniPolySciencia')
-                </li>
-                <li>
-                    <b><i><u>Molecule(s)</u></i></b>: Composition of a Molecule or Composition of Particles (generally
-                    known as 'Folders' according to Computer-Language) (from 'UniPolySciencia')
-                </li>
-                <li>
-                    <b><i><u>Particle(s)</u></i></b>: A Mono-Component where the data is actually stored (generally
-                    known as 'Files' according to Computer-Language) (from 'UniPolySciencia')
-                </li>
-                <li>
-                    <b><i><u>Data/Datum</u></i></b>: A form of composition of energy which generates structure of
-                    waves,etc... that can be used for Analyzing the Particle / to generate Particle by manipulating the
-                    Particle (Mono-Structuared(File Transmitting without manipulating (File-Copy)) / Di-Structured(File
-                    Transmitting with manipulating))
-                </li>
-                <li>
-                    <b><i><u>Data-Point(s)</u></i></b>: Location/Address where the data is stored in a Particle
-                </li>
-                <li>
-                    <b><i><u>Data-Transmission</u></i></b>: Transmitting the data from one particle to another particle
-                    with or with-out manipulationg the data
-                </li>
-                <li>
-                    <b><i><u>Evolution</u></i></b>: Changes occurred in the structure of the data with-in a Particle
-                    which effects its root Molecule ('insertion','update/alteration','deletion') (from
-                    'UniPolySciencia')
-                </li>
-                <li>
-                    <b><i><u>Evolution-Tree</u></i></b>: Representation of the
-                    System's/Surrounding's/Molecule's/Particle's Evolution in a structure of a tree (from
-                    'UniPolySciencia')
-                </li>
-            </ul>
-        </div>
-
-    </fieldset>
-
-    <br>
-
-    <fieldset id="fs__bts_architecture_protocols">
-        <legend id="l__bts_architecture_protocols">'BiTransSciencia' Architecture Protocols</legend>
-        <div id="div__tree_081" style="text-align: center;">
-            <br>
-            <!-- <object id="tree__081" title="logo__081" type="image/svg+xml" data="svg/081_structure.svg" width="720px" height="360px"></object> -->
-            <img id="structure__081" src="datum/structure_081.svg" alt="structure__081"
-                onclick="openImagePopup(this.src)">
-            <pre><i><u>Schematic representation of tree structure of 'BiTransSciencia [081]'</u></i></pre>
-            <br>
-        </div>
-        <div>
-            <p>
-                The 'BiTransSciencia [081]' initialized by consisting of two points where the data is been stored. The
-                data may be originated with-in the system or from its Surroundings.Since there are two data points and
-                the data to be transmited from one to another, now there should be a medium to transmit the data... Here
-                comes the necessity of 'BiTransSciencia [081]' where '0 and 1' are the data points and '8' be the
-                transmitting medium. This transmiting medium transmits from one to another.
-                <br>
-                There are mainly two transmissions that can be occurred i.e. with-in the system and/or System to &/ from
-                Surroundings. So the Architecture gets splits into three(3) namely {'0','8','1'} such that '0' & '1'
-                consists of the data of the 'System' and 'Surroundings' and '8' consists of transmission protocols which
-                aids to transmits the data that can be accessced only from the system.
-                <br>
-            <pre>The '0'&'1' consists majority of data/interface(s)/programs that has been transmitted to/from System & Surroundings, and '8' consists majority of the System-based data/transmissions</pre>
-            </p>
-            <p>
-                Since '0' and '1' has two(2) kinds of data (i.e. System and Surroundings), these gets splits into
-                three(3) sub-divisions namely:
-            <p>
-                '0' gets splits into:
-            <dl>
-                <dt><b>'<u>10</u>'</b>:</dt>
-                <dd>Consists of Partial/Source data of System's Surroundings</dd>
-                <dt><b>'<u>88</u>'</b>:</dt>
-                <dd>Consists of protocols to transmit the extracted data into the System [into '/8/_0_/10/']</dd>
-                <dt><b>'<u>01</u>'</b>:</dt>
-                <dd>Consists of Partial/Source data from '/8/_0_/' that to be transmitted to its Surroundings</dd>
-            </dl>
-            <pre>The molecules/particles '10' is required if to extract partial data from its surroundings, else the data directly be stored directly into '/8/_0_/10/' via surrounding's/system transmissions</pre>
-            <pre>The molecules/particles '01' is required if to transmit partial data to its surroundings, else the data produced from the '/8/_0_/' can be directly accessed to its surroundings. The '01' Molecule mainly aids in data abstraction while transmitting to its surroundings...</pre>
-            </p>
-            <p>
-                '1' gets splits into:
-            <dl>
-                <dt><b>'<u>01</u>'</b>:</dt>
-                <dd>Consists of Partial/Source data of System's Surroundings</dd>
-                <dt><b>'<u>88</u>'</b>:</dt>
-                <dd>Consists of protocols to transmit the extracted data into the System [into '/8/_1_/01/']</dd>
-                <dt><b>'<u>10</u>'</b>:</dt>
-                <dd>Consists of Partial/Source data from '/8/_1_/' that to be transmitted to its Surroundings</dd>
-            </dl>
-            <pre>The molecules/particles '01' is required if to extract partial data from its surroundings, else the data directly be stored directly into '/8/_1_/01/' via surrounding's/system transmissions</pre>
-            <pre>The molecules/particles '10' is required if to transmit partial data to its surroundings, else the data produced from the '/8/_1_/' can be directly accessed to its surroundings. The '10' Molecule mainly aids in data abstraction while transmitting to its surroundings...</pre>
-            </p>
-            </p>
-            <p>
-                Till now, the data has been transmitted across System-Surroundings... Now, we explore how the data is
-                transmitted with-in the System i.e. the actual data transmission occurs in between '0' & '1' is at '8'
-                namely '_0_' &harr; '__8__' &harr; '_1_'.
-                <br>
-                The '_0_' molecule consists of three(3) further molecules namely '10','00','01' where the actual '0's
-                System data been addressed. The '10' molecule consists of data that have been extracted from
-                Surroundings via '/0/88/' or directly from Surroundings. The molecule '00' consists of Origin data of
-                '0' that has been evoluted within-it or via System transmissions. The '01' molecule consists of data
-                that can be accessed by its Surroundings.
-                <br>
-                The '_1_' molecule consists of three(3) further molecules namely '01','11','10' where the actual '1's
-                System data been addressed. The '01' molecule consists of data that have been extracted from
-                Surroundings via '/1/88/' or directly from Surroundings. The molecule '11' Consists of Origin data of
-                '1' that has been evoluted within-it or via System transmissions. The '10' molecule consists of data
-                that can be accessed by its Surroundings.
-                <br>
-                The '__8__' molecule consists of three(3) further molecules namely '_0_','_8_','_1_'.
-            <p>
-                The '_0_' molecule consists of three(3) further molecules namely '081','080','180'.
-            <p>
-                The '081' molecule consists of transmission protocols to transmit data from '_0_' to '_1_'
-                <br>
-                The '080' molecule consists of transmission protocols to transmit data with-in '_0_' molecule.
-                <br>
-                The '180' molecule consists of transmission protocols to transmit data from '_1_' to '_0_' on the basis
-                of Server request...
-            </p>
-            <br>
-            The '_1_' molecule consists of three(3) further molecules namely '180','181','081'.
-            <p>
-                The '180' molecule consists of transmission protocols to transmit data from '_1_' to '_0_'
-                <br>
-                The '181' molecule consists of transmission protocols to transmit data with-in '_1_' molecule.
-                <br>
-                The '081' molecule consists of transmission protocols to transmit data from '_0_' to '_1_' on the basis
-                of Sensor request...
-            </p>
-            <br>
-            The '_8_' molecule consists of three(3) further molecules namely '808','818','888'.
-            <p>
-                The '808' molecule consists of two(2) further molecules namely '0' and '8'.
-            <p>
-                The '8' molecule consists of interfaces that helps in calling the '/__8__/_0_/' protocols for
-                transmitting the data.
-                <br>
-                The '0' molecule consists of "input &rarr; output" log files for the respective call of the protocols
-                and molecule(s)/particles(s) of processed data (such like 'cached data',etc...). This is an optional
-                molecule.
-            </p>
-            <br>
-            The '818' molecule consists of two(2) further molecules namely '1' and '8'.
-            <p>
-                The '8' molecule consists of interfaces that helps in calling the '/__8__/_1_/' protocols for
-                transmitting the data.
-                <br>
-                The '1' molecule consists of "input &rarr; output" log files for respective call of the protocols and
-                molecule(s)/particles(s) of processed data (such like 'cached data',etc...). This is an optional
-                molecule.
-            </p>
-            <br>
-            The '888' molecule consists of three(3) further molecules namely '0', '8', '1'.
-            <p>
-                The '0' and '1' molecule(s) consists of protocols that aids in transmitting of data across '1' and '0'
-                data transmissions. These are intermediate transmitting data molecule.
-                <br>
-                The '8' molecule consists of "input &harr; transmitting &harr; output" log files for respective call of
-                the protocols and molecule(s)/particles(s) of processed data (such like 'cached data',etc...). This is
-                an optional molecule.
-            </p>
-            <br>
-            The '888' molecule is required to transmit across the '_0_' and '_1_' and optional to transmit within '_0_'
-            and '_1_'. In 'Open System', the 'interface' transmissions can be embedded within this molecule such that
-            either 'Server[0]' or 'Sensors[1]' can access directly without separate interfaces.(i.e. interface{'Closed
-            System','Closely-Opened System' : Optional; 'Open System': Recommended})
-            <br>
-            The '808' and '818' molecules are required for transmitting the data via interfaces{'Closed
-            System','Closely-Opened System' : Recommended; 'Open System': Optional} and Complex transmissions, for
-            simple
-            transmissions, the data can be transmitted via '/__8__/_0_/' and '/__8__/_1_/' directly, but the log files
-            to be generated in '808' and '818' molecules respectively.
-            </p>
-            </p>
-
-            </p>
-            <p>
-                There is no specific location where the '8' to be located since it is an intermediate point. It depends
-                of 'Eminencial' properties (from "'BiTransSciencia' Properties")
-            </p>
-            <br>
-            <!-- --- -->
-            <br>
-            <div>
-                There are two special particles named '.Ss' and '.et' which represents 'Evolution Tree' and
-                'System-Surrounding' characteristics.
-                <br>
-                <br>
-                <div>
-              
-                    &emsp;&emsp;&emsp;:&rarr;&emsp;The '.Ss' particle is used to represent the 'System-Surroundings'
-                    characteristics on the basis of "'FSE' properties of '081'"(from the "'BiTransSciencia'
-                    Properties"). The
-                    syntax of '.Ss' is "__[F]_[S]_[E]__"; where {F,S,E} is represented based on the abbreviations of
-                    these sub-divisional properties.
-                    <br>
-                    <p>
-                        &emsp;&emsp;&emsp;&emsp;&emsp;
-                        The molecules/particles can be outside the [081] and inside the System. These molecules/particles have
-                        Universal access w.r.t the System and the Surroundings those may be of any of {'0','8','1'}. These are
-                        termed as '__Ss__' molecules and '_Ss_' particles.
-                    </p>
-                    <p>
-                        &emsp;&emsp;&emsp;&emsp;&emsp;There are three(3) types of '.Ss' molecules/particles i.e {_,__,#}:
-                        <br>
-                        <dl>
-                            <dt>&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;<b>_</b>    -</dt>
-                            <dd>&emsp;&emsp;&emsp;&emsp;&emsp;
-                                -  
-                                The molecule '_' consists of molecules/particles which are connected to any/all of the molecules/particles of the System(majority) &/( i.e. and/or) Surroundings which are 'Private' i.e. these can only be accessed by the '0' or '1' ['_' represents '2<sup>0</sup>' (where '0' describes 'Private')].
-                            </dd>
-                            <dt>&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;<b>__</b>    -</dt>
-                            <dd>&emsp;&emsp;&emsp;&emsp;&emsp;
-                                -  
-                                The molecule '__' consists of molecules/particles which are connected to any/all of the molecules/particles of the System(majority) &/ Surroundings which are 'Public' i.e. these can be accessed by the '0' and '1' ['__' represents '2<sup>1</sup>' (where '1' describes 'Public')].
-                            </dd>
-                            <dt>&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;<b>#</b>    -</dt>
-                            <dd>&emsp;&emsp;&emsp;&emsp;&emsp;
-                                -  
-                                The '#' represents the molecule(s)/particle(s) which are related to any/all the molecules/particles of the System &/ Surroundings and other molecules/particles which don't meet the properties of '_' and/or '__'. The molecules/particles are of superset of '_', '__' by which these can be accessed by any of the molecules/particles of the System and/or Surroundings. The '.et' is also a type of '#' molecule/particle which represents the System's Evolution-Tree but it also has its own properties i.e. it is superset of all the Molecules/Particles of the System &/ Surroundings on the basis of its specification. Note that '#' is not a specific molecule/particle, it's just a representation of the molecules/particles which are not of '_' , '__' and the 081 Molecules...
-                            </dd>
-
-                        </dl>
-                        
-                    </p>
-                </div>
-                <br>
-                <div>
-                    &emsp;&emsp;&emsp;:&rarr;&emsp;The '.et' particle is used to represent the evolution of the System (in
-                    general-commonly known as 'Version'). The syntax of '.et' is "__[Stem(s)]_[Branch(s)]_[Leaf(s)]__";
-                    where 'Stem' count represents the number of evolutions added or removed or 'altered' in the system i.e.
-                    the evolutions that occurred in the System's root molecule, 'Branch(s)' count represents the number of
-                    evolutions that occurred inside the System's root molecule, 'Leaf(s)' count represents the number of
-                    evolutions that occurred inside the System's particles.For every system the "Root" is a "BlackHole".
-                    Initially the '.et' is "__0_0_0__" while evoluting, after its initial evolution the '.et' is as
-                    '__1_1_1__', then after it depends on the evolutions... The name of the System to be initialized within
-                    the [.et] Particle
-                    <p>
-                        &emsp;&emsp;&emsp;&emsp;&emsp;The types of 'Evolution-Tree':
-                        <br>
-                        &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;i.&emsp;<b><i>Balanced Evolution-Tree</i></b> :
-                        <br>
-                        &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;: This represents the initial
-                        System
-                        that has been Evoluted (Either by Hirerchial/Derived or via BlackHole/Neutral[The System is evoluted
-                        by
-                        no dependecy on surrounding Systems]).The syntax of this Evolution-Tree is as
-                        "__[Stem(s)]_[Branch(s)]_[Leaf(s)]__"
-                        <br>
-                        &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;ii.&emsp;<b><i>Pseudo Evolution Tree</i></b> :
-                        <br>
-                        &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;If a System has evoluted but if
-                        this
-                        has not effected the initial System, then the '.et' is represented by 'Pseudo Evolution-Tree'. This
-                        is
-                        only applicable for the evolutions with-in only one System. The syntax of this Evolution-Tree is
-                        "_[Branch(s)]_[Leaf(s)]_"."__[Stem(s)]_[Branch(s)]_[Leaf(s)]__" ('.'(Single Dot) read as "pseudo
-                        of"(here)); in
-                        which left of "." is the System that has been evoluted of the System of the right of the ".". Here
-                        the
-                        Pseudo System doesn't Consists [Stem] since the [Stem] of the Pseudo System is same as its Balanced
-                        System [right of "."].
-                        <br>
-                        &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;iii.&emsp;<b><i>Hierarchical Evolution Tree</i></b> :
-                        <br>
-                        &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;If a Balanced System derives any
-                        of
-                        the Molecules/Particles from the surrounding System(s) [Balanced/Hierarchical], the evolution of
-                        this
-                        System is represented by "Hirarchial Evolution-Tree". The syntax of the Evolution-Tree is
-                        "__[Stem(s)]_[Branch(s)]_[Leaf(s)]__"-"__[Stem(s)]_[Branch(s)]_[Leaf(s)]__"....-"__[Stem(s)]_[Branch(s)]_[Leaf(s)]__"('-'
-                        read as "Hierarchical of"(here)); in which the left of "-" is the Balanced System[not to be Black
-                        Holed
-                        since Hierarchical] which derives 'n' number of surrounding Systems which are separated by "-". Here
-                        the
-                        Stems can be different for left and right "-" since the Systems are non-Identical.
-                        This Evolution-Tree can be represented with-in the Particle [.et] or by only initializing the
-                        instance
-                        System Evolution-Tree [left of "-"] and the derived System(s) to be inside the [.et] Particle by
-                        initializing the Hierarchical System(s) with "-" with suffix of its Hierarchical System's Name
-                        separated by
-                        '--' and the successive Hierarchical System(s) are separated by "\n"(new line)
-                    </p>
-                    <p>
-                        &emsp;&emsp;&emsp;&emsp;&emsp;The '.et' can be initialized by:
-                        <br>
-                        &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;i.&emsp;(as a Molecule): The {Molecules,Particles} will be
-                        initiated with-in the '.et' Molecule.
-                        <br>
-                        &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;ii.&emsp;(as a Particle): The '.et' Particle will be
-                        initiated
-                        with-in the Root/Molecule.
-    
-                    </p>
-                </div>
-
-            </div>
-            <br><br>
-            <p>
-                The different line-strokes represents different relations in between the molecules/particles with
-                respect to the data/transmission medium; i.e.
-                <br>
-            <p>The "&mdash;&mdash;&mdash;" represents the direct(or)indirect[while accessing with "---" type
-                molecules/particles] relation; "---" represents indirect relation; "..." represents about the
-                System-Surround relations</p>
-            </p>
-            <p>
-                The {Black,Gray, White, Blue, Green, Red} color-codes are used in the schematic-architecture
-                which indicates:
-                <br>
-            <dl>
-                <dt>Black -</dt>
-                <dd>- Representation of the Universe</dd>
-                <dt>Gray -</dt>
-                <dd>- 'Gray' is used to represent System's Molecule(s)/Particle(s) {'.et', '.Ss'} and/or 'Hierarchical
-                    Transmission'</dd>
-                <dt>White -</dt>
-                <dd>- 'White' is used to represent data-transmission across System-Surroundings and the Boundary(s) of
-                    the Molecule(s)/Particle(s)</dd>
-                <dt>Blue -</dt>
-                <dd>- 'Blue' is used to represent 'Server[0]' (which simulates 'Back-end' on the basis of its
-                    wavelength)</dd>
-                <dt>Green -</dt>
-                <dd>- 'Green' is used to represent 'Transmission medium[8]' (which simulates 'intermediate of '0' and
-                    '1'' on the basis of its wavelength)</dd>
-                <dt>Red -</dt>
-                <dd>- 'Red' is used to represent 'Sensors[1]' (which simulates 'Front-end' on the basis of its
-                    wavelength)</dd>
-            </dl>
-            </p>
-
-        </div>
-    </fieldset>
-
-    <br>
-
-    <fieldset id="fs__fse">
-        <legend id="l__fse">'BiTransSciencia' Properties</legend>
-        <p>
-            For a System developed by 'BiTransSciencia' consists of three(3) Properties [applicable for System and
-            Surroundings]:
-        <ul>
-            <li>Functional Properties</li>
-            <li>Structural Properties</li>
-            <li>Eminencial Properties</li>
-        </ul>
-        These Properties are commonly called as "'FSE' Properties of '081'"
-        </p>
-        <p>
-            <b><u>Functional Properties</u>:</b> Functional Properties describes about the function of the System for
-            which it was developed for.
-            <br>
-            These are of three(3) types:
-        <ul>
-            <li>Data Oriented System</li>
-            <li>Transmission Oriented System</li>
-            <li>Data-Transmission Oriented System</li>
-        </ul>
-        <br>
-        <b><i>'Data Oriented System'</i>:</b>&emsp;&emsp;&emsp;'Data Oriented' based systems mainly focusses on the
-        'where to store the data' based on required conditions with minimum transmission protocols
-        <br>
-        <b><i>'Transmission Oriented System'</i></b>&emsp;&emsp;&emsp;'Transmission Oriented' based systems mainly
-        focusses on 'how to store the data' based on required conditions with minimum data with-in the system
-        <br>
-        <b><i>'Data-Transmission Oriented System'</i></b>&emsp;&emsp;&emsp;'Data-Transmission' based systems focusses on
-        both 'where and how the data to be stored' with required transmission protocols
-        </p>
-        <p>
-            <b><u>Structural Properties</u></b>: Structural Properties describes about the Structure of the System
-            based on the design of the System
-            <br>
-            These are of three(3) types:
-        <ul>
-            <li>Pseudo System</li>
-            <li>Neutral System</li>
-            <li>Balanced System</li>
-        </ul>
-        <br>
-        <b><i>'Pseudo System'</i>:</b>&emsp;&emsp;&emsp;'Pseudo System' is a system in which it transmits to or from
-        with-in the System or Surroundings. The nature of the system is flexible and unstable(to make system Stable and
-        may or may not follow all the protocols of 'BiTransSciencia [081]'. To be converted to any of the above
-        Properties i.e. 'Natural/Balanced' which may loose flexible characteristic). At the initial Evolution of a
-        System
-        (i.e. et: __0_0_0__) possesses 'Pseudo System' util the System is initially Evoluted (et: __1_1_1__) by
-        transforming to 'Neutral'/'Balanced' System(s). Some of the System(s) possess the 'Pseudo System' even after
-        their initial evolution based on their functonality...
-        <br>
-        <b><i>'Balanced System'</i>:</b>&emsp;&emsp;&emsp;'Balanced System' is a system in which it must transmits the
-        data with-in the System and Surroundings. The nature of the system is Stable and Dynamic
-        <br>
-        <b><i>'Neutral System'</i>:</b>&emsp;&emsp;&emsp;'Neutral System' is a system in which it must transmits with-in
-        the system and doesn't transmit to and from Surroundings. The nature of the system is Stable and Static.
-
-        </p>
-        <p>
-            <b><u>Eminencial Properties</u></b>: Eminencial Properties describes about accessing of the data and
-            transmission of data with respective to the Data-Points [i.e. 'Server' and 'Sensors']
-            <br>
-            These are of three(3) types:
-            <br>
-        <ul>
-            <li>Open System</li>
-            <li>Closed System</li>
-            <li>Closely-Opened System</li>
-        </ul>
-        <br>
-        <b><i>'Open System'</i>:</b>&emsp;&emsp;&emsp;The data in 'Server', 'Sensor' and 'Transmission of Data' can be
-        accessed from any of the Data-Point within the transmission medium. The '8' resides at any of the data-points or
-        at an individual location
-        <br>
-        <b><i>'Closed System'</i>:</b>&emsp;&emsp;&emsp;The data-point can only access the data with-in a data-point and
-        its protocols and the system protocols and system data [example: the 'Server' can only access the data with-in
-        'Server' , and the protocols with-in '__8__/_0_', and '__8__/_8_'].The molecule '8' gets splits and resides at
-        the data-points except '/8/__8__/_8_/888/' which resides at an individual location that can be accessed by the
-        data-points
-        <br>
-        <b><i>'Closely-Opened System'</i>:</b>&emsp;&emsp;&emsp;In this, any of the data-point have control over the
-        system in which it prescribes the manipulation of the data by a data-point.
-        <br>
-        &emsp;In 'Open-System' the folders '0' and '1' can be accessed by any of the data-point but, in 'Closed System'
-        &
-        'Closely-Opened System' the folders '0' and '1' can only be accessed by the respective data folder unless the
-        folder becomes the input/output of the surrounding Systems.The '8' resides as per the protocols of controlling
-        data-point (i.e. 'Server[0]' or 'Sensors[1]')
-        </p>
-    </fieldset>
-    <br>
-    <fieldset id="fs__download">
-        <legend id="l__download">Download 'BiTransSciencia [081]' Schematic Structure</legend>
-        <div class="div__dl">
-            <code>Download the below zip file to install 'BiTransSciencia [081]' Schematic Structure... The folders/files can be manipulated as per your System requriments w.r.t 'BiTransSciencia [081]' Protocols</code>
-            <br><br>
-            <a id="a__dl" href="/datum/zip__081/__0_0_0__.et--zip__BiTransSciencia.zip" download>Download 'BiTransSciencia [081]' Schematic Structure</a>
-            <br>
-            <pre>!! Thank You choosing 'BiTransSciencia [081]' Data-Architecture for evoluting your System !!</pre>
-        </div>
-    </fieldset>
-    <br>
-    <fieldset id="fs__ack">
-        <legend id="l__ack">Surrounding Acknowledgement(s)</legend>
-
-        <!-- <div id="div__ack">
-                <a title="UniPolySciencia"  href="#" target="_self"><img class="img__bts_ext_rs__sq" alt="UniPolySciencia" src="svg/ico_logo__ups.svg"></a>
-                <a title="Inkscape"  href="https://inkscape.org/" target="_blank"><img class="img__bts_ext_rs__sq" alt="Inkscape" src="svg/inkscape-flat-logo-2color-text.svg" style="background-color: white;"></a>
-                <a title="Anaconda"  href="https://www.anaconda.com/" target="_blank"><img class="img__bts_ext_rs__rec" alt="Anaconda" src="https://upload.wikimedia.org/wikipedia/commons/e/ea/Conda_logo.svg"></a>
-                <a title="Python Software Foundation"  href="https://www.python.org/" target="_blank"><img class="img__bts_ext_rs__rec" alt="Python Software Foundation" src="https://staging.python.org/static/community_logos/python-logo-inkscape.svg"></a>
-                <a title="W3C"  href="https://www.w3.org/" target="_blank"><img class="img__bts_ext_rs__rec" alt="W3C" src="https://www.w3.org/Icons/WWW/w3c_home_nb-v.svg"></a>
-                <a title="Visual Studio Code (from 'MicroSoft')"  href="https://code.visualstudio.com/" target="_blank"><img class="img__bts_ext_rs__sq" alt="Visual Studio Code (Microsoft)" src="svg/vscode.svg"></a>
-                <a title="git"  href="https://git-scm.com/" target="_blank"><img class="img__bts_ext_rs__sq" alt="git-scm" src="https://git-scm.com/images/logos/downloads/Git-Logo-2Color.svg" style="background-color: white; width: 100px;"></a>
-                <a title="FileZilla"  href="https://filezilla-project.org/" target="_blank"><img class="img__bts_ext_rs__sq" alt="FileZilla" src="https://upload.wikimedia.org/wikipedia/commons/0/01/FileZilla_logo.svg"></a>
-                <a title="SourceForge"  href="https://sourceforge.net" target="_blank"><img class="img__bts_ext_rs__rec" alt="SourceForge" src="https://sourceforge.net/sflogo.php?type=18&group_id=3558441"></a>
-                <a title="Firefox"  href="https://www.mozilla.org/" target="_blank"><img class="img__bts_ext_rs__sq" alt="Firefox" src="svg/Fx-Browser-lockup-vertical.svg" style="background-color: white; width: 100px;"></a>
-                <a title="Creative Commons"  href="https://creativecommons.org/" target="_blank"><img class="img__bts_ext_rs__rec" alt="Creative commons" src="https://mirrors.creativecommons.org/presskit/logos/cc.logo.svg" style="background-color: white; width: 100px;"></a>
-                <br>
-                <a id="a__ack" title="Windows (by 'Microsoft')">Windows (from 'MicroSoft')</a>
-
-            </div> -->
-
-
-        <div id="div__ack">
-            <a title="UniPolySciencia" href="#" target="_self">UniPolySciencia</a>
-            <a title="Inkscape" href="https://inkscape.org/" target="_blank">Inkscape</a>
-            <a title="Anaconda" href="https://www.anaconda.com/" target="_blank">Anaconda</a>
-            <a title="Python Software Foundation" href="https://www.python.org/" target="_blank">Python Software
-                Foundation</a>
-            <a title="W3C" href="https://www.w3.org/" target="_blank">W3C</a>
-            <a title="Visual Studio Code (from 'MicroSoft')" href="https://code.visualstudio.com/"
-                target="_blank">Visual Studio Code (from 'MicroSoft')</a>
-            <a title="git" href="https://git-scm.com/" target="_blank">Git</a>
-            <a title="FileZilla" href="https://filezilla-project.org/" target="_blank">FileZilla</a>
-            <a title="SourceForge" href="https://sourceforge.net" target="_blank">SourceForge</a>
-            <a title="Firefox" href="https://www.mozilla.org/" target="_blank">Firefox</a>
-            <a title="Creative Commons" href="https://creativecommons.org/" target="_blank">creative Commons</a>
-            <a title="Windows (by 'Microsoft')">Windows (from 'MicroSoft')</a>
-
-        </div>
-
-        <hr>
-
-        <p id="p__ack_logo">
-            !! All the logos, names, trademarks and registered trademarks are property of the respective owners(if
-            applicable*). These were used to show gratitude for usage of the product in developing the 'BiTransSciencia
-            [081]'. These products are used with-out any direct affiliation of the respective product owner. Usage of
-            these names, trademarks and brands does not imply endorsement or affiliation. with the Organization(s)!!
-        </p>
-
-    </fieldset>
-
-    <br>
-
-
-    <fieldset id="fs__license">
-        <legend id="l__license">Licensed to 'K.V.N.Aditya'...</legend>
-        <p class="p__license">
-            The elements of 'UniPolySciencia' is licensed under:
-            <b>
-                <p class="p__cc" xmlns:cc="http://creativecommons.org/ns#" xmlns:dct="http://purl.org/dc/terms/"><a
-                        class="a__cc" property="dct:title" rel="cc:attributionURL" href="#">UniPolySciencia (Icon)</a>
-                    by <a class="a__cc" rel="cc:attributionURL dct:creator" property="cc:attributionName"
-                        href="https://kvnaditya.users.sourceforge.net">Venkata Naga Aditya Kothapalli</a> is licensed
-                    under <a class="a__cc" href="http://creativecommons.org/licenses/by-nc-nd/4.0/?ref=chooser-v1"
-                        target="_blank" rel="license noopener noreferrer" style="display:inline-block;">CC BY-NC-ND
-                        4.0<img class="img__cc"
-                            style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1"><img
-                            class="img__cc" style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1"><img
-                            class="img__cc" style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/nc.svg?ref=chooser-v1"><img
-                            class="img__cc" style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/nd.svg?ref=chooser-v1"></a></p>
-            </b>
-        </p>
-        <p class="p__license" id="p_id__license">
-            The elements of 'BiTransSciencia' is licensed under:
-            <b>
-                <p class="p__cc" xmlns:cc="http://creativecommons.org/ns#" xmlns:dct="http://purl.org/dc/terms/"><a
-                        class="a__cc" property="dct:title" rel="cc:attributionURL" href="#p_id__license"
-                        onclick="a_openImagePopup('datum/logo_081.svg')">BiTransSciencia [081] (Logo)</a>
-                    by <a class="a__cc" rel="cc:attributionURL dct:creator" property="cc:attributionName"
-                        href="https://kvnaditya.users.sourceforge.net">Venkata Naga Aditya Kothapalli</a> is licensed
-                    under <a class="a__cc" href="http://creativecommons.org/licenses/by-nc-nd/4.0/?ref=chooser-v1"
-                        target="_blank" rel="license noopener noreferrer" style="display:inline-block;">CC BY-NC-ND
-                        4.0<img class="img__cc"
-                            style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1"><img
-                            class="img__cc" style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1"><img
-                            class="img__cc" style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/nc.svg?ref=chooser-v1"><img
-                            class="img__cc" style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/nd.svg?ref=chooser-v1"></a></p>
-            </b>
-            <b>
-                <p class="p__cc" xmlns:cc="http://creativecommons.org/ns#" xmlns:dct="http://purl.org/dc/terms/"><a
-                        class="a__cc" property="dct:title" rel="cc:attributionURL" href="#p_id__license"
-                        onclick="a_openImagePopup('datum/icon_081.svg')">BiTransSciencia [081] (Icon)</a>
-                    by <a class="a__cc" rel="cc:attributionURL dct:creator" property="cc:attributionName"
-                        href="https://kvnaditya.users.sourceforge.net">Venkata Naga Aditya Kothapalli</a> is licensed
-                    under <a class="a__cc" href="http://creativecommons.org/licenses/by-nc-nd/4.0/?ref=chooser-v1"
-                        target="_blank" rel="license noopener noreferrer" style="display:inline-block;">CC BY-NC-ND
-                        4.0<img class="img__cc"
-                            style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1"><img
-                            class="img__cc" style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1"><img
-                            class="img__cc" style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/nc.svg?ref=chooser-v1"><img
-                            class="img__cc" style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/nd.svg?ref=chooser-v1"></a></p>
-            </b>
-            <b>
-                <p class="p__cc" xmlns:cc="http://creativecommons.org/ns#" xmlns:dct="http://purl.org/dc/terms/"><a
-                        class="a__cc" property="dct:title" rel="cc:attributionURL" href="#p_id__license"
-                        onclick="a_openImagePopup('datum/structure_081.svg')">BiTransSciencia [081] (Tree
-                        Structure)</a> by <a class="a__cc" rel="cc:attributionURL dct:creator"
-                        property="cc:attributionName" href="https://kvnaditya.users.sourceforge.net">Venkata Naga Aditya
-                        Kothapalli</a> is licensed under <a class="a__cc"
-                        href="http://creativecommons.org/licenses/by-nc-nd/4.0/?ref=chooser-v1" target="_blank"
-                        rel="license noopener noreferrer" style="display:inline-block;">CC BY-NC-ND 4.0<img
-                            class="img__cc" style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1"><img
-                            class="img__cc" style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1"><img
-                            class="img__cc" style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/nc.svg?ref=chooser-v1"><img
-                            class="img__cc" style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/nd.svg?ref=chooser-v1"></a></p>
-            </b>
-            <b>
-                <p class="p__cc" xmlns:cc="http://creativecommons.org/ns#" xmlns:dct="http://purl.org/dc/terms/"><a
-                        class="a__cc" property="dct:title" rel="cc:attributionURL" href="#">BiTransSciencia [081]</a> by
-                    <a class="a__cc" rel="cc:attributionURL dct:creator" property="cc:attributionName"
-                        href="https://kvnaditya.users.sourceforge.net">Venkata Naga Aditya Kothapalli</a> is licensed
-                    under <a class="a__cc" href="http://creativecommons.org/licenses/by-nd/4.0/?ref=chooser-v1"
-                        target="_blank" rel="license noopener noreferrer" style="display:inline-block;">CC BY-ND 4.0<img
-                            class="img__cc" style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1"><img
-                            class="img__cc" style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1"><img
-                            class="img__cc" style="height:22px!important;margin-left:3px;vertical-align:text-bottom;"
-                            src="https://mirrors.creativecommons.org/presskit/icons/nd.svg?ref=chooser-v1"></a>
-                </p>
-            </b>
-        </p>
-        <hr>
-        <p class="p__bts_license_usage">
-            The images (any format) from 'UniPolySciencia' and 'BiTransSciencia' can be used on their respective work as
-            per the 'Creative Commons'. These images can only be manipulated accordingly as per requirement w.r.t the
-            'Color', 'Font-Style', 'Alignment' without manipulating the structure of the actual image...
-            <br>
-            The 'BiTransSciencia [081]' Data-Architecture can be used for developing 'Open-Source','Closed-Source' and
-            'Commercial' applications without manipulating the protocols.
-        </p>
-
-    </fieldset>
-    <br>
-    <!--s: source-->
-    <fieldset id="fs__s">
-        <legend id="l__s">Source &harr; Resource</legend>
-        <s>
-            <div id="div__s">
-                The 'BiTransSciencia [081]' has discovered on the basis of general working of data-transmission in the
-                Universe. There is no further refference(s) has been used in this. If you think the content used in this
-                was
-                a source of your's / other liable one's, mail to <a
-                    href="mailto:[email protected]"
-                    target="_blank"><s>mailto:bitranssciencia@081</s></a>
-            </div>
-        </s>
-
-    </fieldset>
-</body>
-<footer>
-    <hr>
-    <div class="div__et" style="text-align: center;">
-        <p id="initial_evolution">
-            Initial Evolution: <b>Mon, 24 Oct 2022 02:36:02 GMT</b> (apx.)
-        </p>
-
-        <p id="instantaneous_evolution"></p>
-        <!-- <p id="initial_evolution" style="text-align: center;"></p> -->
-        <script>
-            var x = new Date(document.lastModified).toUTCString();
-            document.getElementById("instantaneous_evolution").innerHTML = "Instantaneous Evolution:\t" + "<b>" + x + "</b>";
-            /* Inital Evolution: 2022*/
-        </script>
-    </div>
-    <hr>
-    <div class="d3__KVNAditya">
-        Discovered Designed & Developed by "<p id="id__kvna"><a id="id__kvna_data"
-                href="https://kvnaditya.users.sourceforge.net" target="_blank">K.V.N.Aditya</a></p>"
-    </div>
-    <hr>
-</footer>
-
-</html>

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/util/retry.py

@@ -1,620 +0,0 @@
-from __future__ import absolute_import
-
-import email
-import logging
-import re
-import time
-import warnings
-from collections import namedtuple
-from itertools import takewhile
-
-from ..exceptions import (
-    ConnectTimeoutError,
-    InvalidHeader,
-    MaxRetryError,
-    ProtocolError,
-    ProxyError,
-    ReadTimeoutError,
-    ResponseError,
-)
-from ..packages import six
-
-log = logging.getLogger(__name__)
-
-
-# Data structure for representing the metadata of requests that result in a retry.
-RequestHistory = namedtuple(
-    "RequestHistory", ["method", "url", "error", "status", "redirect_location"]
-)
-
-
-# TODO: In v2 we can remove this sentinel and metaclass with deprecated options.
-_Default = object()
-
-
-class _RetryMeta(type):
-    @property
-    def DEFAULT_METHOD_WHITELIST(cls):
-        warnings.warn(
-            "Using 'Retry.DEFAULT_METHOD_WHITELIST' is deprecated and "
-            "will be removed in v2.0. Use 'Retry.DEFAULT_ALLOWED_METHODS' instead",
-            DeprecationWarning,
-        )
-        return cls.DEFAULT_ALLOWED_METHODS
-
-    @DEFAULT_METHOD_WHITELIST.setter
-    def DEFAULT_METHOD_WHITELIST(cls, value):
-        warnings.warn(
-            "Using 'Retry.DEFAULT_METHOD_WHITELIST' is deprecated and "
-            "will be removed in v2.0. Use 'Retry.DEFAULT_ALLOWED_METHODS' instead",
-            DeprecationWarning,
-        )
-        cls.DEFAULT_ALLOWED_METHODS = value
-
-    @property
-    def DEFAULT_REDIRECT_HEADERS_BLACKLIST(cls):
-        warnings.warn(
-            "Using 'Retry.DEFAULT_REDIRECT_HEADERS_BLACKLIST' is deprecated and "
-            "will be removed in v2.0. Use 'Retry.DEFAULT_REMOVE_HEADERS_ON_REDIRECT' instead",
-            DeprecationWarning,
-        )
-        return cls.DEFAULT_REMOVE_HEADERS_ON_REDIRECT
-
-    @DEFAULT_REDIRECT_HEADERS_BLACKLIST.setter
-    def DEFAULT_REDIRECT_HEADERS_BLACKLIST(cls, value):
-        warnings.warn(
-            "Using 'Retry.DEFAULT_REDIRECT_HEADERS_BLACKLIST' is deprecated and "
-            "will be removed in v2.0. Use 'Retry.DEFAULT_REMOVE_HEADERS_ON_REDIRECT' instead",
-            DeprecationWarning,
-        )
-        cls.DEFAULT_REMOVE_HEADERS_ON_REDIRECT = value
-
-    @property
-    def BACKOFF_MAX(cls):
-        warnings.warn(
-            "Using 'Retry.BACKOFF_MAX' is deprecated and "
-            "will be removed in v2.0. Use 'Retry.DEFAULT_BACKOFF_MAX' instead",
-            DeprecationWarning,
-        )
-        return cls.DEFAULT_BACKOFF_MAX
-
-    @BACKOFF_MAX.setter
-    def BACKOFF_MAX(cls, value):
-        warnings.warn(
-            "Using 'Retry.BACKOFF_MAX' is deprecated and "
-            "will be removed in v2.0. Use 'Retry.DEFAULT_BACKOFF_MAX' instead",
-            DeprecationWarning,
-        )
-        cls.DEFAULT_BACKOFF_MAX = value
-
-
-@six.add_metaclass(_RetryMeta)
-class Retry(object):
-    """Retry configuration.
-
-    Each retry attempt will create a new Retry object with updated values, so
-    they can be safely reused.
-
-    Retries can be defined as a default for a pool::
-
-        retries = Retry(connect=5, read=2, redirect=5)
-        http = PoolManager(retries=retries)
-        response = http.request('GET', 'http://example.com/')
-
-    Or per-request (which overrides the default for the pool)::
-
-        response = http.request('GET', 'http://example.com/', retries=Retry(10))
-
-    Retries can be disabled by passing ``False``::
-
-        response = http.request('GET', 'http://example.com/', retries=False)
-
-    Errors will be wrapped in :class:`~urllib3.exceptions.MaxRetryError` unless
-    retries are disabled, in which case the causing exception will be raised.
-
-    :param int total:
-        Total number of retries to allow. Takes precedence over other counts.
-
-        Set to ``None`` to remove this constraint and fall back on other
-        counts.
-
-        Set to ``0`` to fail on the first retry.
-
-        Set to ``False`` to disable and imply ``raise_on_redirect=False``.
-
-    :param int connect:
-        How many connection-related errors to retry on.
-
-        These are errors raised before the request is sent to the remote server,
-        which we assume has not triggered the server to process the request.
-
-        Set to ``0`` to fail on the first retry of this type.
-
-    :param int read:
-        How many times to retry on read errors.
-
-        These errors are raised after the request was sent to the server, so the
-        request may have side-effects.
-
-        Set to ``0`` to fail on the first retry of this type.
-
-    :param int redirect:
-        How many redirects to perform. Limit this to avoid infinite redirect
-        loops.
-
-        A redirect is a HTTP response with a status code 301, 302, 303, 307 or
-        308.
-
-        Set to ``0`` to fail on the first retry of this type.
-
-        Set to ``False`` to disable and imply ``raise_on_redirect=False``.
-
-    :param int status:
-        How many times to retry on bad status codes.
-
-        These are retries made on responses, where status code matches
-        ``status_forcelist``.
-
-        Set to ``0`` to fail on the first retry of this type.
-
-    :param int other:
-        How many times to retry on other errors.
-
-        Other errors are errors that are not connect, read, redirect or status errors.
-        These errors might be raised after the request was sent to the server, so the
-        request might have side-effects.
-
-        Set to ``0`` to fail on the first retry of this type.
-
-        If ``total`` is not set, it's a good idea to set this to 0 to account
-        for unexpected edge cases and avoid infinite retry loops.
-
-    :param iterable allowed_methods:
-        Set of uppercased HTTP method verbs that we should retry on.
-
-        By default, we only retry on methods which are considered to be
-        idempotent (multiple requests with the same parameters end with the
-        same state). See :attr:`Retry.DEFAULT_ALLOWED_METHODS`.
-
-        Set to a ``False`` value to retry on any verb.
-
-        .. warning::
-
-            Previously this parameter was named ``method_whitelist``, that
-            usage is deprecated in v1.26.0 and will be removed in v2.0.
-
-    :param iterable status_forcelist:
-        A set of integer HTTP status codes that we should force a retry on.
-        A retry is initiated if the request method is in ``allowed_methods``
-        and the response status code is in ``status_forcelist``.
-
-        By default, this is disabled with ``None``.
-
-    :param float backoff_factor:
-        A backoff factor to apply between attempts after the second try
-        (most errors are resolved immediately by a second try without a
-        delay). urllib3 will sleep for::
-
-            {backoff factor} * (2 ** ({number of total retries} - 1))
-
-        seconds. If the backoff_factor is 0.1, then :func:`.sleep` will sleep
-        for [0.0s, 0.2s, 0.4s, ...] between retries. It will never be longer
-        than :attr:`Retry.DEFAULT_BACKOFF_MAX`.
-
-        By default, backoff is disabled (set to 0).
-
-    :param bool raise_on_redirect: Whether, if the number of redirects is
-        exhausted, to raise a MaxRetryError, or to return a response with a
-        response code in the 3xx range.
-
-    :param bool raise_on_status: Similar meaning to ``raise_on_redirect``:
-        whether we should raise an exception, or return a response,
-        if status falls in ``status_forcelist`` range and retries have
-        been exhausted.
-
-    :param tuple history: The history of the request encountered during
-        each call to :meth:`~Retry.increment`. The list is in the order
-        the requests occurred. Each list item is of class :class:`RequestHistory`.
-
-    :param bool respect_retry_after_header:
-        Whether to respect Retry-After header on status codes defined as
-        :attr:`Retry.RETRY_AFTER_STATUS_CODES` or not.
-
-    :param iterable remove_headers_on_redirect:
-        Sequence of headers to remove from the request when a response
-        indicating a redirect is returned before firing off the redirected
-        request.
-    """
-
-    #: Default methods to be used for ``allowed_methods``
-    DEFAULT_ALLOWED_METHODS = frozenset(
-        ["HEAD", "GET", "PUT", "DELETE", "OPTIONS", "TRACE"]
-    )
-
-    #: Default status codes to be used for ``status_forcelist``
-    RETRY_AFTER_STATUS_CODES = frozenset([413, 429, 503])
-
-    #: Default headers to be used for ``remove_headers_on_redirect``
-    DEFAULT_REMOVE_HEADERS_ON_REDIRECT = frozenset(["Authorization"])
-
-    #: Maximum backoff time.
-    DEFAULT_BACKOFF_MAX = 120
-
-    def __init__(
-        self,
-        total=10,
-        connect=None,
-        read=None,
-        redirect=None,
-        status=None,
-        other=None,
-        allowed_methods=_Default,
-        status_forcelist=None,
-        backoff_factor=0,
-        raise_on_redirect=True,
-        raise_on_status=True,
-        history=None,
-        respect_retry_after_header=True,
-        remove_headers_on_redirect=_Default,
-        # TODO: Deprecated, remove in v2.0
-        method_whitelist=_Default,
-    ):
-
-        if method_whitelist is not _Default:
-            if allowed_methods is not _Default:
-                raise ValueError(
-                    "Using both 'allowed_methods' and "
-                    "'method_whitelist' together is not allowed. "
-                    "Instead only use 'allowed_methods'"
-                )
-            warnings.warn(
-                "Using 'method_whitelist' with Retry is deprecated and "
-                "will be removed in v2.0. Use 'allowed_methods' instead",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-            allowed_methods = method_whitelist
-        if allowed_methods is _Default:
-            allowed_methods = self.DEFAULT_ALLOWED_METHODS
-        if remove_headers_on_redirect is _Default:
-            remove_headers_on_redirect = self.DEFAULT_REMOVE_HEADERS_ON_REDIRECT
-
-        self.total = total
-        self.connect = connect
-        self.read = read
-        self.status = status
-        self.other = other
-
-        if redirect is False or total is False:
-            redirect = 0
-            raise_on_redirect = False
-
-        self.redirect = redirect
-        self.status_forcelist = status_forcelist or set()
-        self.allowed_methods = allowed_methods
-        self.backoff_factor = backoff_factor
-        self.raise_on_redirect = raise_on_redirect
-        self.raise_on_status = raise_on_status
-        self.history = history or tuple()
-        self.respect_retry_after_header = respect_retry_after_header
-        self.remove_headers_on_redirect = frozenset(
-            [h.lower() for h in remove_headers_on_redirect]
-        )
-
-    def new(self, **kw):
-        params = dict(
-            total=self.total,
-            connect=self.connect,
-            read=self.read,
-            redirect=self.redirect,
-            status=self.status,
-            other=self.other,
-            status_forcelist=self.status_forcelist,
-            backoff_factor=self.backoff_factor,
-            raise_on_redirect=self.raise_on_redirect,
-            raise_on_status=self.raise_on_status,
-            history=self.history,
-            remove_headers_on_redirect=self.remove_headers_on_redirect,
-            respect_retry_after_header=self.respect_retry_after_header,
-        )
-
-        # TODO: If already given in **kw we use what's given to us
-        # If not given we need to figure out what to pass. We decide
-        # based on whether our class has the 'method_whitelist' property
-        # and if so we pass the deprecated 'method_whitelist' otherwise
-        # we use 'allowed_methods'. Remove in v2.0
-        if "method_whitelist" not in kw and "allowed_methods" not in kw:
-            if "method_whitelist" in self.__dict__:
-                warnings.warn(
-                    "Using 'method_whitelist' with Retry is deprecated and "
-                    "will be removed in v2.0. Use 'allowed_methods' instead",
-                    DeprecationWarning,
-                )
-                params["method_whitelist"] = self.allowed_methods
-            else:
-                params["allowed_methods"] = self.allowed_methods
-
-        params.update(kw)
-        return type(self)(**params)
-
-    @classmethod
-    def from_int(cls, retries, redirect=True, default=None):
-        """Backwards-compatibility for the old retries format."""
-        if retries is None:
-            retries = default if default is not None else cls.DEFAULT
-
-        if isinstance(retries, Retry):
-            return retries
-
-        redirect = bool(redirect) and None
-        new_retries = cls(retries, redirect=redirect)
-        log.debug("Converted retries value: %r -> %r", retries, new_retries)
-        return new_retries
-
-    def get_backoff_time(self):
-        """Formula for computing the current backoff
-
-        :rtype: float
-        """
-        # We want to consider only the last consecutive errors sequence (Ignore redirects).
-        consecutive_errors_len = len(
-            list(
-                takewhile(lambda x: x.redirect_location is None, reversed(self.history))
-            )
-        )
-        if consecutive_errors_len <= 1:
-            return 0
-
-        backoff_value = self.backoff_factor * (2 ** (consecutive_errors_len - 1))
-        return min(self.DEFAULT_BACKOFF_MAX, backoff_value)
-
-    def parse_retry_after(self, retry_after):
-        # Whitespace: https://tools.ietf.org/html/rfc7230#section-3.2.4
-        if re.match(r"^\s*[0-9]+\s*$", retry_after):
-            seconds = int(retry_after)
-        else:
-            retry_date_tuple = email.utils.parsedate_tz(retry_after)
-            if retry_date_tuple is None:
-                raise InvalidHeader("Invalid Retry-After header: %s" % retry_after)
-            if retry_date_tuple[9] is None:  # Python 2
-                # Assume UTC if no timezone was specified
-                # On Python2.7, parsedate_tz returns None for a timezone offset
-                # instead of 0 if no timezone is given, where mktime_tz treats
-                # a None timezone offset as local time.
-                retry_date_tuple = retry_date_tuple[:9] + (0,) + retry_date_tuple[10:]
-
-            retry_date = email.utils.mktime_tz(retry_date_tuple)
-            seconds = retry_date - time.time()
-
-        if seconds < 0:
-            seconds = 0
-
-        return seconds
-
-    def get_retry_after(self, response):
-        """Get the value of Retry-After in seconds."""
-
-        retry_after = response.headers.get("Retry-After")
-
-        if retry_after is None:
-            return None
-
-        return self.parse_retry_after(retry_after)
-
-    def sleep_for_retry(self, response=None):
-        retry_after = self.get_retry_after(response)
-        if retry_after:
-            time.sleep(retry_after)
-            return True
-
-        return False
-
-    def _sleep_backoff(self):
-        backoff = self.get_backoff_time()
-        if backoff <= 0:
-            return
-        time.sleep(backoff)
-
-    def sleep(self, response=None):
-        """Sleep between retry attempts.
-
-        This method will respect a server's ``Retry-After`` response header
-        and sleep the duration of the time requested. If that is not present, it
-        will use an exponential backoff. By default, the backoff factor is 0 and
-        this method will return immediately.
-        """
-
-        if self.respect_retry_after_header and response:
-            slept = self.sleep_for_retry(response)
-            if slept:
-                return
-
-        self._sleep_backoff()
-
-    def _is_connection_error(self, err):
-        """Errors when we're fairly sure that the server did not receive the
-        request, so it should be safe to retry.
-        """
-        if isinstance(err, ProxyError):
-            err = err.original_error
-        return isinstance(err, ConnectTimeoutError)
-
-    def _is_read_error(self, err):
-        """Errors that occur after the request has been started, so we should
-        assume that the server began processing it.
-        """
-        return isinstance(err, (ReadTimeoutError, ProtocolError))
-
-    def _is_method_retryable(self, method):
-        """Checks if a given HTTP method should be retried upon, depending if
-        it is included in the allowed_methods
-        """
-        # TODO: For now favor if the Retry implementation sets its own method_whitelist
-        # property outside of our constructor to avoid breaking custom implementations.
-        if "method_whitelist" in self.__dict__:
-            warnings.warn(
-                "Using 'method_whitelist' with Retry is deprecated and "
-                "will be removed in v2.0. Use 'allowed_methods' instead",
-                DeprecationWarning,
-            )
-            allowed_methods = self.method_whitelist
-        else:
-            allowed_methods = self.allowed_methods
-
-        if allowed_methods and method.upper() not in allowed_methods:
-            return False
-        return True
-
-    def is_retry(self, method, status_code, has_retry_after=False):
-        """Is this method/status code retryable? (Based on allowlists and control
-        variables such as the number of total retries to allow, whether to
-        respect the Retry-After header, whether this header is present, and
-        whether the returned status code is on the list of status codes to
-        be retried upon on the presence of the aforementioned header)
-        """
-        if not self._is_method_retryable(method):
-            return False
-
-        if self.status_forcelist and status_code in self.status_forcelist:
-            return True
-
-        return (
-            self.total
-            and self.respect_retry_after_header
-            and has_retry_after
-            and (status_code in self.RETRY_AFTER_STATUS_CODES)
-        )
-
-    def is_exhausted(self):
-        """Are we out of retries?"""
-        retry_counts = (
-            self.total,
-            self.connect,
-            self.read,
-            self.redirect,
-            self.status,
-            self.other,
-        )
-        retry_counts = list(filter(None, retry_counts))
-        if not retry_counts:
-            return False
-
-        return min(retry_counts) < 0
-
-    def increment(
-        self,
-        method=None,
-        url=None,
-        response=None,
-        error=None,
-        _pool=None,
-        _stacktrace=None,
-    ):
-        """Return a new Retry object with incremented retry counters.
-
-        :param response: A response object, or None, if the server did not
-            return a response.
-        :type response: :class:`~urllib3.response.HTTPResponse`
-        :param Exception error: An error encountered during the request, or
-            None if the response was received successfully.
-
-        :return: A new ``Retry`` object.
-        """
-        if self.total is False and error:
-            # Disabled, indicate to re-raise the error.
-            raise six.reraise(type(error), error, _stacktrace)
-
-        total = self.total
-        if total is not None:
-            total -= 1
-
-        connect = self.connect
-        read = self.read
-        redirect = self.redirect
-        status_count = self.status
-        other = self.other
-        cause = "unknown"
-        status = None
-        redirect_location = None
-
-        if error and self._is_connection_error(error):
-            # Connect retry?
-            if connect is False:
-                raise six.reraise(type(error), error, _stacktrace)
-            elif connect is not None:
-                connect -= 1
-
-        elif error and self._is_read_error(error):
-            # Read retry?
-            if read is False or not self._is_method_retryable(method):
-                raise six.reraise(type(error), error, _stacktrace)
-            elif read is not None:
-                read -= 1
-
-        elif error:
-            # Other retry?
-            if other is not None:
-                other -= 1
-
-        elif response and response.get_redirect_location():
-            # Redirect retry?
-            if redirect is not None:
-                redirect -= 1
-            cause = "too many redirects"
-            redirect_location = response.get_redirect_location()
-            status = response.status
-
-        else:
-            # Incrementing because of a server error like a 500 in
-            # status_forcelist and the given method is in the allowed_methods
-            cause = ResponseError.GENERIC_ERROR
-            if response and response.status:
-                if status_count is not None:
-                    status_count -= 1
-                cause = ResponseError.SPECIFIC_ERROR.format(status_code=response.status)
-                status = response.status
-
-        history = self.history + (
-            RequestHistory(method, url, error, status, redirect_location),
-        )
-
-        new_retry = self.new(
-            total=total,
-            connect=connect,
-            read=read,
-            redirect=redirect,
-            status=status_count,
-            other=other,
-            history=history,
-        )
-
-        if new_retry.is_exhausted():
-            raise MaxRetryError(_pool, url, error or ResponseError(cause))
-
-        log.debug("Incremented Retry for (url='%s'): %r", url, new_retry)
-
-        return new_retry
-
-    def __repr__(self):
-        return (
-            "{cls.__name__}(total={self.total}, connect={self.connect}, "
-            "read={self.read}, redirect={self.redirect}, status={self.status})"
-        ).format(cls=type(self), self=self)
-
-    def __getattr__(self, item):
-        if item == "method_whitelist":
-            # TODO: Remove this deprecated alias in v2.0
-            warnings.warn(
-                "Using 'method_whitelist' with Retry is deprecated and "
-                "will be removed in v2.0. Use 'allowed_methods' instead",
-                DeprecationWarning,
-            )
-            return self.allowed_methods
-        try:
-            return getattr(super(Retry, self), item)
-        except AttributeError:
-            return getattr(Retry, item)
-
-
-# For backwards compatibility (equivalent to pre-v1.9):
-Retry.DEFAULT = Retry(3)