Update parquet files (step 66 of 249)

spaces/1gistliPinn/ChatGPT4/Examples/3ds Max 2014 X32 Torrent __FULL__.md

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spaces/1line/AutoGPT/autogpt/speech/say.py

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-""" Text to speech module """
-import threading
-from threading import Semaphore
-
-from autogpt.config import Config
-from autogpt.speech.brian import BrianSpeech
-from autogpt.speech.eleven_labs import ElevenLabsSpeech
-from autogpt.speech.gtts import GTTSVoice
-from autogpt.speech.macos_tts import MacOSTTS
-
-CFG = Config()
-DEFAULT_VOICE_ENGINE = GTTSVoice()
-VOICE_ENGINE = None
-if CFG.elevenlabs_api_key:
-    VOICE_ENGINE = ElevenLabsSpeech()
-elif CFG.use_mac_os_tts == "True":
-    VOICE_ENGINE = MacOSTTS()
-elif CFG.use_brian_tts == "True":
-    VOICE_ENGINE = BrianSpeech()
-else:
-    VOICE_ENGINE = GTTSVoice()
-
-
-QUEUE_SEMAPHORE = Semaphore(
-    1
-)  # The amount of sounds to queue before blocking the main thread
-
-
-def say_text(text: str, voice_index: int = 0) -> None:
-    """Speak the given text using the given voice index"""
-
-    def speak() -> None:
-        success = VOICE_ENGINE.say(text, voice_index)
-        if not success:
-            DEFAULT_VOICE_ENGINE.say(text)
-
-        QUEUE_SEMAPHORE.release()
-
-    QUEUE_SEMAPHORE.acquire(True)
-    thread = threading.Thread(target=speak)
-    thread.start()

spaces/1phancelerku/anime-remove-background/Dig Digger Pro APK 1.0-pro-2022-09-16 The Ultimate File Recovery Tool for Rooted Devices.md

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-<p>Have you ever accidentally deleted some important photos, videos, documents, or music files from your Android device? Or have you ever lost some files due to formatting, virus attack, system crash, or other reasons? If yes, then you might be wondering how to get them back. Well, don't worry, because there is a solution for you: Dig Digger Pro APK.</p>
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-<p>To use Dig Digger Pro APK to recover lost files from your Android device, you need to follow these steps:</p>
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-<p>The app requires root access for full functionality, which means that you need to have a rooted device to use the Full Scan mode and recover files from your entire memory. Rooting your device may void your warranty, expose your device to security risks, or cause system instability.</p>
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spaces/1phancelerku/anime-remove-background/Download Real Racing 3 APK from APKPure and Enjoy the Premier Racing Experience on Android.md

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-<h1>Download Real Racing 3 APKPure: The Ultimate Guide</h1>
-<p>If you are a fan of racing games, you have probably heard of Real Racing 3, one of the most realistic and immersive racing games on mobile devices. But did you know that you can download it from APKPure, a third-party app store that offers fast and safe downloads of free and updated apps? In this article, we will tell you everything you need to know about Real Racing 3 and APKPure, and how to download them on your Android device.</p>
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-<p>Real Racing 3 is a racing game developed by Firemonkeys Studios and published by Electronic Arts. It was released in 2013 for iOS and Android devices, and has since become one of the most popular and acclaimed racing games on mobile platforms. It has won several awards, such as the Best Mobile Game at the BAFTA Games Awards, and has been downloaded over 500 million times.</p>
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-<li>Locate the Sonic Forces Running Battle Mod file that you downloaded from HappyMod or another source. It should be a .zip or .rar file.</li>
-<li>Select the mod file and click on "Install" or "Apply". The tool will automatically copy and replace the game files with the modded ones.</li>
-<li>Wait for the installation process to finish. It may take a few minutes depending on the size of the mod and your device's speed.</li>
-<li>Launch the game and enjoy the mod. You may need to restart your device or clear the game's cache if you encounter any issues.</li>
-</ol>
- <h2>Tips and tricks for playing Sonic Forces Running Battle Mod</h2>
-<h3>Choose your species, Wispon, and Star Runner wisely</h3>
-<p>One of the fun aspects of Sonic Forces Running Battle Mod is that you can create your own custom character with different options. You can choose from seven different species: wolf, rabbit, cat, dog, bear, bird, or hedgehog. Each species has its own unique ability that can help you in the game. For example, wolves can automatically draw in nearby rings, rabbits can jump higher, cats can always keep one ring after being hit, etc.</p>
-<p>You can also choose from different Wispons that have different effects and attacks. Some Wispons are better suited for certain stages than others. For example, lightning Wispons can activate electric rails, drill Wispons can dig through sand or snow, fire Wispons can burn wooden bridges or crates, etc. You can also use your Wispon to attack other players or enemies by tapping on them.</p>
-<p>You can also choose from different Star Runners that have different stats and bonuses. Star Runners are special outfits that you can unlock by collecting red star rings or trophies. They can boost your speed, acceleration, strength, or ring bonus. Some Star Runners also have special effects such as invincibility, magnetism, or double jump.</p>
- <h3>Collect rings, items, and power-ups</h3>
-<p>Rings are the main currency of Sonic Forces Running Battle Mod. You can use them to buy new Wispons, outfits, items, or upgrades. You can also use them to revive yourself if you fall off the stage or get hit by an enemy. You can collect rings by running through them or by using your Wispon ability. You can also get rings by completing missions, achievements, or daily challenges.</p>
-<p>Items are consumables that you can use before or during a race to give you an edge over your opponents. You can buy items with rings or get them as rewards. Some items are speed shoes, shield, invincibility, bomb, boost, etc. You can use items by tapping on their icons on the screen.</p>
-<p>Power-ups are temporary effects that you can get by running through special capsules on the stage. They can help you or hinder your opponents. Some power-ups are magnet, lightning, fireball, ice cube, tornado, etc. You can activate power-ups by tapping on their icons on the screen.</p>
- <h3>Use your Wispon abilities and attacks strategically</h3>
-<p>Your Wispon is not only a weapon but also a tool that can help you navigate the stage and reach hidden areas. You can use your Wispon ability by swiping up on the screen. Depending on your Wispon type, you can do different things such as fly, drill, dash, hover, etc. You can also use your Wispon ability to collect rings or items that are out of reach.</p>
-<p>You can also use your Wispon to attack other players or enemies by tapping on them. Depending on your Wispon type, you can do different things such as shoot projectiles, create explosions, freeze opponents, etc. You can also use your Wispon attack to destroy obstacles or traps that are blocking your way.</p>
- <h3>Avoid obstacles and traps</h3>
-<p>The stages in Sonic Forces Running Battle Mod are full of obstacles and traps that can slow you down or damage you. You need to avoid them or destroy them with your Wispon attack. Some obstacles and traps are spikes, lasers, mines, swinging axes, falling rocks, etc. You can also use obstacles and traps to your advantage by pushing other players into them or by using them as shortcuts.</p>
- <h3>Race against other players online</h3>
-<p>The multiplayer mode in Sonic Forces Running Battle Mod is where you can test your skills and compete with other players online. You can join or create a room with up to four players and race on any stage that you have unlocked. You can also chat with other players and send them emojis. You can choose to race for fun or for ranking points and medals. You can also join or create a team with other players and cooperate or challenge them.</p>
-<p>The multiplayer mode is fast-paced and competitive, so you need to be quick and smart. You need to use your Wispon abilities and attacks, items, power-ups, and shortcuts to gain an advantage over your rivals. You also need to avoid or counter their attacks, obstacles, and traps. You can also use your Star Runner bonus to boost your performance. The player who reaches the finish line first wins the race.</p>
- <h2>Conclusion and FAQs</h2>
-<h3>Summary of the article and benefits of the mod</h3>
-<p>Sonic Forces Running Battle Mod is a great mod for Sonic fans who want to enjoy a more fun and exciting racing game. The mod adds God Mode, unlimited money, increased speed, and all unlocked characters, stages, and items to the original game. The mod also improves the graphics, sound, and performance of the game. The mod has the same gameplay as the original game, but with some enhancements. You can create your own custom character with different species, Wispons, and Star Runners. You can run through various stages that are based on the Sonic Forces console game. You can collect rings, items, power-ups, and trophies as you run. You can also race against other players online in a multiplayer mode.</p>
-<p>The mod is easy to download and install using HedgeModManager or Sonic Forces Mod Installer. You just need to have an Android device with at least Android 4.1 or higher and 300 MB of free space. You also need to have the original game installed on your device. You can download the mod from HappyMod or other sources online.</p>
-<p>The mod is fun and challenging to play with some tips and tricks. You need to choose your species, Wispon, and Star Runner wisely based on their abilities, effects, and bonuses. You need to collect rings, items, and power-ups to help you in the game. You need to use your Wispon abilities and attacks strategically to navigate the stage and attack your opponents. You need to avoid obstacles and traps that can slow you down or damage you. You need to race against other players online and use your skills and items to win.</p>
- <h3>Five unique FAQs about the mod</h3>
-<table>
-<tr><td><b>Question</b></td><td><b>Answer</b></td></tr>
-<tr><td>Is Sonic Forces Running Battle Mod safe to use?</td><td>Yes, Sonic Forces Running Battle Mod is safe to use as long as you download it from a trusted source and use a reliable mod installer or manager. However, you should always backup your game data before installing any mod.</td></tr>
-<tr><td>Can I play Sonic Forces Running Battle Mod offline?</td><td>Yes, you can play Sonic Forces Running Battle Mod offline in the single-player mode. However, you will not be able to access the multiplayer mode or some features that require an internet connection.</td></tr>
-<tr><td>Can I play Sonic Forces Running Battle Mod with my friends?</td><td>Yes, you can play Sonic Forces Running Battle Mod with your friends in the multiplayer mode. You can join or create a room with up to four players and race on any stage that you have unlocked. You can also chat with your friends and send them emojis.</td></tr>
-<tr><td>How do I update Sonic Forces Running Battle Mod?</td><td>To update Sonic Forces Running Battle Mod, you need to download the latest version of the mod from HappyMod or another source online. Then, you need to uninstall the previous version of the mod using HedgeModManager or Sonic Forces Mod Installer. After that, you need to install the new version of the mod using the same tool.</td></tr>
-<tr><td>How do I uninstall Sonic Forces Running Battle Mod?</td><td>To uninstall Sonic Forces Running Battle Mod, you need to use HedgeModManager or Sonic Forces Mod Installer. You need to open the tool and locate the mod file that you installed. Then, you need to select the mod file and click on "Uninstall" or "Remove". The tool will automatically restore the original game files.</td></tr>
-</table></p> 401be4b1e0<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Enjoy Live Streaming of Over 100 Football and Basketball Leagues with Live Score 808 APK.md

@@ -1,125 +0,0 @@
-
-<h1>Live Score 808 APK: A Unique Football Game for Android</h1>
-<p>Are you a football fan who loves to watch and play the game? Do you want to experience a different kind of football game on your Android device? If yes, then you should try Live Score 808 APK, a free smartphone application that lets you compete against your favorite football teams in a fun and challenging way. In this article, we will tell you everything you need to know about Live Score 808 APK, including its features, how to download and install it, why you should play it, and some tips and tricks to help you win. Let's get started!</p>
- <h2>What is Live Score 808 APK?</h2>
-<p>Live Score 808 APK is a football game that is unlike any other. Instead of controlling an entire team, you can only manipulate a single player at a time. Your goal is to score as many goals as possible while avoiding the opponents and obstacles on the field. You can choose from hundreds of players from different countries and leagues, each with their own skills and abilities. You can also use power-ups and boosters to enhance your performance and overcome the challenges. The game has realistic graphics, sound effects, and animations that make you feel like you are in a real stadium. You can also check the live scores of real matches and compare your results with other players around the world.</p>
-<h2>live score 808 apk</h2><br /><p><b><b>Download</b> &#11088; <a href="https://jinyurl.com/2uNNnU">https://jinyurl.com/2uNNnU</a></b></p><br /><br />
- <h3>Features of Live Score 808 APK</h3>
-<p>Some of the features that make Live Score 808 APK an amazing football game are:</p>
-<ul>
-<li>It is free to download and play.</li>
-<li>It has simple and intuitive controls that are easy to learn and use.</li>
-<li>It has hundreds of players from different countries and leagues to choose from.</li>
-<li>It has various power-ups and boosters that can help you score more goals and avoid the enemies.</li>
-<li>It has realistic graphics, sound effects, and animations that create an immersive gaming experience.</li>
-<li>It has live scores of real matches that you can follow and compare with your own results.</li>
-<li>It has leaderboards and achievements that you can unlock and share with your friends.</li>
-</ul>
- <h3>How to download and install Live Score 808 APK</h3>
-<p>To download and install Live Score 808 APK on your Android device, you need to follow these steps:</p>
-<ol>
-<li>Go to the official website of Live Score 808 APK or click on this link: <a href="(^1^)">https://score808-live.en.softonic.com/android</a></li>
-<li>Click on the "Download" button and wait for the file to be downloaded on your device.</li>
-<li>Once the file is downloaded, go to your device's settings and enable the option to install apps from unknown sources.</li>
-<li>Locate the downloaded file in your device's storage and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to be completed.</li>
-<li>Launch the app and enjoy playing Live Score 808 APK!</li>
-</ol>
- <h2>Why play Live Score 808 APK?</h2>
-<p>If you are still wondering why you should play Live Score 808 APK, here are some reasons that might convince you:</p>
- <h3>Pros of Live Score 808 APK</h3>
-<ul>
-<li>It is a unique and innovative football game that tests your skills and reflexes.</li>
-<li>It is a fun and entertaining way to pass the time and relieve stress.</li>
-<li>It is a great way to show your love and support for your favorite football teams and players.</li>
-<li>It is a challenging and rewarding game that keeps you motivated and engaged.</li>
-</ul>
- <h3>Cons <h3>Cons of Live Score 808 APK</h3>
-<ul>
-<li>It is a relatively new game that might have some bugs and glitches.</li>
-<li>It requires a stable internet connection to play and access the live scores.</li>
-<li>It might consume a lot of battery and data on your device.</li>
-<li>It might not be compatible with some older or low-end devices.</li>
-</ul>
- <h2>Tips and tricks for playing Live Score 808 APK</h2>
-<p>If you want to improve your skills and performance in Live Score 808 APK, here are some tips and tricks that you can use:</p>
- <h3>Choose your player wisely</h3>
-<p>Before you start a match, you can choose from hundreds of players from different countries and leagues. Each player has their own stats and abilities, such as speed, power, accuracy, and stamina. You should choose a player that suits your playstyle and strategy. For example, if you want to score fast and furious goals, you should pick a player with high speed and power. If you want to play more defensively and tactically, you should pick a player with high accuracy and stamina.</p>
- <h3>Use the power-ups and boosters</h3>
-<p>During the match, you can collect and use various power-ups and boosters that can help you score more goals and avoid the enemies. Some of the power-ups and boosters are:</p>
-<ul>
-<li>Shield: It protects you from the opponents and obstacles for a short time.</li>
-<li>Magnet: It attracts the coins and gems to you for a short time.</li>
-<li>Rocket: It boosts your speed and power for a short time.</li>
-<li>Bomb: It explodes and clears the enemies and obstacles around you for a short time.</li>
-<li>Freeze: It freezes the opponents and obstacles for a short time.</li>
-</ul>
- <h3>Watch out for the opponents and obstacles</h3>
-<p>As you play, you will encounter various opponents and obstacles that will try to stop you from scoring. Some of the opponents and obstacles are:</p>
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-<ul>
-<li>Goalkeeper: He will try to block your shots and save the goals.</li>
-<li>Defender: He will try to tackle you and steal the ball from you.</li>
-<li>Mine: It will explode if you touch it and damage your health.</li>
-<li>Spike: It will pierce you if you touch it and damage your health.</li>
-<li>Hole: It will make you fall if you step on it and end your run.</li>
-</ul>
- <h3>Collect coins and gems</h3>
-<p>As you play, you can also collect coins and gems that are scattered on the field. Coins and gems are useful for buying new players, power-ups, boosters, and other items in the shop. You can also use coins and gems to revive yourself if you lose or to continue playing if you run out of time. The more coins and gems you collect, the more options and advantages you have in the game.</p>
- <h2>Conclusion</h2>
-<p>Live Score 808 APK is a unique football game that lets you compete against your favorite football teams in a fun and challenging way. You can choose from hundreds of players from different countries and leagues, each with their own skills and abilities. You can also use power-ups and boosters to enhance your performance and overcome the challenges. The game has realistic graphics, sound effects, and animations that make you feel like you are in a real stadium. You can also check the live scores of real matches and compare your results with other players around the world. Live Score 808 APK is a free smartphone application that is easy to download and install on your Android device. If you are a football fan who loves to watch and play the game, you should definitely try Live Score 808 APK!</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about Live Score 808 APK:</p>
- <ol>
-<li><b>What is the latest version of Live Score 808 APK?</b></li>
-<p>The latest version of Live Score 808 APK is 1.0.8, which was released on June 22, 2023. It has some bug fixes and performance improvements.</p>
- <li><b>Is Live Score 808 APK safe to use?</b></li>
-<p>Yes, Live Score 808 APK is safe to use as long as you download it from the official website or a trusted source. It does not contain any viruses or malware that can harm your device or data.</p>
- <li><b>How can I contact the developer of Live Score 808 APK?</b></li>
-<p>You can contact the developer of Live Score 808 APK by sending an email to [email protected] or by visiting their Facebook page at < a href="">https://www.facebook.com/LiveScore808</a>.</p>
- <li><b>Can I play Live Score 808 APK offline?</b></li>
-<p>No, you cannot play Live Score 808 APK offline. You need a stable internet connection to play and access the live scores.</p>
- <li><b>Can I play Live Score 808 APK on other devices?</b></li>
-<p>Yes, you can play Live Score 808 APK on other devices that support Android operating system. However, you might need to adjust the settings and graphics according to your device's specifications.</p>
-</ol></p> 197e85843d<br />
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spaces/2023Liu2023/bingo/src/components/ui/dialog.tsx

@@ -1,128 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as DialogPrimitive from '@radix-ui/react-dialog'
-
-import { cn } from '@/lib/utils'
-import { IconClose } from '@/components/ui/icons'
-
-const Dialog = DialogPrimitive.Root
-
-const DialogTrigger = DialogPrimitive.Trigger
-
-const DialogPortal = ({
-  className,
-  children,
-  ...props
-}: DialogPrimitive.DialogPortalProps) => (
-  <DialogPrimitive.Portal className={cn(className)} {...props}>
-    <div className="fixed inset-0 z-50 flex items-start justify-center sm:items-center">
-      {children}
-    </div>
-  </DialogPrimitive.Portal>
-)
-DialogPortal.displayName = DialogPrimitive.Portal.displayName
-
-const DialogOverlay = React.forwardRef<
-  React.ElementRef<typeof DialogPrimitive.Overlay>,
-  React.ComponentPropsWithoutRef<typeof DialogPrimitive.Overlay>
->(({ className, ...props }, ref) => (
-  <DialogPrimitive.Overlay
-    ref={ref}
-    className={cn(
-      'bg-white fixed inset-0 z-50 bg-background/80 backdrop-blur-sm transition-all duration-100 data-[state=closed]:animate-out data-[state=closed]:fade-out data-[state=open]:fade-in',
-      className
-    )}
-    {...props}
-  />
-))
-DialogOverlay.displayName = DialogPrimitive.Overlay.displayName
-
-const DialogContent = React.forwardRef<
-  React.ElementRef<typeof DialogPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof DialogPrimitive.Content>
->(({ className, children, ...props }, ref) => (
-  <DialogPortal>
-    <DialogOverlay />
-    <DialogPrimitive.Content
-      ref={ref}
-      className={cn(
-        'fixed z-50 grid w-full gap-4 rounded-b-lg border bg-background p-6 shadow-sm animate-in data-[state=open]:fade-in-90 data-[state=open]:slide-in-from-bottom-10 sm:max-w-lg sm:rounded-lg sm:zoom-in-90 data-[state=open]:sm:slide-in-from-bottom-0',
-        className
-      )}
-      {...props}
-    >
-      {children}
-      <DialogPrimitive.Close className="absolute right-4 top-4 rounded-sm opacity-70 ring-offset-background transition-opacity hover:opacity-100 focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 disabled:pointer-events-none data-[state=open]:bg-accent data-[state=open]:text-muted-foreground">
-        <IconClose />
-        <span className="sr-only">Close</span>
-      </DialogPrimitive.Close>
-    </DialogPrimitive.Content>
-  </DialogPortal>
-))
-DialogContent.displayName = DialogPrimitive.Content.displayName
-
-const DialogHeader = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col space-y-1.5 text-center sm:text-left',
-      className
-    )}
-    {...props}
-  />
-)
-DialogHeader.displayName = 'DialogHeader'
-
-const DialogFooter = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2',
-      className
-    )}
-    {...props}
-  />
-)
-DialogFooter.displayName = 'DialogFooter'
-
-const DialogTitle = React.forwardRef<
-  React.ElementRef<typeof DialogPrimitive.Title>,
-  React.ComponentPropsWithoutRef<typeof DialogPrimitive.Title>
->(({ className, ...props }, ref) => (
-  <DialogPrimitive.Title
-    ref={ref}
-    className={cn(
-      'text-lg font-semibold leading-none tracking-tight',
-      className
-    )}
-    {...props}
-  />
-))
-DialogTitle.displayName = DialogPrimitive.Title.displayName
-
-const DialogDescription = React.forwardRef<
-  React.ElementRef<typeof DialogPrimitive.Description>,
-  React.ComponentPropsWithoutRef<typeof DialogPrimitive.Description>
->(({ className, ...props }, ref) => (
-  <DialogPrimitive.Description
-    ref={ref}
-    className={cn('text-sm text-muted-foreground', className)}
-    {...props}
-  />
-))
-DialogDescription.displayName = DialogPrimitive.Description.displayName
-
-export {
-  Dialog,
-  DialogTrigger,
-  DialogContent,
-  DialogHeader,
-  DialogFooter,
-  DialogTitle,
-  DialogDescription
-}

spaces/2ndelement/voicevox/voicevox_engine/engine_manifest/EngineManifest.py

@@ -1,58 +0,0 @@
-from typing import List, Optional
-
-from pydantic import BaseModel, Field
-
-
-class UpdateInfo(BaseModel):
-    """
-    エンジンのアップデート情報
-    """
-
-    version: str = Field(title="エンジンのバージョン名")
-    descriptions: List[str] = Field(title="アップデートの詳細についての説明")
-    contributors: Optional[List[str]] = Field(title="貢献者名")
-
-
-class LicenseInfo(BaseModel):
-    """
-    依存ライブラリのライセンス情報
-    """
-
-    name: str = Field(title="依存ライブラリ名")
-    version: Optional[str] = Field(title="依存ライブラリのバージョン")
-    license: Optional[str] = Field(title="依存ライブラリのライセンス名")
-    text: str = Field(title="依存ライブラリのライセンス本文")
-
-
-class SupportedFeatures(BaseModel):
-    """
-    エンジンが持つ機能の一覧
-    """
-
-    adjust_mora_pitch: bool = Field(title="モーラごとの音高の調整")
-    adjust_phoneme_length: bool = Field(title="音素ごとの長さの調整")
-    adjust_speed_scale: bool = Field(title="全体の話速の調整")
-    adjust_pitch_scale: bool = Field(title="全体の音高の調整")
-    adjust_intonation_scale: bool = Field(title="全体の抑揚の調整")
-    adjust_volume_scale: bool = Field(title="全体の音量の調整")
-    interrogative_upspeak: bool = Field(title="疑問文の自動調整")
-    synthesis_morphing: bool = Field(title="2人の話者でモーフィングした音声を合成")
-    manage_library: bool = Field(title="音声ライブラリのインストール・アンインストール")
-
-
-class EngineManifest(BaseModel):
-    """
-    エンジン自体に関する情報
-    """
-
-    manifest_version: str = Field(title="マニフェストのバージョン")
-    name: str = Field(title="エンジン名")
-    brand_name: str = Field(title="ブランド名")
-    uuid: str = Field(title="エンジンのUUID")
-    url: str = Field(title="エンジンのURL")
-    icon: str = Field(title="エンジンのアイコンをBASE64エンコードしたもの")
-    default_sampling_rate: int = Field(title="デフォルトのサンプリング周波数")
-    terms_of_service: str = Field(title="エンジンの利用規約")
-    update_infos: List[UpdateInfo] = Field(title="エンジンのアップデート情報")
-    dependency_licenses: List[LicenseInfo] = Field(title="依存関係のライセンス情報")
-    supported_features: SupportedFeatures = Field(title="エンジンが持つ機能")

spaces/AK-12/llama-gradio-chat/app.py

@@ -1,19 +0,0 @@
-import gradio as gr
-import requests
-# def greet(name):
-#     return "Hello " + name + "!!"
-#     import requests
-
-# API_URL = "https://api-inference.huggingface.co/models/AK-12/llama-2-medical-fine-tune"
-# headers = {"Authorization": "Bearer "}
-
-def query(payload):
-	response = requests.post("https://api-inference.huggingface.co/models/AK-12/llama-2-medical-fine-tune", headers={"Authorization": "Bearer "}, json=payload)
-	return response.json()
-	
-# output = query({
-# 	"inputs": "What is haller cells? ",
-# })
-
-iface = gr.Interface(fn=query, inputs="text", outputs="text")
-iface.launch()

spaces/Ababababababbababa/Ashaar/poetry_diacritizer/predict.py

@@ -1,167 +0,0 @@
-import os
-from typing import Dict
-
-from diacritization_evaluation import der, wer
-import torch
-from torch import nn
-from torch import optim
-from torch.cuda.amp import autocast
-from torch.utils.tensorboard.writer import SummaryWriter
-from tqdm.notebook import tqdm
-from tqdm import trange
-from diacritization_evaluation import util 
-
-from .config_manager import ConfigManager
-from .dataset import load_iterators
-from .diacritizer import CBHGDiacritizer, Seq2SeqDiacritizer
-from .options import OptimizerType
-import gdown
-
-class Trainer:
-    def run(self):
-        raise NotImplementedError
-
-
-class GeneralTrainer(Trainer):
-    def __init__(self, config_path: str, model_kind: str) -> None:
-        self.config_path = config_path
-        self.model_kind = model_kind
-        self.config_manager = ConfigManager(
-            config_path=config_path, model_kind=model_kind
-        )
-        self.config = self.config_manager.config
-        self.losses = []
-        self.lr = 0
-        self.pad_idx = 0
-        self.criterion = nn.CrossEntropyLoss(ignore_index=self.pad_idx)
-        self.set_device()
-
-        self.config_manager.create_remove_dirs()
-        self.text_encoder = self.config_manager.text_encoder
-        self.start_symbol_id = self.text_encoder.start_symbol_id
-        self.summary_manager = SummaryWriter(log_dir=self.config_manager.log_dir)
-
-        self.model = self.config_manager.get_model()
-
-        self.optimizer = self.get_optimizer()
-        self.model = self.model.to(self.device)
-
-        self.load_model(model_path=self.config.get("train_resume_model_path"))
-        self.load_diacritizer()
-
-        self.initialize_model()
-
-
-    def set_device(self):
-        if self.config.get("device"):
-            self.device = self.config["device"]
-        else:
-            self.device = "cuda" if torch.cuda.is_available() else "cpu"
-
-    def load_diacritizer(self):
-        if self.model_kind in ["cbhg", "baseline"]:
-            self.diacritizer = CBHGDiacritizer(self.config_path, self.model_kind)
-        elif self.model_kind in ["seq2seq", "tacotron_based"]:
-            self.diacritizer = Seq2SeqDiacritizer(self.config_path, self.model_kind)
-
-    def initialize_model(self):
-        if self.global_step > 1:
-            return
-        if self.model_kind == "transformer":
-            print("Initializing using xavier_uniform_")
-            self.model.apply(initialize_weights)
-
-
-    def load_model(self, model_path: str = None, load_optimizer: bool = True):
-      with open(
-          self.config_manager.base_dir / f"{self.model_kind}_network.txt", "w"
-      ) as file:
-          file.write(str(self.model))
-
-      if model_path is None:
-          last_model_path = self.config_manager.get_last_model_path()
-          if last_model_path is None:
-              self.global_step = 1
-              return
-      else:
-          last_model_path = model_path
-
-      print(f"loading from {last_model_path}")
-      saved_model = torch.load(last_model_path, torch.device(self.config.get("device")))
-      self.model.load_state_dict(saved_model["model_state_dict"])
-      if load_optimizer:
-          self.optimizer.load_state_dict(saved_model["optimizer_state_dict"])
-      self.global_step = saved_model["global_step"] + 1
-
-class DiacritizationTester(GeneralTrainer):
-    def __init__(self, config_path: str, model_kind: str, model_path: str) -> None:
-        # if config_path == 'config/test.yml' or config_path == "Arabic_Diacritization/config/test.yml":
-        #   print("Exporting the pretrained models ... ")
-        #   url = 'https://drive.google.com/uc?id=12aYNY7cbsLNzhdPdC2K3u1sgrb1lpzwO' 
-        #   gdown.cached_download(url,'model.zip', quiet=False, postprocess=gdown.extractall)
-        
-        self.config_path = config_path
-        self.model_kind = model_kind
-        self.config_manager = ConfigManager(
-            config_path=config_path, model_kind=model_kind
-        )
-        self.config = self.config_manager.config
-        # print(self.config)
-        self.pad_idx = 0
-        self.criterion = nn.CrossEntropyLoss(ignore_index=self.pad_idx)
-        self.set_device()
-
-        self.text_encoder = self.config_manager.text_encoder
-        self.start_symbol_id = self.text_encoder.start_symbol_id
-
-        self.model = self.config_manager.get_model()
-
-        self.model = self.model.to(self.device)
-        self.load_model(model_path=model_path, load_optimizer=False)
-        self.load_diacritizer()
-        self.diacritizer.set_model(self.model)
-        self.initialize_model()
-
-    def collate_fn(self, data):
-        """
-        Padding the input and output sequences
-        """
-
-        def merge(sequences):
-            lengths = [len(seq) for seq in sequences]
-            padded_seqs = torch.zeros(len(sequences), max(lengths)).long()
-            for i, seq in enumerate(sequences):
-                end = lengths[i]
-                padded_seqs[i, :end] = seq[:end]
-            return padded_seqs, lengths
-
-        data.sort(key=lambda x: len(x[0]), reverse=True)
-
-        # separate source and target sequences
-        src_seqs, trg_seqs, original = zip(*data)
-
-        # merge sequences (from tuple of 1D tensor to 2D tensor)
-        src_seqs, src_lengths = merge(src_seqs)
-        trg_seqs, trg_lengths = merge(trg_seqs)
-
-        batch = {
-            "original": original,
-            "src": src_seqs,
-            "target": trg_seqs,
-            "lengths": torch.LongTensor(src_lengths),  # src_lengths = trg_lengths
-        }
-        return batch
-    
-    def get_batch(self, sentence):
-      data = self.text_encoder.clean(sentence)
-      text, inputs, diacritics = util.extract_haraqat(data)
-      inputs = torch.Tensor(self.text_encoder.input_to_sequence("".join(inputs)))
-      diacritics = torch.Tensor(self.text_encoder.target_to_sequence(diacritics))
-      batch = self.collate_fn([(inputs, diacritics, text)])
-      return batch
-  
-    def infer(self, sentence):
-        self.model.eval()
-        batch = self.get_batch(sentence)
-        predicted = self.diacritizer.diacritize_batch(batch)
-        return predicted[0]

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

@@ -1,293 +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 sys
-import typing as tp
-
-import julius
-import torch
-import torchaudio
-
-
-def convert_audio_channels(wav: torch.Tensor, channels: int = 2) -> torch.Tensor:
-    """Convert audio to the given number of channels.
-
-    Args:
-        wav (torch.Tensor): Audio wave of shape [B, C, T].
-        channels (int): Expected number of channels as output.
-    Returns:
-        torch.Tensor: Downmixed or unchanged audio wave [B, C, T].
-    """
-    *shape, src_channels, length = wav.shape
-    if src_channels == channels:
-        pass
-    elif channels == 1:
-        # Case 1:
-        # The caller asked 1-channel audio, and the stream has multiple
-        # channels, downmix all channels.
-        wav = wav.mean(dim=-2, keepdim=True)
-    elif src_channels == 1:
-        # Case 2:
-        # The caller asked for multiple channels, but the input file has
-        # a single channel, replicate the audio over all channels.
-        wav = wav.expand(*shape, channels, length)
-    elif src_channels >= channels:
-        # Case 3:
-        # The caller asked for multiple channels, and the input file has
-        # more channels than requested. In that case return the first channels.
-        wav = wav[..., :channels, :]
-    else:
-        # Case 4: What is a reasonable choice here?
-        raise ValueError('The audio file has less channels than requested but is not mono.')
-    return wav
-
-
-def convert_audio(wav: torch.Tensor, from_rate: float,
-                  to_rate: float, to_channels: int) -> torch.Tensor:
-    """Convert audio to new sample rate and number of audio channels.
-    """
-    wav = julius.resample_frac(wav, int(from_rate), int(to_rate))
-    wav = convert_audio_channels(wav, to_channels)
-    return wav
-
-
-def normalize_loudness(wav: torch.Tensor, sample_rate: int, loudness_headroom_db: float = 14,
-                       loudness_compressor: bool = False, energy_floor: float = 2e-3):
-    """Normalize an input signal to a user loudness in dB LKFS.
-    Audio loudness is defined according to the ITU-R BS.1770-4 recommendation.
-
-    Args:
-        wav (torch.Tensor): Input multichannel audio data.
-        sample_rate (int): Sample rate.
-        loudness_headroom_db (float): Target loudness of the output in dB LUFS.
-        loudness_compressor (bool): Uses tanh for soft clipping.
-        energy_floor (float): anything below that RMS level will not be rescaled.
-    Returns:
-        output (torch.Tensor): Loudness normalized output data.
-    """
-    energy = wav.pow(2).mean().sqrt().item()
-    if energy < energy_floor:
-        return wav
-    transform = torchaudio.transforms.Loudness(sample_rate)
-    input_loudness_db = transform(wav).item()
-    # calculate the gain needed to scale to the desired loudness level
-    delta_loudness = -loudness_headroom_db - input_loudness_db
-    gain = 10.0 ** (delta_loudness / 20.0)
-    output = gain * wav
-    if loudness_compressor:
-        output = torch.tanh(output)
-    assert output.isfinite().all(), (input_loudness_db, wav.pow(2).mean().sqrt())
-    return output
-
-
-def _clip_wav(wav: torch.Tensor, log_clipping: bool = False, stem_name: tp.Optional[str] = None) -> None:
-    """Utility function to clip the audio with logging if specified."""
-    max_scale = wav.abs().max()
-    if log_clipping and max_scale > 1:
-        clamp_prob = (wav.abs() > 1).float().mean().item()
-        print(f"CLIPPING {stem_name or ''} happening with proba (a bit of clipping is okay):",
-              clamp_prob, "maximum scale: ", max_scale.item(), file=sys.stderr)
-    wav.clamp_(-1, 1)
-
-
-def normalize_audio(wav: torch.Tensor, normalize: bool = True,
-                    strategy: str = 'peak', peak_clip_headroom_db: float = 1,
-                    rms_headroom_db: float = 18, loudness_headroom_db: float = 14,
-                    loudness_compressor: bool = False, log_clipping: bool = False,
-                    sample_rate: tp.Optional[int] = None,
-                    stem_name: tp.Optional[str] = None) -> torch.Tensor:
-    """Normalize the audio according to the prescribed strategy (see after).
-
-    Args:
-        wav (torch.Tensor): Audio data.
-        normalize (bool): if `True` (default), normalizes according to the prescribed
-            strategy (see after). If `False`, the strategy is only used in case clipping
-            would happen.
-        strategy (str): Can be either 'clip', 'peak', or 'rms'. Default is 'peak',
-            i.e. audio is normalized by its largest value. RMS normalizes by root-mean-square
-            with extra headroom to avoid clipping. 'clip' just clips.
-        peak_clip_headroom_db (float): Headroom in dB when doing 'peak' or 'clip' strategy.
-        rms_headroom_db (float): Headroom in dB when doing 'rms' strategy. This must be much larger
-            than the `peak_clip` one to avoid further clipping.
-        loudness_headroom_db (float): Target loudness for loudness normalization.
-        loudness_compressor (bool): If True, uses tanh based soft clipping.
-        log_clipping (bool): If True, basic logging on stderr when clipping still
-            occurs despite strategy (only for 'rms').
-        sample_rate (int): Sample rate for the audio data (required for loudness).
-        stem_name (Optional[str]): Stem name for clipping logging.
-    Returns:
-        torch.Tensor: Normalized audio.
-    """
-    scale_peak = 10 ** (-peak_clip_headroom_db / 20)
-    scale_rms = 10 ** (-rms_headroom_db / 20)
-    if strategy == 'peak':
-        rescaling = (scale_peak / wav.abs().max())
-        if normalize or rescaling < 1:
-            wav = wav * rescaling
-    elif strategy == 'clip':
-        wav = wav.clamp(-scale_peak, scale_peak)
-    elif strategy == 'rms':
-        mono = wav.mean(dim=0)
-        rescaling = scale_rms / mono.pow(2).mean().sqrt()
-        if normalize or rescaling < 1:
-            wav = wav * rescaling
-        _clip_wav(wav, log_clipping=log_clipping, stem_name=stem_name)
-    elif strategy == 'loudness':
-        assert sample_rate is not None, "Loudness normalization requires sample rate."
-        wav = normalize_loudness(wav, sample_rate, loudness_headroom_db, loudness_compressor)
-        _clip_wav(wav, log_clipping=log_clipping, stem_name=stem_name)
-    else:
-        assert wav.abs().max() < 1
-        assert strategy == '' or strategy == 'none', f"Unexpected strategy: '{strategy}'"
-    return wav
-
-
-def f32_pcm(wav: torch.Tensor) -> torch.Tensor:
-    """Convert audio to float 32 bits PCM format.
-    """
-    if wav.dtype.is_floating_point:
-        return wav
-    else:
-        assert wav.dtype == torch.int16
-        return wav.float() / 2**15
-
-
-def i16_pcm(wav: torch.Tensor) -> torch.Tensor:
-    """Convert audio to int 16 bits PCM format.
-
-    ..Warning:: There exist many formula for doing this convertion. None are perfect
-    due to the asymetry of the int16 range. One either have possible clipping, DC offset,
-    or inconsistancies with f32_pcm. If the given wav doesn't have enough headroom,
-    it is possible that `i16_pcm(f32_pcm)) != Identity`.
-    """
-    if wav.dtype.is_floating_point:
-        assert wav.abs().max() <= 1
-        candidate = (wav * 2 ** 15).round()
-        if candidate.max() >= 2 ** 15:  # clipping would occur
-            candidate = (wav * (2 ** 15 - 1)).round()
-        return candidate.short()
-    else:
-        assert wav.dtype == torch.int16
-        return wav
-
-def apply_tafade(audio: torch.Tensor, sample_rate, duration=3.0, out=True, start=True, shape: str = "linear", stem_name: tp.Optional[str] = None) -> torch.Tensor:
-    """
-    Apply fade-in and/or fade-out effects to the audio tensor.
-
-    Args:
-        audio (torch.Tensor): The input audio tensor of shape (C, L).
-        sample_rate (int): The sample rate of the audio.
-        duration (float, optional): The duration of the fade in seconds. Defaults to 3.0.
-        out (bool, optional): Determines whether to apply fade-in (False) or fade-out (True) effect. Defaults to True.
-        start (bool, optional): Determines whether the fade is applied to the beginning (True) or end (False) of the audio. Defaults to True.
-        shape (str, optional): The shape of the fade. Must be one of: "quarter_sine", "half_sine", "linear", "logarithmic", "exponential". Defaults to "linear".
-
-    Returns:
-        torch.Tensor: The audio tensor with the fade effect applied.
-
-    """
-    fade_samples = int(sample_rate * duration)  # Number of samples for the fade duration
-
-    # Create the fade transform
-    fade_transform = torchaudio.transforms.Fade(fade_in_len=0, fade_out_len=0, fade_shape=shape)
-
-    if out:
-        fade_transform.fade_out_len = fade_samples
-    else:
-        fade_transform.fade_in_len = fade_samples        
-
-    # Select the portion of the audio to apply the fade
-    if start:
-        audio_fade_section = audio[:, :fade_samples]
-    else:
-        audio_fade_section = audio[:, -fade_samples:]
-
-    # Apply the fade transform to the audio section
-    audio_faded = fade_transform(audio)
-
-    # Replace the selected portion of the audio with the faded section
-    if start:
-        audio_faded[:, :fade_samples] = audio_fade_section
-    else:
-        audio_faded[:, -fade_samples:] = audio_fade_section
-
-    wav = normalize_loudness(audio_faded,sample_rate, loudness_headroom_db=18, loudness_compressor=True)
-    _clip_wav(wav, log_clipping=False, stem_name=stem_name)
-    return wav
-
-
-def apply_fade(audio: torch.Tensor, sample_rate, duration=3.0, out=True, start=True, curve_start:float=0.0, curve_end:float=1.0, current_device:str="cpu", stem_name: tp.Optional[str] = None) -> torch.Tensor:
-    """
-    Apply fade-in and/or fade-out effects to the audio tensor.
-
-    Args:
-        audio (torch.Tensor): The input audio tensor of shape (C, L).
-        sample_rate (int): The sample rate of the audio.
-        duration (float, optional): The duration of the fade in seconds. Defaults to 3.0.
-        out (bool, optional): Determines whether to apply fade-in (False) or fade-out (True) effect. Defaults to True.
-        start (bool, optional): Determines whether the fade is applied to the beginning (True) or end (False) of the audio. Defaults to True.
-        curve_start (float, optional): The starting amplitude of the fade curve. Defaults to 0.0.
-        curve_end (float, optional): The ending amplitude of the fade curve. Defaults to 1.0.
-        current_device (str, optional): The device on which the fade curve tensor should be created. Defaults to "cpu".
-
-    Returns:
-        torch.Tensor: The audio tensor with the fade effect applied.
-
-    """
-    fade_samples = int(sample_rate * duration)  # Number of samples for the fade duration
-    fade_curve = torch.linspace(curve_start, curve_end, fade_samples, device=current_device)  # Generate linear fade curve
-
-    if out:
-        fade_curve = fade_curve.flip(0)  # Reverse the fade curve for fade out
-
-    # Select the portion of the audio to apply the fade
-    if start:
-        audio_fade_section = audio[:, :fade_samples]
-    else:
-        audio_fade_section = audio[:, -fade_samples:]
-
-    # Apply the fade curve to the audio section
-    audio_faded = audio.clone()
-    audio_faded[:, :fade_samples] *= fade_curve.unsqueeze(0)
-    audio_faded[:, -fade_samples:] *= fade_curve.unsqueeze(0)
-
-    # Replace the selected portion of the audio with the faded section
-    if start:
-        audio_faded[:, :fade_samples] = audio_fade_section
-    else:
-        audio_faded[:, -fade_samples:] = audio_fade_section
-
-    wav = normalize_loudness(audio_faded,sample_rate, loudness_headroom_db=18, loudness_compressor=True)
-    _clip_wav(wav, log_clipping=False, stem_name=stem_name)
-    return wav
-
-def apply_splice_effect(waveform1, sample_rate1, waveform2, sample_rate2, overlap):
-    # Convert sample rates to integers
-    sample_rate1 = int(sample_rate1)
-    sample_rate2 = int(sample_rate2)
-
-    # Convert tensors to mono-channel if needed
-    if waveform1.ndim > 2:
-        waveform1 = waveform1.mean(dim=1)
-    if waveform2.ndim > 2:
-        waveform2 = waveform2.mean(dim=1)
-
-    ## Convert tensors to numpy arrays
-    #waveform1_np = waveform1.numpy()
-    #waveform2_np = waveform2.numpy()
-
-    # Apply splice effect using torchaudio.sox_effects.apply_effects_tensor
-    effects = [
-        ["splice", f"-q {waveform1},{overlap}"],
-    ]
-    output_waveform, output_sample_rate = torchaudio.sox_effects.apply_effects_tensor(
-        torch.cat([waveform1.unsqueeze(0), waveform2.unsqueeze(0)], dim=2),
-        sample_rate1,
-        effects
-    )
-
-    return output_waveform.squeeze(0), output_sample_rate
-

spaces/Abhilashvj/planogram-compliance/utils/loggers/wandb/wandb_utils.py

@@ -1,788 +0,0 @@
-"""Utilities and tools for tracking runs with Weights & Biases."""
-
-import logging
-import os
-import sys
-from contextlib import contextmanager
-from pathlib import Path
-from typing import Dict
-
-import yaml
-from tqdm import tqdm
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[3]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-
-from utils.dataloaders import LoadImagesAndLabels, img2label_paths
-from utils.general import LOGGER, check_dataset, check_file
-
-try:
-    import wandb
-
-    assert hasattr(wandb, "__version__")  # verify package import not local dir
-except (ImportError, AssertionError):
-    wandb = None
-
-RANK = int(os.getenv("RANK", -1))
-WANDB_ARTIFACT_PREFIX = "wandb-artifact://"
-
-
-def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX):
-    return from_string[len(prefix) :]
-
-
-def check_wandb_config_file(data_config_file):
-    wandb_config = "_wandb.".join(
-        data_config_file.rsplit(".", 1)
-    )  # updated data.yaml path
-    if Path(wandb_config).is_file():
-        return wandb_config
-    return data_config_file
-
-
-def check_wandb_dataset(data_file):
-    is_trainset_wandb_artifact = False
-    is_valset_wandb_artifact = False
-    if isinstance(data_file, dict):
-        # In that case another dataset manager has already processed it and we don't have to
-        return data_file
-    if check_file(data_file) and data_file.endswith(".yaml"):
-        with open(data_file, errors="ignore") as f:
-            data_dict = yaml.safe_load(f)
-        is_trainset_wandb_artifact = isinstance(
-            data_dict["train"], str
-        ) and data_dict["train"].startswith(WANDB_ARTIFACT_PREFIX)
-        is_valset_wandb_artifact = isinstance(
-            data_dict["val"], str
-        ) and data_dict["val"].startswith(WANDB_ARTIFACT_PREFIX)
-    if is_trainset_wandb_artifact or is_valset_wandb_artifact:
-        return data_dict
-    else:
-        return check_dataset(data_file)
-
-
-def get_run_info(run_path):
-    run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX))
-    run_id = run_path.stem
-    project = run_path.parent.stem
-    entity = run_path.parent.parent.stem
-    model_artifact_name = "run_" + run_id + "_model"
-    return entity, project, run_id, model_artifact_name
-
-
-def check_wandb_resume(opt):
-    process_wandb_config_ddp_mode(opt) if RANK not in [-1, 0] else None
-    if isinstance(opt.resume, str):
-        if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
-            if RANK not in [-1, 0]:  # For resuming DDP runs
-                entity, project, run_id, model_artifact_name = get_run_info(
-                    opt.resume
-                )
-                api = wandb.Api()
-                artifact = api.artifact(
-                    entity
-                    + "/"
-                    + project
-                    + "/"
-                    + model_artifact_name
-                    + ":latest"
-                )
-                modeldir = artifact.download()
-                opt.weights = str(Path(modeldir) / "last.pt")
-            return True
-    return None
-
-
-def process_wandb_config_ddp_mode(opt):
-    with open(check_file(opt.data), errors="ignore") as f:
-        data_dict = yaml.safe_load(f)  # data dict
-    train_dir, val_dir = None, None
-    if isinstance(data_dict["train"], str) and data_dict["train"].startswith(
-        WANDB_ARTIFACT_PREFIX
-    ):
-        api = wandb.Api()
-        train_artifact = api.artifact(
-            remove_prefix(data_dict["train"]) + ":" + opt.artifact_alias
-        )
-        train_dir = train_artifact.download()
-        train_path = Path(train_dir) / "data/images/"
-        data_dict["train"] = str(train_path)
-
-    if isinstance(data_dict["val"], str) and data_dict["val"].startswith(
-        WANDB_ARTIFACT_PREFIX
-    ):
-        api = wandb.Api()
-        val_artifact = api.artifact(
-            remove_prefix(data_dict["val"]) + ":" + opt.artifact_alias
-        )
-        val_dir = val_artifact.download()
-        val_path = Path(val_dir) / "data/images/"
-        data_dict["val"] = str(val_path)
-    if train_dir or val_dir:
-        ddp_data_path = str(Path(val_dir) / "wandb_local_data.yaml")
-        with open(ddp_data_path, "w") as f:
-            yaml.safe_dump(data_dict, f)
-        opt.data = ddp_data_path
-
-
-class WandbLogger:
-    """Log training runs, datasets, models, and predictions to Weights & Biases.
-
-    This logger sends information to W&B at wandb.ai. By default, this information
-    includes hyperparameters, system configuration and metrics, model metrics,
-    and basic data metrics and analyses.
-
-    By providing additional command line arguments to train.py, datasets,
-    models and predictions can also be logged.
-
-    For more on how this logger is used, see the Weights & Biases documentation:
-    https://docs.wandb.com/guides/integrations/yolov5
-    """
-
-    def __init__(self, opt, run_id=None, job_type="Training"):
-        """
-        - Initialize WandbLogger instance
-        - Upload dataset if opt.upload_dataset is True
-        - Setup training processes if job_type is 'Training'
-
-        arguments:
-        opt (namespace) -- Commandline arguments for this run
-        run_id (str) -- Run ID of W&B run to be resumed
-        job_type (str) -- To set the job_type for this run
-
-        """
-        # Temporary-fix
-        if opt.upload_dataset:
-            opt.upload_dataset = False
-            # LOGGER.info("Uploading Dataset functionality is not being supported temporarily due to a bug.")
-
-        # Pre-training routine --
-        self.job_type = job_type
-        self.wandb, self.wandb_run = wandb, None if not wandb else wandb.run
-        self.val_artifact, self.train_artifact = None, None
-        self.train_artifact_path, self.val_artifact_path = None, None
-        self.result_artifact = None
-        self.val_table, self.result_table = None, None
-        self.bbox_media_panel_images = []
-        self.val_table_path_map = None
-        self.max_imgs_to_log = 16
-        self.wandb_artifact_data_dict = None
-        self.data_dict = None
-        # It's more elegant to stick to 1 wandb.init call,
-        #  but useful config data is overwritten in the WandbLogger's wandb.init call
-        if isinstance(opt.resume, str):  # checks resume from artifact
-            if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
-                entity, project, run_id, model_artifact_name = get_run_info(
-                    opt.resume
-                )
-                model_artifact_name = (
-                    WANDB_ARTIFACT_PREFIX + model_artifact_name
-                )
-                assert wandb, "install wandb to resume wandb runs"
-                # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config
-                self.wandb_run = wandb.init(
-                    id=run_id,
-                    project=project,
-                    entity=entity,
-                    resume="allow",
-                    allow_val_change=True,
-                )
-                opt.resume = model_artifact_name
-        elif self.wandb:
-            self.wandb_run = (
-                wandb.init(
-                    config=opt,
-                    resume="allow",
-                    project="YOLOv5"
-                    if opt.project == "runs/train"
-                    else Path(opt.project).stem,
-                    entity=opt.entity,
-                    name=opt.name if opt.name != "exp" else None,
-                    job_type=job_type,
-                    id=run_id,
-                    allow_val_change=True,
-                )
-                if not wandb.run
-                else wandb.run
-            )
-        if self.wandb_run:
-            if self.job_type == "Training":
-                if opt.upload_dataset:
-                    if not opt.resume:
-                        self.wandb_artifact_data_dict = (
-                            self.check_and_upload_dataset(opt)
-                        )
-
-                if isinstance(opt.data, dict):
-                    # This means another dataset manager has already processed the dataset info (e.g. ClearML)
-                    # and they will have stored the already processed dict in opt.data
-                    self.data_dict = opt.data
-                elif opt.resume:
-                    # resume from artifact
-                    if isinstance(opt.resume, str) and opt.resume.startswith(
-                        WANDB_ARTIFACT_PREFIX
-                    ):
-                        self.data_dict = dict(self.wandb_run.config.data_dict)
-                    else:  # local resume
-                        self.data_dict = check_wandb_dataset(opt.data)
-                else:
-                    self.data_dict = check_wandb_dataset(opt.data)
-                    self.wandb_artifact_data_dict = (
-                        self.wandb_artifact_data_dict or self.data_dict
-                    )
-
-                    # write data_dict to config. useful for resuming from artifacts. Do this only when not resuming.
-                    self.wandb_run.config.update(
-                        {"data_dict": self.wandb_artifact_data_dict},
-                        allow_val_change=True,
-                    )
-                self.setup_training(opt)
-
-            if self.job_type == "Dataset Creation":
-                self.wandb_run.config.update({"upload_dataset": True})
-                self.data_dict = self.check_and_upload_dataset(opt)
-
-    def check_and_upload_dataset(self, opt):
-        """
-        Check if the dataset format is compatible and upload it as W&B artifact
-
-        arguments:
-        opt (namespace)-- Commandline arguments for current run
-
-        returns:
-        Updated dataset info dictionary where local dataset paths are replaced by WAND_ARFACT_PREFIX links.
-        """
-        assert wandb, "Install wandb to upload dataset"
-        config_path = self.log_dataset_artifact(
-            opt.data,
-            opt.single_cls,
-            "YOLOv5"
-            if opt.project == "runs/train"
-            else Path(opt.project).stem,
-        )
-        with open(config_path, errors="ignore") as f:
-            wandb_data_dict = yaml.safe_load(f)
-        return wandb_data_dict
-
-    def setup_training(self, opt):
-        """
-        Setup the necessary processes for training YOLO models:
-          - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
-          - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
-          - Setup log_dict, initialize bbox_interval
-
-        arguments:
-        opt (namespace) -- commandline arguments for this run
-
-        """
-        self.log_dict, self.current_epoch = {}, 0
-        self.bbox_interval = opt.bbox_interval
-        if isinstance(opt.resume, str):
-            modeldir, _ = self.download_model_artifact(opt)
-            if modeldir:
-                self.weights = Path(modeldir) / "last.pt"
-                config = self.wandb_run.config
-                (
-                    opt.weights,
-                    opt.save_period,
-                    opt.batch_size,
-                    opt.bbox_interval,
-                    opt.epochs,
-                    opt.hyp,
-                    opt.imgsz,
-                ) = (
-                    str(self.weights),
-                    config.save_period,
-                    config.batch_size,
-                    config.bbox_interval,
-                    config.epochs,
-                    config.hyp,
-                    config.imgsz,
-                )
-        data_dict = self.data_dict
-        if (
-            self.val_artifact is None
-        ):  # If --upload_dataset is set, use the existing artifact, don't download
-            (
-                self.train_artifact_path,
-                self.train_artifact,
-            ) = self.download_dataset_artifact(
-                data_dict.get("train"), opt.artifact_alias
-            )
-            (
-                self.val_artifact_path,
-                self.val_artifact,
-            ) = self.download_dataset_artifact(
-                data_dict.get("val"), opt.artifact_alias
-            )
-
-        if self.train_artifact_path is not None:
-            train_path = Path(self.train_artifact_path) / "data/images/"
-            data_dict["train"] = str(train_path)
-        if self.val_artifact_path is not None:
-            val_path = Path(self.val_artifact_path) / "data/images/"
-            data_dict["val"] = str(val_path)
-
-        if self.val_artifact is not None:
-            self.result_artifact = wandb.Artifact(
-                "run_" + wandb.run.id + "_progress", "evaluation"
-            )
-            columns = ["epoch", "id", "ground truth", "prediction"]
-            columns.extend(self.data_dict["names"])
-            self.result_table = wandb.Table(columns)
-            self.val_table = self.val_artifact.get("val")
-            if self.val_table_path_map is None:
-                self.map_val_table_path()
-        if opt.bbox_interval == -1:
-            self.bbox_interval = opt.bbox_interval = (
-                (opt.epochs // 10) if opt.epochs > 10 else 1
-            )
-            if opt.evolve or opt.noplots:
-                self.bbox_interval = opt.bbox_interval = (
-                    opt.epochs + 1
-                )  # disable bbox_interval
-        train_from_artifact = (
-            self.train_artifact_path is not None
-            and self.val_artifact_path is not None
-        )
-        # Update the the data_dict to point to local artifacts dir
-        if train_from_artifact:
-            self.data_dict = data_dict
-
-    def download_dataset_artifact(self, path, alias):
-        """
-        download the model checkpoint artifact if the path starts with WANDB_ARTIFACT_PREFIX
-
-        arguments:
-        path -- path of the dataset to be used for training
-        alias (str)-- alias of the artifact to be download/used for training
-
-        returns:
-        (str, wandb.Artifact) -- path of the downladed dataset and it's corresponding artifact object if dataset
-        is found otherwise returns (None, None)
-        """
-        if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):
-            artifact_path = Path(
-                remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias
-            )
-            dataset_artifact = wandb.use_artifact(
-                artifact_path.as_posix().replace("\\", "/")
-            )
-            assert (
-                dataset_artifact is not None
-            ), "'Error: W&B dataset artifact doesn't exist'"
-            datadir = dataset_artifact.download()
-            return datadir, dataset_artifact
-        return None, None
-
-    def download_model_artifact(self, opt):
-        """
-        download the model checkpoint artifact if the resume path starts with WANDB_ARTIFACT_PREFIX
-
-        arguments:
-        opt (namespace) -- Commandline arguments for this run
-        """
-        if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
-            model_artifact = wandb.use_artifact(
-                remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest"
-            )
-            assert (
-                model_artifact is not None
-            ), "Error: W&B model artifact doesn't exist"
-            modeldir = model_artifact.download()
-            # epochs_trained = model_artifact.metadata.get('epochs_trained')
-            total_epochs = model_artifact.metadata.get("total_epochs")
-            is_finished = total_epochs is None
-            assert (
-                not is_finished
-            ), "training is finished, can only resume incomplete runs."
-            return modeldir, model_artifact
-        return None, None
-
-    def log_model(self, path, opt, epoch, fitness_score, best_model=False):
-        """
-        Log the model checkpoint as W&B artifact
-
-        arguments:
-        path (Path)   -- Path of directory containing the checkpoints
-        opt (namespace) -- Command line arguments for this run
-        epoch (int)  -- Current epoch number
-        fitness_score (float) -- fitness score for current epoch
-        best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.
-        """
-        model_artifact = wandb.Artifact(
-            "run_" + wandb.run.id + "_model",
-            type="model",
-            metadata={
-                "original_url": str(path),
-                "epochs_trained": epoch + 1,
-                "save period": opt.save_period,
-                "project": opt.project,
-                "total_epochs": opt.epochs,
-                "fitness_score": fitness_score,
-            },
-        )
-        model_artifact.add_file(str(path / "last.pt"), name="last.pt")
-        wandb.log_artifact(
-            model_artifact,
-            aliases=[
-                "latest",
-                "last",
-                "epoch " + str(self.current_epoch),
-                "best" if best_model else "",
-            ],
-        )
-        LOGGER.info(f"Saving model artifact on epoch {epoch + 1}")
-
-    def log_dataset_artifact(
-        self, data_file, single_cls, project, overwrite_config=False
-    ):
-        """
-        Log the dataset as W&B artifact and return the new data file with W&B links
-
-        arguments:
-        data_file (str) -- the .yaml file with information about the dataset like - path, classes etc.
-        single_class (boolean)  -- train multi-class data as single-class
-        project (str) -- project name. Used to construct the artifact path
-        overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new
-        file with _wandb postfix. Eg -> data_wandb.yaml
-
-        returns:
-        the new .yaml file with artifact links. it can be used to start training directly from artifacts
-        """
-        upload_dataset = self.wandb_run.config.upload_dataset
-        log_val_only = (
-            isinstance(upload_dataset, str) and upload_dataset == "val"
-        )
-        self.data_dict = check_dataset(data_file)  # parse and check
-        data = dict(self.data_dict)
-        nc, names = (
-            (1, ["item"]) if single_cls else (int(data["nc"]), data["names"])
-        )
-        names = {k: v for k, v in enumerate(names)}  # to index dictionary
-
-        # log train set
-        if not log_val_only:
-            self.train_artifact = (
-                self.create_dataset_table(
-                    LoadImagesAndLabels(
-                        data["train"], rect=True, batch_size=1
-                    ),
-                    names,
-                    name="train",
-                )
-                if data.get("train")
-                else None
-            )
-            if data.get("train"):
-                data["train"] = WANDB_ARTIFACT_PREFIX + str(
-                    Path(project) / "train"
-                )
-
-        self.val_artifact = (
-            self.create_dataset_table(
-                LoadImagesAndLabels(data["val"], rect=True, batch_size=1),
-                names,
-                name="val",
-            )
-            if data.get("val")
-            else None
-        )
-        if data.get("val"):
-            data["val"] = WANDB_ARTIFACT_PREFIX + str(Path(project) / "val")
-
-        path = Path(data_file)
-        # create a _wandb.yaml file with artifacts links if both train and test set are logged
-        if not log_val_only:
-            path = (
-                path.stem if overwrite_config else path.stem + "_wandb"
-            ) + ".yaml"  # updated data.yaml path
-            path = ROOT / "data" / path
-            data.pop("download", None)
-            data.pop("path", None)
-            with open(path, "w") as f:
-                yaml.safe_dump(data, f)
-                LOGGER.info(f"Created dataset config file {path}")
-
-        if (
-            self.job_type == "Training"
-        ):  # builds correct artifact pipeline graph
-            if not log_val_only:
-                self.wandb_run.log_artifact(
-                    self.train_artifact
-                )  # calling use_artifact downloads the dataset. NOT NEEDED!
-            self.wandb_run.use_artifact(self.val_artifact)
-            self.val_artifact.wait()
-            self.val_table = self.val_artifact.get("val")
-            self.map_val_table_path()
-        else:
-            self.wandb_run.log_artifact(self.train_artifact)
-            self.wandb_run.log_artifact(self.val_artifact)
-        return path
-
-    def map_val_table_path(self):
-        """
-        Map the validation dataset Table like name of file -> it's id in the W&B Table.
-        Useful for - referencing artifacts for evaluation.
-        """
-        self.val_table_path_map = {}
-        LOGGER.info("Mapping dataset")
-        for i, data in enumerate(tqdm(self.val_table.data)):
-            self.val_table_path_map[data[3]] = data[0]
-
-    def create_dataset_table(
-        self,
-        dataset: LoadImagesAndLabels,
-        class_to_id: Dict[int, str],
-        name: str = "dataset",
-    ):
-        """
-        Create and return W&B artifact containing W&B Table of the dataset.
-
-        arguments:
-        dataset -- instance of LoadImagesAndLabels class used to iterate over the data to build Table
-        class_to_id -- hash map that maps class ids to labels
-        name -- name of the artifact
-
-        returns:
-        dataset artifact to be logged or used
-        """
-        # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging
-        artifact = wandb.Artifact(name=name, type="dataset")
-        img_files = (
-            tqdm([dataset.path])
-            if isinstance(dataset.path, str) and Path(dataset.path).is_dir()
-            else None
-        )
-        img_files = tqdm(dataset.im_files) if not img_files else img_files
-        for img_file in img_files:
-            if Path(img_file).is_dir():
-                artifact.add_dir(img_file, name="data/images")
-                labels_path = "labels".join(dataset.path.rsplit("images", 1))
-                artifact.add_dir(labels_path, name="data/labels")
-            else:
-                artifact.add_file(
-                    img_file, name="data/images/" + Path(img_file).name
-                )
-                label_file = Path(img2label_paths([img_file])[0])
-                artifact.add_file(
-                    str(label_file), name="data/labels/" + label_file.name
-                ) if label_file.exists() else None
-        table = wandb.Table(columns=["id", "train_image", "Classes", "name"])
-        class_set = wandb.Classes(
-            [{"id": id, "name": name} for id, name in class_to_id.items()]
-        )
-        for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)):
-            box_data, img_classes = [], {}
-            for cls, *xywh in labels[:, 1:].tolist():
-                cls = int(cls)
-                box_data.append(
-                    {
-                        "position": {
-                            "middle": [xywh[0], xywh[1]],
-                            "width": xywh[2],
-                            "height": xywh[3],
-                        },
-                        "class_id": cls,
-                        "box_caption": "%s" % (class_to_id[cls]),
-                    }
-                )
-                img_classes[cls] = class_to_id[cls]
-            boxes = {
-                "ground_truth": {
-                    "box_data": box_data,
-                    "class_labels": class_to_id,
-                }
-            }  # inference-space
-            table.add_data(
-                si,
-                wandb.Image(paths, classes=class_set, boxes=boxes),
-                list(img_classes.values()),
-                Path(paths).name,
-            )
-        artifact.add(table, name)
-        return artifact
-
-    def log_training_progress(self, predn, path, names):
-        """
-        Build evaluation Table. Uses reference from validation dataset table.
-
-        arguments:
-        predn (list): list of predictions in the native space in the format - [xmin, ymin, xmax, ymax, confidence, class]
-        path (str): local path of the current evaluation image
-        names (dict(int, str)): hash map that maps class ids to labels
-        """
-        class_set = wandb.Classes(
-            [{"id": id, "name": name} for id, name in names.items()]
-        )
-        box_data = []
-        avg_conf_per_class = [0] * len(self.data_dict["names"])
-        pred_class_count = {}
-        for *xyxy, conf, cls in predn.tolist():
-            if conf >= 0.25:
-                cls = int(cls)
-                box_data.append(
-                    {
-                        "position": {
-                            "minX": xyxy[0],
-                            "minY": xyxy[1],
-                            "maxX": xyxy[2],
-                            "maxY": xyxy[3],
-                        },
-                        "class_id": cls,
-                        "box_caption": f"{names[cls]} {conf:.3f}",
-                        "scores": {"class_score": conf},
-                        "domain": "pixel",
-                    }
-                )
-                avg_conf_per_class[cls] += conf
-
-                if cls in pred_class_count:
-                    pred_class_count[cls] += 1
-                else:
-                    pred_class_count[cls] = 1
-
-        for pred_class in pred_class_count.keys():
-            avg_conf_per_class[pred_class] = (
-                avg_conf_per_class[pred_class] / pred_class_count[pred_class]
-            )
-
-        boxes = {
-            "predictions": {"box_data": box_data, "class_labels": names}
-        }  # inference-space
-        id = self.val_table_path_map[Path(path).name]
-        self.result_table.add_data(
-            self.current_epoch,
-            id,
-            self.val_table.data[id][1],
-            wandb.Image(
-                self.val_table.data[id][1], boxes=boxes, classes=class_set
-            ),
-            *avg_conf_per_class,
-        )
-
-    def val_one_image(self, pred, predn, path, names, im):
-        """
-        Log validation data for one image. updates the result Table if validation dataset is uploaded and log bbox media panel
-
-        arguments:
-        pred (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
-        predn (list): list of predictions in the native space - [xmin, ymin, xmax, ymax, confidence, class]
-        path (str): local path of the current evaluation image
-        """
-        if (
-            self.val_table and self.result_table
-        ):  # Log Table if Val dataset is uploaded as artifact
-            self.log_training_progress(predn, path, names)
-
-        if (
-            len(self.bbox_media_panel_images) < self.max_imgs_to_log
-            and self.current_epoch > 0
-        ):
-            if self.current_epoch % self.bbox_interval == 0:
-                box_data = [
-                    {
-                        "position": {
-                            "minX": xyxy[0],
-                            "minY": xyxy[1],
-                            "maxX": xyxy[2],
-                            "maxY": xyxy[3],
-                        },
-                        "class_id": int(cls),
-                        "box_caption": f"{names[int(cls)]} {conf:.3f}",
-                        "scores": {"class_score": conf},
-                        "domain": "pixel",
-                    }
-                    for *xyxy, conf, cls in pred.tolist()
-                ]
-                boxes = {
-                    "predictions": {
-                        "box_data": box_data,
-                        "class_labels": names,
-                    }
-                }  # inference-space
-                self.bbox_media_panel_images.append(
-                    wandb.Image(im, boxes=boxes, caption=path.name)
-                )
-
-    def log(self, log_dict):
-        """
-        save the metrics to the logging dictionary
-
-        arguments:
-        log_dict (Dict) -- metrics/media to be logged in current step
-        """
-        if self.wandb_run:
-            for key, value in log_dict.items():
-                self.log_dict[key] = value
-
-    def end_epoch(self, best_result=False):
-        """
-        commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.
-
-        arguments:
-        best_result (boolean): Boolean representing if the result of this evaluation is best or not
-        """
-        if self.wandb_run:
-            with all_logging_disabled():
-                if self.bbox_media_panel_images:
-                    self.log_dict[
-                        "BoundingBoxDebugger"
-                    ] = self.bbox_media_panel_images
-                try:
-                    wandb.log(self.log_dict)
-                except BaseException as e:
-                    LOGGER.info(
-                        f"An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}"
-                    )
-                    self.wandb_run.finish()
-                    self.wandb_run = None
-
-                self.log_dict = {}
-                self.bbox_media_panel_images = []
-            if self.result_artifact:
-                self.result_artifact.add(self.result_table, "result")
-                wandb.log_artifact(
-                    self.result_artifact,
-                    aliases=[
-                        "latest",
-                        "last",
-                        "epoch " + str(self.current_epoch),
-                        ("best" if best_result else ""),
-                    ],
-                )
-
-                wandb.log({"evaluation": self.result_table})
-                columns = ["epoch", "id", "ground truth", "prediction"]
-                columns.extend(self.data_dict["names"])
-                self.result_table = wandb.Table(columns)
-                self.result_artifact = wandb.Artifact(
-                    "run_" + wandb.run.id + "_progress", "evaluation"
-                )
-
-    def finish_run(self):
-        """
-        Log metrics if any and finish the current W&B run
-        """
-        if self.wandb_run:
-            if self.log_dict:
-                with all_logging_disabled():
-                    wandb.log(self.log_dict)
-            wandb.run.finish()
-
-
-@contextmanager
-def all_logging_disabled(highest_level=logging.CRITICAL):
-    """source - https://gist.github.com/simon-weber/7853144
-    A context manager that will prevent any logging messages triggered during the body from being processed.
-    :param highest_level: the maximum logging level in use.
-      This would only need to be changed if a custom level greater than CRITICAL is defined.
-    """
-    previous_level = logging.root.manager.disable
-    logging.disable(highest_level)
-    try:
-        yield
-    finally:
-        logging.disable(previous_level)

spaces/Adapter/CoAdapter/README.md

@@ -1,12 +0,0 @@
----
-license: openrail
-title: CoAdapter
-sdk: gradio
-sdk_version: 3.19.1
-emoji: 😻
-colorFrom: pink
-colorTo: blue
-pinned: false
-python_version: 3.8.16
-app_file: app.py
----

spaces/AgentVerse/agentVerse/agentverse/llms/utils/jsonrepair.py

@@ -1,660 +0,0 @@
-# jsonrepair.py - Repair invalid JSON documents in Python
-#
-# Just https://github.com/josdejong/jsonrepair ported from TypeScript to Python.
-#
-# This port won't get updates, because the goal should be to generate this library instead.
-#
-# See: https://github.com/josdejong/jsonrepair/issues/84
-#
-
-import json
-import re
-from typing import Optional
-
-CONTROL_CHARACTERS = {"\b": "\\b", "\f": "\\f", "\n": "\\n", "\r": "\\r", "\t": "\\t"}
-
-ESCAPE_CHARACTERS = {
-    '"': '"',
-    "\\": "\\",
-    "/": "/",
-    "b": "\b",
-    "f": "\f",
-    "n": "\n",
-    "r": "\r",
-    "t": "\t"
-    # note that \u is handled separately in parseString()
-}
-
-
-def remove_at_index(text: str, start: int, count: int) -> str:
-    return text[0:start] + text[start + count :]
-
-
-def is_control_character(char: str) -> bool:
-    return char in CONTROL_CHARACTERS
-
-
-def is_valid_string_character(char: str) -> bool:
-    return 0x20 <= ord(char) <= 0x10FFFF
-
-
-def is_quote(char: str) -> bool:
-    return is_single_quote(char) or is_double_quote(char)
-
-
-def is_single_quote(char: str) -> bool:
-    """Test whether the given character is a single quote character.
-    Also tests for special variants of single quotes.
-    """
-    return char in (
-        "'",  # U+0027
-        "‘",  # U+2018
-        "’",  # U+2019
-        "`",  # U+0060
-        "´",  # U+00B4
-    )
-
-
-def is_double_quote(char: str) -> bool:
-    return (
-        is_ascii_double_quote(char)
-        or is_double_quote_left(char)
-        or is_double_quote_right(char)
-    )
-
-
-def is_ascii_double_quote(char: str) -> bool:
-    return char == '"'  # U+0022
-
-
-def is_double_quote_left(char: str) -> bool:
-    return char == "“"  # U+201C
-
-
-def is_double_quote_right(char: str) -> bool:
-    return char == "”"  # U+201D
-
-
-def is_start_of_value(char: str) -> bool:
-    regex_start_of_value = (
-        r"^[[{\w-]$"  # alpha, number, minus, or opening bracket or brace
-    )
-    return bool(re.search(regex_start_of_value, char)) or is_quote(char)
-
-
-def ends_with_comma_or_newline(text: str) -> bool:
-    return bool(re.search(r"[,\n][ \t\r]*$", text))
-
-
-def is_whitespace(char: str) -> bool:
-    return char.isspace()
-
-
-def is_special_whitespace(char: str) -> bool:
-    """Check if the given character is a special whitespace character, some unicode variant"""
-    return (
-        char == "\u00A0"  # non-breaking space
-        or ord("\u2000") <= ord(char) <= ord("\u200A")
-        or char == "\u202F"
-        or char == "\u205F"
-        or char == "\u3000"
-    )
-
-
-def insert_before_last_whitespace(text: str, text_to_insert: str) -> str:
-    index = len(text)
-
-    if not is_whitespace(text[index - 1]):
-        # no trailing whitespaces
-        return text + text_to_insert
-
-    while is_whitespace(text[index - 1]):
-        index -= 1
-
-    return text[:index] + text_to_insert + text[index:]
-
-
-def strip_last_occurrence(
-    text: str, text_to_strip: str, strip_remaining: bool = False
-) -> str:
-    index = text.rindex(text_to_strip)
-    try:
-        return text[:index] + ("" if strip_remaining else text[index + 1 :])
-    except ValueError:
-        return text
-
-
-def is_hex(char: str) -> bool:
-    try:
-        int(char, 16)
-        return True
-    except ValueError:
-        return False
-
-
-def is_delimiter(char: str) -> bool:
-    return char in ",:[]{}()\n'" or is_quote(char)
-
-
-def at_end_of_block_comment(text: str, i: int) -> bool:
-    return text[i] == "*" and text[i + 1] == "/"
-
-
-class JsonRepairError(Exception):
-    def __init__(self, message: str, position: int):
-        super(JsonRepairError, self).__init__(message + f" at position {position}")
-        self.position = position
-
-
-class JsonRepair:
-    """Repairs invalid JSON, i.e. change JavaScript notation into JSON notation.
-
-    Example:
-
-    try:
-        json = "{name: 'John'}"
-        repaired = JsonRepair(json).repair()
-        print(repaired)
-        # '{"name": "John"}'
-    except JsonRepairFailed as err:
-        print(err)
-
-    """
-
-    def __init__(self, text: str):
-        self.text = text
-        self.i = 0  # current index in text
-        self.output = ""  # generated output
-
-    def char(self, pos: int = 0) -> str:
-        return self.text[self.i + pos]
-
-    def inc(self, by: int = 1) -> None:
-        self.i += by
-
-    def dec(self, by: int = 1) -> None:
-        self.i -= by
-
-    def is_start_of_document(self, pos: int = 0) -> bool:
-        return self.i + pos == 0
-
-    def is_end_of_document(self, pos: int = 0) -> bool:
-        return self.i + pos >= len(self.text)
-
-    def repair(self) -> str:
-        processed = self.parse_value()
-        if not processed:
-            raise self.unexpected_end()
-
-        processed_comma = self.parse_character(",")
-        if processed_comma:
-            self.parse_whitespace_and_skip_comments()
-
-        if (
-            not self.is_end_of_document()
-            and is_start_of_value(self.char())
-            and ends_with_comma_or_newline(self.output)
-        ):
-            # start of a new value after end of the root level object: looks like
-            # newline delimited JSON -> turn into a root level array
-            if not processed_comma:
-                # repair missing comma
-                self.output = insert_before_last_whitespace(self.output, ",")
-
-            self.parse_newline_delimited_json()
-        elif processed_comma:
-            # repair: remove trailing comma
-            self.output = strip_last_occurrence(self.output, ",")
-
-        if self.is_end_of_document():
-            # reached the end of the document properly
-            return self.output
-
-        raise self.unexpected_character()
-
-    def parse_value(self) -> bool:
-        self.parse_whitespace_and_skip_comments()
-        processed = (
-            self.parse_object()
-            or self.parse_array()
-            or self.parse_string()
-            or self.parse_number()
-            or self.parse_keywords()
-            or self.parse_unquoted_string()
-        )
-        self.parse_whitespace_and_skip_comments()
-        return processed
-
-    def parse_whitespace_and_skip_comments(self) -> bool:
-        start = self.i
-
-        changed = self.parse_whitespace()
-        while True:
-            changed = self.parse_comment()
-            if changed:
-                changed = self.parse_whitespace()
-            if not changed:
-                break
-
-        return self.i > start
-
-    def parse_whitespace(self) -> bool:
-        whitespace = ""
-
-        while not self.is_end_of_document():
-            char = self.char()
-
-            normal = is_whitespace(char)
-            special = is_special_whitespace(char)
-
-            if not normal and not special:
-                break
-
-            if special:
-                whitespace += " "  # repair special whitespace
-            else:
-                whitespace += char
-
-            self.inc()
-
-        if whitespace:
-            self.output += whitespace
-            return True
-        return False
-
-    def parse_comment(self) -> bool:
-        # find a block comment '/* ... */'
-        if not self.is_end_of_document() and not self.is_end_of_document(pos=+1):
-            if self.char() == "/" and self.char(pos=+1) == "*":
-                # repair block comment by skipping it
-                while not self.is_end_of_document() and not at_end_of_block_comment(
-                    self.text, self.i
-                ):
-                    self.inc()
-                self.inc(by=2)
-                return True
-
-            # find a line comment '// ...'
-            if self.char() == "/" and self.char(pos=+1) == "/":
-                # repair line comment by skipping it
-                while not self.is_end_of_document() and self.char() != "\n":
-                    self.inc()
-                return True
-
-        return False
-
-    def parse_character(self, char: str) -> bool:
-        if not self.is_end_of_document():
-            if self.char() == char:
-                self.output += char
-                self.inc()
-                return True
-        return False
-
-    def skip_character(self, char: str) -> bool:
-        if not self.is_end_of_document() and self.char() == char:
-            self.inc()
-            return True
-        return False
-
-    def skip_escape_character(self) -> bool:
-        return self.skip_character("\\")
-
-    def parse_object(self) -> bool:
-        """Parse an object like '{"key": "value"}'"""
-        if not self.is_end_of_document() and self.char() == "{":
-            self.output += "{"
-            self.inc()
-            self.parse_whitespace_and_skip_comments()
-
-            initial = True
-            while not self.is_end_of_document() and self.char() != "}":
-                if not initial:
-                    processed_comma = self.parse_character(",")
-                    if not processed_comma:
-                        # repair missing comma
-                        self.output = insert_before_last_whitespace(self.output, ",")
-                    self.parse_whitespace_and_skip_comments()
-                else:
-                    processed_comma = True
-                    initial = False
-
-                processed_key = self.parse_string() or self.parse_unquoted_string()
-                if not processed_key:
-                    if self.is_end_of_document() or self.char() in "{}[]":
-                        # repair trailing comma
-                        self.output = strip_last_occurrence(self.output, ",")
-                        break
-                    raise self.object_key_expected()
-
-                self.parse_whitespace_and_skip_comments()
-                processed_colon = self.parse_character(":")
-                if not processed_colon:
-                    if is_start_of_value(self.char()):
-                        # repair missing colon
-                        self.output = insert_before_last_whitespace(self.output, ":")
-                    else:
-                        raise self.colon_expected()
-                processed_value = self.parse_value()
-                if not processed_value:
-                    if processed_colon:
-                        raise self.object_value_expected()
-                    raise self.colon_expected()
-
-            if not self.is_end_of_document() and self.char() == "}":
-                self.output += "}"
-                self.inc()
-            else:
-                # repair missing end bracket
-                self.output = insert_before_last_whitespace(self.output, "}")
-
-            return True
-
-        return False
-
-    def parse_array(self) -> bool:
-        """Parse an array like '["item1", "item2", ...]'"""
-        if not self.is_end_of_document() and self.char() == "[":
-            self.output += "["
-            self.inc()
-            self.parse_whitespace_and_skip_comments()
-
-            initial = True
-            while not self.is_end_of_document() and self.char() != "]":
-                if not initial:
-                    processed_comma = self.parse_character(",")
-                    if not processed_comma:
-                        # repair missing comma
-                        self.output = insert_before_last_whitespace(self.output, ",")
-                else:
-                    initial = False
-
-                processed_value = self.parse_value()
-                if not processed_value:
-                    # repair trailing comma
-                    self.output = strip_last_occurrence(self.output, ",")
-                    break
-
-            if not self.is_end_of_document() and self.char() == "]":
-                self.output += "]"
-                self.inc()
-            else:
-                # repair missing closing array bracket
-                self.output = insert_before_last_whitespace(self.output, "]")
-
-            return True
-
-        return False
-
-    def parse_newline_delimited_json(self):
-        """Parse and repair Newline Delimited JSON (NDJSON):
-        multiple JSON objects separated by a newline character
-        """
-        # repair NDJSON
-        initial = True
-        processed_value = True
-        while processed_value:
-            if not initial:
-                # parse optional comma, insert when missing
-                processed_comma = self.parse_character(",")
-                if not processed_comma:
-                    # repair: add missing comma
-                    self.output = insert_before_last_whitespace(self.output, ",")
-            else:
-                initial = False
-
-            processed_value = self.parse_value()
-
-            if not processed_value:
-                # repair: remove trailing comma
-                self.output = strip_last_occurrence(self.output, ",")
-
-        # repair: wrap the output inside array brackets
-        self.output = f"[\n{self.output}\n]"
-
-    def parse_string(self) -> bool:
-        """Parse a string enclosed by double quotes "...". Can contain escaped quotes
-        Repair strings enclosed in single quotes or special quotes
-        Repair an escaped string
-        """
-        if not self.is_end_of_document():
-            skip_escape_chars = self.char() == "\\"
-            if skip_escape_chars:
-                # repair: remove the first escape character
-                self.inc()
-                skip_escape_chars = True
-
-            if not self.is_end_of_document() and is_quote(self.char()):
-                is_end_quote = (
-                    is_single_quote if is_single_quote(self.char()) else is_double_quote
-                )
-
-                if self.char() != '"':
-                    pass  # TODO?: repair non-normalized quote
-                self.output += '"'
-                self.inc()
-
-                while not self.is_end_of_document() and not is_end_quote(self.char()):
-                    if self.char() == "\\":
-                        char = self.char(pos=+1)
-                        escape_char = ESCAPE_CHARACTERS.get(char)
-                        if escape_char:
-                            self.output += self.text[self.i : self.i + 2]
-                            self.inc(by=2)
-                        elif char == "u":
-                            if (
-                                not self.is_end_of_document(pos=+5)
-                                and is_hex(self.char(pos=+2))
-                                and is_hex(self.char(pos=+3))
-                                and is_hex(self.char(pos=+4))
-                                and is_hex(self.char(pos=+5))
-                            ):
-                                self.output += self.text[self.i : self.i + 6]
-                                self.inc(by=6)
-                            else:
-                                raise self.invalid_unicode_character(self.i)
-                        else:
-                            # repair invalid escape character: remove it
-                            self.output += char
-                            self.inc(by=2)
-                    else:
-                        char = self.char()
-
-                        if char == '"' and self.char(pos=-1) != "\\":
-                            # repair unescaped double quote
-                            self.output += "\\" + char
-                            self.inc()
-                        elif is_control_character(char):
-                            # unescaped control character
-                            self.output += CONTROL_CHARACTERS[char]
-                            self.inc()
-                        else:
-                            if not is_valid_string_character(char):
-                                raise self.invalid_character(char)
-                            self.output += char
-                            self.inc()
-
-                    if skip_escape_chars:
-                        processed = self.skip_escape_character()
-                        if processed:
-                            pass  # repair: skipped escape character (nothing to do)
-
-                if not self.is_end_of_document() and is_quote(self.char()):
-                    if self.char() != '"':
-                        pass  # TODO:? repair non-normalized quote
-
-                    self.output += '"'
-                    self.inc()
-                else:
-                    # repair missing end quote
-                    self.output += '"'
-
-                self.parse_concatenated_string()
-
-                return True
-
-        return False
-
-    def parse_concatenated_string(self) -> bool:
-        """Repair concatenated strings like \"hello\" + \"world\", change this into \"helloworld\" """
-        processed = False
-
-        self.parse_whitespace_and_skip_comments()
-        while not self.is_end_of_document() and self.char() == "+":
-            processed = True
-            self.inc()
-            self.parse_whitespace_and_skip_comments()
-
-            # repair: remove the end quote of the first string
-            self.output = strip_last_occurrence(self.output, '"', True)
-            start = len(self.output)
-            self.parse_string()
-
-            # repair: remove the start quote of the second string
-            self.output = remove_at_index(self.output, start, 1)
-
-        return processed
-
-    def parse_number(self) -> bool:
-        """Parse a number like 2.4 or 2.4e6"""
-        if not self.is_end_of_document():
-            start = self.i
-            if self.char() == "-":
-                self.inc()
-                err = self.expect_digit(start)
-                if err:
-                    raise err
-
-            if not self.is_end_of_document() and self.char() == "0":
-                self.inc()
-            elif not self.is_end_of_document() and self.char() in "123456789":
-                self.inc()
-                while not self.is_end_of_document() and self.char().isdigit():
-                    self.inc()
-
-            if not self.is_end_of_document() and self.char() == ".":
-                self.inc()
-                err = self.expect_digit(start)
-                if err:
-                    raise err
-                while not self.is_end_of_document() and self.char().isdigit():
-                    self.inc()
-
-            if not self.is_end_of_document() and self.char() in "eE":
-                self.inc()
-                if not self.is_end_of_document() and self.char() in "+-":
-                    self.inc()
-                err = self.expect_digit(start)
-                if err:
-                    raise err
-                while not self.is_end_of_document() and self.char().isdigit():
-                    self.inc()
-
-            if self.i > start:
-                self.output += self.text[start : self.i]
-                return True
-
-        return False
-
-    def parse_keywords(self) -> bool:
-        """Parse keywords true, false, null
-        Repair Python keywords True, False, None
-        """
-        return (
-            self.parse_keyword("true", "true")
-            or self.parse_keyword("false", "false")
-            or self.parse_keyword("null", "null")
-            # repair Python keywords True, False, None
-            or self.parse_keyword("True", "true")
-            or self.parse_keyword("False", "false")
-            or self.parse_keyword("None", "null")
-        )
-
-    def parse_keyword(self, name: str, value: str) -> bool:
-        if self.text[self.i : self.i + len(name)] == name:
-            self.output += value
-            self.inc(by=len(name))
-            return True
-
-        return False
-
-    def parse_unquoted_string(self) -> bool:
-        """Repair and unquoted string by adding quotes around it
-        Repair a MongoDB function call like NumberLong("2")
-        Repair a JSONP function call like callback({...});
-        """
-        # note that the symbol can end with whitespaces: we stop at the next delimiter
-        start = self.i
-        while not self.is_end_of_document() and not is_delimiter(self.char()):
-            self.inc()
-
-        if self.i > start:
-            if not self.is_end_of_document() and self.char() == "(":
-                # repair a MongoDB function call like NumberLong("2")
-                # repair a JSONP function call like callback({...});
-                self.inc()
-
-                self.parse_value()
-
-                if not self.is_end_of_document() and self.char() == ")":
-                    # repair: skip close bracket of function call
-                    self.inc()
-                    if not self.is_end_of_document() and self.char() == ";":
-                        # repair: skip semicolon after JSONP call
-                        self.inc()
-
-                return True
-
-            # else repair unquoted string
-
-            # first, go back to prevent getting trailing whitespaces in the string
-            while not self.is_start_of_document() and is_whitespace(self.char(pos=-1)):
-                self.dec()
-
-            symbol = self.text[start : self.i]
-            self.output += json.dumps(symbol)
-
-            return True
-
-        return False
-
-    def expect_digit(self, start: int) -> Optional[JsonRepairError]:
-        if self.is_end_of_document() or not self.char().isdigit():
-            num_so_far = self.text[start : self.i]
-            return JsonRepairError(
-                f"Invalid number '{num_so_far}', expecting a digit {self.got()}", 2
-            )
-
-    def invalid_character(self, char: str) -> JsonRepairError:
-        return JsonRepairError("Invalid character " + json.dumps(char), self.i)
-
-    def unexpected_character(self) -> JsonRepairError:
-        return JsonRepairError(
-            "Unexpected character " + json.dumps(self.text[self.i]), self.i
-        )
-
-    def unexpected_end(self) -> JsonRepairError:
-        return JsonRepairError("Unexpected end of json string", len(self.text))
-
-    def object_key_expected(self) -> JsonRepairError:
-        return JsonRepairError("Object key expected", self.i)
-
-    def object_value_expected(self) -> JsonRepairError:
-        return JsonRepairError("Object value expected", self.i)
-
-    def colon_expected(self) -> JsonRepairError:
-        return JsonRepairError("Colon expected", self.i)
-
-    def invalid_unicode_character(self, start: int) -> JsonRepairError:
-        end = start + 2
-        while re.match(r"\w", self.text[end]):
-            end += 1
-        chars = self.text[start:end]
-        return JsonRepairError(f'Invalid unicode character "{chars}"', self.i)
-
-    def got(self) -> str:
-        return (
-            f"but got '{self.char()}'"
-            if not self.is_end_of_document()
-            else "but reached end of input"
-        )

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/bracketparser2-plugin.js

@@ -1,18 +0,0 @@
-import BracketParser from './bracketparser2.js';
-
-class BracketParserPlugin extends Phaser.Plugins.BasePlugin {
-    constructor(pluginManager) {
-        super(pluginManager);
-    }
-
-    start() {
-        var eventEmitter = this.game.events;
-        eventEmitter.on('destroy', this.destroy, this);
-    }
-
-    add(config) {
-        return new BracketParser(config);
-    }
-}
-
-export default BracketParserPlugin;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/dialog/methods/ModalMethods.js

@@ -1,43 +0,0 @@
-import ModalMethods from '../../basesizer/ModalMethods.js';
-
-export default {
-    onCreateModalBehavior(self) {
-        self.on('button.click', function (button, groupName, index, pointer, event) {
-            if (groupName !== 'actions') {
-                return;
-            }
-
-            var closeEventData = {
-                index: index,
-                text: button.text,
-                button: button,
-                dialog: self
-            }
-
-
-            switch (self.buttonsType) {
-                case 'radio':
-                    closeEventData.value = self.getChoicesSelectedButtonName();
-                    break;
-                case 'checkboxes':
-                    closeEventData.value = self.getChoicesButtonStates();
-                    break;
-                default:
-                    closeEventData.value = undefined;
-            }
-
-            self.modalClose(closeEventData);
-        });
-    },
-
-    modal(config, onClose) {
-        if (config && (config.defaultBehavior === false)) {
-            this.onCreateModalBehavior = false;
-        } else {
-            delete this.onCreateModalBehavior;
-        }
-
-        ModalMethods.modal.call(this, config, onClose);
-        return this;
-    }
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sides/childbehaviors/index.js

@@ -1,14 +0,0 @@
-import Visible from './Visible.js';
-import Fade from './Fade.js';
-import Move from './Move.js';
-
-var methods = {};
-
-Object.assign(
-    methods,
-    Visible,
-    Fade,
-    Move
-);
-
-export default methods;

spaces/AlexWang/lama/bin/evaluate_predicts.py

@@ -1,79 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-
-import pandas as pd
-
-from saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset
-from saicinpainting.evaluation.evaluator import InpaintingEvaluator, lpips_fid100_f1
-from saicinpainting.evaluation.losses.base_loss import SegmentationAwareSSIM, \
-    SegmentationClassStats, SSIMScore, LPIPSScore, FIDScore, SegmentationAwareLPIPS, SegmentationAwareFID
-from saicinpainting.evaluation.utils import load_yaml
-
-
-def main(args):
-    config = load_yaml(args.config)
-
-    dataset = PrecomputedInpaintingResultsDataset(args.datadir, args.predictdir, **config.dataset_kwargs)
-
-    metrics = {
-        'ssim': SSIMScore(),
-        'lpips': LPIPSScore(),
-        'fid': FIDScore()
-    }
-    enable_segm = config.get('segmentation', dict(enable=False)).get('enable', False)
-    if enable_segm:
-        weights_path = os.path.expandvars(config.segmentation.weights_path)
-        metrics.update(dict(
-            segm_stats=SegmentationClassStats(weights_path=weights_path),
-            segm_ssim=SegmentationAwareSSIM(weights_path=weights_path),
-            segm_lpips=SegmentationAwareLPIPS(weights_path=weights_path),
-            segm_fid=SegmentationAwareFID(weights_path=weights_path)
-        ))
-    evaluator = InpaintingEvaluator(dataset, scores=metrics,
-                                    integral_title='lpips_fid100_f1', integral_func=lpips_fid100_f1,
-                                    **config.evaluator_kwargs)
-
-    os.makedirs(os.path.dirname(args.outpath), exist_ok=True)
-
-    results = evaluator.evaluate()
-
-    results = pd.DataFrame(results).stack(1).unstack(0)
-    results.dropna(axis=1, how='all', inplace=True)
-    results.to_csv(args.outpath, sep='\t', float_format='%.4f')
-
-    if enable_segm:
-        only_short_results = results[[c for c in results.columns if not c[0].startswith('segm_')]].dropna(axis=1, how='all')
-        only_short_results.to_csv(args.outpath + '_short', sep='\t', float_format='%.4f')
-
-        print(only_short_results)
-
-        segm_metrics_results = results[['segm_ssim', 'segm_lpips', 'segm_fid']].dropna(axis=1, how='all').transpose().unstack(0).reorder_levels([1, 0], axis=1)
-        segm_metrics_results.drop(['mean', 'std'], axis=0, inplace=True)
-
-        segm_stats_results = results['segm_stats'].dropna(axis=1, how='all').transpose()
-        segm_stats_results.index = pd.MultiIndex.from_tuples(n.split('/') for n in segm_stats_results.index)
-        segm_stats_results = segm_stats_results.unstack(0).reorder_levels([1, 0], axis=1)
-        segm_stats_results.sort_index(axis=1, inplace=True)
-        segm_stats_results.dropna(axis=0, how='all', inplace=True)
-
-        segm_results = pd.concat([segm_metrics_results, segm_stats_results], axis=1, sort=True)
-        segm_results.sort_values(('mask_freq', 'total'), ascending=False, inplace=True)
-
-        segm_results.to_csv(args.outpath + '_segm', sep='\t', float_format='%.4f')
-    else:
-        print(results)
-
-
-if __name__ == '__main__':
-    import argparse
-
-    aparser = argparse.ArgumentParser()
-    aparser.add_argument('config', type=str, help='Path to evaluation config')
-    aparser.add_argument('datadir', type=str,
-                         help='Path to folder with images and masks (output of gen_mask_dataset.py)')
-    aparser.add_argument('predictdir', type=str,
-                         help='Path to folder with predicts (e.g. predict_hifill_baseline.py)')
-    aparser.add_argument('outpath', type=str, help='Where to put results')
-
-    main(aparser.parse_args())

spaces/Alfasign/diffusers-gallery/index.html

@@ -1,218 +0,0 @@
-<!DOCTYPE html>
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width" />
-
-    <title>Diffusers gallery</title>
-    <meta name="description" content="Discover all difussion models on the Hugging Face hub." />
-
-    <meta property="og:url" content="https://huggingface-projects-diffusers-gallery.hf.space/" />
-    <meta property="og:type" content="website" />
-    <meta property="og:title" content="Hugging Face - Diffusers Models Gallery" />
-    <meta property="og:description" content="Discover all difussion models on the Hugging Face hub." />
-    <meta property="og:image" content="https://huggingface-projects-diffusers-gallery.hf.space/Fo6vR6JX0AEjbw1.jpeg" />
-
-    <meta name="twitter:card" content="player" />
-    <meta property="twitter:url" content="https://huggingface-projects-diffusers-gallery.hf.space/" />
-    <meta name="twitter:description" content="Discover all difussion models on the Hugging Face hub." />
-
-    <meta name="twitter:site" content="@huggingface" />
-    <meta name="twitter:title" content="Hugging Face - Diffusers Models Gallery" />
-
-    <meta name="twitter:image" content="https://huggingface-projects-diffusers-gallery.hf.space/Fo6vR6JX0AEjbw1.jpeg" />
-    <meta name="twitter:player" content="https://huggingface-projects-diffusers-gallery.hf.space/index.html" />
-    <meta name="twitter:player:width" content="100%" />
-    <meta name="twitter:player:height" content="600" />
-
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script>
-    <script src="https://cdn.tailwindcss.com"></script>
-
-    <script type="module">
-      import Alpine from "https://cdn.skypack.dev/alpinejs";
-      import Intersect from "https://cdn.skypack.dev/@alpinejs/intersect";
-      Alpine.plugin(Intersect);
-
-      Alpine.data("modelsData", () => ({
-        async init() {
-          const data = await this.getModels(this.page, this.sort, this.filter);
-          this.models = data.models;
-          this.totalPages = data.totalPages;
-        },
-        ASSETS_URL: "https://d26smi9133w0oo.cloudfront.net/diffusers-gallery/",
-        models: [],
-        filter: "all",
-        sort: "trending",
-        page: 1,
-        totalPages: -1,
-        buttonClass(attr, filter) {
-          if (this[attr] === filter) {
-            return "bg-black dark:bg-white shadow-lg text-white dark:text-black hover:bg-black hover:text-white";
-          }
-          return "text-gray-600 dark:text-gray-300 hover:bg-gray-200 dark:hover:bg-gray-500 hover:text-gray-800";
-        },
-        async filterModels(style) {
-          this.filter = style;
-          this.page = 1;
-          const data = await this.getModels(this.page, this.sort, this.filter);
-          this.models = data.models;
-          this.totalPages = data.totalPages;
-        },
-        async sortModels(sort) {
-          this.sort = sort;
-          this.page = 1;
-          const data = await this.getModels(this.page, this.sort, this.filter);
-          this.models = data.models;
-          this.totalPages = data.totalPages;
-        },
-        async getModels(page, sort, style) {
-          // const res = await fetch(`http://localhost:8000/api/models?page=${page}&sort=${sort}&style=${style}`)
-          const res = await fetch(
-            `https://huggingface-projects-diffusers-gallery-bot.hf.space/api/models?page=${page}&sort=${sort}&style=${style}`
-          );
-          const data = await res.json();
-          const models = data.models.map((model) => ({
-            id: model.id,
-            likes: model.likes,
-            class: model.class,
-            isNFSW: model.isNFSW,
-            images: model.images.filter((image) => image && image.endsWith(".jpg")),
-          }));
-
-          return {
-            models,
-            totalPages: data.totalPages,
-          };
-        },
-        async nextPage() {
-          if (this.page < this.totalPages) {
-            this.page += 1;
-            const data = await this.getModels(this.page, this.sort, this.filter);
-            this.models = this.models.concat(data.models);
-            this.totalPages = data.totalPages;
-          }
-        },
-      }));
-      Alpine.start();
-    </script>
-  </head>
-
-  <body class="pb-10 pt-5 bg-gray-100 dark:bg-gray-900 relative">
-    <section
-      class="container px-6 grid grid-cols-2 md:grid-cols-3 lg:grid-cols-4 gap-4 mx-auto relative"
-      x-data="modelsData"
-    >
-      <div class="col-span-2 lg:col-span-1 flex flex-col gap-2 row-start">
-        <h1 class="text-lg font-semibold dark:text-white whitespace-nowrap">Diffusers Models Gallery</h1>
-      </div>
-
-      <div class="col-span-2 md:col-span-3 flex items-center gap-4 flex flex-wrap lg-auto lg:ml-auto text-sm">
-        <div class="flex gap-2">
-          <span class="md:px-3 py-1 dark:text-white text-gray-400">sort by</span>
-          <button
-            :class="buttonClass('sort', 'trending')"
-            class="px-2 md:px-3 py-1 rounded-full text"
-            @click="sortModels('trending')"
-          >
-            Trending
-          </button>
-          <button
-            :class="buttonClass('sort', 'recent')"
-            class="text-gray-600 hover:bg-gray-200 dark:hover:bg-gray-500 hover:text-gray-800 px-2 md:px-3 py-1 rounded-full"
-            @click="sortModels('recent')"
-          >
-            Recent
-          </button>
-          <button
-            :class="buttonClass('sort', 'likes')"
-            class="text-gray-600 hover:bg-gray-200 dark:hover:bg-gray-500 hover:text-gray-800 px-2 md:px-3 py-1 rounded-full"
-            @click="sortModels('likes')"
-          >
-            Most Likes
-          </button>
-        </div>
-        <div class="flex gap-2">
-          <span class="md:px-3 py-1 dark:text-white text-gray-400"> style</span>
-          <button
-            :class="buttonClass('filter', 'all')"
-            class="px-2 md:px-3 py-1 rounded-full"
-            @click="filterModels('all')"
-          >
-            All
-          </button>
-          <button
-            :class="buttonClass('filter', 'anime')"
-            class="text-gray-600 hover:bg-gray-200 dark:hover:bg-gray-500 hover:text-gray-800 px-2 md:px-3 py-1 rounded-full"
-            @click="filterModels('anime')"
-          >
-            Anime
-          </button>
-          <button
-            :class="buttonClass('filter', '3d')"
-            class="text-gray-600 hover:bg-gray-200 dark:hover:bg-gray-500 hover:text-gray-800 px-2 md:px-3 py-1 rounded-full"
-            @click="filterModels('3d')"
-          >
-            3D
-          </button>
-          <button
-            :class="buttonClass('filter', 'realistic')"
-            class="text-gray-600 hover:bg-gray-200 dark:hover:bg-gray-500 hover:text-gray-800 px-2 md:px-3 py-1 rounded-full"
-            @click="filterModels('realistic')"
-          >
-            Realistic
-          </button>
-        </div>
-      </div>
-
-      <template x-for="model in models" :key="model.id">
-        <template x-if="model.images.length > 0">
-          <a
-            :href="`https://huggingface.co/${model.id}`"
-            class="block bg-gray-900 rounded-xl overflow-hidden relative group aspect-square text-white"
-            target="_blank"
-          >
-            <div
-              class="absolute bottom-0 p-4 bg-gradient-to-t text-white pt-10 from-black/90 via-black/70 to-transparent w-full z-10"
-            >
-              <div class="text-sm flex items-center group-hover:translate-x-0.5 transition">
-                <svg
-                  class="mr-1.5 text-white/70"
-                  xmlns="http://www.w3.org/2000/svg"
-                  xmlns:xlink="http://www.w3.org/1999/xlink"
-                  aria-hidden="true"
-                  focusable="false"
-                  role="img"
-                  width="1em"
-                  height="1em"
-                  preserveAspectRatio="xMidYMid meet"
-                  viewBox="0 0 32 32"
-                  fill="currentColor"
-                >
-                  <path
-                    d="M22.5,4c-2,0-3.9,0.8-5.3,2.2L16,7.4l-1.1-1.1C12,3.3,7.2,3.3,4.3,6.2c0,0-0.1,0.1-0.1,0.1c-3,3-3,7.8,0,10.8L16,29l11.8-11.9c3-3,3-7.8,0-10.8C26.4,4.8,24.5,4,22.5,4z"
-                  ></path>
-                </svg>
-                <span x-text="model.likes"></span>
-              </div>
-              <div
-                x-text="model.id"
-                class="text-sm md:text-lg lg:text-xl font-semibold group-hover:translate-x-0.5 transition"
-              ></div>
-            </div>
-            <div class="group-hover:brightness-90 h-full" :class="model.isNFSW ? 'blur-md' : ''">
-              <template x-if="model.images[0]">
-                <img
-                  :src="()=> ASSETS_URL + model.images[0]"
-                  :alt="model.id"
-                  alt=""
-                  class="w-full h-full object-cover group-hover:scale-[1.01] transition"
-                />
-              </template>
-            </div>
-          </a>
-        </template>
-      </template>
-      <div class="h-12 relative" x-intersect="nextPage" data-iframe-height></div>
-    </section>
-  </body>
-</html>

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/using-diffusers/depth2img.md

@@ -1,56 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Text-guided depth-to-image generation
-
-[[open-in-colab]]
-
-The [`StableDiffusionDepth2ImgPipeline`] lets you pass a text prompt and an initial image to condition the generation of new images. In addition, you can also pass a `depth_map` to preserve the image structure. If no `depth_map` is provided, the pipeline automatically predicts the depth via an integrated [depth-estimation model](https://github.com/isl-org/MiDaS).
-
-Start by creating an instance of the [`StableDiffusionDepth2ImgPipeline`]:
-
-```python
-import torch
-import requests
-from PIL import Image
-
-from diffusers import StableDiffusionDepth2ImgPipeline
-
-pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-2-depth",
-    torch_dtype=torch.float16,
-).to("cuda")
-```
-
-Now pass your prompt to the pipeline. You can also pass a `negative_prompt` to prevent certain words from guiding how an image is generated:
-
-```python
-url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-init_image = Image.open(requests.get(url, stream=True).raw)
-prompt = "two tigers"
-n_prompt = "bad, deformed, ugly, bad anatomy"
-image = pipe(prompt=prompt, image=init_image, negative_prompt=n_prompt, strength=0.7).images[0]
-image
-```
-
-| Input                                                                           | Output                                                                                                                                |
-|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|
-| <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/coco-cats.png" width="500"/> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/depth2img-tigers.png" width="500"/> |
-
-Play around with the Spaces below and see if you notice a difference between generated images with and without a depth map!
-
-<iframe
-	src="https://radames-stable-diffusion-depth2img.hf.space"
-	frameborder="0"
-	width="850"
-	height="500"
-></iframe>

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/t5_film_transformer.py

@@ -1,321 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import math
-
-import torch
-from torch import nn
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from .attention_processor import Attention
-from .embeddings import get_timestep_embedding
-from .modeling_utils import ModelMixin
-
-
-class T5FilmDecoder(ModelMixin, ConfigMixin):
-    @register_to_config
-    def __init__(
-        self,
-        input_dims: int = 128,
-        targets_length: int = 256,
-        max_decoder_noise_time: float = 2000.0,
-        d_model: int = 768,
-        num_layers: int = 12,
-        num_heads: int = 12,
-        d_kv: int = 64,
-        d_ff: int = 2048,
-        dropout_rate: float = 0.1,
-    ):
-        super().__init__()
-
-        self.conditioning_emb = nn.Sequential(
-            nn.Linear(d_model, d_model * 4, bias=False),
-            nn.SiLU(),
-            nn.Linear(d_model * 4, d_model * 4, bias=False),
-            nn.SiLU(),
-        )
-
-        self.position_encoding = nn.Embedding(targets_length, d_model)
-        self.position_encoding.weight.requires_grad = False
-
-        self.continuous_inputs_projection = nn.Linear(input_dims, d_model, bias=False)
-
-        self.dropout = nn.Dropout(p=dropout_rate)
-
-        self.decoders = nn.ModuleList()
-        for lyr_num in range(num_layers):
-            # FiLM conditional T5 decoder
-            lyr = DecoderLayer(d_model=d_model, d_kv=d_kv, num_heads=num_heads, d_ff=d_ff, dropout_rate=dropout_rate)
-            self.decoders.append(lyr)
-
-        self.decoder_norm = T5LayerNorm(d_model)
-
-        self.post_dropout = nn.Dropout(p=dropout_rate)
-        self.spec_out = nn.Linear(d_model, input_dims, bias=False)
-
-    def encoder_decoder_mask(self, query_input, key_input):
-        mask = torch.mul(query_input.unsqueeze(-1), key_input.unsqueeze(-2))
-        return mask.unsqueeze(-3)
-
-    def forward(self, encodings_and_masks, decoder_input_tokens, decoder_noise_time):
-        batch, _, _ = decoder_input_tokens.shape
-        assert decoder_noise_time.shape == (batch,)
-
-        # decoder_noise_time is in [0, 1), so rescale to expected timing range.
-        time_steps = get_timestep_embedding(
-            decoder_noise_time * self.config.max_decoder_noise_time,
-            embedding_dim=self.config.d_model,
-            max_period=self.config.max_decoder_noise_time,
-        ).to(dtype=self.dtype)
-
-        conditioning_emb = self.conditioning_emb(time_steps).unsqueeze(1)
-
-        assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
-
-        seq_length = decoder_input_tokens.shape[1]
-
-        # If we want to use relative positions for audio context, we can just offset
-        # this sequence by the length of encodings_and_masks.
-        decoder_positions = torch.broadcast_to(
-            torch.arange(seq_length, device=decoder_input_tokens.device),
-            (batch, seq_length),
-        )
-
-        position_encodings = self.position_encoding(decoder_positions)
-
-        inputs = self.continuous_inputs_projection(decoder_input_tokens)
-        inputs += position_encodings
-        y = self.dropout(inputs)
-
-        # decoder: No padding present.
-        decoder_mask = torch.ones(
-            decoder_input_tokens.shape[:2], device=decoder_input_tokens.device, dtype=inputs.dtype
-        )
-
-        # Translate encoding masks to encoder-decoder masks.
-        encodings_and_encdec_masks = [(x, self.encoder_decoder_mask(decoder_mask, y)) for x, y in encodings_and_masks]
-
-        # cross attend style: concat encodings
-        encoded = torch.cat([x[0] for x in encodings_and_encdec_masks], dim=1)
-        encoder_decoder_mask = torch.cat([x[1] for x in encodings_and_encdec_masks], dim=-1)
-
-        for lyr in self.decoders:
-            y = lyr(
-                y,
-                conditioning_emb=conditioning_emb,
-                encoder_hidden_states=encoded,
-                encoder_attention_mask=encoder_decoder_mask,
-            )[0]
-
-        y = self.decoder_norm(y)
-        y = self.post_dropout(y)
-
-        spec_out = self.spec_out(y)
-        return spec_out
-
-
-class DecoderLayer(nn.Module):
-    def __init__(self, d_model, d_kv, num_heads, d_ff, dropout_rate, layer_norm_epsilon=1e-6):
-        super().__init__()
-        self.layer = nn.ModuleList()
-
-        # cond self attention: layer 0
-        self.layer.append(
-            T5LayerSelfAttentionCond(d_model=d_model, d_kv=d_kv, num_heads=num_heads, dropout_rate=dropout_rate)
-        )
-
-        # cross attention: layer 1
-        self.layer.append(
-            T5LayerCrossAttention(
-                d_model=d_model,
-                d_kv=d_kv,
-                num_heads=num_heads,
-                dropout_rate=dropout_rate,
-                layer_norm_epsilon=layer_norm_epsilon,
-            )
-        )
-
-        # Film Cond MLP + dropout: last layer
-        self.layer.append(
-            T5LayerFFCond(d_model=d_model, d_ff=d_ff, dropout_rate=dropout_rate, layer_norm_epsilon=layer_norm_epsilon)
-        )
-
-    def forward(
-        self,
-        hidden_states,
-        conditioning_emb=None,
-        attention_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        encoder_decoder_position_bias=None,
-    ):
-        hidden_states = self.layer[0](
-            hidden_states,
-            conditioning_emb=conditioning_emb,
-            attention_mask=attention_mask,
-        )
-
-        if encoder_hidden_states is not None:
-            encoder_extended_attention_mask = torch.where(encoder_attention_mask > 0, 0, -1e10).to(
-                encoder_hidden_states.dtype
-            )
-
-            hidden_states = self.layer[1](
-                hidden_states,
-                key_value_states=encoder_hidden_states,
-                attention_mask=encoder_extended_attention_mask,
-            )
-
-        # Apply Film Conditional Feed Forward layer
-        hidden_states = self.layer[-1](hidden_states, conditioning_emb)
-
-        return (hidden_states,)
-
-
-class T5LayerSelfAttentionCond(nn.Module):
-    def __init__(self, d_model, d_kv, num_heads, dropout_rate):
-        super().__init__()
-        self.layer_norm = T5LayerNorm(d_model)
-        self.FiLMLayer = T5FiLMLayer(in_features=d_model * 4, out_features=d_model)
-        self.attention = Attention(query_dim=d_model, heads=num_heads, dim_head=d_kv, out_bias=False, scale_qk=False)
-        self.dropout = nn.Dropout(dropout_rate)
-
-    def forward(
-        self,
-        hidden_states,
-        conditioning_emb=None,
-        attention_mask=None,
-    ):
-        # pre_self_attention_layer_norm
-        normed_hidden_states = self.layer_norm(hidden_states)
-
-        if conditioning_emb is not None:
-            normed_hidden_states = self.FiLMLayer(normed_hidden_states, conditioning_emb)
-
-        # Self-attention block
-        attention_output = self.attention(normed_hidden_states)
-
-        hidden_states = hidden_states + self.dropout(attention_output)
-
-        return hidden_states
-
-
-class T5LayerCrossAttention(nn.Module):
-    def __init__(self, d_model, d_kv, num_heads, dropout_rate, layer_norm_epsilon):
-        super().__init__()
-        self.attention = Attention(query_dim=d_model, heads=num_heads, dim_head=d_kv, out_bias=False, scale_qk=False)
-        self.layer_norm = T5LayerNorm(d_model, eps=layer_norm_epsilon)
-        self.dropout = nn.Dropout(dropout_rate)
-
-    def forward(
-        self,
-        hidden_states,
-        key_value_states=None,
-        attention_mask=None,
-    ):
-        normed_hidden_states = self.layer_norm(hidden_states)
-        attention_output = self.attention(
-            normed_hidden_states,
-            encoder_hidden_states=key_value_states,
-            attention_mask=attention_mask.squeeze(1),
-        )
-        layer_output = hidden_states + self.dropout(attention_output)
-        return layer_output
-
-
-class T5LayerFFCond(nn.Module):
-    def __init__(self, d_model, d_ff, dropout_rate, layer_norm_epsilon):
-        super().__init__()
-        self.DenseReluDense = T5DenseGatedActDense(d_model=d_model, d_ff=d_ff, dropout_rate=dropout_rate)
-        self.film = T5FiLMLayer(in_features=d_model * 4, out_features=d_model)
-        self.layer_norm = T5LayerNorm(d_model, eps=layer_norm_epsilon)
-        self.dropout = nn.Dropout(dropout_rate)
-
-    def forward(self, hidden_states, conditioning_emb=None):
-        forwarded_states = self.layer_norm(hidden_states)
-        if conditioning_emb is not None:
-            forwarded_states = self.film(forwarded_states, conditioning_emb)
-
-        forwarded_states = self.DenseReluDense(forwarded_states)
-        hidden_states = hidden_states + self.dropout(forwarded_states)
-        return hidden_states
-
-
-class T5DenseGatedActDense(nn.Module):
-    def __init__(self, d_model, d_ff, dropout_rate):
-        super().__init__()
-        self.wi_0 = nn.Linear(d_model, d_ff, bias=False)
-        self.wi_1 = nn.Linear(d_model, d_ff, bias=False)
-        self.wo = nn.Linear(d_ff, d_model, bias=False)
-        self.dropout = nn.Dropout(dropout_rate)
-        self.act = NewGELUActivation()
-
-    def forward(self, hidden_states):
-        hidden_gelu = self.act(self.wi_0(hidden_states))
-        hidden_linear = self.wi_1(hidden_states)
-        hidden_states = hidden_gelu * hidden_linear
-        hidden_states = self.dropout(hidden_states)
-
-        hidden_states = self.wo(hidden_states)
-        return hidden_states
-
-
-class T5LayerNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        Construct a layernorm module in the T5 style. No bias and no subtraction of mean.
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
-        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
-        # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
-        # half-precision inputs is done in fp32
-
-        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-
-        # convert into half-precision if necessary
-        if self.weight.dtype in [torch.float16, torch.bfloat16]:
-            hidden_states = hidden_states.to(self.weight.dtype)
-
-        return self.weight * hidden_states
-
-
-class NewGELUActivation(nn.Module):
-    """
-    Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see
-    the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415
-    """
-
-    def forward(self, input: torch.Tensor) -> torch.Tensor:
-        return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0))))
-
-
-class T5FiLMLayer(nn.Module):
-    """
-    FiLM Layer
-    """
-
-    def __init__(self, in_features, out_features):
-        super().__init__()
-        self.scale_bias = nn.Linear(in_features, out_features * 2, bias=False)
-
-    def forward(self, x, conditioning_emb):
-        emb = self.scale_bias(conditioning_emb)
-        scale, shift = torch.chunk(emb, 2, -1)
-        x = x * (1 + scale) + shift
-        return x

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/auto_pipeline.py

@@ -1,886 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from collections import OrderedDict
-
-from ..configuration_utils import ConfigMixin
-from .controlnet import (
-    StableDiffusionControlNetImg2ImgPipeline,
-    StableDiffusionControlNetInpaintPipeline,
-    StableDiffusionControlNetPipeline,
-    StableDiffusionXLControlNetPipeline,
-)
-from .deepfloyd_if import IFImg2ImgPipeline, IFInpaintingPipeline, IFPipeline
-from .kandinsky import (
-    KandinskyCombinedPipeline,
-    KandinskyImg2ImgCombinedPipeline,
-    KandinskyImg2ImgPipeline,
-    KandinskyInpaintCombinedPipeline,
-    KandinskyInpaintPipeline,
-    KandinskyPipeline,
-)
-from .kandinsky2_2 import (
-    KandinskyV22CombinedPipeline,
-    KandinskyV22Img2ImgCombinedPipeline,
-    KandinskyV22Img2ImgPipeline,
-    KandinskyV22InpaintCombinedPipeline,
-    KandinskyV22InpaintPipeline,
-    KandinskyV22Pipeline,
-)
-from .stable_diffusion import (
-    StableDiffusionImg2ImgPipeline,
-    StableDiffusionInpaintPipeline,
-    StableDiffusionPipeline,
-)
-from .stable_diffusion_xl import (
-    StableDiffusionXLImg2ImgPipeline,
-    StableDiffusionXLInpaintPipeline,
-    StableDiffusionXLPipeline,
-)
-
-
-AUTO_TEXT2IMAGE_PIPELINES_MAPPING = OrderedDict(
-    [
-        ("stable-diffusion", StableDiffusionPipeline),
-        ("stable-diffusion-xl", StableDiffusionXLPipeline),
-        ("if", IFPipeline),
-        ("kandinsky", KandinskyCombinedPipeline),
-        ("kandinsky22", KandinskyV22CombinedPipeline),
-        ("stable-diffusion-controlnet", StableDiffusionControlNetPipeline),
-        ("stable-diffusion-xl-controlnet", StableDiffusionXLControlNetPipeline),
-    ]
-)
-
-AUTO_IMAGE2IMAGE_PIPELINES_MAPPING = OrderedDict(
-    [
-        ("stable-diffusion", StableDiffusionImg2ImgPipeline),
-        ("stable-diffusion-xl", StableDiffusionXLImg2ImgPipeline),
-        ("if", IFImg2ImgPipeline),
-        ("kandinsky", KandinskyImg2ImgCombinedPipeline),
-        ("kandinsky22", KandinskyV22Img2ImgCombinedPipeline),
-        ("stable-diffusion-controlnet", StableDiffusionControlNetImg2ImgPipeline),
-    ]
-)
-
-AUTO_INPAINT_PIPELINES_MAPPING = OrderedDict(
-    [
-        ("stable-diffusion", StableDiffusionInpaintPipeline),
-        ("stable-diffusion-xl", StableDiffusionXLInpaintPipeline),
-        ("if", IFInpaintingPipeline),
-        ("kandinsky", KandinskyInpaintCombinedPipeline),
-        ("kandinsky22", KandinskyV22InpaintCombinedPipeline),
-        ("stable-diffusion-controlnet", StableDiffusionControlNetInpaintPipeline),
-    ]
-)
-
-_AUTO_TEXT2IMAGE_DECODER_PIPELINES_MAPPING = OrderedDict(
-    [
-        ("kandinsky", KandinskyPipeline),
-        ("kandinsky22", KandinskyV22Pipeline),
-    ]
-)
-_AUTO_IMAGE2IMAGE_DECODER_PIPELINES_MAPPING = OrderedDict(
-    [
-        ("kandinsky", KandinskyImg2ImgPipeline),
-        ("kandinsky22", KandinskyV22Img2ImgPipeline),
-    ]
-)
-_AUTO_INPAINT_DECODER_PIPELINES_MAPPING = OrderedDict(
-    [
-        ("kandinsky", KandinskyInpaintPipeline),
-        ("kandinsky22", KandinskyV22InpaintPipeline),
-    ]
-)
-
-SUPPORTED_TASKS_MAPPINGS = [
-    AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
-    AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
-    AUTO_INPAINT_PIPELINES_MAPPING,
-    _AUTO_TEXT2IMAGE_DECODER_PIPELINES_MAPPING,
-    _AUTO_IMAGE2IMAGE_DECODER_PIPELINES_MAPPING,
-    _AUTO_INPAINT_DECODER_PIPELINES_MAPPING,
-]
-
-
-def _get_connected_pipeline(pipeline_cls):
-    # for now connected pipelines can only be loaded from decoder pipelines, such as kandinsky-community/kandinsky-2-2-decoder
-    if pipeline_cls in _AUTO_TEXT2IMAGE_DECODER_PIPELINES_MAPPING.values():
-        return _get_task_class(
-            AUTO_TEXT2IMAGE_PIPELINES_MAPPING, pipeline_cls.__name__, throw_error_if_not_exist=False
-        )
-    if pipeline_cls in _AUTO_IMAGE2IMAGE_DECODER_PIPELINES_MAPPING.values():
-        return _get_task_class(
-            AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, pipeline_cls.__name__, throw_error_if_not_exist=False
-        )
-    if pipeline_cls in _AUTO_INPAINT_DECODER_PIPELINES_MAPPING.values():
-        return _get_task_class(AUTO_INPAINT_PIPELINES_MAPPING, pipeline_cls.__name__, throw_error_if_not_exist=False)
-
-
-def _get_task_class(mapping, pipeline_class_name, throw_error_if_not_exist: bool = True):
-    def get_model(pipeline_class_name):
-        for task_mapping in SUPPORTED_TASKS_MAPPINGS:
-            for model_name, pipeline in task_mapping.items():
-                if pipeline.__name__ == pipeline_class_name:
-                    return model_name
-
-    model_name = get_model(pipeline_class_name)
-
-    if model_name is not None:
-        task_class = mapping.get(model_name, None)
-        if task_class is not None:
-            return task_class
-
-    if throw_error_if_not_exist:
-        raise ValueError(f"AutoPipeline can't find a pipeline linked to {pipeline_class_name} for {model_name}")
-
-
-def _get_signature_keys(obj):
-    parameters = inspect.signature(obj.__init__).parameters
-    required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}
-    optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
-    expected_modules = set(required_parameters.keys()) - {"self"}
-    return expected_modules, optional_parameters
-
-
-class AutoPipelineForText2Image(ConfigMixin):
-    r"""
-
-    AutoPipeline for text-to-image generation.
-
-    [`AutoPipelineForText2Image`] is a generic pipeline class that will be instantiated as one of the text-to-image
-    pipeline class in diffusers.
-
-    The pipeline type (for example [`StableDiffusionPipeline`]) is automatically selected when created with the
-    AutoPipelineForText2Image.from_pretrained(pretrained_model_name_or_path) or
-    AutoPipelineForText2Image.from_pipe(pipeline) class methods .
-
-    This class cannot be instantiated using __init__() (throws an error).
-
-    Class attributes:
-
-        - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
-          diffusion pipeline's components.
-
-    """
-    config_name = "model_index.json"
-
-    def __init__(self, *args, **kwargs):
-        raise EnvironmentError(
-            f"{self.__class__.__name__} is designed to be instantiated "
-            f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
-            f"`{self.__class__.__name__}.from_pipe(pipeline)` methods."
-        )
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_or_path, **kwargs):
-        r"""
-        Instantiates a text-to-image Pytorch diffusion pipeline from pretrained pipeline weight.
-
-        The from_pretrained() method takes care of returning the correct pipeline class instance by:
-            1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its
-               config object
-            2. Find the text-to-image pipeline linked to the pipeline class using pattern matching on pipeline class
-               name.
-
-        If a `controlnet` argument is passed, it will instantiate a [`StableDiffusionControlNetPipeline`] object.
-
-        The pipeline is set in evaluation mode (`model.eval()`) by default.
-
-        If you get the error message below, you need to finetune the weights for your downstream task:
-
-        ```
-        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
-        - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
-        You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
-        ```
-
-        Parameters:
-            pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
-                Can be either:
-
-                    - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline
-                      hosted on the Hub.
-                    - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights
-                      saved using
-                    [`~DiffusionPipeline.save_pretrained`].
-            torch_dtype (`str` or `torch.dtype`, *optional*):
-                Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the
-                dtype is automatically derived from the model's weights.
-            force_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
-                cached versions if they exist.
-            cache_dir (`Union[str, os.PathLike]`, *optional*):
-                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
-                is not used.
-            resume_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
-                incompletely downloaded files are deleted.
-            proxies (`Dict[str, str]`, *optional*):
-                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
-                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
-            output_loading_info(`bool`, *optional*, defaults to `False`):
-                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
-            local_files_only (`bool`, *optional*, defaults to `False`):
-                Whether to only load local model weights and configuration files or not. If set to `True`, the model
-                won't be downloaded from the Hub.
-            use_auth_token (`str` or *bool*, *optional*):
-                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
-                `diffusers-cli login` (stored in `~/.huggingface`) is used.
-            revision (`str`, *optional*, defaults to `"main"`):
-                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
-                allowed by Git.
-            custom_revision (`str`, *optional*, defaults to `"main"`):
-                The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
-                `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
-                custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
-            mirror (`str`, *optional*):
-                Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not
-                guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
-                information.
-            device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
-                A map that specifies where each submodule should go. It doesn’t need to be defined for each
-                parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
-                same device.
-
-                Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For
-                more information about each option see [designing a device
-                map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
-            max_memory (`Dict`, *optional*):
-                A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
-                each GPU and the available CPU RAM if unset.
-            offload_folder (`str` or `os.PathLike`, *optional*):
-                The path to offload weights if device_map contains the value `"disk"`.
-            offload_state_dict (`bool`, *optional*):
-                If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
-                the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`
-                when there is some disk offload.
-            low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
-                Speed up model loading only loading the pretrained weights and not initializing the weights. This also
-                tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
-                Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
-                argument to `True` will raise an error.
-            use_safetensors (`bool`, *optional*, defaults to `None`):
-                If set to `None`, the safetensors weights are downloaded if they're available **and** if the
-                safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
-                weights. If set to `False`, safetensors weights are not loaded.
-            kwargs (remaining dictionary of keyword arguments, *optional*):
-                Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
-                class). The overwritten components are passed directly to the pipelines `__init__` method. See example
-                below for more information.
-            variant (`str`, *optional*):
-                Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
-                loading `from_flax`.
-
-        <Tip>
-
-        To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
-        `huggingface-cli login`.
-
-        </Tip>
-
-        Examples:
-
-        ```py
-        >>> from diffusers import AutoPipelineForTextToImage
-
-        >>> pipeline = AutoPipelineForTextToImage.from_pretrained("runwayml/stable-diffusion-v1-5")
-        >>> print(pipeline.__class__)
-        ```
-        """
-        config = cls.load_config(pretrained_model_or_path)
-        orig_class_name = config["_class_name"]
-
-        if "controlnet" in kwargs:
-            orig_class_name = config["_class_name"].replace("Pipeline", "ControlNetPipeline")
-
-        text_2_image_cls = _get_task_class(AUTO_TEXT2IMAGE_PIPELINES_MAPPING, orig_class_name)
-
-        return text_2_image_cls.from_pretrained(pretrained_model_or_path, **kwargs)
-
-    @classmethod
-    def from_pipe(cls, pipeline, **kwargs):
-        r"""
-        Instantiates a text-to-image Pytorch diffusion pipeline from another instantiated diffusion pipeline class.
-
-        The from_pipe() method takes care of returning the correct pipeline class instance by finding the text-to-image
-        pipeline linked to the pipeline class using pattern matching on pipeline class name.
-
-        All the modules the pipeline contains will be used to initialize the new pipeline without reallocating
-        additional memoery.
-
-        The pipeline is set in evaluation mode (`model.eval()`) by default.
-
-        Parameters:
-            pipeline (`DiffusionPipeline`):
-                an instantiated `DiffusionPipeline` object
-
-        ```py
-        >>> from diffusers import AutoPipelineForTextToImage, AutoPipelineForImageToImage
-
-        >>> pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5", requires_safety_checker=False
-        ... )
-
-        >>> pipe_t2i = AutoPipelineForTextToImage.from_pipe(pipe_t2i)
-        ```
-        """
-
-        original_config = dict(pipeline.config)
-        original_cls_name = pipeline.__class__.__name__
-
-        # derive the pipeline class to instantiate
-        text_2_image_cls = _get_task_class(AUTO_TEXT2IMAGE_PIPELINES_MAPPING, original_cls_name)
-
-        # define expected module and optional kwargs given the pipeline signature
-        expected_modules, optional_kwargs = _get_signature_keys(text_2_image_cls)
-
-        pretrained_model_name_or_path = original_config.pop("_name_or_path", None)
-
-        # allow users pass modules in `kwargs` to override the original pipeline's components
-        passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
-        original_class_obj = {
-            k: pipeline.components[k]
-            for k, v in pipeline.components.items()
-            if k in expected_modules and k not in passed_class_obj
-        }
-
-        # allow users pass optional kwargs to override the original pipelines config attribute
-        passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
-        original_pipe_kwargs = {
-            k: original_config[k]
-            for k, v in original_config.items()
-            if k in optional_kwargs and k not in passed_pipe_kwargs
-        }
-
-        # config that were not expected by original pipeline is stored as private attribute
-        # we will pass them as optional arguments if they can be accepted by the pipeline
-        additional_pipe_kwargs = [
-            k[1:]
-            for k in original_config.keys()
-            if k.startswith("_") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs
-        ]
-        for k in additional_pipe_kwargs:
-            original_pipe_kwargs[k] = original_config.pop(f"_{k}")
-
-        text_2_image_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}
-
-        # store unused config as private attribute
-        unused_original_config = {
-            f"{'' if k.startswith('_') else '_'}{k}": original_config[k]
-            for k, v in original_config.items()
-            if k not in text_2_image_kwargs
-        }
-
-        missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(text_2_image_kwargs.keys())
-
-        if len(missing_modules) > 0:
-            raise ValueError(
-                f"Pipeline {text_2_image_cls} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed"
-            )
-
-        model = text_2_image_cls(**text_2_image_kwargs)
-        model.register_to_config(_name_or_path=pretrained_model_name_or_path)
-        model.register_to_config(**unused_original_config)
-
-        return model
-
-
-class AutoPipelineForImage2Image(ConfigMixin):
-    r"""
-
-    AutoPipeline for image-to-image generation.
-
-    [`AutoPipelineForImage2Image`] is a generic pipeline class that will be instantiated as one of the image-to-image
-    pipeline classes in diffusers.
-
-    The pipeline type (for example [`StableDiffusionImg2ImgPipeline`]) is automatically selected when created with the
-    `AutoPipelineForImage2Image.from_pretrained(pretrained_model_name_or_path)` or
-    `AutoPipelineForImage2Image.from_pipe(pipeline)` class methods.
-
-    This class cannot be instantiated using __init__() (throws an error).
-
-    Class attributes:
-
-        - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
-          diffusion pipeline's components.
-
-    """
-    config_name = "model_index.json"
-
-    def __init__(self, *args, **kwargs):
-        raise EnvironmentError(
-            f"{self.__class__.__name__} is designed to be instantiated "
-            f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
-            f"`{self.__class__.__name__}.from_pipe(pipeline)` methods."
-        )
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_or_path, **kwargs):
-        r"""
-        Instantiates a image-to-image Pytorch diffusion pipeline from pretrained pipeline weight.
-
-        The from_pretrained() method takes care of returning the correct pipeline class instance by:
-            1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its
-               config object
-            2. Find the image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class
-               name.
-
-        If a `controlnet` argument is passed, it will instantiate a StableDiffusionControlNetImg2ImgPipeline object.
-
-        The pipeline is set in evaluation mode (`model.eval()`) by default.
-
-        If you get the error message below, you need to finetune the weights for your downstream task:
-
-        ```
-        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
-        - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
-        You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
-        ```
-
-        Parameters:
-            pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
-                Can be either:
-
-                    - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline
-                      hosted on the Hub.
-                    - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights
-                      saved using
-                    [`~DiffusionPipeline.save_pretrained`].
-            torch_dtype (`str` or `torch.dtype`, *optional*):
-                Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the
-                dtype is automatically derived from the model's weights.
-            force_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
-                cached versions if they exist.
-            cache_dir (`Union[str, os.PathLike]`, *optional*):
-                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
-                is not used.
-            resume_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
-                incompletely downloaded files are deleted.
-            proxies (`Dict[str, str]`, *optional*):
-                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
-                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
-            output_loading_info(`bool`, *optional*, defaults to `False`):
-                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
-            local_files_only (`bool`, *optional*, defaults to `False`):
-                Whether to only load local model weights and configuration files or not. If set to `True`, the model
-                won't be downloaded from the Hub.
-            use_auth_token (`str` or *bool*, *optional*):
-                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
-                `diffusers-cli login` (stored in `~/.huggingface`) is used.
-            revision (`str`, *optional*, defaults to `"main"`):
-                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
-                allowed by Git.
-            custom_revision (`str`, *optional*, defaults to `"main"`):
-                The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
-                `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
-                custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
-            mirror (`str`, *optional*):
-                Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not
-                guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
-                information.
-            device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
-                A map that specifies where each submodule should go. It doesn’t need to be defined for each
-                parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
-                same device.
-
-                Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For
-                more information about each option see [designing a device
-                map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
-            max_memory (`Dict`, *optional*):
-                A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
-                each GPU and the available CPU RAM if unset.
-            offload_folder (`str` or `os.PathLike`, *optional*):
-                The path to offload weights if device_map contains the value `"disk"`.
-            offload_state_dict (`bool`, *optional*):
-                If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
-                the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`
-                when there is some disk offload.
-            low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
-                Speed up model loading only loading the pretrained weights and not initializing the weights. This also
-                tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
-                Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
-                argument to `True` will raise an error.
-            use_safetensors (`bool`, *optional*, defaults to `None`):
-                If set to `None`, the safetensors weights are downloaded if they're available **and** if the
-                safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
-                weights. If set to `False`, safetensors weights are not loaded.
-            kwargs (remaining dictionary of keyword arguments, *optional*):
-                Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
-                class). The overwritten components are passed directly to the pipelines `__init__` method. See example
-                below for more information.
-            variant (`str`, *optional*):
-                Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
-                loading `from_flax`.
-
-        <Tip>
-
-        To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
-        `huggingface-cli login`.
-
-        </Tip>
-
-        Examples:
-
-        ```py
-        >>> from diffusers import AutoPipelineForTextToImage
-
-        >>> pipeline = AutoPipelineForImageToImage.from_pretrained("runwayml/stable-diffusion-v1-5")
-        >>> print(pipeline.__class__)
-        ```
-        """
-        config = cls.load_config(pretrained_model_or_path)
-        orig_class_name = config["_class_name"]
-
-        if "controlnet" in kwargs:
-            orig_class_name = config["_class_name"].replace("Pipeline", "ControlNetPipeline")
-
-        image_2_image_cls = _get_task_class(AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, orig_class_name)
-
-        return image_2_image_cls.from_pretrained(pretrained_model_or_path, **kwargs)
-
-    @classmethod
-    def from_pipe(cls, pipeline, **kwargs):
-        r"""
-        Instantiates a image-to-image Pytorch diffusion pipeline from another instantiated diffusion pipeline class.
-
-        The from_pipe() method takes care of returning the correct pipeline class instance by finding the
-        image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name.
-
-        All the modules the pipeline contains will be used to initialize the new pipeline without reallocating
-        additional memoery.
-
-        The pipeline is set in evaluation mode (`model.eval()`) by default.
-
-        Parameters:
-            pipeline (`DiffusionPipeline`):
-                an instantiated `DiffusionPipeline` object
-
-        Examples:
-
-        ```py
-        >>> from diffusers import AutoPipelineForTextToImage, AutoPipelineForImageToImage
-
-        >>> pipe_t2i = AutoPipelineForText2Image.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5", requires_safety_checker=False
-        ... )
-
-        >>> pipe_i2i = AutoPipelineForImageToImage.from_pipe(pipe_t2i)
-        ```
-        """
-
-        original_config = dict(pipeline.config)
-        original_cls_name = pipeline.__class__.__name__
-
-        # derive the pipeline class to instantiate
-        image_2_image_cls = _get_task_class(AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, original_cls_name)
-
-        # define expected module and optional kwargs given the pipeline signature
-        expected_modules, optional_kwargs = _get_signature_keys(image_2_image_cls)
-
-        pretrained_model_name_or_path = original_config.pop("_name_or_path", None)
-
-        # allow users pass modules in `kwargs` to override the original pipeline's components
-        passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
-        original_class_obj = {
-            k: pipeline.components[k]
-            for k, v in pipeline.components.items()
-            if k in expected_modules and k not in passed_class_obj
-        }
-
-        # allow users pass optional kwargs to override the original pipelines config attribute
-        passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
-        original_pipe_kwargs = {
-            k: original_config[k]
-            for k, v in original_config.items()
-            if k in optional_kwargs and k not in passed_pipe_kwargs
-        }
-
-        # config attribute that were not expected by original pipeline is stored as its private attribute
-        # we will pass them as optional arguments if they can be accepted by the pipeline
-        additional_pipe_kwargs = [
-            k[1:]
-            for k in original_config.keys()
-            if k.startswith("_") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs
-        ]
-        for k in additional_pipe_kwargs:
-            original_pipe_kwargs[k] = original_config.pop(f"_{k}")
-
-        image_2_image_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}
-
-        # store unused config as private attribute
-        unused_original_config = {
-            f"{'' if k.startswith('_') else '_'}{k}": original_config[k]
-            for k, v in original_config.items()
-            if k not in image_2_image_kwargs
-        }
-
-        missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(image_2_image_kwargs.keys())
-
-        if len(missing_modules) > 0:
-            raise ValueError(
-                f"Pipeline {image_2_image_cls} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed"
-            )
-
-        model = image_2_image_cls(**image_2_image_kwargs)
-        model.register_to_config(_name_or_path=pretrained_model_name_or_path)
-        model.register_to_config(**unused_original_config)
-
-        return model
-
-
-class AutoPipelineForInpainting(ConfigMixin):
-    r"""
-
-    AutoPipeline for inpainting generation.
-
-    [`AutoPipelineForInpainting`] is a generic pipeline class that will be instantiated as one of the inpainting
-    pipeline class in diffusers.
-
-    The pipeline type (for example [`IFInpaintingPipeline`]) is automatically selected when created with the
-    AutoPipelineForInpainting.from_pretrained(pretrained_model_name_or_path) or
-    AutoPipelineForInpainting.from_pipe(pipeline) class methods .
-
-    This class cannot be instantiated using __init__() (throws an error).
-
-    Class attributes:
-
-        - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
-          diffusion pipeline's components.
-
-    """
-    config_name = "model_index.json"
-
-    def __init__(self, *args, **kwargs):
-        raise EnvironmentError(
-            f"{self.__class__.__name__} is designed to be instantiated "
-            f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
-            f"`{self.__class__.__name__}.from_pipe(pipeline)` methods."
-        )
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_or_path, **kwargs):
-        r"""
-        Instantiates a inpainting Pytorch diffusion pipeline from pretrained pipeline weight.
-
-        The from_pretrained() method takes care of returning the correct pipeline class instance by:
-            1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its
-               config object
-            2. Find the inpainting pipeline linked to the pipeline class using pattern matching on pipeline class name.
-
-        If a `controlnet` argument is passed, it will instantiate a StableDiffusionControlNetInpaintPipeline object.
-
-        The pipeline is set in evaluation mode (`model.eval()`) by default.
-
-        If you get the error message below, you need to finetune the weights for your downstream task:
-
-        ```
-        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
-        - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
-        You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
-        ```
-
-        Parameters:
-            pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
-                Can be either:
-
-                    - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline
-                      hosted on the Hub.
-                    - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights
-                      saved using
-                    [`~DiffusionPipeline.save_pretrained`].
-            torch_dtype (`str` or `torch.dtype`, *optional*):
-                Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the
-                dtype is automatically derived from the model's weights.
-            force_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
-                cached versions if they exist.
-            cache_dir (`Union[str, os.PathLike]`, *optional*):
-                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
-                is not used.
-            resume_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
-                incompletely downloaded files are deleted.
-            proxies (`Dict[str, str]`, *optional*):
-                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
-                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
-            output_loading_info(`bool`, *optional*, defaults to `False`):
-                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
-            local_files_only (`bool`, *optional*, defaults to `False`):
-                Whether to only load local model weights and configuration files or not. If set to `True`, the model
-                won't be downloaded from the Hub.
-            use_auth_token (`str` or *bool*, *optional*):
-                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
-                `diffusers-cli login` (stored in `~/.huggingface`) is used.
-            revision (`str`, *optional*, defaults to `"main"`):
-                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
-                allowed by Git.
-            custom_revision (`str`, *optional*, defaults to `"main"`):
-                The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
-                `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
-                custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
-            mirror (`str`, *optional*):
-                Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not
-                guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
-                information.
-            device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
-                A map that specifies where each submodule should go. It doesn’t need to be defined for each
-                parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
-                same device.
-
-                Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For
-                more information about each option see [designing a device
-                map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
-            max_memory (`Dict`, *optional*):
-                A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
-                each GPU and the available CPU RAM if unset.
-            offload_folder (`str` or `os.PathLike`, *optional*):
-                The path to offload weights if device_map contains the value `"disk"`.
-            offload_state_dict (`bool`, *optional*):
-                If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
-                the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`
-                when there is some disk offload.
-            low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
-                Speed up model loading only loading the pretrained weights and not initializing the weights. This also
-                tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
-                Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
-                argument to `True` will raise an error.
-            use_safetensors (`bool`, *optional*, defaults to `None`):
-                If set to `None`, the safetensors weights are downloaded if they're available **and** if the
-                safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
-                weights. If set to `False`, safetensors weights are not loaded.
-            kwargs (remaining dictionary of keyword arguments, *optional*):
-                Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
-                class). The overwritten components are passed directly to the pipelines `__init__` method. See example
-                below for more information.
-            variant (`str`, *optional*):
-                Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
-                loading `from_flax`.
-
-        <Tip>
-
-        To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
-        `huggingface-cli login`.
-
-        </Tip>
-
-        Examples:
-
-        ```py
-        >>> from diffusers import AutoPipelineForTextToImage
-
-        >>> pipeline = AutoPipelineForImageToImage.from_pretrained("runwayml/stable-diffusion-v1-5")
-        >>> print(pipeline.__class__)
-        ```
-        """
-        config = cls.load_config(pretrained_model_or_path)
-        orig_class_name = config["_class_name"]
-
-        if "controlnet" in kwargs:
-            orig_class_name = config["_class_name"].replace("Pipeline", "ControlNetPipeline")
-
-        inpainting_cls = _get_task_class(AUTO_INPAINT_PIPELINES_MAPPING, orig_class_name)
-
-        return inpainting_cls.from_pretrained(pretrained_model_or_path, **kwargs)
-
-    @classmethod
-    def from_pipe(cls, pipeline, **kwargs):
-        r"""
-        Instantiates a inpainting Pytorch diffusion pipeline from another instantiated diffusion pipeline class.
-
-        The from_pipe() method takes care of returning the correct pipeline class instance by finding the inpainting
-        pipeline linked to the pipeline class using pattern matching on pipeline class name.
-
-        All the modules the pipeline class contain will be used to initialize the new pipeline without reallocating
-        additional memoery.
-
-        The pipeline is set in evaluation mode (`model.eval()`) by default.
-
-        Parameters:
-            pipeline (`DiffusionPipeline`):
-                an instantiated `DiffusionPipeline` object
-
-        Examples:
-
-        ```py
-        >>> from diffusers import AutoPipelineForTextToImage, AutoPipelineForInpainting
-
-        >>> pipe_t2i = AutoPipelineForText2Image.from_pretrained(
-        ...     "DeepFloyd/IF-I-XL-v1.0", requires_safety_checker=False
-        ... )
-
-        >>> pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_t2i)
-        ```
-        """
-        original_config = dict(pipeline.config)
-        original_cls_name = pipeline.__class__.__name__
-
-        # derive the pipeline class to instantiate
-        inpainting_cls = _get_task_class(AUTO_INPAINT_PIPELINES_MAPPING, original_cls_name)
-
-        # define expected module and optional kwargs given the pipeline signature
-        expected_modules, optional_kwargs = _get_signature_keys(inpainting_cls)
-
-        pretrained_model_name_or_path = original_config.pop("_name_or_path", None)
-
-        # allow users pass modules in `kwargs` to override the original pipeline's components
-        passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
-        original_class_obj = {
-            k: pipeline.components[k]
-            for k, v in pipeline.components.items()
-            if k in expected_modules and k not in passed_class_obj
-        }
-
-        # allow users pass optional kwargs to override the original pipelines config attribute
-        passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
-        original_pipe_kwargs = {
-            k: original_config[k]
-            for k, v in original_config.items()
-            if k in optional_kwargs and k not in passed_pipe_kwargs
-        }
-
-        # config that were not expected by original pipeline is stored as private attribute
-        # we will pass them as optional arguments if they can be accepted by the pipeline
-        additional_pipe_kwargs = [
-            k[1:]
-            for k in original_config.keys()
-            if k.startswith("_") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs
-        ]
-        for k in additional_pipe_kwargs:
-            original_pipe_kwargs[k] = original_config.pop(f"_{k}")
-
-        inpainting_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}
-
-        # store unused config as private attribute
-        unused_original_config = {
-            f"{'' if k.startswith('_') else '_'}{k}": original_config[k]
-            for k, v in original_config.items()
-            if k not in inpainting_kwargs
-        }
-
-        missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(inpainting_kwargs.keys())
-
-        if len(missing_modules) > 0:
-            raise ValueError(
-                f"Pipeline {inpainting_cls} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed"
-            )
-
-        model = inpainting_cls(**inpainting_kwargs)
-        model.register_to_config(_name_or_path=pretrained_model_name_or_path)
-        model.register_to_config(**unused_original_config)
-
-        return model

spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/dense_test_mixins.py

@@ -1,100 +0,0 @@
-from inspect import signature
-
-import torch
-
-from mmdet.core import bbox2result, bbox_mapping_back, multiclass_nms
-
-
-class BBoxTestMixin(object):
-    """Mixin class for test time augmentation of bboxes."""
-
-    def merge_aug_bboxes(self, aug_bboxes, aug_scores, img_metas):
-        """Merge augmented detection bboxes and scores.
-
-        Args:
-            aug_bboxes (list[Tensor]): shape (n, 4*#class)
-            aug_scores (list[Tensor] or None): shape (n, #class)
-            img_shapes (list[Tensor]): shape (3, ).
-
-        Returns:
-            tuple: (bboxes, scores)
-        """
-        recovered_bboxes = []
-        for bboxes, img_info in zip(aug_bboxes, img_metas):
-            img_shape = img_info[0]['img_shape']
-            scale_factor = img_info[0]['scale_factor']
-            flip = img_info[0]['flip']
-            flip_direction = img_info[0]['flip_direction']
-            bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip,
-                                       flip_direction)
-            recovered_bboxes.append(bboxes)
-        bboxes = torch.cat(recovered_bboxes, dim=0)
-        if aug_scores is None:
-            return bboxes
-        else:
-            scores = torch.cat(aug_scores, dim=0)
-            return bboxes, scores
-
-    def aug_test_bboxes(self, feats, img_metas, rescale=False):
-        """Test det bboxes with test time augmentation.
-
-        Args:
-            feats (list[Tensor]): the outer list indicates test-time
-                augmentations and inner Tensor should have a shape NxCxHxW,
-                which contains features for all images in the batch.
-            img_metas (list[list[dict]]): the outer list indicates test-time
-                augs (multiscale, flip, etc.) and the inner list indicates
-                images in a batch. each dict has image information.
-            rescale (bool, optional): Whether to rescale the results.
-                Defaults to False.
-
-        Returns:
-            list[ndarray]: bbox results of each class
-        """
-        # check with_nms argument
-        gb_sig = signature(self.get_bboxes)
-        gb_args = [p.name for p in gb_sig.parameters.values()]
-        if hasattr(self, '_get_bboxes'):
-            gbs_sig = signature(self._get_bboxes)
-        else:
-            gbs_sig = signature(self._get_bboxes_single)
-        gbs_args = [p.name for p in gbs_sig.parameters.values()]
-        assert ('with_nms' in gb_args) and ('with_nms' in gbs_args), \
-            f'{self.__class__.__name__}' \
-            ' does not support test-time augmentation'
-
-        aug_bboxes = []
-        aug_scores = []
-        aug_factors = []  # score_factors for NMS
-        for x, img_meta in zip(feats, img_metas):
-            # only one image in the batch
-            outs = self.forward(x)
-            bbox_inputs = outs + (img_meta, self.test_cfg, False, False)
-            bbox_outputs = self.get_bboxes(*bbox_inputs)[0]
-            aug_bboxes.append(bbox_outputs[0])
-            aug_scores.append(bbox_outputs[1])
-            # bbox_outputs of some detectors (e.g., ATSS, FCOS, YOLOv3)
-            # contains additional element to adjust scores before NMS
-            if len(bbox_outputs) >= 3:
-                aug_factors.append(bbox_outputs[2])
-
-        # after merging, bboxes will be rescaled to the original image size
-        merged_bboxes, merged_scores = self.merge_aug_bboxes(
-            aug_bboxes, aug_scores, img_metas)
-        merged_factors = torch.cat(aug_factors, dim=0) if aug_factors else None
-        det_bboxes, det_labels = multiclass_nms(
-            merged_bboxes,
-            merged_scores,
-            self.test_cfg.score_thr,
-            self.test_cfg.nms,
-            self.test_cfg.max_per_img,
-            score_factors=merged_factors)
-
-        if rescale:
-            _det_bboxes = det_bboxes
-        else:
-            _det_bboxes = det_bboxes.clone()
-            _det_bboxes[:, :4] *= det_bboxes.new_tensor(
-                img_metas[0][0]['scale_factor'])
-        bbox_results = bbox2result(_det_bboxes, det_labels, self.num_classes)
-        return bbox_results

spaces/Andy1621/uniformer_image_segmentation/configs/gcnet/README.md

@@ -1,48 +0,0 @@
-# GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
-
-## Introduction
-
-<!-- [ALGORITHM] -->
-
-```latex
-@inproceedings{cao2019gcnet,
-  title={Gcnet: Non-local networks meet squeeze-excitation networks and beyond},
-  author={Cao, Yue and Xu, Jiarui and Lin, Stephen and Wei, Fangyun and Hu, Han},
-  booktitle={Proceedings of the IEEE International Conference on Computer Vision Workshops},
-  pages={0--0},
-  year={2019}
-}
-```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                    | download                                                                                                                                                                                                                                                                                                                                               |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| GCNet  | R-50-D8  | 512x1024  |   40000 | 5.8      | 3.93           | 77.69 |         78.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436-4b0fd17b.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436.log.json)     |
-| GCNet  | R-101-D8 | 512x1024  |   40000 | 9.2      | 2.61           | 78.28 |         79.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436-5e62567f.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436.log.json) |
-| GCNet  | R-50-D8  | 769x769   |   40000 | 6.5      | 1.67           | 78.12 |         80.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814-a26f4471.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814.log.json)         |
-| GCNet  | R-101-D8 | 769x769   |   40000 | 10.5     | 1.13           | 78.95 |         80.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550-ca4f0a84.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550.log.json)     |
-| GCNet  | R-50-D8  | 512x1024  |   80000 | -        | -              | 78.48 |         80.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450-ef8f069b.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450.log.json)     |
-| GCNet  | R-101-D8 | 512x1024  |   80000 | -        | -              | 79.03 |         79.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450-778ebf69.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450.log.json) |
-| GCNet  | R-50-D8  | 769x769   |   80000 | -        | -              | 78.68 |         80.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516-4839565b.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516.log.json)         |
-| GCNet  | R-101-D8 | 769x769   |   80000 | -        | -              | 79.18 |         80.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628-8e043423.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628.log.json)     |
-
-### ADE20K
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                | download                                                                                                                                                                                                                                                                                                                               |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| GCNet  | R-50-D8  | 512x512   |   80000 | 8.5      | 23.38          | 41.47 |         42.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146-91a6da41.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146.log.json)         |
-| GCNet  | R-101-D8 | 512x512   |   80000 | 12       | 15.20          | 42.82 |         44.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811-c3fcb6dd.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811.log.json)     |
-| GCNet  | R-50-D8  | 512x512   |  160000 | -        | -              | 42.37 |         43.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122-d95f3e1f.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122.log.json)     |
-| GCNet  | R-101-D8 | 512x512   |  160000 | -        | -              | 43.69 |         45.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406-615528d7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406.log.json) |
-
-### Pascal VOC 2012 + Aug
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                   |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| GCNet  | R-50-D8  | 512x512   |   20000 | 5.8      | 23.35          | 76.42 |         77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701-3cbfdab1.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701.log.json)     |
-| GCNet  | R-101-D8 | 512x512   |   20000 | 9.2      | 14.80          | 77.41 |         78.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713-6c720aa9.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713.log.json) |
-| GCNet  | R-50-D8  | 512x512   |   40000 | -        | -              | 76.24 |         77.63 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105-9797336d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105.log.json)     |
-| GCNet  | R-101-D8 | 512x512   |   40000 | -        | -              | 77.84 |         78.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806-1e38208d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806.log.json) |

spaces/Andy1621/uniformer_image_segmentation/configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py

@@ -1,2 +0,0 @@
-_base_ = './gcnet_r50-d8_769x769_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/configs/_base_/datasets/pascal_context_59.py

@@ -1,60 +0,0 @@
-# dataset settings
-dataset_type = 'PascalContextDataset59'
-data_root = 'data/VOCdevkit/VOC2010/'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-
-img_scale = (520, 520)
-crop_size = (480, 480)
-
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', reduce_zero_label=True),
-    dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
-    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
-    dict(type='RandomFlip', prob=0.5),
-    dict(type='PhotoMetricDistortion'),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=img_scale,
-        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=4,
-    workers_per_gpu=4,
-    train=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='JPEGImages',
-        ann_dir='SegmentationClassContext',
-        split='ImageSets/SegmentationContext/train.txt',
-        pipeline=train_pipeline),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='JPEGImages',
-        ann_dir='SegmentationClassContext',
-        split='ImageSets/SegmentationContext/val.txt',
-        pipeline=test_pipeline),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='JPEGImages',
-        ann_dir='SegmentationClassContext',
-        split='ImageSets/SegmentationContext/val.txt',
-        pipeline=test_pipeline))

spaces/Anonymous-sub/Rerender/gmflow_module/utils/misc.py

@@ -1,42 +0,0 @@
-import os
-import numpy as np
-import sys
-import json
-
-
-def read_text_lines(filepath):
-    with open(filepath, 'r') as f:
-        lines = f.readlines()
-    lines = [l.rstrip() for l in lines]
-    return lines
-
-
-def check_path(path):
-    if not os.path.exists(path):
-        os.makedirs(path, exist_ok=True)  # explicitly set exist_ok when multi-processing
-
-
-def save_command(save_path, filename='command_train.txt'):
-    check_path(save_path)
-    command = sys.argv
-    save_file = os.path.join(save_path, filename)
-    # Save all training commands when resuming training
-    with open(save_file, 'a') as f:
-        f.write(' '.join(command))
-        f.write('\n\n')
-
-
-def save_args(args, filename='args.json'):
-    args_dict = vars(args)
-    check_path(args.checkpoint_dir)
-    save_path = os.path.join(args.checkpoint_dir, filename)
-
-    # Save all training args when resuming training
-    with open(save_path, 'a') as f:
-        json.dump(args_dict, f, indent=4, sort_keys=False)
-        f.write('\n\n')
-
-
-def int_list(s):
-    """Convert string to int list"""
-    return [int(x) for x in s.split(',')]

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/__pip-runner__.py

@@ -1,50 +0,0 @@
-"""Execute exactly this copy of pip, within a different environment.
-
-This file is named as it is, to ensure that this module can't be imported via
-an import statement.
-"""
-
-# /!\ This version compatibility check section must be Python 2 compatible. /!\
-
-import sys
-
-# Copied from setup.py
-PYTHON_REQUIRES = (3, 7)
-
-
-def version_str(version):  # type: ignore
-    return ".".join(str(v) for v in version)
-
-
-if sys.version_info[:2] < PYTHON_REQUIRES:
-    raise SystemExit(
-        "This version of pip does not support python {} (requires >={}).".format(
-            version_str(sys.version_info[:2]), version_str(PYTHON_REQUIRES)
-        )
-    )
-
-# From here on, we can use Python 3 features, but the syntax must remain
-# Python 2 compatible.
-
-import runpy  # noqa: E402
-from importlib.machinery import PathFinder  # noqa: E402
-from os.path import dirname  # noqa: E402
-
-PIP_SOURCES_ROOT = dirname(dirname(__file__))
-
-
-class PipImportRedirectingFinder:
-    @classmethod
-    def find_spec(self, fullname, path=None, target=None):  # type: ignore
-        if fullname != "pip":
-            return None
-
-        spec = PathFinder.find_spec(fullname, [PIP_SOURCES_ROOT], target)
-        assert spec, (PIP_SOURCES_ROOT, fullname)
-        return spec
-
-
-sys.meta_path.insert(0, PipImportRedirectingFinder())
-
-assert __name__ == "__main__", "Cannot run __pip-runner__.py as a non-main module"
-runpy.run_module("pip", run_name="__main__", alter_sys=True)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/network/utils.py

@@ -1,96 +0,0 @@
-from typing import Dict, Generator
-
-from pip._vendor.requests.models import CONTENT_CHUNK_SIZE, Response
-
-from pip._internal.exceptions import NetworkConnectionError
-
-# The following comments and HTTP headers were originally added by
-# Donald Stufft in git commit 22c562429a61bb77172039e480873fb239dd8c03.
-#
-# We use Accept-Encoding: identity here because requests defaults to
-# accepting compressed responses. This breaks in a variety of ways
-# depending on how the server is configured.
-# - Some servers will notice that the file isn't a compressible file
-#   and will leave the file alone and with an empty Content-Encoding
-# - Some servers will notice that the file is already compressed and
-#   will leave the file alone, adding a Content-Encoding: gzip header
-# - Some servers won't notice anything at all and will take a file
-#   that's already been compressed and compress it again, and set
-#   the Content-Encoding: gzip header
-# By setting this to request only the identity encoding we're hoping
-# to eliminate the third case.  Hopefully there does not exist a server
-# which when given a file will notice it is already compressed and that
-# you're not asking for a compressed file and will then decompress it
-# before sending because if that's the case I don't think it'll ever be
-# possible to make this work.
-HEADERS: Dict[str, str] = {"Accept-Encoding": "identity"}
-
-
-def raise_for_status(resp: Response) -> None:
-    http_error_msg = ""
-    if isinstance(resp.reason, bytes):
-        # We attempt to decode utf-8 first because some servers
-        # choose to localize their reason strings. If the string
-        # isn't utf-8, we fall back to iso-8859-1 for all other
-        # encodings.
-        try:
-            reason = resp.reason.decode("utf-8")
-        except UnicodeDecodeError:
-            reason = resp.reason.decode("iso-8859-1")
-    else:
-        reason = resp.reason
-
-    if 400 <= resp.status_code < 500:
-        http_error_msg = (
-            f"{resp.status_code} Client Error: {reason} for url: {resp.url}"
-        )
-
-    elif 500 <= resp.status_code < 600:
-        http_error_msg = (
-            f"{resp.status_code} Server Error: {reason} for url: {resp.url}"
-        )
-
-    if http_error_msg:
-        raise NetworkConnectionError(http_error_msg, response=resp)
-
-
-def response_chunks(
-    response: Response, chunk_size: int = CONTENT_CHUNK_SIZE
-) -> Generator[bytes, None, None]:
-    """Given a requests Response, provide the data chunks."""
-    try:
-        # Special case for urllib3.
-        for chunk in response.raw.stream(
-            chunk_size,
-            # We use decode_content=False here because we don't
-            # want urllib3 to mess with the raw bytes we get
-            # from the server. If we decompress inside of
-            # urllib3 then we cannot verify the checksum
-            # because the checksum will be of the compressed
-            # file. This breakage will only occur if the
-            # server adds a Content-Encoding header, which
-            # depends on how the server was configured:
-            # - Some servers will notice that the file isn't a
-            #   compressible file and will leave the file alone
-            #   and with an empty Content-Encoding
-            # - Some servers will notice that the file is
-            #   already compressed and will leave the file
-            #   alone and will add a Content-Encoding: gzip
-            #   header
-            # - Some servers won't notice anything at all and
-            #   will take a file that's already been compressed
-            #   and compress it again and set the
-            #   Content-Encoding: gzip header
-            #
-            # By setting this not to decode automatically we
-            # hope to eliminate problems with the second case.
-            decode_content=False,
-        ):
-            yield chunk
-    except AttributeError:
-        # Standard file-like object.
-        while True:
-            chunk = response.raw.read(chunk_size)
-            if not chunk:
-                break
-            yield chunk

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/archive_util.py

@@ -1,280 +0,0 @@
-"""distutils.archive_util
-
-Utility functions for creating archive files (tarballs, zip files,
-that sort of thing)."""
-
-import os
-from warnings import warn
-import sys
-
-try:
-    import zipfile
-except ImportError:
-    zipfile = None
-
-
-from distutils.errors import DistutilsExecError
-from distutils.spawn import spawn
-from distutils.dir_util import mkpath
-from distutils import log
-
-try:
-    from pwd import getpwnam
-except ImportError:
-    getpwnam = None
-
-try:
-    from grp import getgrnam
-except ImportError:
-    getgrnam = None
-
-
-def _get_gid(name):
-    """Returns a gid, given a group name."""
-    if getgrnam is None or name is None:
-        return None
-    try:
-        result = getgrnam(name)
-    except KeyError:
-        result = None
-    if result is not None:
-        return result[2]
-    return None
-
-
-def _get_uid(name):
-    """Returns an uid, given a user name."""
-    if getpwnam is None or name is None:
-        return None
-    try:
-        result = getpwnam(name)
-    except KeyError:
-        result = None
-    if result is not None:
-        return result[2]
-    return None
-
-
-def make_tarball(
-    base_name, base_dir, compress="gzip", verbose=0, dry_run=0, owner=None, group=None
-):
-    """Create a (possibly compressed) tar file from all the files under
-    'base_dir'.
-
-    'compress' must be "gzip" (the default), "bzip2", "xz", "compress", or
-    None.  ("compress" will be deprecated in Python 3.2)
-
-    'owner' and 'group' can be used to define an owner and a group for the
-    archive that is being built. If not provided, the current owner and group
-    will be used.
-
-    The output tar file will be named 'base_dir' +  ".tar", possibly plus
-    the appropriate compression extension (".gz", ".bz2", ".xz" or ".Z").
-
-    Returns the output filename.
-    """
-    tar_compression = {
-        'gzip': 'gz',
-        'bzip2': 'bz2',
-        'xz': 'xz',
-        None: '',
-        'compress': '',
-    }
-    compress_ext = {'gzip': '.gz', 'bzip2': '.bz2', 'xz': '.xz', 'compress': '.Z'}
-
-    # flags for compression program, each element of list will be an argument
-    if compress is not None and compress not in compress_ext.keys():
-        raise ValueError(
-            "bad value for 'compress': must be None, 'gzip', 'bzip2', "
-            "'xz' or 'compress'"
-        )
-
-    archive_name = base_name + '.tar'
-    if compress != 'compress':
-        archive_name += compress_ext.get(compress, '')
-
-    mkpath(os.path.dirname(archive_name), dry_run=dry_run)
-
-    # creating the tarball
-    import tarfile  # late import so Python build itself doesn't break
-
-    log.info('Creating tar archive')
-
-    uid = _get_uid(owner)
-    gid = _get_gid(group)
-
-    def _set_uid_gid(tarinfo):
-        if gid is not None:
-            tarinfo.gid = gid
-            tarinfo.gname = group
-        if uid is not None:
-            tarinfo.uid = uid
-            tarinfo.uname = owner
-        return tarinfo
-
-    if not dry_run:
-        tar = tarfile.open(archive_name, 'w|%s' % tar_compression[compress])
-        try:
-            tar.add(base_dir, filter=_set_uid_gid)
-        finally:
-            tar.close()
-
-    # compression using `compress`
-    if compress == 'compress':
-        warn("'compress' is deprecated.", DeprecationWarning)
-        # the option varies depending on the platform
-        compressed_name = archive_name + compress_ext[compress]
-        if sys.platform == 'win32':
-            cmd = [compress, archive_name, compressed_name]
-        else:
-            cmd = [compress, '-f', archive_name]
-        spawn(cmd, dry_run=dry_run)
-        return compressed_name
-
-    return archive_name
-
-
-def make_zipfile(base_name, base_dir, verbose=0, dry_run=0):  # noqa: C901
-    """Create a zip file from all the files under 'base_dir'.
-
-    The output zip file will be named 'base_name' + ".zip".  Uses either the
-    "zipfile" Python module (if available) or the InfoZIP "zip" utility
-    (if installed and found on the default search path).  If neither tool is
-    available, raises DistutilsExecError.  Returns the name of the output zip
-    file.
-    """
-    zip_filename = base_name + ".zip"
-    mkpath(os.path.dirname(zip_filename), dry_run=dry_run)
-
-    # If zipfile module is not available, try spawning an external
-    # 'zip' command.
-    if zipfile is None:
-        if verbose:
-            zipoptions = "-r"
-        else:
-            zipoptions = "-rq"
-
-        try:
-            spawn(["zip", zipoptions, zip_filename, base_dir], dry_run=dry_run)
-        except DistutilsExecError:
-            # XXX really should distinguish between "couldn't find
-            # external 'zip' command" and "zip failed".
-            raise DistutilsExecError(
-                (
-                    "unable to create zip file '%s': "
-                    "could neither import the 'zipfile' module nor "
-                    "find a standalone zip utility"
-                )
-                % zip_filename
-            )
-
-    else:
-        log.info("creating '%s' and adding '%s' to it", zip_filename, base_dir)
-
-        if not dry_run:
-            try:
-                zip = zipfile.ZipFile(
-                    zip_filename, "w", compression=zipfile.ZIP_DEFLATED
-                )
-            except RuntimeError:
-                zip = zipfile.ZipFile(zip_filename, "w", compression=zipfile.ZIP_STORED)
-
-            with zip:
-                if base_dir != os.curdir:
-                    path = os.path.normpath(os.path.join(base_dir, ''))
-                    zip.write(path, path)
-                    log.info("adding '%s'", path)
-                for dirpath, dirnames, filenames in os.walk(base_dir):
-                    for name in dirnames:
-                        path = os.path.normpath(os.path.join(dirpath, name, ''))
-                        zip.write(path, path)
-                        log.info("adding '%s'", path)
-                    for name in filenames:
-                        path = os.path.normpath(os.path.join(dirpath, name))
-                        if os.path.isfile(path):
-                            zip.write(path, path)
-                            log.info("adding '%s'", path)
-
-    return zip_filename
-
-
-ARCHIVE_FORMATS = {
-    'gztar': (make_tarball, [('compress', 'gzip')], "gzip'ed tar-file"),
-    'bztar': (make_tarball, [('compress', 'bzip2')], "bzip2'ed tar-file"),
-    'xztar': (make_tarball, [('compress', 'xz')], "xz'ed tar-file"),
-    'ztar': (make_tarball, [('compress', 'compress')], "compressed tar file"),
-    'tar': (make_tarball, [('compress', None)], "uncompressed tar file"),
-    'zip': (make_zipfile, [], "ZIP file"),
-}
-
-
-def check_archive_formats(formats):
-    """Returns the first format from the 'format' list that is unknown.
-
-    If all formats are known, returns None
-    """
-    for format in formats:
-        if format not in ARCHIVE_FORMATS:
-            return format
-    return None
-
-
-def make_archive(
-    base_name,
-    format,
-    root_dir=None,
-    base_dir=None,
-    verbose=0,
-    dry_run=0,
-    owner=None,
-    group=None,
-):
-    """Create an archive file (eg. zip or tar).
-
-    'base_name' is the name of the file to create, minus any format-specific
-    extension; 'format' is the archive format: one of "zip", "tar", "gztar",
-    "bztar", "xztar", or "ztar".
-
-    'root_dir' is a directory that will be the root directory of the
-    archive; ie. we typically chdir into 'root_dir' before creating the
-    archive.  'base_dir' is the directory where we start archiving from;
-    ie. 'base_dir' will be the common prefix of all files and
-    directories in the archive.  'root_dir' and 'base_dir' both default
-    to the current directory.  Returns the name of the archive file.
-
-    'owner' and 'group' are used when creating a tar archive. By default,
-    uses the current owner and group.
-    """
-    save_cwd = os.getcwd()
-    if root_dir is not None:
-        log.debug("changing into '%s'", root_dir)
-        base_name = os.path.abspath(base_name)
-        if not dry_run:
-            os.chdir(root_dir)
-
-    if base_dir is None:
-        base_dir = os.curdir
-
-    kwargs = {'dry_run': dry_run}
-
-    try:
-        format_info = ARCHIVE_FORMATS[format]
-    except KeyError:
-        raise ValueError("unknown archive format '%s'" % format)
-
-    func = format_info[0]
-    for arg, val in format_info[1]:
-        kwargs[arg] = val
-
-    if format != 'zip':
-        kwargs['owner'] = owner
-        kwargs['group'] = group
-
-    try:
-        filename = func(base_name, base_dir, **kwargs)
-    finally:
-        if root_dir is not None:
-            log.debug("changing back to '%s'", save_cwd)
-            os.chdir(save_cwd)
-
-    return filename

spaces/Atualli/node-media-server/README.md

@@ -1,11 +0,0 @@
----
-title: Node Media Server
-emoji: 🚀
-colorFrom: indigo
-colorTo: gray
-sdk: docker
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/tutorials/lazyconfigs.md

@@ -1,170 +0,0 @@
-# Lazy Configs
-
-The traditional yacs-based config system provides basic, standard functionalities.
-However, it does not offer enough flexibility for many new projects.
-We develop an alternative, non-intrusive config system that can be used with
-detectron2 or potentially any other complex projects.
-
-## Python Syntax
-
-Our config objects are still dictionaries. Instead of using Yaml to define dictionaries,
-we create dictionaries in Python directly. This gives users the following power that
-doesn't exist in Yaml:
-
-* Easily manipulate the dictionary (addition & deletion) using Python.
-* Write simple arithmetics or call simple functions.
-* Use more data types / objects.
-* Import / compose other config files, using the familiar Python import syntax.
-
-A Python config file can be loaded like this:
-```python
-# config.py:
-a = dict(x=1, y=2, z=dict(xx=1))
-b = dict(x=3, y=4)
-
-# my_code.py:
-from detectron2.config import LazyConfig
-cfg = LazyConfig.load("path/to/config.py")  # an omegaconf dictionary
-assert cfg.a.z.xx == 1
-```
-
-After [LazyConfig.load](../modules/config.html#detectron2.config.LazyConfig.load), `cfg` will be a dictionary that contains all dictionaries
-defined in the global scope of the config file. Note that:
-* All dictionaries are turned to an [omegaconf](https://omegaconf.readthedocs.io/)
-  config object during loading. This enables access to omegaconf features,
-  such as its [access syntax](https://omegaconf.readthedocs.io/en/2.1_branch/usage.html#access-and-manipulation)
-  and [interpolation](https://omegaconf.readthedocs.io/en/2.1_branch/usage.html#variable-interpolation).
-* Absolute imports in `config.py` works the same as in regular Python.
-* Relative imports can only import dictionaries from config files.
-  They are simply a syntax sugar for [LazyConfig.load_rel](../modules/config.html#detectron2.config.LazyConfig.load_rel).
-  They can load Python files at relative path without requiring `__init__.py`.
-
-[LazyConfig.save](../modules/config.html#detectron2.config.LazyConfig.save) can save a config object to yaml.
-Note that this is not always successful if non-serializable objects appear in the config file (e.g. lambdas).
-It is up to users whether to sacrifice the ability to save in exchange for flexibility.
-
-## Recursive Instantiation
-
-The LazyConfig system heavily uses recursive instantiation, which is a pattern that
-uses a dictionary to describe a
-call to a function/class. The dictionary consists of:
-
-1. A "\_target\_" key which contains path to the callable, such as "module.submodule.class_name".
-2. Other keys that represent arguments to pass to the callable. Arguments themselves can be defined
-   using recursive instantiation.
-
-We provide a helper function [LazyCall](../modules/config.html#detectron2.config.LazyCall) that helps create such dictionaries.
-The following code using `LazyCall`
-```python
-from detectron2.config import LazyCall as L
-from my_app import Trainer, Optimizer
-cfg = L(Trainer)(
-  optimizer=L(Optimizer)(
-    lr=0.01,
-    algo="SGD"
-  )
-)
-```
-creates a dictionary like this:
-```
-cfg = {
-  "_target_": "my_app.Trainer",
-  "optimizer": {
-    "_target_": "my_app.Optimizer",
-    "lr": 0.01, "algo": "SGD"
-  }
-}
-```
-
-By representing objects using such dictionaries, a general
-[instantiate](../modules/config.html#detectron2.config.instantiate)
-function can turn them into actual objects, i.e.:
-```python
-from detectron2.config import instantiate
-trainer = instantiate(cfg)
-# equivalent to:
-# from my_app import Trainer, Optimizer
-# trainer = Trainer(optimizer=Optimizer(lr=0.01, algo="SGD"))
-```
-
-This pattern is powerful enough to describe very complex objects, e.g.:
-
- <details>
- <summary>
-A Full Mask R-CNN described in recursive instantiation (click to expand)
- </summary>
-
-```eval_rst
-.. literalinclude:: ../../configs/common/models/mask_rcnn_fpn.py
-  :language: python
-  :linenos:
-```
-
- </details>
-
-There are also objects or logic that cannot be described simply by a dictionary,
-such as reused objects or method calls. They may require some refactoring
-to work with recursive instantiation.
-
-## Using Model Zoo LazyConfigs
-
-We provide some configs in the model zoo using the LazyConfig system, for example:
-
-* [common baselines](../../configs/common/).
-* [new Mask R-CNN baselines](../../configs/new_baselines/)
-
-After installing detectron2, they can be loaded by the model zoo API
-[model_zoo.get_config](../modules/model_zoo.html#detectron2.model_zoo.get_config).
-
-Using these as references, you're free to define custom config structure / fields for your own
-project, as long as your training script can understand them.
-Despite of this, our model zoo configs still follow some simple conventions for consistency, e.g.
-`cfg.model` defines a model object, `cfg.dataloader.{train,test}` defines dataloader objects,
-and `cfg.train` contains training options in key-value form.
-In addition to `print()`, a better way to view the structure of a config is like this:
-```
-from detectron2.model_zoo import get_config
-from detectron2.config import LazyConfig
-print(LazyConfig.to_py(get_config("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py")))
-```
-From the output it's easier to find relevant options to change, e.g.
-`dataloader.train.total_batch_size` for the batch size, or `optimizer.lr` for base learning rate.
-
-We provide a reference training script
-[tools/lazyconfig_train_net.py](../../tools/lazyconfig_train_net.py),
-that can train/eval our model zoo configs.
-It also shows how to support command line value overrides.
-
-To demonstrate the power and flexibility of the new system, we show that
-[a simple config file](../../configs/Misc/torchvision_imagenet_R_50.py)
-can let detectron2 train an ImageNet classification model from torchvision, even though
-detectron2 contains no features about ImageNet classification.
-This can serve as a reference for using detectron2 in other deep learning tasks.
-
-## Summary
-
-By using recursive instantiation to create objects,
-we avoid passing a giant config to many places, because `cfg` is only passed to `instantiate`.
-This has the following benefits:
-
-* It's __non-intrusive__: objects to be constructed are config-agnostic, regular Python
-  functions/classes.
-  They can even live in other libraries. For example,
-  `{"_target_": "torch.nn.Conv2d", "in_channels": 10, "out_channels": 10, "kernel_size": 1}`
-  defines a conv layer.
-* __Clarity__ of what function/classes will be called, and what arguments they use.
-* `cfg` doesn't need pre-defined keys and structures. It's valid as long as it translates to valid
-  code. This gives a lot more __flexibility__.
-* You can still pass huge dictionaries as arguments, just like the old way.
-
-Recursive instantiation and Python syntax are orthogonal: you can use one without the other.
-But by putting them together, the config file looks a lot like the code that will be executed:
-
-![img](./lazyconfig.jpg)
-
-However, the config file just defines dictionaries, which can be easily manipulated further
-by composition or overrides.
-The corresponding code will only be executed
-later when `instantiate` is called. In some way,
-in config files we're writing "editable code" that will be "lazily executed" later when needed.
-That's why we call this system "LazyConfig".

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/bifpn_fcos.py

@@ -1,469 +0,0 @@
-# This file is modified from https://github.com/aim-uofa/AdelaiDet/blob/master/adet/modeling/backbone/bifpn.py
-# The original file is under 2-clause BSD License for academic use, and *non-commercial use*.
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from detectron2.layers import Conv2d, ShapeSpec, get_norm
-
-from detectron2.modeling.backbone import Backbone, build_resnet_backbone
-from detectron2.modeling import BACKBONE_REGISTRY
-from .dlafpn import dla34
-
-__all__ = []
-
-
-def swish(x):
-    return x * x.sigmoid()
-
-
-def split_name(name):
-    for i, c in enumerate(name):
-        if not c.isalpha():
-            return name[:i], int(name[i:])
-    raise ValueError()
-
-
-class FeatureMapResampler(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, norm=""):
-        super(FeatureMapResampler, self).__init__()
-        if in_channels != out_channels:
-            self.reduction = Conv2d(
-                in_channels, out_channels, kernel_size=1,
-                bias=(norm == ""),
-                norm=get_norm(norm, out_channels),
-                activation=None
-            )
-        else:
-            self.reduction = None
-
-        assert stride <= 2
-        self.stride = stride
-
-    def forward(self, x):
-        if self.reduction is not None:
-            x = self.reduction(x)
-
-        if self.stride == 2:
-            x = F.max_pool2d(
-                x, kernel_size=self.stride + 1,
-                stride=self.stride, padding=1
-            )
-        elif self.stride == 1:
-            pass
-        else:
-            raise NotImplementedError()
-        return x
-
-
-class BackboneWithTopLevels(Backbone):
-    def __init__(self, backbone, out_channels, num_top_levels, norm=""):
-        super(BackboneWithTopLevels, self).__init__()
-        self.backbone = backbone
-        backbone_output_shape = backbone.output_shape()
-
-        self._out_feature_channels = {name: shape.channels for name, shape in backbone_output_shape.items()}
-        self._out_feature_strides = {name: shape.stride for name, shape in backbone_output_shape.items()}
-        self._out_features = list(self._out_feature_strides.keys())
-
-        last_feature_name = max(self._out_feature_strides.keys(), key=lambda x: split_name(x)[1])
-        self.last_feature_name = last_feature_name
-        self.num_top_levels = num_top_levels
-
-        last_channels = self._out_feature_channels[last_feature_name]
-        last_stride = self._out_feature_strides[last_feature_name]
-
-        prefix, suffix = split_name(last_feature_name)
-        prev_channels = last_channels
-        for i in range(num_top_levels):
-            name = prefix + str(suffix + i + 1)
-            self.add_module(name, FeatureMapResampler(
-                prev_channels, out_channels, 2, norm
-            ))
-            prev_channels = out_channels
-
-            self._out_feature_channels[name] = out_channels
-            self._out_feature_strides[name] = last_stride * 2 ** (i + 1)
-            self._out_features.append(name)
-
-    def forward(self, x):
-        outputs = self.backbone(x)
-        last_features = outputs[self.last_feature_name]
-        prefix, suffix = split_name(self.last_feature_name)
-
-        x = last_features
-        for i in range(self.num_top_levels):
-            name = prefix + str(suffix + i + 1)
-            x = self.__getattr__(name)(x)
-            outputs[name] = x
-
-        return outputs
-
-
-class SingleBiFPN(Backbone):
-    """
-    This module implements Feature Pyramid Network.
-    It creates pyramid features built on top of some input feature maps.
-    """
-
-    def __init__(
-        self, in_channels_list, out_channels, norm=""
-    ):
-        """
-        Args:
-            bottom_up (Backbone): module representing the bottom up subnetwork.
-                Must be a subclass of :class:`Backbone`. The multi-scale feature
-                maps generated by the bottom up network, and listed in `in_features`,
-                are used to generate FPN levels.
-            in_features (list[str]): names of the input feature maps coming
-                from the backbone to which FPN is attached. For example, if the
-                backbone produces ["res2", "res3", "res4"], any *contiguous* sublist
-                of these may be used; order must be from high to low resolution.
-            out_channels (int): number of channels in the output feature maps.
-            norm (str): the normalization to use.
-        """
-        super(SingleBiFPN, self).__init__()
-
-        self.out_channels = out_channels
-        # build 5-levels bifpn
-        if len(in_channels_list) == 5:
-            self.nodes = [
-                {'feat_level': 3, 'inputs_offsets': [3, 4]},
-                {'feat_level': 2, 'inputs_offsets': [2, 5]},
-                {'feat_level': 1, 'inputs_offsets': [1, 6]},
-                {'feat_level': 0, 'inputs_offsets': [0, 7]},
-                {'feat_level': 1, 'inputs_offsets': [1, 7, 8]},
-                {'feat_level': 2, 'inputs_offsets': [2, 6, 9]},
-                {'feat_level': 3, 'inputs_offsets': [3, 5, 10]},
-                {'feat_level': 4, 'inputs_offsets': [4, 11]},
-            ]
-        elif len(in_channels_list) == 3:
-            self.nodes = [
-                {'feat_level': 1, 'inputs_offsets': [1, 2]},
-                {'feat_level': 0, 'inputs_offsets': [0, 3]},
-                {'feat_level': 1, 'inputs_offsets': [1, 3, 4]},
-                {'feat_level': 2, 'inputs_offsets': [2, 5]},
-            ]
-        else:
-            raise NotImplementedError
-
-        node_info = [_ for _ in in_channels_list]
-
-        num_output_connections = [0 for _ in in_channels_list]
-        for fnode in self.nodes:
-            feat_level = fnode["feat_level"]
-            inputs_offsets = fnode["inputs_offsets"]
-            inputs_offsets_str = "_".join(map(str, inputs_offsets))
-            for input_offset in inputs_offsets:
-                num_output_connections[input_offset] += 1
-
-                in_channels = node_info[input_offset]
-                if in_channels != out_channels:
-                    lateral_conv = Conv2d(
-                        in_channels,
-                        out_channels,
-                        kernel_size=1,
-                        norm=get_norm(norm, out_channels)
-                    )
-                    self.add_module(
-                        "lateral_{}_f{}".format(input_offset, feat_level), lateral_conv
-                    )
-            node_info.append(out_channels)
-            num_output_connections.append(0)
-
-            # generate attention weights
-            name = "weights_f{}_{}".format(feat_level, inputs_offsets_str)
-            self.__setattr__(name, nn.Parameter(
-                    torch.ones(len(inputs_offsets), dtype=torch.float32),
-                    requires_grad=True
-                ))
-
-            # generate convolutions after combination
-            name = "outputs_f{}_{}".format(feat_level, inputs_offsets_str)
-            self.add_module(name, Conv2d(
-                out_channels,
-                out_channels,
-                kernel_size=3,
-                padding=1,
-                norm=get_norm(norm, out_channels),
-                bias=(norm == "")
-            ))
-
-    def forward(self, feats):
-        """
-        Args:
-            input (dict[str->Tensor]): mapping feature map name (e.g., "p5") to
-                feature map tensor for each feature level in high to low resolution order.
-        Returns:
-            dict[str->Tensor]:
-                mapping from feature map name to FPN feature map tensor
-                in high to low resolution order. Returned feature names follow the FPN
-                paper convention: "p<stage>", where stage has stride = 2 ** stage e.g.,
-                ["n2", "n3", ..., "n6"].
-        """
-        feats = [_ for _ in feats]
-        num_levels = len(feats)
-        num_output_connections = [0 for _ in feats]
-        for fnode in self.nodes:
-            feat_level = fnode["feat_level"]
-            inputs_offsets = fnode["inputs_offsets"]
-            inputs_offsets_str = "_".join(map(str, inputs_offsets))
-            input_nodes = []
-            _, _, target_h, target_w = feats[feat_level].size()
-            for input_offset in inputs_offsets:
-                num_output_connections[input_offset] += 1
-                input_node = feats[input_offset]
-
-                # reduction
-                if input_node.size(1) != self.out_channels:
-                    name = "lateral_{}_f{}".format(input_offset, feat_level)
-                    input_node = self.__getattr__(name)(input_node)
-
-                # maybe downsample
-                _, _, h, w = input_node.size()
-                if h > target_h and w > target_w:
-                    height_stride_size = int((h - 1) // target_h + 1)
-                    width_stride_size = int((w - 1) // target_w + 1)
-                    assert height_stride_size == width_stride_size == 2
-                    input_node = F.max_pool2d(
-                        input_node, kernel_size=(height_stride_size + 1, width_stride_size + 1),
-                        stride=(height_stride_size, width_stride_size), padding=1
-                    )
-                elif h <= target_h and w <= target_w:
-                    if h < target_h or w < target_w:
-                        input_node = F.interpolate(
-                            input_node,
-                            size=(target_h, target_w),
-                            mode="nearest"
-                        )
-                else:
-                    raise NotImplementedError()
-                input_nodes.append(input_node)
-
-            # attention
-            name = "weights_f{}_{}".format(feat_level, inputs_offsets_str)
-            weights = F.relu(self.__getattr__(name))
-            norm_weights = weights / (weights.sum() + 0.0001)
-
-            new_node = torch.stack(input_nodes, dim=-1)
-            new_node = (norm_weights * new_node).sum(dim=-1)
-            new_node = swish(new_node)
-
-            name = "outputs_f{}_{}".format(feat_level, inputs_offsets_str)
-            feats.append(self.__getattr__(name)(new_node))
-
-            num_output_connections.append(0)
-
-        output_feats = []
-        for idx in range(num_levels):
-            for i, fnode in enumerate(reversed(self.nodes)):
-                if fnode['feat_level'] == idx:
-                    output_feats.append(feats[-1 - i])
-                    break
-            else:
-                raise ValueError()
-        return output_feats
-
-
-class BiFPN(Backbone):
-    """
-    This module implements Feature Pyramid Network.
-    It creates pyramid features built on top of some input feature maps.
-    """
-
-    def __init__(
-        self, bottom_up, in_features, out_channels, num_top_levels, num_repeats, norm=""
-    ):
-        """
-        Args:
-            bottom_up (Backbone): module representing the bottom up subnetwork.
-                Must be a subclass of :class:`Backbone`. The multi-scale feature
-                maps generated by the bottom up network, and listed in `in_features`,
-                are used to generate FPN levels.
-            in_features (list[str]): names of the input feature maps coming
-                from the backbone to which FPN is attached. For example, if the
-                backbone produces ["res2", "res3", "res4"], any *contiguous* sublist
-                of these may be used; order must be from high to low resolution.
-            out_channels (int): number of channels in the output feature maps.
-            num_top_levels (int): the number of the top levels (p6 or p7).
-            num_repeats (int): the number of repeats of BiFPN.
-            norm (str): the normalization to use.
-        """
-        super(BiFPN, self).__init__()
-        assert isinstance(bottom_up, Backbone)
-
-        # add extra feature levels (i.e., 6 and 7)
-        self.bottom_up = BackboneWithTopLevels(
-            bottom_up, out_channels,
-            num_top_levels, norm
-        )
-        bottom_up_output_shapes = self.bottom_up.output_shape()
-
-        in_features = sorted(in_features, key=lambda x: split_name(x)[1])
-        self._size_divisibility = 128 #bottom_up_output_shapes[in_features[-1]].stride
-        self.out_channels = out_channels
-        self.min_level = split_name(in_features[0])[1]
-
-        # add the names for top blocks
-        prefix, last_suffix = split_name(in_features[-1])
-        for i in range(num_top_levels):
-            in_features.append(prefix + str(last_suffix + i + 1))
-        self.in_features = in_features
-
-        # generate output features
-        self._out_features = ["p{}".format(split_name(name)[1]) for name in in_features]
-        self._out_feature_strides = {
-            out_name: bottom_up_output_shapes[in_name].stride
-            for out_name, in_name in zip(self._out_features, in_features)
-        }
-        self._out_feature_channels = {k: out_channels for k in self._out_features}
-
-        # build bifpn
-        self.repeated_bifpn = nn.ModuleList()
-        for i in range(num_repeats):
-            if i == 0:
-                in_channels_list = [
-                    bottom_up_output_shapes[name].channels for name in in_features
-                ]
-            else:
-                in_channels_list = [
-                    self._out_feature_channels[name] for name in self._out_features
-                ]
-            self.repeated_bifpn.append(SingleBiFPN(
-                in_channels_list, out_channels, norm
-            ))
-
-    @property
-    def size_divisibility(self):
-        return self._size_divisibility
-
-    def forward(self, x):
-        """
-        Args:
-            input (dict[str->Tensor]): mapping feature map name (e.g., "p5") to
-                feature map tensor for each feature level in high to low resolution order.
-        Returns:
-            dict[str->Tensor]:
-                mapping from feature map name to FPN feature map tensor
-                in high to low resolution order. Returned feature names follow the FPN
-                paper convention: "p<stage>", where stage has stride = 2 ** stage e.g.,
-                ["n2", "n3", ..., "n6"].
-        """
-        bottom_up_features = self.bottom_up(x)
-        feats = [bottom_up_features[f] for f in self.in_features]
-
-        for bifpn in self.repeated_bifpn:
-             feats = bifpn(feats)
-
-        return dict(zip(self._out_features, feats))
-
-
-def _assert_strides_are_log2_contiguous(strides):
-    """
-    Assert that each stride is 2x times its preceding stride, i.e. "contiguous in log2".
-    """
-    for i, stride in enumerate(strides[1:], 1):
-        assert stride == 2 * strides[i - 1], "Strides {} {} are not log2 contiguous".format(
-            stride, strides[i - 1]
-        )
-
-
-@BACKBONE_REGISTRY.register()
-def build_fcos_resnet_bifpn_backbone(cfg, input_shape: ShapeSpec):
-    """
-    Args:
-        cfg: a detectron2 CfgNode
-    Returns:
-        backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
-    """
-    bottom_up = build_resnet_backbone(cfg, input_shape)
-    in_features = cfg.MODEL.FPN.IN_FEATURES
-    out_channels = cfg.MODEL.BIFPN.OUT_CHANNELS
-    num_repeats = cfg.MODEL.BIFPN.NUM_BIFPN
-    top_levels = 2
-
-    backbone = BiFPN(
-        bottom_up=bottom_up,
-        in_features=in_features,
-        out_channels=out_channels,
-        num_top_levels=top_levels,
-        num_repeats=num_repeats,
-        norm=cfg.MODEL.BIFPN.NORM
-    )
-    return backbone
-
-
-
-@BACKBONE_REGISTRY.register()
-def build_p35_fcos_resnet_bifpn_backbone(cfg, input_shape: ShapeSpec):
-    """
-    Args:
-        cfg: a detectron2 CfgNode
-    Returns:
-        backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
-    """
-    bottom_up = build_resnet_backbone(cfg, input_shape)
-    in_features = cfg.MODEL.FPN.IN_FEATURES
-    out_channels = cfg.MODEL.BIFPN.OUT_CHANNELS
-    num_repeats = cfg.MODEL.BIFPN.NUM_BIFPN
-    top_levels = 0
-
-    backbone = BiFPN(
-        bottom_up=bottom_up,
-        in_features=in_features,
-        out_channels=out_channels,
-        num_top_levels=top_levels,
-        num_repeats=num_repeats,
-        norm=cfg.MODEL.BIFPN.NORM
-    )
-    return backbone
-
-
-@BACKBONE_REGISTRY.register()
-def build_p35_fcos_dla_bifpn_backbone(cfg, input_shape: ShapeSpec):
-    """
-    Args:
-        cfg: a detectron2 CfgNode
-    Returns:
-        backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
-    """
-    bottom_up = dla34(cfg)
-    in_features = cfg.MODEL.FPN.IN_FEATURES
-    out_channels = cfg.MODEL.BIFPN.OUT_CHANNELS
-    num_repeats = cfg.MODEL.BIFPN.NUM_BIFPN
-    top_levels = 0
-
-    backbone = BiFPN(
-        bottom_up=bottom_up,
-        in_features=in_features,
-        out_channels=out_channels,
-        num_top_levels=top_levels,
-        num_repeats=num_repeats,
-        norm=cfg.MODEL.BIFPN.NORM
-    )
-    return backbone
-
-@BACKBONE_REGISTRY.register()
-def build_p37_fcos_dla_bifpn_backbone(cfg, input_shape: ShapeSpec):
-    """
-    Args:
-        cfg: a detectron2 CfgNode
-    Returns:
-        backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
-    """
-    bottom_up = dla34(cfg)
-    in_features = cfg.MODEL.FPN.IN_FEATURES
-    out_channels = cfg.MODEL.BIFPN.OUT_CHANNELS
-    num_repeats = cfg.MODEL.BIFPN.NUM_BIFPN
-    assert cfg.MODEL.BIFPN.NUM_LEVELS == 5
-    top_levels = 2
-
-    backbone = BiFPN(
-        bottom_up=bottom_up,
-        in_features=in_features,
-        out_channels=out_channels,
-        num_top_levels=top_levels,
-        num_repeats=num_repeats,
-        norm=cfg.MODEL.BIFPN.NORM
-    )
-    return backbone

spaces/Awiny/Image2Paragraph/pretrained_models/download_pretrain.sh

@@ -1,5 +0,0 @@
-git clone https://huggingface.co/openai/clip-vit-large-patch14
-git clone https://huggingface.co/shi-labs/oneformer_ade20k_swin_large
-git clone https://huggingface.co/shi-labs/oneformer_coco_swin_large
-git clone https://huggingface.co/Salesforce/blip-image-captioning-large
-git clone https://huggingface.co/CIDAS/clipseg-rd64-refined

spaces/Banbri/zcvzcv/src/components/ui/dropdown-menu.tsx

@@ -1,200 +0,0 @@
-"use client"
-
-import * as React from "react"
-import * as DropdownMenuPrimitive from "@radix-ui/react-dropdown-menu"
-import { Check, ChevronRight, Circle } from "lucide-react"
-
-import { cn } from "@/lib/utils"
-
-const DropdownMenu = DropdownMenuPrimitive.Root
-
-const DropdownMenuTrigger = DropdownMenuPrimitive.Trigger
-
-const DropdownMenuGroup = DropdownMenuPrimitive.Group
-
-const DropdownMenuPortal = DropdownMenuPrimitive.Portal
-
-const DropdownMenuSub = DropdownMenuPrimitive.Sub
-
-const DropdownMenuRadioGroup = DropdownMenuPrimitive.RadioGroup
-
-const DropdownMenuSubTrigger = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.SubTrigger>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.SubTrigger> & {
-    inset?: boolean
-  }
->(({ className, inset, children, ...props }, ref) => (
-  <DropdownMenuPrimitive.SubTrigger
-    ref={ref}
-    className={cn(
-      "flex cursor-default select-none items-center rounded-sm px-2 py-1.5 text-sm outline-none focus:bg-stone-100 data-[state=open]:bg-stone-100 dark:focus:bg-stone-800 dark:data-[state=open]:bg-stone-800",
-      inset && "pl-8",
-      className
-    )}
-    {...props}
-  >
-    {children}
-    <ChevronRight className="ml-auto h-4 w-4" />
-  </DropdownMenuPrimitive.SubTrigger>
-))
-DropdownMenuSubTrigger.displayName =
-  DropdownMenuPrimitive.SubTrigger.displayName
-
-const DropdownMenuSubContent = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.SubContent>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.SubContent>
->(({ className, ...props }, ref) => (
-  <DropdownMenuPrimitive.SubContent
-    ref={ref}
-    className={cn(
-      "z-50 min-w-[8rem] overflow-hidden rounded-md border border-stone-200 bg-white p-1 text-stone-950 shadow-lg data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 data-[state=closed]:zoom-out-95 data-[state=open]:zoom-in-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2 dark:border-stone-800 dark:bg-stone-950 dark:text-stone-50",
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuSubContent.displayName =
-  DropdownMenuPrimitive.SubContent.displayName
-
-const DropdownMenuContent = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Content>
->(({ className, sideOffset = 4, ...props }, ref) => (
-  <DropdownMenuPrimitive.Portal>
-    <DropdownMenuPrimitive.Content
-      ref={ref}
-      sideOffset={sideOffset}
-      className={cn(
-        "z-50 min-w-[8rem] overflow-hidden rounded-md border border-stone-200 bg-white p-1 text-stone-950 shadow-md data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 data-[state=closed]:zoom-out-95 data-[state=open]:zoom-in-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2 dark:border-stone-800 dark:bg-stone-950 dark:text-stone-50",
-        className
-      )}
-      {...props}
-    />
-  </DropdownMenuPrimitive.Portal>
-))
-DropdownMenuContent.displayName = DropdownMenuPrimitive.Content.displayName
-
-const DropdownMenuItem = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Item>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Item> & {
-    inset?: boolean
-  }
->(({ className, inset, ...props }, ref) => (
-  <DropdownMenuPrimitive.Item
-    ref={ref}
-    className={cn(
-      "relative flex cursor-default select-none items-center rounded-sm px-2 py-1.5 text-sm outline-none transition-colors focus:bg-stone-100 focus:text-stone-900 data-[disabled]:pointer-events-none data-[disabled]:opacity-50 dark:focus:bg-stone-800 dark:focus:text-stone-50",
-      inset && "pl-8",
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuItem.displayName = DropdownMenuPrimitive.Item.displayName
-
-const DropdownMenuCheckboxItem = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.CheckboxItem>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.CheckboxItem>
->(({ className, children, checked, ...props }, ref) => (
-  <DropdownMenuPrimitive.CheckboxItem
-    ref={ref}
-    className={cn(
-      "relative flex cursor-default select-none items-center rounded-sm py-1.5 pl-8 pr-2 text-sm outline-none transition-colors focus:bg-stone-100 focus:text-stone-900 data-[disabled]:pointer-events-none data-[disabled]:opacity-50 dark:focus:bg-stone-800 dark:focus:text-stone-50",
-      className
-    )}
-    checked={checked}
-    {...props}
-  >
-    <span className="absolute left-2 flex h-3.5 w-3.5 items-center justify-center">
-      <DropdownMenuPrimitive.ItemIndicator>
-        <Check className="h-4 w-4" />
-      </DropdownMenuPrimitive.ItemIndicator>
-    </span>
-    {children}
-  </DropdownMenuPrimitive.CheckboxItem>
-))
-DropdownMenuCheckboxItem.displayName =
-  DropdownMenuPrimitive.CheckboxItem.displayName
-
-const DropdownMenuRadioItem = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.RadioItem>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.RadioItem>
->(({ className, children, ...props }, ref) => (
-  <DropdownMenuPrimitive.RadioItem
-    ref={ref}
-    className={cn(
-      "relative flex cursor-default select-none items-center rounded-sm py-1.5 pl-8 pr-2 text-sm outline-none transition-colors focus:bg-stone-100 focus:text-stone-900 data-[disabled]:pointer-events-none data-[disabled]:opacity-50 dark:focus:bg-stone-800 dark:focus:text-stone-50",
-      className
-    )}
-    {...props}
-  >
-    <span className="absolute left-2 flex h-3.5 w-3.5 items-center justify-center">
-      <DropdownMenuPrimitive.ItemIndicator>
-        <Circle className="h-2 w-2 fill-current" />
-      </DropdownMenuPrimitive.ItemIndicator>
-    </span>
-    {children}
-  </DropdownMenuPrimitive.RadioItem>
-))
-DropdownMenuRadioItem.displayName = DropdownMenuPrimitive.RadioItem.displayName
-
-const DropdownMenuLabel = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Label>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Label> & {
-    inset?: boolean
-  }
->(({ className, inset, ...props }, ref) => (
-  <DropdownMenuPrimitive.Label
-    ref={ref}
-    className={cn(
-      "px-2 py-1.5 text-sm font-semibold",
-      inset && "pl-8",
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuLabel.displayName = DropdownMenuPrimitive.Label.displayName
-
-const DropdownMenuSeparator = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Separator>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Separator>
->(({ className, ...props }, ref) => (
-  <DropdownMenuPrimitive.Separator
-    ref={ref}
-    className={cn("-mx-1 my-1 h-px bg-stone-100 dark:bg-stone-800", className)}
-    {...props}
-  />
-))
-DropdownMenuSeparator.displayName = DropdownMenuPrimitive.Separator.displayName
-
-const DropdownMenuShortcut = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLSpanElement>) => {
-  return (
-    <span
-      className={cn("ml-auto text-xs tracking-widest opacity-60", className)}
-      {...props}
-    />
-  )
-}
-DropdownMenuShortcut.displayName = "DropdownMenuShortcut"
-
-export {
-  DropdownMenu,
-  DropdownMenuTrigger,
-  DropdownMenuContent,
-  DropdownMenuItem,
-  DropdownMenuCheckboxItem,
-  DropdownMenuRadioItem,
-  DropdownMenuLabel,
-  DropdownMenuSeparator,
-  DropdownMenuShortcut,
-  DropdownMenuGroup,
-  DropdownMenuPortal,
-  DropdownMenuSub,
-  DropdownMenuSubContent,
-  DropdownMenuSubTrigger,
-  DropdownMenuRadioGroup,
-}

spaces/Benson/text-generation/Examples/Arquero Inactivo Torre De Defensa Mod Apk.md

@@ -1,88 +0,0 @@
-
-<tabla>
-<tr>
-<td>
-<h1>Idle Archer Tower Defense Mod APK: Un divertido y desafiante juego de rol</h1>
-<p>¿Estás buscando un juego de rol divertido y desafiante que te mantenga entretenido durante horas? ¿Quieres experimentar un emocionante juego de torre de defensa con increíbles gráficos y efectos de sonido? Si es así, entonces usted debe tratar Idle Archer Tower Defense Mod APK! </p>
-<p>Idle Archer Tower Defense es un popular juego de rol que combina la defensa de la torre y elementos inactivos. Juegas como un arquero solitario que tiene que defender tu torre contra las olas de monstruos malvados invocados por el señor oscuro. Puedes mejorar tus habilidades de arquero, armas y torre para volverte más fuerte y derrotar a más enemigos. También puedes recoger monedas y gemas para desbloquear nuevos elementos y características. </p>
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-<p>Pero ¿y si quieres disfrutar del juego sin limitaciones ni restricciones? ¿Qué pasa si quieres tener recursos y potenciadores ilimitados para que tu juego sea más fácil y divertido? Bueno, ahí es donde Idle Archer Tower Defense Mod APK viene muy bien! </p>
-<p>Idle Archer Tower Defense Mod APK es una versión modificada del juego original que le da acceso a varios beneficios y ventajas que no están disponibles en la versión regular. Usted puede disfrutar de características como el modo de dios, un golpe matar, monedas y gemas ilimitadas, y más. Puede descargar e instalar Idle Archer Tower Defense Mod APK gratis desde nuestro sitio web. </p>
-<p>En este artículo, le diremos todo lo que necesita saber sobre Idle Archer Tower Defense Mod APK. Te explicaremos qué es, por qué deberías descargarlo, cómo descargarlo, cómo instalarlo y cómo jugarlo. También vamos a responder a algunas preguntas frecuentes sobre Idle Archer Tower Defense Mod APK.</p>
-<p>Así que, sin más preámbulos, ¡empecemos! </p>
-<h2>¿Qué es Idle Archer Tower Defense? </ debilidades. También tienes que enfrentarte a jefes poderosos que pondrán a prueba tus habilidades y estrategia. Tienes que completar varias misiones y logros para ganar recompensas y desbloquear nuevas características. </p>
-
-<h2>El juego de Idle Archer Tower Defense</h2>
-<h3>Cómo jugar Idle Archer Tower Defense</h3>
-<p>Jugar Idle Archer Tower Defense es fácil y agradable. Solo tienes que seguir estos sencillos pasos:</p>
-<ol>
-<li>Descargar e instalar Idle Archer Tower Defense desde Google Play Store o desde nuestro sitio web. </li>
-<li>Inicie el juego y elija su idioma preferido. </li>
-<li>Ver el tutorial y aprender los fundamentos del juego. </li>
-<li>Comienza el juego y defiende tu torre de los enemigos. </li>
-<li>Toque en la pantalla para apuntar y disparar las flechas. </li>
-<li>Desliza el dedo en la pantalla para mover el arquero y esquivar los ataques enemigos. </li>
-<li>Usa hechizos, objetos y aliados para aumentar tu potencia y rendimiento. </li>
-<li>Gana monedas y gemas matando enemigos y completando misiones. </li>
-<li>Mejora tus habilidades, armas y torre en la tienda. </li>
-<li>Progreso a través de las etapas y niveles del juego. </li>
-<li>Disfruta de la historia, gráficos, sonido y jugabilidad del juego. </li>
-</ol>
-<h3>¿Cuáles son las características de Idle Archer Tower Defense</h3>
-<p>Idle Archer Tower Defense tiene muchas características que lo convierten en un juego de rol divertido y desafiante. Algunas de estas características son:</p>
-<p></p>
-<ul>
-<li>Una combinación única de defensa de torre y elementos inactivos. </li>
-<li>Un sistema de control simple e intuitivo. </li>
-<li>Un estilo gráfico colorido y caricaturesco. </li>
-<li>Una banda sonora animada y pegadiza. </li>
-<li>Una variedad de enemigos, jefes, escenarios y escenarios. </li>
-<li>Un modo inactivo que te permite ganar monedas y gemas incluso cuando no estás conectado. </li>
-<li>Una historia rica e inmersiva que te mantendrá enganchado y motivado para jugar. </li>
-<li>Una tienda donde puedes comprar nuevos artículos y características. </li>
-<li>Un sistema de misión que te recompensa por completar tareas y logros. </li>
-<li>Un sistema de eventos que añade variedad y diversión al juego. </li>
-</ul> <h2>¿Por qué descargar Idle Archer Tower Defense Mod APK? </h2>
-
-<p>Es por eso que usted debe descargar Idle Archer Tower Defense Mod APK de nuestro sitio web. Idle Archer Tower Defense Mod APK es una versión modificada del juego original que le da acceso a varios beneficios y ventajas que no están disponibles en la versión regular. Usted puede disfrutar de características como el modo de dios, un golpe matar, monedas y gemas ilimitadas, y más. Puede descargar e instalar Idle Archer Tower Defense Mod APK gratis desde nuestro sitio web. </p>
-<h3> ¿Qué es Idle Archer Tower Defense Mod APK? </h3>
-<p>Idle Archer Tower Defense Mod APK es una versión modificada del juego original que ha sido creado por desarrolladores de terceros. El archivo mod APK contiene algunos cambios y modificaciones que alteran el código y la funcionalidad del juego. El archivo APK mod le permite evitar el sistema de seguridad del juego y acceder a las características que normalmente están bloqueadas o restringidas. </p>
-<p>Idle Archer Tower Defense Mod APK no es una versión oficial del juego. No está avalado o apoyado por los desarrolladores originales o editores del juego. No está disponible en Google Play Store ni en ninguna otra tienda de aplicaciones oficial. Solo está disponible en nuestro sitio web y en otras fuentes no oficiales. </p>
-<h3>¿Cuáles son los beneficios de Idle Archer Tower Defense Mod APK? </h3>
-<p>Idle Archer Tower Defense Mod APK tiene muchos beneficios que harán que su juego más fácil y divertido. Algunos de estos beneficios son:</p>
-<h4>Modo de Dios</h4>
-<p>El modo Dios es una característica que te hace invencible e inmune a cualquier daño. Puede activar el modo dios en Idle Archer Tower Defense Mod APK y disfrutar del juego sin preocuparse por morir o perder la salud. Puedes sobrevivir a cualquier ataque enemigo, lucha contra jefes o desafío de escenario con facilidad. También puedes explorar el mundo del juego sin ningún miedo o riesgo. </p>
-<h4>Matar con un solo golpe</h4>
-
-<h4>Monedas y gemas ilimitadas</h4>
-<p>Monedas y gemas ilimitadas son características que te hacen rico e ingenioso. Puede activar monedas y gemas ilimitadas en Idle Archer Tower Defense Mod APK y disfrutar del juego sin gastar dinero real o moler duro. Puedes tener tantas monedas y gemas como quieras en tu cuenta. Puedes usarlos para comprar nuevos artículos, características, mejoras, habilidades, armas, torres, hechizos, aliados y más en la tienda. También puedes usarlos para desbloquear nuevas etapas, niveles, modos, eventos, escenarios, personajes y más en el juego. </p> <h2>Cómo descargar e instalar Idle Archer Tower Defense Mod APK? </h2>
-<p>Descargar e instalar Idle Archer Tower Defense Mod APK es fácil y simple. Solo tienes que seguir estos sencillos pasos:</p>
-<h3>Paso 1: Descargar el archivo APK</h3>
-<p>El primer paso es descargar el archivo APK de Idle Archer Tower Defense Mod APK de nuestro sitio web. Puede encontrar el enlace de descarga al final de este artículo. Solo tiene que hacer clic en el enlace y esperar a que comience la descarga. El tamaño del archivo es de unos 100 MB, así que asegúrese de tener suficiente espacio de almacenamiento y una conexión a Internet estable. </p>
-<h3>Paso 2: Habilitar fuentes desconocidas</h3>
-<p>El segundo paso es habilitar fuentes desconocidas en su dispositivo. Esta es una configuración de seguridad que le permite instalar aplicaciones desde fuentes distintas de Google Play Store u otras tiendas de aplicaciones oficiales. Es necesario habilitar esta configuración porque Idle Archer Tower Defense Mod APK no es una aplicación oficial y no está disponible en ninguna tienda de aplicaciones oficial. </p>
-<p>Para habilitar fuentes desconocidas, debe ir a la configuración de su dispositivo y buscar la opción de seguridad o privacidad. Luego, debe encontrar la opción de fuentes desconocidas y activarla. Es posible que vea un mensaje de advertencia que indica que instalar aplicaciones de fuentes desconocidas puede dañar su dispositivo o datos. Puede ignorar este mensaje y proceder con la instalación. </p>
-<h3>Paso 3: Instalar el archivo APK</h3>
-
-<h3>Paso 4: Disfruta del juego</h3>
-<p>El cuarto y último paso es disfrutar del juego. Puede iniciar el juego desde el cajón de aplicaciones de su dispositivo o la pantalla de inicio. También puede crear un icono de acceso directo en el escritorio para facilitar el acceso. Ahora puede disfrutar de todas las características y beneficios de Idle Archer Tower Defense Mod APK sin limitaciones o restricciones. </p>
-<h2>Conclusión</h2>
-<p>Idle Archer Tower Defense Mod APK es un divertido y desafiante juego de rol que combina la torre de defensa y elementos inactivos. Puedes defender tu torre de oleadas de enemigos, mejorar tus habilidades, armas y torre, recoger monedas y gemas, y disfrutar de una historia rica e inmersiva. También puede disfrutar de características como el modo de dios, un solo golpe matar, monedas y gemas ilimitadas, y más con Idle Archer Tower Defense Mod APK.</p>
-<p>Si desea descargar e instalar Idle Archer Tower Defense Mod APK, puede seguir los pasos que hemos proporcionado en este artículo. También puede encontrar el enlace de descarga al final de este artículo. Puede descargar e instalar Idle Archer Tower Defense Mod APK gratis desde nuestro sitio web. </p>
-<p>Esperamos que haya encontrado este artículo útil e informativo. Si tiene alguna pregunta o comentario sobre Idle Archer Tower Defense Mod APK, puede dejar un comentario a continuación. Intentaremos responder a sus preguntas lo antes posible. </p>
-<h3>Preguntas frecuentes</h3>
-<p>Aquí hay algunas preguntas frecuentes sobre Idle Archer Tower Defense Mod APK:</p>
-<ol>
-<li> ¿Es seguro usar Idle Archer Tower Defense Mod APK? </li>
-<p>Sí, Idle Archer Tower Defense Mod APK es seguro de usar. No contiene ningún virus, malware o spyware que pueda dañar su dispositivo o datos. Tampoco requiere ningún acceso root o permisos especiales que puedan comprometer la seguridad o privacidad de su dispositivo. </p>
-<li> ¿Es Idle Archer Tower Defense Mod APK compatible con mi dispositivo? </li>
-
-<li> ¿Puedo jugar Idle Archer Tower Defense Mod APK en línea? </li>
-<p>No, Idle Archer Tower Defense Mod APK es un juego sin conexión que no requiere una conexión a Internet para jugar. Sin embargo, es posible que necesite una conexión a Internet para descargar e instalar el juego, así como para acceder a algunas funciones en línea como actualizaciones, eventos o tablas de clasificación. </p>
-<li> ¿Puedo actualizar Idle Archer Tower Defense Mod APK? </li>
-<p>Sí, puede actualizar Idle Archer Tower Defense Mod APK siempre que haya una nueva versión disponible en nuestro sitio web. Sin embargo, puedes perder parte de tu progreso o datos si actualizas el juego sin hacer una copia de seguridad. </p>
-<li> ¿Puedo jugar Idle Archer Tower Defense Mod APK con mis amigos? </li>
-<p>No, Idle Archer Tower Defense Mod APK no tiene un modo multijugador o característica que le permite jugar con tus amigos. Sin embargo, puedes comparar tus puntajes y logros con otros jugadores en las tablas de clasificación o plataformas de redes sociales. </p>
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-<br />
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spaces/Benson/text-generation/Examples/Descargar Conquista Paraso Riddim.md

@@ -1,71 +0,0 @@
-<br />
-<h1>Cómo descargar Conquest Paradise Riddim</h1>
-<p>Si eres un fan de la música reggae, es posible que hayas oído hablar de Conquest Paradise Riddim, una compilación de canciones que comparten la misma pista instrumental o "riddim". Este riddim fue producido por Damage Musiq, un sello discográfico jamaicano especializado en dancehall y reggae. En este artículo, te mostraremos cómo descargar Conquest Paradise Riddim gratis o por un cargo, dependiendo de tu preferencia y presupuesto. También explicaremos qué hace que este riddim sea tan especial y por qué deberías añadirlo a tu biblioteca de música. </p>
-<h2>descargar conquista paraíso riddim</h2><br /><p><b><b>DOWNLOAD</b> &#9658; <a href="https://bltlly.com/2v6Mgq">https://bltlly.com/2v6Mgq</a></b></p><br /><br />
- <h2>¿Qué es Conquest Paradise Riddim? </h2>
-<h3>Una breve introducción al riddim y su productor</h3>
-<p>Un riddim es un término utilizado en la música jamaicana para describir un ritmo o pista instrumental que es utilizado por diferentes artistas para crear sus propias canciones. Un riddim puede ser original o muestreado de otra canción, y generalmente consiste en un patrón de batería, una línea de bajo y algunos otros elementos. Un riddim puede tener un tema o un estado de ánimo que refleje su nombre o productor. </p>
-<p>Conquest Paradise Riddim es un riddim que fue lanzado en febrero de 2019 por Damage Musiq, un sello discográfico fundado por Dwayne Parkinson, también conocido como Damage. Damage Musiq es conocido por producir riddims de alta calidad que cuentan con algunos de los mejores artistas de dancehall y reggae en Jamaica y más allá. Algunos de sus riddims anteriores incluyen Holocaust Riddim, Levels Riddim, Rope Riddim y Dark Temptation Riddim.</p>
- <h3>Los artistas y canciones que aparecen en el riddim</h3>
-<p>Conquest Paradise Riddim cuenta con 10 canciones de 9 artistas diferentes, incluyendo algunos de los nombres más populares en la escena reggae. Las canciones son las siguientes:</p>
-<p></p>
-<tabla>
-<tr><th>Artista</th><th>Canción</th></tr>
-<tr><td>Tommy Lee Sparta</td><td>Bendiciones</td></tr>
-<tr><td>Ley Crónica</td><td>Bendíceme</td></tr>
-<tr><td>Shane O</td><td>Mantenga pulsado </td></tr>
-<tr><td>Shatta Wale</td><td>Hola</td></tr>
-<tr><td>Singer J</td><td>Pray</td></tr>
-<tr><td>Tamo J</td><td>Victoria</td></tr>
-
-<tr><td>Jah Vinci</td><td>Guerra de lucha</td></tr>
-<tr><td>Jahmiel</td><td>Mankind</td></tr>
-<tr><td>Damage Musiq</td><td>Conquest Paradise Riddim (Instrumental)</td></tr>
-</tabla>
-<p>Las canciones de Conquest Paradise Riddim son en su mayoría positivas y edificantes, con temas como la gratitud, la perseverancia, la fe, el éxito y el amor. El riddim tiene un sonido suave y melódico, con un riff de guitarra pegadizo, un bajo pulsante y un ritmo de batería constante. El riddim es adecuado para relajarse y bailar, y muestra la versatilidad y el talento de los artistas y el productor. </p>
- <h2>¿Por qué descargar Conquest Paradise Riddim? </h2>
-<h3>Los beneficios de poseer tu música</h3>
-<p>Download Conquest Paradise Riddim es una gran manera de disfrutar de su música en cualquier momento y en cualquier lugar, sin depender de la conexión a Internet o servicios de transmisión. Al descargar el riddim, puedes tener control total sobre tu biblioteca de música, y puedes crear tus propias listas de reproducción, mixtapes o álbumes. También puede compartir su música con sus amigos y familiares, o reproducirla en cualquier dispositivo o altavoz que desee. Descargar tu música también significa que puedes apoyar a los artistas y al productor directamente, mostrándoles tu aprecio y lealtad. </p>
- <h3>La calidad y variedad del riddim</h3>
-<p>Otra razón para descargar Conquest Paradise Riddim es que es un riddim de alta calidad y diversa que satisfará a cualquier amante del reggae. El riddim tiene un sonido nítido y claro, sin distorsión ni ruido. Las canciones están bien producidas y bien mezcladas, con voces e instrumentos equilibrados. El riddim también ofrece una variedad de estilos y vibraciones, desde las canciones inspiradoras y motivadoras de Tommy Lee Sparta, Chronic Law, Shane O, Singer J, Tamo J y Gott Yo, hasta las canciones románticas y sensuales de Shatta Wale y Jah Vinci, y las canciones conscientes y sociales de Jahmiel. Hay algo para todos en Conquest Paradise Riddim, y no te aburrirás de escucharlo. </p>
-
-<h3>Las cuestiones legales y éticas de descargar música gratuita</h3>
-<p>Antes de mostrarle cómo descargar Conquest Paradise Riddim gratis, tenemos que advertirle sobre los problemas legales y éticos de hacerlo. Descargar música gratis de fuentes no autorizadas se considera piratería, que es una forma de robo que viola los derechos de propiedad intelectual de los artistas y el productor. La piratería puede tener consecuencias negativas para la industria musical, como una reducción de los ingresos, una menor calidad, menos innovación y menos incentivos para que surjan nuevos artistas. La piratería también puede exponerlo a malware, virus, spyware u otro software dañino que puede dañar su dispositivo o comprometer su privacidad. Por lo tanto, no recomendamos ni aprobamos la descarga de música gratuita de fuentes ilegales, y le aconsejamos que lo haga bajo su propio riesgo. </p>
- <h3>Los sitios web y aplicaciones que ofrecen descargas gratuitas del riddim</h3>
-<p>Si todavía quieres descargar Conquest Paradise Riddim gratis, a pesar de los riesgos que implica, hay algunos sitios web y aplicaciones que afirman ofrecer descargas gratuitas de la riddim. Sin embargo, no podemos garantizar su seguridad, fiabilidad o calidad. Algunos de estos sitios web y aplicaciones son:</p>
-<ul>
-<li><a href="">Reggae4us.com</a>: Este sitio web afirma ser "el destino online definitivo para los amantes de la música reggae". Ofrece descargas gratuitas de varias canciones de reggae y álbumes, incluyendo Conquest Paradise Riddim. Sin embargo, el sitio web tiene muchos anuncios emergentes y redirecciones que pueden ser molestos o peligrosos. </li>
-<li><a href="">Audiomack.com</a>: Este sitio web es una plataforma para que los artistas suban su música y la compartan con sus fans. También permite a los usuarios transmitir o descargar música de forma gratuita. Sin embargo, no todas las canciones de Conquest Paradise Riddim están disponibles en este sitio web, y algunas de ellas tienen versiones de baja calidad o incompletas. </li>
-
-</ul> <h2>Cómo descargar Conquest Paradise Riddim por una tarifa? </h2>
-<h3>Las ventajas de comprar música online</h3>
-<p>Si desea descargar Conquest Paradise Riddim legal y éticamente, puede optar por comprarlo en línea de fuentes autorizadas. Comprar música online tiene muchas ventajas, como:</p>
-<ul>
-<li>Puedes apoyar financieramente a los artistas y al productor, y mostrarles tu respeto y aprecio por su trabajo. </li>
-<li> Puede obtener la mejor calidad y la versión completa de la riddim, sin interrupciones o errores. </li>
-<li> Puede acceder a su música en cualquier momento y en cualquier lugar, sin depender de la conexión a Internet o servicios de streaming. </li>
-<li>Puedes disfrutar de tu música sin anuncios, pop-ups, redirecciones o malware. </li>
-<li>Puedes obtener características o beneficios adicionales, como letras, ilustraciones, reseñas, recomendaciones o descuentos. </li>
-</ul>
- <h3>Las plataformas y servicios que venden el riddim</h3>
-<p>Hay muchas plataformas y servicios que venden Conquest Paradise Riddim en línea, por un precio razonable. Algunas de estas plataformas y servicios son:</p>
-<ul>
-<li><a href="">iTunes</a>: Esta es una de las plataformas más populares y confiables para comprar música en línea. Puedes comprar Conquest Paradise Riddim por $9.99 USD, o canciones individuales por $0.99 USD cada una. También puede transmitir el riddim en Apple Music, si tiene una suscripción. </li>
-<li><a href="">Amazon Music</a>: Esta es otra plataforma conocida y confiable para comprar música en línea. Puedes comprar Conquest Paradise Riddim por $8.99 USD, o canciones individuales por $0.99 USD cada una. También puede transmitir el riddim en Amazon Music Unlimited, si tiene una suscripción. </li>
-<li><a href="">Spotify</a>: Esta es una de las plataformas más populares y ampliamente utilizadas para la transmisión de música en línea. Puedes transmitir Conquest Paradise Riddim en Spotify de forma gratuita, con anuncios o con una suscripción premium, sin anuncios. También puede descargar el riddim en Spotify Premium, para escuchar sin conexión. </li>
-</ul>
-
-<h3>Un resumen de los puntos principales y una llamada a la acción</h3>
-<p>En conclusión, Conquest Paradise Riddim es un imprescindible para cualquier amante de la música reggae. Es un riddim de alta calidad y diversa que cuenta con algunos de los mejores artistas y canciones del género. También es un riddim positivo y edificante que te inspirará y te hará sentir bien. Puedes descargar Conquest Paradise Riddim gratis o por una tarifa, dependiendo de tu preferencia y presupuesto. Sin embargo, le recomendamos que compre el riddim en línea de fuentes autorizadas, para apoyar a los artistas y al productor, y para disfrutar de la mejor calidad y experiencia. Entonces, ¿qué estás esperando? Descargar Conquest Paradise Riddim hoy y disfrutar de las vibraciones! </p>
- <h2>Preguntas frecuentes</h2>
-<h3>¿Qué es un riddim? </h3>
-<p>Un riddim es un término utilizado en la música jamaicana para describir un ritmo o pista instrumental que es utilizado por diferentes artistas para crear sus propias canciones. </p>
- <h3>¿Qué es la música reggae? </h3>
-<p>El reggae es un género de música que se originó en Jamaica a finales de los años 60. Se caracteriza por su ritmo distintivo, línea de bajo, acordes de guitarra, voces y letras. La música reggae a menudo expresa mensajes sociales y políticos, así como valores culturales y espirituales. </p>
- <h3>¿Cómo puedo reproducir la música descargada en mi dispositivo? </h3>
-<p>Puede reproducir la música descargada en su dispositivo mediante el uso de cualquier aplicación de reproductor de medios que soporta mp3 u otros formatos de audio. También puede transferir la música descargada a otros dispositivos o altavoces a través de Bluetooth, USB o Wi-Fi.</p>
- <h3>¿Cómo puedo apoyar a los artistas y productores del riddim? </h3>
-<p>Puedes apoyar a los artistas y productores del riddim comprando su música en línea de fuentes autorizadas, transmitiendo su música en plataformas legales, siguiéndola en redes sociales, compartiendo su música con tus amigos y familiares, asistiendo a sus conciertos o eventos, o enviándoles comentarios o donaciones. </p>
- <h3>¿Dónde puedo encontrar más música reggae online? </h3> 64aa2da5cf<br />
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spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/mbcsgroupprober.py

@@ -1,57 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Universal charset detector code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 2001
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#   Shy Shalom - original C code
-#   Proofpoint, Inc.
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from .big5prober import Big5Prober
-from .charsetgroupprober import CharSetGroupProber
-from .cp949prober import CP949Prober
-from .enums import LanguageFilter
-from .eucjpprober import EUCJPProber
-from .euckrprober import EUCKRProber
-from .euctwprober import EUCTWProber
-from .gb2312prober import GB2312Prober
-from .johabprober import JOHABProber
-from .sjisprober import SJISProber
-from .utf8prober import UTF8Prober
-
-
-class MBCSGroupProber(CharSetGroupProber):
-    def __init__(self, lang_filter: LanguageFilter = LanguageFilter.NONE) -> None:
-        super().__init__(lang_filter=lang_filter)
-        self.probers = [
-            UTF8Prober(),
-            SJISProber(),
-            EUCJPProber(),
-            GB2312Prober(),
-            EUCKRProber(),
-            CP949Prober(),
-            Big5Prober(),
-            EUCTWProber(),
-            JOHABProber(),
-        ]
-        self.reset()

spaces/Billius/VizLib-TopLargeHospitalsNewJersey-04-07-2023/README.md

@@ -1,13 +0,0 @@
----
-title: VizLib TopLargeHospitalsNewJersey 04 07 2023
-emoji: 💻
-colorFrom: yellow
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/.github/ISSUE_TEMPLATE/feature-request.md

@@ -1,32 +0,0 @@
----
-name: "\U0001F680Feature Request"
-about: Submit a proposal/request for a new detectron2 feature
-
----
-
-## 🚀 Feature
-A clear and concise description of the feature proposal.
-
-
-## Motivation & Examples
-
-Tell us why the feature is useful.
-
-Describe what the feature would look like, if it is implemented.
-Best demonstrated using **code examples** in addition to words.
-
-## Note
-
-We only consider adding new features if they are relevant to many users.
-
-If you request implementation of research papers --
-we only consider papers that have enough significance and prevalance.
-
-We do not take requests for most projects in the `projects/` directory,
-because they are research code release that is mainly for other researchers to reproduce results.
-
-Instead of adding features inside detectron2,
-you can implement many features by [extending detectron2](https://detectron2.readthedocs.io/tutorials/extend.html).
-The [projects/](https://github.com/facebookresearch/detectron2/tree/master/projects/) directory
-contains many of such examples.
-

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/dev/packaging/gen_wheel_index.sh

@@ -1,26 +0,0 @@
-#!/bin/bash -e
-
-
-root=$1
-if [[ -z "$root" ]]; then
-  echo "Usage: ./gen_wheel_index.sh /path/to/wheels"
-  exit
-fi
-
-index=$root/index.html
-
-cd "$root"
-for cu in cpu cu92 cu100 cu101; do
-  cd $cu
-  echo "Creating $PWD/index.html ..."
-  for whl in *.whl; do
-    echo "<a href=\"${whl/+/%2B}\">$whl</a><br>"
-  done > index.html
-  cd "$root"
-done
-
-echo "Creating $index ..."
-for whl in $(find . -type f -name '*.whl' -printf '%P\n' | sort); do
-  echo "<a href=\"${whl/+/%2B}\">$whl</a><br>"
-done > "$index"
-

spaces/CVPR/LIVE/pybind11/tests/test_opaque_types.cpp

@@ -1,67 +0,0 @@
-/*
-    tests/test_opaque_types.cpp -- opaque types, passing void pointers
-
-    Copyright (c) 2016 Wenzel Jakob <[email protected]>
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-#include "pybind11_tests.h"
-#include <pybind11/stl.h>
-#include <vector>
-
-// IMPORTANT: Disable internal pybind11 translation mechanisms for STL data structures
-//
-// This also deliberately doesn't use the below StringList type alias to test
-// that MAKE_OPAQUE can handle a type containing a `,`.  (The `std::allocator`
-// bit is just the default `std::vector` allocator).
-PYBIND11_MAKE_OPAQUE(std::vector<std::string, std::allocator<std::string>>);
-
-using StringList = std::vector<std::string, std::allocator<std::string>>;
-
-TEST_SUBMODULE(opaque_types, m) {
-    // test_string_list
-    py::class_<StringList>(m, "StringList")
-        .def(py::init<>())
-        .def("pop_back", &StringList::pop_back)
-        /* There are multiple versions of push_back(), etc. Select the right ones. */
-        .def("push_back", (void (StringList::*)(const std::string &)) &StringList::push_back)
-        .def("back", (std::string &(StringList::*)()) &StringList::back)
-        .def("__len__", [](const StringList &v) { return v.size(); })
-        .def("__iter__", [](StringList &v) {
-           return py::make_iterator(v.begin(), v.end());
-        }, py::keep_alive<0, 1>());
-
-    class ClassWithSTLVecProperty {
-    public:
-        StringList stringList;
-    };
-    py::class_<ClassWithSTLVecProperty>(m, "ClassWithSTLVecProperty")
-        .def(py::init<>())
-        .def_readwrite("stringList", &ClassWithSTLVecProperty::stringList);
-
-    m.def("print_opaque_list", [](const StringList &l) {
-        std::string ret = "Opaque list: [";
-        bool first = true;
-        for (auto entry : l) {
-            if (!first)
-                ret += ", ";
-            ret += entry;
-            first = false;
-        }
-        return ret + "]";
-    });
-
-    // test_pointers
-    m.def("return_void_ptr", []() { return (void *) 0x1234; });
-    m.def("get_void_ptr_value", [](void *ptr) { return reinterpret_cast<std::intptr_t>(ptr); });
-    m.def("return_null_str", []() { return (char *) nullptr; });
-    m.def("get_null_str_value", [](char *ptr) { return reinterpret_cast<std::intptr_t>(ptr); });
-
-    m.def("return_unique_ptr", []() -> std::unique_ptr<StringList> {
-        StringList *result = new StringList();
-        result->push_back("some value");
-        return std::unique_ptr<StringList>(result);
-    });
-}

spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/uninitialized_fill.h

@@ -1,57 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/generic/tag.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-template<typename DerivedPolicy,
-         typename ForwardIterator,
-         typename T>
-__host__ __device__
-  void uninitialized_fill(thrust::execution_policy<DerivedPolicy> &exec,
-                          ForwardIterator first,
-                          ForwardIterator last,
-                          const T &x);
-
-template<typename DerivedPolicy,
-         typename ForwardIterator,
-         typename Size,
-         typename T>
-__host__ __device__
-  ForwardIterator uninitialized_fill_n(thrust::execution_policy<DerivedPolicy> &exec,
-                                       ForwardIterator first,
-                                       Size n,
-                                       const T &x);
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/generic/uninitialized_fill.inl>
-

spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/transform_reduce.h

@@ -1,22 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system has no special transform_reduce functions
-

spaces/CVPR/SPOTER_Sign_Language_Recognition/spoter/gaussian_noise.py

@@ -1,18 +0,0 @@
-
-import torch
-
-
-class GaussianNoise(object):
-    def __init__(self, mean=0., std=1.):
-        self.std = std
-        self.mean = mean
-
-    def __call__(self, tensor):
-        return tensor + torch.randn(tensor.size()) * self.std + self.mean
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
-
-
-if __name__ == "__main__":
-    pass

spaces/CVPR/WALT/mmdet/core/utils/dist_utils.py

@@ -1,69 +0,0 @@
-import warnings
-from collections import OrderedDict
-
-import torch.distributed as dist
-from mmcv.runner import OptimizerHook
-from torch._utils import (_flatten_dense_tensors, _take_tensors,
-                          _unflatten_dense_tensors)
-
-
-def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
-    if bucket_size_mb > 0:
-        bucket_size_bytes = bucket_size_mb * 1024 * 1024
-        buckets = _take_tensors(tensors, bucket_size_bytes)
-    else:
-        buckets = OrderedDict()
-        for tensor in tensors:
-            tp = tensor.type()
-            if tp not in buckets:
-                buckets[tp] = []
-            buckets[tp].append(tensor)
-        buckets = buckets.values()
-
-    for bucket in buckets:
-        flat_tensors = _flatten_dense_tensors(bucket)
-        dist.all_reduce(flat_tensors)
-        flat_tensors.div_(world_size)
-        for tensor, synced in zip(
-                bucket, _unflatten_dense_tensors(flat_tensors, bucket)):
-            tensor.copy_(synced)
-
-
-def allreduce_grads(params, coalesce=True, bucket_size_mb=-1):
-    """Allreduce gradients.
-
-    Args:
-        params (list[torch.Parameters]): List of parameters of a model
-        coalesce (bool, optional): Whether allreduce parameters as a whole.
-            Defaults to True.
-        bucket_size_mb (int, optional): Size of bucket, the unit is MB.
-            Defaults to -1.
-    """
-    grads = [
-        param.grad.data for param in params
-        if param.requires_grad and param.grad is not None
-    ]
-    world_size = dist.get_world_size()
-    if coalesce:
-        _allreduce_coalesced(grads, world_size, bucket_size_mb)
-    else:
-        for tensor in grads:
-            dist.all_reduce(tensor.div_(world_size))
-
-
-class DistOptimizerHook(OptimizerHook):
-    """Deprecated optimizer hook for distributed training."""
-
-    def __init__(self, *args, **kwargs):
-        warnings.warn('"DistOptimizerHook" is deprecated, please switch to'
-                      '"mmcv.runner.OptimizerHook".')
-        super().__init__(*args, **kwargs)
-
-
-def reduce_mean(tensor):
-    """"Obtain the mean of tensor on different GPUs."""
-    if not (dist.is_available() and dist.is_initialized()):
-        return tensor
-    tensor = tensor.clone()
-    dist.all_reduce(tensor.div_(dist.get_world_size()), op=dist.ReduceOp.SUM)
-    return tensor

spaces/ChristopherMarais/Andrew_AI-BB_classification-beta/mysite/mysite/urls.py

@@ -1,23 +0,0 @@
-"""
-URL configuration for mysite project.
-
-The `urlpatterns` list routes URLs to views. For more information please see:
-    https://docs.djangoproject.com/en/4.2/topics/http/urls/
-Examples:
-Function views
-    1. Add an import:  from my_app import views
-    2. Add a URL to urlpatterns:  path('', views.home, name='home')
-Class-based views
-    1. Add an import:  from other_app.views import Home
-    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')
-Including another URLconf
-    1. Import the include() function: from django.urls import include, path
-    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))
-"""
-from django.contrib import admin
-from django.urls import path, include
-
-urlpatterns = [
-    path('admin/', admin.site.urls),
-    path('', include('andrew_alpha.urls')),
-]

spaces/CikeyQI/Yunzai/Yunzai/lib/plugins/stdin.js

@@ -1,159 +0,0 @@
-import fetch from "node-fetch"
-import fs from "node:fs"
-import path from "node:path"
-import common from "../common/common.js"
-import { fileTypeFromBuffer } from "file-type"
-
-Bot.adapter.push(new class stdinAdapter {
-  constructor() {
-    this.id = "stdin"
-    this.name = "标准输入"
-    this.path = "data/stdin/"
-    common.mkdirs(this.path)
-  }
-
-  async makeBuffer(file) {
-    if (file.match(/^base64:\/\//))
-      return Buffer.from(file.replace(/^base64:\/\//, ""), "base64")
-    else if (file.match(/^https?:\/\//))
-      return Buffer.from(await (await fetch(file)).arrayBuffer())
-    else if (fs.existsSync(file))
-      return Buffer.from(fs.readFileSync(file))
-    return file
-  }
-
-  async fileType(data) {
-    const file = {}
-    try {
-      file.url = data.replace(/^base64:\/\/.*/, "base64://...")
-      file.buffer = await this.makeBuffer(data)
-      file.type = await fileTypeFromBuffer(file.buffer)
-      file.path = `${this.path}${Date.now()}.${file.type.ext}`
-    } catch (err) {
-      logger.error(`文件类型检测错误：${logger.red(err)}`)
-    }
-    return file
-  }
-
-  async sendMsg(msg) {
-    if (!Array.isArray(msg))
-      msg = [msg]
-    for (let i of msg) {
-      if (typeof i != "object")
-        i = { type: "text", data: { text: i }}
-      else if (!i.data)
-        i = { type: i.type, data: { ...i, type: undefined }}
-
-      let file
-      if (i.data.file)
-        file = await this.fileType(i.data.file)
-
-      switch (i.type) {
-        case "text":
-          if (i.data.text.match("\n"))
-            i.data.text = `\n${i.data.text}`
-          logger.info(`${logger.blue(`[${this.id}]`)} 发送文本：${i.data.text}`)
-          break
-        case "image":
-          logger.info(`${logger.blue(`[${this.id}]`)} 发送图片：${file.url}\n文件已保存到：${logger.cyan(file.path)}`)
-          fs.writeFileSync(file.path, file.buffer)
-          break
-        case "record":
-          logger.info(`${logger.blue(`[${this.id}]`)} 发送音频：${file.url}\n文件已保存到：${logger.cyan(file.path)}`)
-          fs.writeFileSync(file.path, file.buffer)
-          break
-        case "video":
-          logger.info(`${logger.blue(`[${this.id}]`)} 发送视频：${file.url}\n文件已保存到：${logger.cyan(file.path)}`)
-          fs.writeFileSync(file.path, file.buffer)
-          break
-        case "reply":
-          break
-        case "at":
-          break
-        case "node":
-          Bot.sendForwardMsg(msg => this.sendMsg(msg), i.data)
-          break
-        default:
-          i = JSON.stringify(i)
-          if (i.match("\n"))
-            i = `\n${i}`
-          logger.info(`${logger.blue(`[${this.id}]`)} 发送消息：${i}`)
-      }
-    }
-    return { message_id: Date.now() }
-  }
-
-  recallMsg(message_id) {
-    logger.info(`${logger.blue(`[${this.id}]`)} 撤回消息：${message_id}`)
-  }
-
-  async sendFile(file, name = path.basename(file)) {
-    const buffer = await this.makeBuffer(file)
-    if (!Buffer.isBuffer(buffer)) {
-      logger.error(`${logger.blue(`[${this.id}]`)} 发送文件错误：找不到文件 ${logger.red(file)}`)
-      return false
-    }
-
-    const files = `${this.path}${Date.now()}-${name}`
-    logger.info(`${logger.blue(`[${this.id}]`)} 发送文件：${file}\n文件已保存到：${logger.cyan(files)}`)
-    return fs.writeFileSync(files, buffer)
-  }
-
-  pickFriend() {
-    return {
-      user_id: this.id,
-      nickname: this.name,
-      group_id: this.id,
-      group_name: this.name,
-      sendMsg: msg => this.sendMsg(msg),
-      recallMsg: message_id => this.recallMsg(message_id),
-      sendFile: (file, name) => this.sendFile(file, name),
-    }
-  }
-
-  message(msg) {
-    const data = {
-      bot: Bot[this.id],
-      self_id: this.id,
-      user_id: this.id,
-      post_type: "message",
-      message_type: "private",
-      sender: { user_id: this.id, nickname: this.name },
-      message: [{ type: "text", text: msg }],
-      raw_message: msg,
-      friend: this.pickFriend(),
-    }
-    logger.info(`${logger.blue(`[${data.self_id}]`)} 系统消息：[${data.sender.nickname}(${data.user_id})] ${data.raw_message}`)
-
-    Bot.em(`${data.post_type}.${data.message_type}`, data)
-  }
-
-  load() {
-    Bot[this.id] = {
-      adapter: this,
-      uin: this.id,
-      nickname: this.name,
-      stat: { start_time: Date.now()/1000 },
-      version: { id: this.id, name: this.name },
-      pickFriend: () => this.pickFriend(),
-      get pickUser() { return this.pickFriend },
-      get pickMember() { return this.pickFriend },
-      get pickGroup() { return this.pickFriend },
-
-      fl: new Map().set(this.id, {
-        user_id: this.id,
-        nickname: this.name,
-        group_id: this.id,
-        group_name: this.name,
-      }),
-      get gl() { return this.fl },
-      gml: new Map,
-    }
-    Bot[this.id].gml.set(this.id, Bot[this.id].fl)
-
-    process[this.id].on("data", data => this.message(data.toString()))
-
-    logger.mark(`${logger.blue(`[${this.id}]`)} ${this.name}(${this.id}) 已连接`)
-    Bot.em(`connect.${this.id}`, { self_id: this.id })
-  }
-})