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spaces/1phancelerku/anime-remove-background/Brawlhalla Mod Apk 6.06 Unlock All Legends and Crossovers with Data.md

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-<h1>Brawlhalla Mod Apk 6.06: A Guide for Beginners</h1>
-<p>If you are looking for a fun and exciting platform fighting game that you can play with your friends or online players across different platforms, you might want to check out Brawlhalla. And if you want to enjoy unlimited money and access to all the characters in the game, you might want to try Brawlhalla Mod Apk 6.06. In this article, we will tell you everything you need to know about this modded version of the game, including what it is, how to download and install it, how to play it, and some tips and tricks to improve your skills.</p>
-<h2>What is Brawlhalla?</h2>
-<h3>A free platform fighting game with over 80 million players</h3>
-<p>Brawlhalla is a free-to-play 2D platform fighting game developed by Blue Mammoth Games and published by Ubisoft. It supports up to 8 players online or local in a single match with full cross-play for PC, PS5, PS4, Xbox Series X|S, Xbox One, Nintendo Switch, iOS, and Android devices. You can join casual free-for-alls, queue for ranked matches, or make a custom room with your friends.</p>
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-<h3>A game with cross-play, frequent updates, and 50+ characters</h3>
-<p>Brawlhalla is constantly updated with new features, events, and characters. There are over 50 unique characters (called Legends) to choose from, each with their own stats, abilities, and weapons. You can also unlock crossover characters from other popular franchises such as Adventure Time, Ben 10, The Walking Dead, Tomb Raider, WWE, Shovel Knight, Hellboy, Rayman, Steven Universe, and more.</p>
-<h3>A game with various modes, maps, and cosmetics</h3>
-<p>Brawlhalla offers a variety of game modes to suit your preferences and mood. You can play classic modes such as Stock (last man standing), Timed (most points), Strikeout (switch characters), or Brawlball (score goals). You can also try out fun party modes such as Kung Foot (soccer), Bombsketball (basketball), Capture the Flag (CTF), Bubble Tag (tag), Temple Climb (climb), Morph (change weapons), Walker Attack! (zombies), Showdown (battle royale), Crew Battle (team elimination), Street Brawl (gang fight), Bounty ( collect bounties), and more. You can also play custom games with your own rules and settings, or join tournaments and events to compete for glory and prizes. Brawlhalla has over 40 maps to fight on, each with different layouts, hazards, and themes. You can also customize your characters with hundreds of skins, weapons, colors, emotes, taunts, and podiums that you can buy with in-game currency or real money.</p>
-<h2>What is Brawlhalla Mod Apk 6.06?</h2>
-<h3>A modified version of the game that offers unlimited money and unlocked characters</h3>
-<p>Brawlhalla Mod Apk 6.06 is a modified version of the game that gives you unlimited money (gold and mammoth coins) and access to all the characters (including the crossover ones) in the game. With this mod, you can buy any cosmetic item you want, and play with any character you like. You can also enjoy the game without ads or interruptions.</p>
-<h3>A version that requires data download and installation</h3>
-<p>Brawlhalla Mod Apk 6.06 is not available on the official app stores, so you need to download it from a third-party source. You also need to download a separate data file that contains the game assets and resources. You need to install the mod apk file and copy the data file to the obb folder in your device storage. This process may take some time and space, depending on your device and internet speed.</p>
-<h3>A version that may not be compatible with the official game or safe to use</h3>
-<p>Brawlhalla Mod Apk 6.06 is not an official version of the game, so it may not be compatible with the latest updates or patches of the game. It may also cause errors, glitches, or crashes in your device. Moreover, using a modded version of the game may violate the terms of service of the game, and result in a ban or suspension of your account. You may also risk exposing your device to malware or viruses from untrusted sources. Therefore, use this mod at your own risk and discretion.</p>
-<h2>How to download and install Brawlhalla Mod Apk 6.06?</h2>
-<h3>Find a reliable source for the mod apk file and the data file</h3>
-<p>The first step to download and install Brawlhalla Mod Apk 6.06 is to find a reliable source for the mod apk file and the data file. You can search online for websites or blogs that offer these files for free download. However, be careful of fake or malicious links that may harm your device or steal your information. Always check the reviews, ratings, and comments of other users before downloading anything.</p>
-<h3>Enable unknown sources in your device settings</h3>
-<p>The next step is to enable unknown sources in your device settings. This will allow you to install apps that are not from the official app stores. To do this, go to your device settings > security > unknown sources > enable. You may also need to disable any antivirus or firewall software that may block the installation.</p>
-<h3>Install the mod apk file and copy the data file to the obb folder</h3>
-<p>The final step is to install the mod apk file and copy the data file to the obb folder in your device storage. To do this, follow these steps:</p>
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-<ol>
-<li>Download the mod apk file and the data file from your chosen source.</li>
-<li>Locate the downloaded files in your device storage using a file manager app.</li>
-<li>Tap on the mod apk file and follow the instructions to install it.</li>
-<li>Do not open the game yet after installation.</li>
-<li>Extract or unzip the data file using a zip extractor app.</li>
-<li>Copy or move the extracted folder named "com.bmg.brawlhalla" to your device storage > Android > obb.</li>
-<li>Make sure that the folder is placed inside the obb folder correctly.</li>
-<li>Now you can open the game and enjoy Brawlhalla Mod Apk 6.06.</li>
-</ol> <h2>How to play Brawlhalla Mod Apk 6.06?</h2>
-<h3>Choose your character from the unlocked roster</h3>
-<p>Once you have successfully installed Brawlhalla Mod Apk 6.06, you can start playing the game by choosing your character from the unlocked roster. You can scroll through the list of characters and select the one that suits your playstyle and preference. You can also see the stats, abilities, and weapons of each character by tapping on them.</p>
-<h3>Customize your character with skins, weapons, colors, and emotes</h3>
-<p>After choosing your character, you can customize it with skins, weapons, colors, and emotes that you can buy with the unlimited money you have in the mod. You can access the store by tapping on the shopping cart icon at the bottom of the screen. You can also change your character's name, avatar, and title by tapping on the profile icon at the top of the screen.</p>
-<h3>Join online or offline matches with up to 8 players</h3>
-<p>Now you are ready to join online or offline matches with up to 8 players in Brawlhalla Mod Apk 6.06. You can choose from various game modes and maps by tapping on the play icon at the bottom of the screen. You can also invite your friends or join random players by tapping on the social icon at the top of the screen. You can also chat with other players by tapping on the chat icon at the bottom of the screen.</p>
-<h2>Tips and tricks for Brawlhalla Mod Apk 6.06</h2>
-<h3>Practice your movement, recovery, dodging, and combos in training mode</h3>
-<p>If you are new to Brawlhalla or want to improve your skills, you should practice your movement, recovery, dodging, and combos in training mode. You can access training mode by tapping on the play icon > offline > training. In training mode, you can choose any character, weapon, map, and settings to practice with. You can also see your damage, hitboxes, stun frames, and other useful information on the screen.</p>
-<h3>Learn the strengths and weaknesses of different characters and weapons</h3>
-<p>Brawlhalla has over 50 characters and 13 weapons to choose from, each with their own strengths and weaknesses. You should learn how each character and weapon works, what their advantages and disadvantages are, and how to counter them. You can also watch videos or guides from other players or experts online to learn more tips and tricks.</p>
-<h3>Experiment with different modes and maps to find your favorite</h3>
-<p>Brawlhalla has a variety of modes and maps to suit your preferences and mood. You should experiment with different modes and maps to find your favorite ones. You can also create your own custom games with your own rules and settings by tapping on the play icon > custom > create room. You can also join other players' custom games by tapping on join room.</p>
-<h2>Conclusion</h2>
-<p>Brawlhalla Mod Apk 6.06 is a modified version of Brawlhalla that offers unlimited money and unlocked characters for free. It is a fun and exciting platform fighting game that you can play with your friends or online players across different platforms. However, it is not an official version of the game, so it may not be compatible with the latest updates or patches of the game. It may also cause errors, glitches, or crashes in your device. Moreover, using a modded version of the game may violate the terms of service of the game, and result in a ban or suspension of your account. You may also risk exposing your device to malware or viruses from untrusted sources. Therefore, use this mod at your own risk and discretion.</p>
-<h2>FAQs</h2>
-<ul>
-<li><b>Q: Is Brawlhalla Mod Apk 6.06 safe to use?</b></li>
-<li>A: Brawlhalla Mod Apk 6.06 is not an official version of the game, so it may not be safe to use. It may contain malware or viruses that may harm your device or steal your information. It may also violate the terms of service of the game, and result in a ban or suspension of your account.</li>
-<li><b>Q: How do I update Brawlhalla Mod Apk 6.06?</b></li>
-<li>A: Brawlhalla Mod Apk 6.06 may not be compatible with the latest updates or patches of the game. To update it, you need to find a new version of the mod apk file and the data file from a reliable source online. Then you need to uninstall the old version of the mod apk file and install the new one. You also need to copy the new data file to the obb folder as well.</li>
-<li><b>Q: Can I play Brawlhalla Mod Apk 6.06 with other players online?</b></li>
-<li>A: Brawlhalla Mod Apk 6.06 supports online multiplayer with up to 8 players in a single match. However, you may not be able to play with players who are using the official version of the game, or players who are using a different version of the mod. You may also face lag, disconnects, or bans from the game servers.</li>
-<li><b>Q: Can I use Brawlhalla Mod Apk 6.06 on PC, PS5, PS4, Xbox Series X|S, Xbox One, Nintendo Switch, iOS, or Android devices?</b></li>
-<li>A: Brawlhalla Mod Apk 6.06 is only compatible with Android devices. You cannot use it on PC, PS5, PS4, Xbox Series X|S, Xbox One, Nintendo Switch, or iOS devices. However, you can use an Android emulator on your PC to run the mod apk file and the data file.</li>
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spaces/1phancelerku/anime-remove-background/Download Instagram APK for Android 4.0 and enjoy the latest features.md

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-<h1>How to Download Instagram APK for Android 4.0</h1>
-<p>Instagram is one of the most popular social media platforms in the world, with over one billion monthly active users. It allows you to create and share your photos, stories, reels and videos with the friends and followers you care about. You can also connect with people who share your interests, discover new content, and chat with your favorite creators.</p>
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-<p>But what if you have an older device that runs on Android 4.0 Ice Cream Sandwich, which is no longer supported by the official Instagram app? Does that mean you can't enjoy the latest features and updates of Instagram? Not necessarily. In this article, we will show you how to download Instagram APK for Android 4.0, which is a modified version of the original app that works on older devices. We will also explain what Instagram APK is, why you might want to download it, how to install it, and how to use it.</p>
- <h2>What is Instagram APK?</h2>
-<p>Instagram APK is a file that contains the installation package of the Instagram app for Android devices. APK stands for Android Package Kit, and it is the standard format for distributing and installing apps on Android devices. You can download APK files from various sources online, such as third-party websites, app stores, or file-sharing platforms.</p>
-<p>However, not all APK files are safe or reliable. Some of them may contain malware, viruses, or unwanted ads that can harm your device or compromise your privacy. Therefore, you should always be careful when downloading APK files from unknown sources, and only use trusted and reputable websites.</p>
-<p>One of the advantages of downloading Instagram APK is that you can access the latest version of the app even if your device is not compatible with it. For example, if you have an Android 4.0 device, you can download Instagram APK for Android 4.0 and enjoy the new features and improvements of Instagram without having to upgrade your device.</p>
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-<li>You have an older device that runs on Android 4.0 Ice Cream Sandwich, which is no longer supported by the official Instagram app.</li>
-<li>You want to access the latest version of Instagram with all the new features and updates, such as reels, dark mode, stickers, filters, etc.</li>
-<li>You want to bypass some of the restrictions or limitations imposed by the official app, such as the number of accounts you can log in with, the size of the videos you can upload, or the content you can view.</li>
-<li>You want to customize or modify some aspects of the app according to your preferences, such as the theme, layout, icons, fonts, etc.</li>
-</ul>
-<p>However, there are also some drawbacks or risks associated with downloading Instagram APK for Android 4.0 that you should be aware of before proceeding. Here are some of them:</p>
-<ul>
-<li>You may violate the terms of service or policies of Instagram by using an unofficial or modified version of the app.</li>
-<li>You may encounter some bugs, errors, or compatibility issues that affect the performance or functionality of the app.</li>
-<li>You may expose your device or data to security threats or privacy breaches by downloading an unverified or malicious APK file.</li>
-<li>You may not receive automatic updates or technical support from Instagram if you encounter any problems with the app.</li <h2>How to Download Instagram APK for Android 4.0?</h2>
-<p>If you have decided to download Instagram APK for Android 4.0, you need to follow some simple steps to get the file and install it on your device. Here is a step-by-step guide on how to do it:</p>
-<ol>
-<li>Go to a reliable and reputable website that offers Instagram APK for Android 4.0, such as [APKPure] or [APKMirror]. You can also use a search engine to find other sources, but make sure they are safe and trustworthy.</li>
-<li>On the website, find the latest version of Instagram APK for Android 4.0 and click on the download button. You may need to allow the download from unknown sources in your device settings.</li>
-<li>Wait for the download to complete and locate the file in your device storage. It should have a name like "com.instagram.android.apk" or something similar.</li>
-<li>Tap on the file and follow the instructions to install it on your device. You may need to grant some permissions or accept some terms and conditions.</li>
-<li>Once the installation is done, you can launch the app and log in with your existing account or create a new one.</li>
-</ol>
- <h2>How to Install Instagram APK for Android 4.0?</h2>
-<p>If you have already downloaded Instagram APK for Android 4.0, you need to install it on your device to use it. The installation process is similar to any other app, but you may need to enable some settings or options first. Here is a step-by-step guide on how to do it:</p>
-<ol>
-<li>Before installing Instagram APK for Android 4.0, you need to enable the installation from unknown sources in your device settings. To do this, go to Settings > Security > Unknown Sources and toggle the switch on.</li>
-<li>Now, go to your device storage and find the Instagram APK file that you downloaded earlier. It should have a name like "com.instagram.android.apk" or something similar.</li>
-<li>Tap on the file and follow the instructions to install it on your device. You may need to grant some permissions or accept some terms and conditions.</li>
-<li>Once the installation is done, you can launch the app and log in with your existing account or create a new one.</li>
-</ol>
- <h2>How to Use Instagram APK for Android 4.0?</h2>
-<p>After installing Instagram APK for Android 4.0, you can use it just like the official app, with some minor differences or limitations. Here are some tips and tricks on how to use Instagram APK for Android 4.0:</p>
-<ul>
-<li>To access the latest features and updates of Instagram, such as reels, dark mode, stickers, filters, etc., you may need to update the app regularly by downloading the latest version of Instagram APK for Android 4.0 from the same website that you used before.</li>
-<li>To avoid any compatibility issues or errors, you may need to clear the cache and data of the app occasionally by going to Settings > Apps > Instagram > Storage > Clear Cache and Clear Data.</li>
-<li>To customize or modify some aspects of the app according to your preferences, such as the theme, layout, icons, fonts, etc., you may need to use some third-party apps or tools that work with Instagram APK for Android 4.0, such as [GBInsta] or [InstaMod].</li>
-<li>To bypass some of the restrictions or limitations imposed by the official app, such as the number of accounts you can log in with, the size of the videos you can upload, or the content you can view, you may need to use some tricks or hacks that work with Instagram APK for Android 4.0, such as [Parallel Space] or [VPN].</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, downloading Instagram APK for Android 4.0 is a possible solution for those who have an older device that runs on Android 4.0 Ice Cream Sandwich and want to enjoy the latest features and updates of Instagram without having to upgrade their device. However, there are also some drawbacks and risks involved in using an unofficial or modified version of the app that should be considered before proceeding.</p>
-<p>If you decide to download Instagram APK for Android 4.0, make sure you follow the steps above carefully and use a reliable and reputable website that offers safe and verified APK files. Also, be aware of the potential security threats or privacy breaches that may occur by downloading an unverified or malicious APK file.</p>
-<p>We hope this article has helped you understand what Instagram APK is, why you might want to download it, how to download it, how to install it, and how to use it. If you have any questions or feedback, feel free to leave a comment below or contact us through our website. We would love to hear from you!</p>
- <h2>FAQs</h2>
-<p>Here are some of the most frequently asked questions and answers about Instagram APK for Android 4.0:</p>
-<h3>Is Instagram APK for Android 4.0 legal?</h3>
-<p>There is no definitive answer to this question, as different countries may have different laws and regulations regarding the use of unofficial or modified apps. However, in general, downloading and using Instagram APK for Android 4.0 is not illegal, as long as you do not use it for any malicious or fraudulent purposes. However, you may violate the terms of service or policies of Instagram by using an unofficial or modified version of the app, which may result in your account being suspended or banned.</p>
-<h3>Is Instagram APK for Android 4.0 safe?</h3>
-<p>Not all Instagram APK files are safe or reliable. Some of them may contain malware, viruses, or unwanted ads that can harm your device or compromise your privacy. Therefore, you should always be careful when downloading Instagram APK files from unknown sources, and only use trusted and reputable websites that offer safe and verified APK files. You should also scan the APK file with an antivirus or malware detector before installing it on your device.</p>
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spaces/1phancelerku/anime-remove-background/Download Sonic Dash 2 Sonic Boom MOD APK with Unlimited Money 3.8.1.md

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-<h1>Sonic Dash 2: Sonic Boom Mod Apk Free Download</h1>
- <p>If you are a fan of Sonic the Hedgehog and endless runner games, you might have heard of Sonic Dash 2: Sonic Boom. This is a sequel to the popular Sonic Dash game, featuring the characters and world of the Sonic Boom TV series. In this game, you can run, jump, dash, and swing through various levels, collecting rings, orbs, and sprites, while avoiding obstacles and enemies. You can also play as different characters, each with their own special abilities and powers.</p>
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- <p>But what if you want to enhance your gaming experience even more? What if you want to unlock all the characters, get unlimited rings and orbs, and access all the features of the game without spending any money? This is where a mod apk comes in handy. A mod apk is a modified version of an original app that has been altered to provide some extra features or benefits. By downloading and installing a mod apk for Sonic Dash 2: Sonic Boom, you can enjoy the game with more fun and ease.</p>
- <h2>How to Download and Install the Mod Apk for Sonic Dash 2: Sonic Boom</h2>
- <p>Downloading and installing the mod apk for Sonic Dash 2: Sonic Boom is not very difficult, but you need to follow some steps carefully. Here is what you need to do:</p>
- <ol>
-<li>First, you need to enable unknown sources on your device. This will allow you to install apps that are not from the official app store. To do this, go to your device settings, then security, then unknown sources, and turn it on.</li>
-<li>Next, you need to find a reliable source for downloading the mod apk file. There are many websites that offer mod apks for various games, but not all of them are safe and trustworthy. You need to be careful about malware, viruses, and other harmful software that might harm your device or steal your personal information. One of the websites that we recommend is [text](^1^), where you can find the latest version of the mod apk for Sonic Dash 2: Sonic Boom.</li>
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-<li>After downloading the file, you need to locate it on your device. You can use a file manager app or your device's default file explorer to find it. The file name should be something like "sonic-dash-2-sonic-boom-v2-2-1-mod.apk".</li>
-<li>Now, you need to install the file on your device. To do this, simply tap on the file and follow the instructions on the screen. You might need to grant some permissions for the app to work properly.</li>
-<li>Finally, you need to launch the game and enjoy it with the mod features. You should see a message saying "Modded by HappyMod" on the loading screen. This means that the mod apk has been successfully installed.</li>
-</ol>
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-<li>You can get unlimited rings and orbs in the game. Rings and orbs are the main currencies in the game, which you can use to buy upgrades, power-ups, and boosters. With unlimited rings and orbs, you can get the best items and enhance your performance in the game.</li>
-<li>You can access all the features of the game, such as the events, the missions, the daily challenges, and the leaderboards. You can also play online with other players and compete for the highest scores and rewards.</li>
-<li>You can enjoy the game without any ads or interruptions. The mod apk removes all the annoying ads that might pop up during the game or after completing a level. You can also skip the waiting time for loading or reviving.</li>
-</ul>
- <h2>What are some Tips and Tricks for Playing the Game with the Mod Apk</h2>
- <p>Playing Sonic Dash 2: Sonic Boom with the mod apk is fun and easy, but there are some tips and tricks that you can use to make the most out of it. Here are some of them:</p>
- <ul>
-<li>Try to switch between different characters during the game, depending on the situation. For example, you can use Sonic's dash ability to speed up and break through obstacles, Tails' flight ability to soar over gaps and enemies, Amy's hammer ability to smash everything in her way, Knuckles' punch ability to deal more damage and collect more rings, Sticks' boomerang ability to hit multiple targets and collect sprites, Shadow's chaos blast ability to destroy everything around him, and Vector's music ability to create shockwaves and attract rings.</li>
-<li>Use the sprites wisely. Sprites are cute creatures that can help you in various ways, such as increasing your score, boosting your speed, protecting you from damage, or giving you extra lives. You can equip up to three sprites at a time, and each sprite has a different rarity and effect. You can also upgrade your sprites to make them more powerful.</li>
-<li>Collect as many rings and orbs as possible. Rings and orbs are not only useful for buying items, but also for increasing your score and activating your character's special power. The more rings and orbs you collect, the faster you can fill up your power meter and unleash your character's ultimate move.</li>
-<li>Avoid hitting obstacles and enemies. Hitting obstacles and enemies will slow you down, damage you, or end your run. You need to be alert and agile to dodge or jump over them. You can also use your character's abilities or power-ups to deal with them.</li>
-<li>Complete the events, missions, and daily challenges. These are optional tasks that you can do to earn more rewards and bonuses. They also add more variety and fun to the game. You can check them out on the main menu or on the top of the screen during the game.</li>
-</ul>
- <h1>Conclusion</h1>
- <p>Sonic Dash 2: Sonic Boom is an exciting and addictive endless runner game that features the beloved characters and world of Sonic the Hedgehog. If you want to enjoy the game with more features and benefits, you can download and install the mod apk for Sonic Dash 2: Sonic Boom from [text]. This will allow you to unlock all the characters, get unlimited rings and orbs, access all the features of the game, and play without any ads or interruptions. You can also use some tips and tricks to improve your skills and score in the game. So what are you waiting for? Download the mod apk now and have fun with Sonic Dash 2: Sonic Boom!</p>
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- <ol>
-<li><b>Is the mod apk safe to download and install?</b><br>
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spaces/1phancelerku/anime-remove-background/Experience Realistic Car Parking and Racing with Car Parking Multiplayer on Windows 7.md

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-<br />
-<h1>Car Parking Multiplayer Free Download for Windows 7</h1>
-<p>Are you looking for a realistic and fun driving simulator game that you can play on your PC? If yes, then you should try Car Parking Multiplayer, a game that offers more than just parking. In this article, we will show you how to download and install Car Parking Multiplayer on Windows 7 using two different methods. We will also tell you about the features, tips, and tricks of this amazing game. So, let's get started!</p>
- <h2>Introduction</h2>
-<p>Car Parking Multiplayer is a game that can fool you with its rather deceiving name. But, it's much more than just being about parking your car. It's an open-world experience where you can drive free and yes, still work on that parking if you wish. You can even jump out of your car and walk around. There are different areas that can be explored in the game. Each one is like its own open-world. You can choose to play either single-player mode or online mode if you want a more chaotic scene (in a fun way).</p>
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-<p>Car Parking Multiplayer is a simulation game developed by olzhass, a developer based in Kazakhstan. The game was released in 2017 for Android devices and later for iOS devices. The game has more than 100 million downloads on Google Play Store and more than 130 cars to choose from.</p>
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-<p>While Car Parking Multiplayer is designed for mobile devices, playing it on PC has some advantages. For example, you can enjoy the game on a bigger screen with better graphics and sound quality. You can also use your keyboard and mouse to control your car more easily and precisely. Moreover, playing on PC can save your battery life and avoid overheating issues of your mobile device.</p>
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- <h3>Method 1: Using an Android emulator</h3>
-<p>An Android emulator is a software that creates a virtual Android device on your PC, allowing you to run Android applications and games on your computer. There are many Android emulators available online, such as BlueStacks, LDPlayer, NoxPlayer, etc. Here are the steps to use an Android emulator to play Car Parking Multiplayer on Windows 7:</p>
- <h4>Step 1: Download and install an Android emulator</h4>
-<p>The first step is to download and install an Android emulator of your choice on your PC. You can go to the official website of the emulator or use a third-party source to download the emulator file. After downloading the file, you need to run it and follow the instructions to install the emulator on your PC. This may take some time depending on your internet speed and PC performance.</p>
- <h4>Step 2: Download the APK/XAPK file of Car Parking Multiplayer</h4>
-<p>The next step is to download the APK or XAPK file of Car Parking Multiplayer on your PC. APK and XAPK are file formats that contain the installation package of an Android application or game. You can download the APK/XAPK file of Car Parking Multiplayer from various sources online, such as APKPure, APKMirror, Uptodown, etc. Make sure to download the latest version of the file and save it in a folder that you can easily access.</p>
- <h4>Step 3: Install and launch Car Parking Multiplayer on the emulator</h4>
-<p>The final step is to install and launch Car Parking Multiplayer on the emulator. There are two ways to do this. The first way is to drag and drop the APK/XAPK file into the emulator window and wait for the installation to complete. The second way is to open the emulator and go to the built-in app store or browser and search for Car Parking Multiplayer and install it from there. After installing the game, you can launch it from the emulator's home screen or app drawer and enjoy playing it on your PC.</p>
- <h3>Method 2: Using a web browser</h3>
-<p>Another method that you can use to play Car Parking Multiplayer on Windows 7 is using a web browser. This method is simpler but less immersive than using an emulator. You don't need to download or install anything on your PC, but you need a stable internet connection and a compatible web browser. Here are the steps to use a web browser to play Car Parking Multiplayer on Windows 7:</p>
- <h4>Step 1: Go to the official website of Car Parking Multiplayer</h4>
-<p>The first step is to go to the official website of Car Parking Multiplayer, which is https://carparkingmultiplayer.com/. You can use any web browser that supports HTML5, such as Chrome, Firefox, Edge, Safari, etc.</p>
- <h4>Step 2: Click on the "Play Now" button</h4>
-<p>The next step is to click on the "Play Now" button on the website's homepage. This will open a new tab or window where you can play the game online.</p>
- <h4>Step 3: Enjoy the game on your browser</h4>
-<p>The final step is to enjoy the game on your browser. You can use your mouse and keyboard to control your car and interact with other players. You can also adjust the settings, such as graphics quality, sound volume, language, etc., by clicking on the gear icon on the top right corner of the screen.</p>
- <h2>Features of Car Parking Multiplayer</h2>
-<p>Car Parking Multiplayer is a game that offers many features that make it fun and realistic. Here are some of the features that you can enjoy in this game:</p>
- <h3>Open-world multiplayer mode</h3>
-<p>One of the main features of Car Parking Multiplayer is its open-world multiplayer mode, where you can join thousands of other players from around the world in various maps and locations. You can choose from different modes, such as free roam, racing, police chase, etc., and have fun with your friends or strangers. You can also chat with other players using voice or text messages.</p>
- <h3>Car customization and tuning</h3>
-<p>Another feature of Car Parking Multiplayer is its car customization and tuning system, where you can modify your car's appearance and performance according to your preference. You can change your car's color, wheels, spoilers, stickers, lights, etc., and make it look unique and cool. You can also tune your car's engine, suspension, brakes, transmission, etc., and improve its speed, handling, acceleration, etc.</p>
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-<p>Car Parking Multiplayer is a game that requires some skills and strategies to master. Here are some tips and tricks that can help you improve your gameplay and have more fun in this game:</p>
- <h3>How to select a car and a player</h3>
-<p>When you start the game, you can choose your car and your player from the garage menu. You can scroll through different categories of cars, such as sports, classic, SUV, etc., and select the one that suits your style and budget. You can also customize your car's appearance and performance from the same menu. To change your player, you can click on the avatar icon on the top left corner of the screen and select from different options of gender, skin color, hair style, clothing, etc.</p>
- <h3>How to understand gear ratio and drift</h3>
-<p>One of the features of Car Parking Multiplayer is its realistic gear ratio and drift system, which affects how your car behaves on the road. Gear ratio is the relationship between the engine speed and the wheel speed, which determines how fast or slow your car accelerates or decelerates. Drift is the sideways movement of your car when you turn at high speed, which can be controlled by using the handbrake or steering. To understand how gear ratio and drift work in this game, you can go to the settings menu and enable the "Gear Ratio" and "Drift" options. This will show you a graph and a meter that indicate how your car's gear ratio and drift change according to your actions.</p>
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-<p>Money is an important resource in Car Parking Multiplayer, as it allows you to buy new cars, upgrade your existing ones, or access premium features. There are several ways to make money in this game, such as completing missions, winning races, selling cars, watching ads, etc. You can also buy money with real money if you want to support the developers or get some extra cash. To buy new cars, you can go to the shop menu and browse through different categories of cars, such as exclusive, VIP, police, etc. You can also see the specifications and prices of each car before buying it.</p>
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-<p>Car Parking Multiplayer is a social game where you can communicate and interact with other players from around the world. You can chat with other players using voice or text messages by clicking on the microphone or keyboard icons on the bottom right corner of the screen. You can also join or create a room where you can invite your friends or strangers to play together. You can also follow or unfollow other players by clicking on their names or avatars on the map or leaderboard.</p>
- <h2>Conclusion</h2>
-<p>Car Parking Multiplayer is a game that offers a realistic and fun driving simulator experience that you can play on your PC. You can download and install Car Parking Multiplayer on Windows 7 using an Android emulator or a web browser. You can also enjoy various features, such as open-world multiplayer mode, car customization and tuning, high-quality graphics and sound effects, interesting gameplay and challenges, etc. You can also use some tips and tricks to improve your gameplay and have more fun in this game.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Car Parking Multiplayer:</p>
- <ol>
-<li><b>Is Car Parking Multiplayer free to play?</b></li>
-<p>Yes, Car Parking Multiplayer is free to play on both mobile devices and PC. However, there are some in-app purchases that you can make to get more money, cars, or features.</p>
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spaces/232labs/VToonify/vtoonify/model/encoder/readme.md

@@ -1,9 +0,0 @@
-# Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
-
-## Description   
-Official Implementation of pSp paper for both training and evaluation. The pSp method extends the StyleGAN model to 
-allow solving different image-to-image translation problems using its encoder.
-
-Fork from [https://github.com/eladrich/pixel2style2pixel](https://github.com/eladrich/pixel2style2pixel).
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-In VToonify, we modify pSp to accept z+ latent code.

spaces/52Hz/SRMNet_real_world_denoising/README.md

@@ -1,37 +0,0 @@
----
-title: SRMNet_real_denoising
-emoji: 🌪
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
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-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
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-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

spaces/AFischer1985/German-Flan-T5/README.md

@@ -1,12 +0,0 @@
----
-title: German Flan T5
-emoji: 🐠
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/utils.py

@@ -1,120 +0,0 @@
-import torch
-import numpy as np
-from tqdm import tqdm
-import json
-
-
-def load_data(file_name: str = "./uvr5_pack/name_params.json") -> dict:
-    with open(file_name, "r") as f:
-        data = json.load(f)
-
-    return data
-
-
-def make_padding(width, cropsize, offset):
-    left = offset
-    roi_size = cropsize - left * 2
-    if roi_size == 0:
-        roi_size = cropsize
-    right = roi_size - (width % roi_size) + left
-
-    return left, right, roi_size
-
-
-def inference(X_spec, device, model, aggressiveness, data):
-    """
-    data ： dic configs
-    """
-
-    def _execute(
-        X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True
-    ):
-        model.eval()
-        with torch.no_grad():
-            preds = []
-
-            iterations = [n_window]
-
-            total_iterations = sum(iterations)
-            for i in tqdm(range(n_window)):
-                start = i * roi_size
-                X_mag_window = X_mag_pad[
-                    None, :, :, start : start + data["window_size"]
-                ]
-                X_mag_window = torch.from_numpy(X_mag_window)
-                if is_half:
-                    X_mag_window = X_mag_window.half()
-                X_mag_window = X_mag_window.to(device)
-
-                pred = model.predict(X_mag_window, aggressiveness)
-
-                pred = pred.detach().cpu().numpy()
-                preds.append(pred[0])
-
-            pred = np.concatenate(preds, axis=2)
-        return pred
-
-    def preprocess(X_spec):
-        X_mag = np.abs(X_spec)
-        X_phase = np.angle(X_spec)
-
-        return X_mag, X_phase
-
-    X_mag, X_phase = preprocess(X_spec)
-
-    coef = X_mag.max()
-    X_mag_pre = X_mag / coef
-
-    n_frame = X_mag_pre.shape[2]
-    pad_l, pad_r, roi_size = make_padding(n_frame, data["window_size"], model.offset)
-    n_window = int(np.ceil(n_frame / roi_size))
-
-    X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
-
-    if list(model.state_dict().values())[0].dtype == torch.float16:
-        is_half = True
-    else:
-        is_half = False
-    pred = _execute(
-        X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
-    )
-    pred = pred[:, :, :n_frame]
-
-    if data["tta"]:
-        pad_l += roi_size // 2
-        pad_r += roi_size // 2
-        n_window += 1
-
-        X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
-
-        pred_tta = _execute(
-            X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
-        )
-        pred_tta = pred_tta[:, :, roi_size // 2 :]
-        pred_tta = pred_tta[:, :, :n_frame]
-
-        return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.0j * X_phase)
-    else:
-        return pred * coef, X_mag, np.exp(1.0j * X_phase)
-
-
-def _get_name_params(model_path, model_hash):
-    data = load_data()
-    flag = False
-    ModelName = model_path
-    for type in list(data):
-        for model in list(data[type][0]):
-            for i in range(len(data[type][0][model])):
-                if str(data[type][0][model][i]["hash_name"]) == model_hash:
-                    flag = True
-                elif str(data[type][0][model][i]["hash_name"]) in ModelName:
-                    flag = True
-
-                if flag:
-                    model_params_auto = data[type][0][model][i]["model_params"]
-                    param_name_auto = data[type][0][model][i]["param_name"]
-                    if type == "equivalent":
-                        return param_name_auto, model_params_auto
-                    else:
-                        flag = False
-    return param_name_auto, model_params_auto

spaces/AIConsultant/MusicGen/tests/modules/test_rope.py

@@ -1,168 +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 torch
-
-from audiocraft.modules.rope import RotaryEmbedding
-from audiocraft.modules.transformer import StreamingTransformer, set_efficient_attention_backend
-
-
-def test_rope():
-    set_efficient_attention_backend('xformers')
-    B, T, H, C = 8, 75, 16, 128
-
-    rope = RotaryEmbedding(dim=C)
-    xq = torch.rand((B, T, H, C))
-    xk = torch.rand((B, T, H, C))
-    xq_out, xk_out = rope.rotate_qk(xq, xk, start=7)
-
-    assert list(xq_out.shape) == [B, T, H, C]
-    assert list(xk_out.shape) == [B, T, H, C]
-
-
-def test_rope_io_dtypes():
-    set_efficient_attention_backend('xformers')
-    B, T, H, C = 8, 75, 16, 128
-
-    rope_32 = RotaryEmbedding(dim=C, dtype=torch.float32)
-    rope_64 = RotaryEmbedding(dim=C, dtype=torch.float64)
-
-    # Test bfloat16 inputs w/ both 32 and 64 precision rope.
-    xq_16 = torch.rand((B, T, H, C)).to(torch.bfloat16)
-    xk_16 = torch.rand((B, T, H, C)).to(torch.bfloat16)
-    xq_out, xk_out = rope_32.rotate_qk(xq_16, xk_16)
-    assert xq_out.dtype == torch.bfloat16
-    xq_out, xk_out = rope_64.rotate_qk(xq_16, xk_16)
-    assert xq_out.dtype == torch.bfloat16
-
-    # Test float32 inputs w/ both 32 and 64 precision rope.
-    xq_32 = torch.rand((B, T, H, C)).to(torch.float32)
-    xk_32 = torch.rand((B, T, H, C)).to(torch.float32)
-    xq_out, xk_out = rope_32.rotate_qk(xq_32, xk_32)
-    assert xq_out.dtype == torch.float32
-    xq_out, xk_out = rope_64.rotate_qk(xq_32, xk_32)
-    assert xq_out.dtype == torch.float32
-
-
-def test_transformer_with_rope():
-    set_efficient_attention_backend('xformers')
-    torch.manual_seed(1234)
-    for pos in ['rope', 'sin_rope']:
-        tr = StreamingTransformer(
-            16, 4, 2, custom=True, dropout=0., layer_scale=0.1,
-            positional_embedding=pos)
-        tr.eval()
-        steps = 12
-        x = torch.randn(3, steps, 16)
-
-        out = tr(x)
-        assert list(out.shape) == list(x.shape)
-
-
-@torch.no_grad()
-def test_rope_streaming():
-    set_efficient_attention_backend('xformers')
-    torch.manual_seed(1234)
-    tr = StreamingTransformer(
-        16, 4, 2, causal=True, dropout=0.,
-        custom=True, positional_embedding='rope')
-    tr.eval()
-    steps = 12
-    x = torch.randn(3, steps, 16)
-
-    ref = tr(x)
-
-    with tr.streaming():
-        outs = []
-        frame_sizes = [1] * steps
-
-        for frame_size in frame_sizes:
-            frame = x[:, :frame_size]
-            x = x[:, frame_size:]
-            outs.append(tr(frame))
-
-    out = torch.cat(outs, dim=1)
-    assert list(out.shape) == [3, steps, 16]
-    delta = torch.norm(out - ref) / torch.norm(out)
-    assert delta < 1e-6, delta
-
-
-@torch.no_grad()
-def test_rope_streaming_past_context():
-    set_efficient_attention_backend('xformers')
-    torch.manual_seed(1234)
-
-    for context in [None, 10]:
-        tr = StreamingTransformer(
-            16, 4, 1 if context else 2,
-            causal=True, past_context=context, custom=True,
-            dropout=0., positional_embedding='rope')
-        tr.eval()
-
-        steps = 20
-        x = torch.randn(3, steps, 16)
-        ref = tr(x)
-
-        with tr.streaming():
-            outs = []
-            frame_sizes = [1] * steps
-
-            for frame_size in frame_sizes:
-                frame = x[:, :frame_size]
-                x = x[:, frame_size:]
-                outs.append(tr(frame))
-
-        out = torch.cat(outs, dim=1)
-        assert list(out.shape) == [3, steps, 16]
-        delta = torch.norm(out - ref) / torch.norm(out)
-        assert delta < 1e-6, delta
-
-
-def test_rope_memory_efficient():
-    set_efficient_attention_backend('xformers')
-    torch.manual_seed(1234)
-    tr = StreamingTransformer(
-        16, 4, 2, custom=True, dropout=0., layer_scale=0.1,
-        positional_embedding='rope')
-    tr_mem_efficient = StreamingTransformer(
-        16, 4, 2, dropout=0., memory_efficient=True, layer_scale=0.1,
-        positional_embedding='rope')
-    tr_mem_efficient.load_state_dict(tr.state_dict())
-    tr.eval()
-    steps = 12
-    x = torch.randn(3, steps, 16)
-
-    with torch.no_grad():
-        y = tr(x)
-        y2 = tr_mem_efficient(x)
-        # Check at float precision b/c this is the rope default.
-        assert torch.allclose(y, y2, atol=1e-7), (y - y2).norm()
-
-
-def test_rope_with_xpos():
-    set_efficient_attention_backend('xformers')
-    B, T, H, C = 8, 75, 16, 128
-
-    rope = RotaryEmbedding(dim=C, xpos=True)
-    xq = torch.rand((B, T, H, C))
-    xk = torch.rand((B, T, H, C))
-    xq_out, xk_out = rope.rotate_qk(xq, xk, start=7)
-
-    assert list(xq_out.shape) == [B, T, H, C]
-    assert list(xk_out.shape) == [B, T, H, C]
-
-
-def test_positional_scale():
-    set_efficient_attention_backend('xformers')
-    B, T, H, C = 8, 75, 16, 128
-
-    rope = RotaryEmbedding(dim=C, xpos=True, scale=0.0)
-    xq = torch.rand((B, T, H, C))
-    xk = torch.rand((B, T, H, C))
-    xq_out, xk_out = rope.rotate_qk(xq, xk, start=7)
-
-    assert torch.allclose(xq, xq_out)
-    assert torch.allclose(xk, xk_out)

spaces/AIFILMS/StyleGANEX/models/psp.py

@@ -1,148 +0,0 @@
-"""
-This file defines the core research contribution
-"""
-import matplotlib
-matplotlib.use('Agg')
-import math
-
-import torch
-from torch import nn
-from models.encoders import psp_encoders
-from models.stylegan2.model import Generator
-from configs.paths_config import model_paths
-import torch.nn.functional as F
-
-def get_keys(d, name):
-    if 'state_dict' in d:
-        d = d['state_dict']
-    d_filt = {k[len(name) + 1:]: v for k, v in d.items() if k[:len(name)] == name}
-    return d_filt
-
-
-class pSp(nn.Module):
-
-    def __init__(self, opts, ckpt=None):
-        super(pSp, self).__init__()
-        self.set_opts(opts)
-        # compute number of style inputs based on the output resolution
-        self.opts.n_styles = int(math.log(self.opts.output_size, 2)) * 2 - 2
-        # Define architecture
-        self.encoder = self.set_encoder()
-        self.decoder = Generator(self.opts.output_size, 512, 8)
-        self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256))
-        # Load weights if needed
-        self.load_weights(ckpt)
-
-    def set_encoder(self):
-        if self.opts.encoder_type == 'GradualStyleEncoder':
-            encoder = psp_encoders.GradualStyleEncoder(50, 'ir_se', self.opts)
-        elif self.opts.encoder_type == 'BackboneEncoderUsingLastLayerIntoW':
-            encoder = psp_encoders.BackboneEncoderUsingLastLayerIntoW(50, 'ir_se', self.opts)
-        elif self.opts.encoder_type == 'BackboneEncoderUsingLastLayerIntoWPlus':
-            encoder = psp_encoders.BackboneEncoderUsingLastLayerIntoWPlus(50, 'ir_se', self.opts)
-        else:
-            raise Exception('{} is not a valid encoders'.format(self.opts.encoder_type))
-        return encoder
-
-    def load_weights(self, ckpt=None):
-        if self.opts.checkpoint_path is not None:
-            print('Loading pSp from checkpoint: {}'.format(self.opts.checkpoint_path))
-            if ckpt is None:
-                ckpt = torch.load(self.opts.checkpoint_path, map_location='cpu')
-            self.encoder.load_state_dict(get_keys(ckpt, 'encoder'), strict=False)
-            self.decoder.load_state_dict(get_keys(ckpt, 'decoder'), strict=False)
-            self.__load_latent_avg(ckpt)
-        else:
-            print('Loading encoders weights from irse50!')
-            encoder_ckpt = torch.load(model_paths['ir_se50'])
-            # if input to encoder is not an RGB image, do not load the input layer weights
-            if self.opts.label_nc != 0:
-                encoder_ckpt = {k: v for k, v in encoder_ckpt.items() if "input_layer" not in k}
-            self.encoder.load_state_dict(encoder_ckpt, strict=False)
-            print('Loading decoder weights from pretrained!')
-            ckpt = torch.load(self.opts.stylegan_weights)
-            self.decoder.load_state_dict(ckpt['g_ema'], strict=False)
-            if self.opts.learn_in_w:
-                self.__load_latent_avg(ckpt, repeat=1)
-            else:
-                self.__load_latent_avg(ckpt, repeat=self.opts.n_styles)
-        # for video toonification, we load G0' model
-        if self.opts.toonify_weights is not None: ##### modified
-            ckpt = torch.load(self.opts.toonify_weights)
-            self.decoder.load_state_dict(ckpt['g_ema'], strict=False)
-            self.opts.toonify_weights = None
-
-    # x1: image for first-layer feature f. 
-    # x2: image for style latent code w+. If not specified, x2=x1.
-    # inject_latent: for sketch/mask-to-face translation, another latent code to fuse with w+
-    # latent_mask: fuse w+ and inject_latent with the mask (1~7 use w+ and 8~18 use inject_latent)
-    # use_feature: use f. Otherwise, use the orginal StyleGAN first-layer constant 4*4 feature 
-    # first_layer_feature_ind: always=0, means the 1st layer of G accept f
-    # use_skip: use skip connection.
-    # zero_noise: use zero noises. 
-    # editing_w: the editing vector v for video face editing
-    def forward(self, x1, x2=None, resize=True, latent_mask=None, randomize_noise=True,
-                inject_latent=None, return_latents=False, alpha=None, use_feature=True, 
-                first_layer_feature_ind=0, use_skip=False, zero_noise=False, editing_w=None): ##### modified
-        
-        feats = None # f and the skipped encoder features
-        codes, feats = self.encoder(x1, return_feat=True, return_full=use_skip) ##### modified
-        if x2 is not None: ##### modified
-            codes = self.encoder(x2) ##### modified
-        # normalize with respect to the center of an average face
-        if self.opts.start_from_latent_avg:
-            if self.opts.learn_in_w:
-                codes = codes + self.latent_avg.repeat(codes.shape[0], 1)
-            else:
-                codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1)
-
-        # E_W^{1:7}(T(x1)) concatenate E_W^{8:18}(w~)
-        if latent_mask is not None:
-            for i in latent_mask:
-                if inject_latent is not None:
-                    if alpha is not None:
-                        codes[:, i] = alpha * inject_latent[:, i] + (1 - alpha) * codes[:, i]
-                    else:
-                        codes[:, i] = inject_latent[:, i]
-                else:
-                    codes[:, i] = 0
-                    
-        first_layer_feats, skip_layer_feats, fusion = None, None, None ##### modified            
-        if use_feature: ##### modified
-            first_layer_feats = feats[0:2] # use f
-            if use_skip: ##### modified
-                skip_layer_feats = feats[2:] # use skipped encoder feature
-                fusion = self.encoder.fusion # use fusion layer to fuse encoder feature and decoder feature.
-            
-        images, result_latent = self.decoder([codes],
-                                             input_is_latent=True,
-                                             randomize_noise=randomize_noise,
-                                             return_latents=return_latents,
-                                             first_layer_feature=first_layer_feats,
-                                             first_layer_feature_ind=first_layer_feature_ind,
-                                             skip_layer_feature=skip_layer_feats,
-                                             fusion_block=fusion,
-                                             zero_noise=zero_noise,
-                                             editing_w=editing_w) ##### modified
-
-        if resize:
-            if self.opts.output_size == 1024:  ##### modified
-                images = F.adaptive_avg_pool2d(images, (images.shape[2]//4, images.shape[3]//4))  ##### modified
-            else:
-                images = self.face_pool(images)
-
-        if return_latents:
-            return images, result_latent
-        else:
-            return images
-
-    def set_opts(self, opts):
-        self.opts = opts
-
-    def __load_latent_avg(self, ckpt, repeat=None):
-        if 'latent_avg' in ckpt:
-            self.latent_avg = ckpt['latent_avg'].to(self.opts.device)
-            if repeat is not None:
-                self.latent_avg = self.latent_avg.repeat(repeat, 1)
-        else:
-            self.latent_avg = None

spaces/AIGC-Audio/AudioGPT/text_to_speech/egs/datasets/audio/biaobei/preprocess.py

@@ -1,16 +0,0 @@
-from data_gen.tts.base_preprocess import BasePreprocessor
-import re
-
-
-class BiaobeiPreprocess(BasePreprocessor):
-    def meta_data(self):
-        input_dir = self.raw_data_dir
-        with open(f"{input_dir}/ProsodyLabeling/000001-010000.txt", encoding='utf-8') as f:
-            bb_lines = f.readlines()[::2]
-        for l_idx, l in (enumerate([re.sub("\#\d+", "", l.split('\t')[1].strip()) for l in bb_lines])):
-            item_name = f'{l_idx + 1:06d}'
-            wav_fn = f"{input_dir}/wav/{l_idx + 1:06d}.wav"
-            yield {'item_name': item_name, 'wav_fn': wav_fn, 'txt': l}
-
-if __name__ == "__main__":
-    BiaobeiPreprocess().process()

spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/losses_audio/vggishish/metrics.py

@@ -1,69 +0,0 @@
-import logging
-
-import numpy as np
-import scipy
-import torch
-from sklearn.metrics import average_precision_score, roc_auc_score
-
-logger = logging.getLogger(f'main.{__name__}')
-
-def metrics(targets, outputs, topk=(1, 5)):
-    """
-    Adapted from https://github.com/hche11/VGGSound/blob/master/utils.py
-
-    Calculate statistics including mAP, AUC, and d-prime.
-        Args:
-            output: 2d tensors, (dataset_size, classes_num) - before softmax
-            target: 1d tensors, (dataset_size, )
-            topk: tuple
-        Returns:
-            metric_dict: a dict of metrics
-    """
-    metrics_dict = dict()
-
-    num_cls = outputs.shape[-1]
-
-    # accuracy@k
-    _, preds = torch.topk(outputs, k=max(topk), dim=1)
-    correct_for_maxtopk = preds == targets.view(-1, 1).expand_as(preds)
-    for k in topk:
-        metrics_dict[f'accuracy_{k}'] = float(correct_for_maxtopk[:, :k].sum() / correct_for_maxtopk.shape[0])
-
-    # avg precision, average roc_auc, and dprime
-    targets = torch.nn.functional.one_hot(targets, num_classes=num_cls)
-
-    # ids of the predicted classes (same as softmax)
-    targets_pred = torch.softmax(outputs, dim=1)
-
-    targets = targets.numpy()
-    targets_pred = targets_pred.numpy()
-
-    # one-vs-rest
-    avg_p = [average_precision_score(targets[:, c], targets_pred[:, c], average=None) for c in range(num_cls)]
-    try:
-        roc_aucs = [roc_auc_score(targets[:, c], targets_pred[:, c], average=None) for c in range(num_cls)]
-    except ValueError:
-        logger.warning('Weird... Some classes never occured in targets. Do not trust the metrics.')
-        roc_aucs = np.array([0.5])
-        avg_p = np.array([0])
-
-    metrics_dict['mAP'] = np.mean(avg_p)
-    metrics_dict['mROCAUC'] = np.mean(roc_aucs)
-    # Percent point function (ppf) (inverse of cdf — percentiles).
-    metrics_dict['dprime'] = scipy.stats.norm().ppf(metrics_dict['mROCAUC']) * np.sqrt(2)
-
-    return metrics_dict
-
-
-if __name__ == '__main__':
-    targets = torch.tensor([3, 3, 1, 2, 1, 0])
-    outputs = torch.tensor([
-        [1.2, 1.3, 1.1, 1.5],
-        [1.3, 1.4, 1.0, 1.1],
-        [1.5, 1.1, 1.4, 1.3],
-        [1.0, 1.2, 1.4, 1.5],
-        [1.2, 1.3, 1.1, 1.1],
-        [1.2, 1.1, 1.1, 1.1],
-    ]).float()
-    metrics_dict = metrics(targets, outputs, topk=(1, 3))
-    print(metrics_dict)

spaces/AIML-TUDA/unsafe-vs-safe-stable-diffusion/README.md

@@ -1,13 +0,0 @@
----
-title: Stable Diffusion vs. Safe Stable Diffusion
-colorFrom: blue
-colorTo: red
-emoji: 😇
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: true
-license: creativeml-openrail-m
-duplicated_from: AIML-TUDA/safe-stable-diffusion
----
-

spaces/Abhilashvj/planogram-compliance/detect.py

@@ -1,460 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
-
-Usage - sources:
-    $ python detect.py --weights yolov5s.pt --source 0                               # webcam
-                                                     img.jpg                         # image
-                                                     vid.mp4                         # video
-                                                     screen                          # screenshot
-                                                     path/                           # directory
-                                                     list.txt                        # list of images
-                                                     list.streams                    # list of streams
-                                                     'path/*.jpg'                    # glob
-                                                     'https://youtu.be/Zgi9g1ksQHc'  # YouTube
-                                                     'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
-
-Usage - formats:
-    $ python detect.py --weights yolov5s.pt                 # PyTorch
-                                 yolov5s.torchscript        # TorchScript
-                                 yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
-                                 yolov5s_openvino_model     # OpenVINO
-                                 yolov5s.engine             # TensorRT
-                                 yolov5s.mlmodel            # CoreML (macOS-only)
-                                 yolov5s_saved_model        # TensorFlow SavedModel
-                                 yolov5s.pb                 # TensorFlow GraphDef
-                                 yolov5s.tflite             # TensorFlow Lite
-                                 yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
-                                 yolov5s_paddle_model       # PaddlePaddle
-"""
-
-import argparse
-import os
-import platform
-import sys
-from pathlib import Path
-
-import torch
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-from models.common import DetectMultiBackend
-from utils.dataloaders import (
-    IMG_FORMATS,
-    VID_FORMATS,
-    LoadImages,
-    LoadScreenshots,
-    LoadStreams,
-)
-from utils.general import (
-    LOGGER,
-    Profile,
-    check_file,
-    check_img_size,
-    check_imshow,
-    check_requirements,
-    colorstr,
-    cv2,
-    increment_path,
-    non_max_suppression,
-    print_args,
-    scale_boxes,
-    strip_optimizer,
-    xyxy2xywh,
-)
-from utils.plots import Annotator, colors, save_one_box
-from utils.torch_utils import select_device, smart_inference_mode
-
-
-@smart_inference_mode()
-def run(
-    weights=ROOT / "yolov5s.pt",  # model path or triton URL
-    source=ROOT / "data/images",  # file/dir/URL/glob/screen/0(webcam)
-    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
-    imgsz=(640, 640),  # inference size (height, width)
-    conf_thres=0.25,  # confidence threshold
-    iou_thres=0.45,  # NMS IOU threshold
-    max_det=1000,  # maximum detections per image
-    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-    view_img=False,  # show results
-    save_txt=False,  # save results to *.txt
-    save_conf=False,  # save confidences in --save-txt labels
-    save_crop=False,  # save cropped prediction boxes
-    nosave=False,  # do not save images/videos
-    classes=None,  # filter by class: --class 0, or --class 0 2 3
-    agnostic_nms=False,  # class-agnostic NMS
-    augment=False,  # augmented inference
-    visualize=False,  # visualize features
-    update=False,  # update all models
-    project=ROOT / "runs/detect",  # save results to project/name
-    name="exp",  # save results to project/name
-    exist_ok=False,  # existing project/name ok, do not increment
-    line_thickness=3,  # bounding box thickness (pixels)
-    hide_labels=False,  # hide labels
-    hide_conf=False,  # hide confidences
-    half=False,  # use FP16 half-precision inference
-    dnn=False,  # use OpenCV DNN for ONNX inference
-    vid_stride=1,  # video frame-rate stride
-):
-    source = str(source)
-    save_img = not nosave and not source.endswith(
-        ".txt"
-    )  # save inference images
-    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
-    is_url = source.lower().startswith(
-        ("rtsp://", "rtmp://", "http://", "https://")
-    )
-    webcam = (
-        source.isnumeric()
-        or source.endswith(".streams")
-        or (is_url and not is_file)
-    )
-    screenshot = source.lower().startswith("screen")
-    if is_url and is_file:
-        source = check_file(source)  # download
-
-    # Directories
-    save_dir = increment_path(
-        Path(project) / name, exist_ok=exist_ok
-    )  # increment run
-    (save_dir / "labels" if save_txt else save_dir).mkdir(
-        parents=True, exist_ok=True
-    )  # make dir
-
-    # Load model
-    device = select_device(device)
-    model = DetectMultiBackend(
-        weights, device=device, dnn=dnn, data=data, fp16=half
-    )
-    stride, names, pt = model.stride, model.names, model.pt
-    imgsz = check_img_size(imgsz, s=stride)  # check image size
-
-    # Dataloader
-    bs = 1  # batch_size
-    if webcam:
-        view_img = check_imshow(warn=True)
-        dataset = LoadStreams(
-            source,
-            img_size=imgsz,
-            stride=stride,
-            auto=pt,
-            vid_stride=vid_stride,
-        )
-        bs = len(dataset)
-    elif screenshot:
-        dataset = LoadScreenshots(
-            source, img_size=imgsz, stride=stride, auto=pt
-        )
-    else:
-        dataset = LoadImages(
-            source,
-            img_size=imgsz,
-            stride=stride,
-            auto=pt,
-            vid_stride=vid_stride,
-        )
-    vid_path, vid_writer = [None] * bs, [None] * bs
-
-    # Run inference
-    model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmup
-    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
-    for path, im, im0s, vid_cap, s in dataset:
-        with dt[0]:
-            im = torch.from_numpy(im).to(model.device)
-            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
-            im /= 255  # 0 - 255 to 0.0 - 1.0
-            if len(im.shape) == 3:
-                im = im[None]  # expand for batch dim
-
-        # Inference
-        with dt[1]:
-            visualize = (
-                increment_path(save_dir / Path(path).stem, mkdir=True)
-                if visualize
-                else False
-            )
-            pred = model(im, augment=augment, visualize=visualize)
-
-        # NMS
-        with dt[2]:
-            pred = non_max_suppression(
-                pred,
-                conf_thres,
-                iou_thres,
-                classes,
-                agnostic_nms,
-                max_det=max_det,
-            )
-
-        # Second-stage classifier (optional)
-        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
-
-        # Process predictions
-        for i, det in enumerate(pred):  # per image
-            seen += 1
-            if webcam:  # batch_size >= 1
-                p, im0, frame = path[i], im0s[i].copy(), dataset.count
-                s += f"{i}: "
-            else:
-                p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
-
-            p = Path(p)  # to Path
-            save_path = str(save_dir / p.name)  # im.jpg
-            txt_path = str(save_dir / "labels" / p.stem) + (
-                "" if dataset.mode == "image" else f"_{frame}"
-            )  # im.txt
-            s += "%gx%g " % im.shape[2:]  # print string
-            gn = torch.tensor(im0.shape)[
-                [1, 0, 1, 0]
-            ]  # normalization gain whwh
-            imc = im0.copy() if save_crop else im0  # for save_crop
-            annotator = Annotator(
-                im0, line_width=line_thickness, example=str(names)
-            )
-            if len(det):
-                # Rescale boxes from img_size to im0 size
-                det[:, :4] = scale_boxes(
-                    im.shape[2:], det[:, :4], im0.shape
-                ).round()
-
-                # Print results
-                for c in det[:, 5].unique():
-                    n = (det[:, 5] == c).sum()  # detections per class
-                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
-
-                # Write results
-                for *xyxy, conf, cls in reversed(det):
-                    if save_txt:  # Write to file
-                        xywh = (
-                            (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn)
-                            .view(-1)
-                            .tolist()
-                        )  # normalized xywh
-                        line = (
-                            (cls, *xywh, conf) if save_conf else (cls, *xywh)
-                        )  # label format
-                        with open(f"{txt_path}.txt", "a") as f:
-                            f.write(("%g " * len(line)).rstrip() % line + "\n")
-
-                    if save_img or save_crop or view_img:  # Add bbox to image
-                        c = int(cls)  # integer class
-                        label = (
-                            None
-                            if hide_labels
-                            else (
-                                names[c]
-                                if hide_conf
-                                else f"{names[c]} {conf:.2f}"
-                            )
-                        )
-                        annotator.box_label(xyxy, label, color=colors(c, True))
-                    if save_crop:
-                        save_one_box(
-                            xyxy,
-                            imc,
-                            file=save_dir
-                            / "crops"
-                            / names[c]
-                            / f"{p.stem}.jpg",
-                            BGR=True,
-                        )
-
-            # Stream results
-            im0 = annotator.result()
-            if view_img:
-                if platform.system() == "Linux" and p not in windows:
-                    windows.append(p)
-                    cv2.namedWindow(
-                        str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO
-                    )  # allow window resize (Linux)
-                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
-                cv2.imshow(str(p), im0)
-                cv2.waitKey(1)  # 1 millisecond
-
-            # Save results (image with detections)
-            if save_img:
-                if dataset.mode == "image":
-                    cv2.imwrite(save_path, im0)
-                else:  # 'video' or 'stream'
-                    if vid_path[i] != save_path:  # new video
-                        vid_path[i] = save_path
-                        if isinstance(vid_writer[i], cv2.VideoWriter):
-                            vid_writer[
-                                i
-                            ].release()  # release previous video writer
-                        if vid_cap:  # video
-                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
-                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-                        else:  # stream
-                            fps, w, h = 30, im0.shape[1], im0.shape[0]
-                        save_path = str(
-                            Path(save_path).with_suffix(".mp4")
-                        )  # force *.mp4 suffix on results videos
-                        vid_writer[i] = cv2.VideoWriter(
-                            save_path,
-                            cv2.VideoWriter_fourcc(*"mp4v"),
-                            fps,
-                            (w, h),
-                        )
-                    vid_writer[i].write(im0)
-
-        # Print time (inference-only)
-        LOGGER.info(
-            f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms"
-        )
-
-    # Print results
-    t = tuple(x.t / seen * 1e3 for x in dt)  # speeds per image
-    LOGGER.info(
-        f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}"
-        % t
-    )
-    if save_txt or save_img:
-        s = (
-            f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}"
-            if save_txt
-            else ""
-        )
-        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
-    if update:
-        strip_optimizer(
-            weights[0]
-        )  # update model (to fix SourceChangeWarning)
-
-
-def parse_opt():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--weights",
-        nargs="+",
-        type=str,
-        default=ROOT / "yolov5s.pt",
-        help="model path or triton URL",
-    )
-    parser.add_argument(
-        "--source",
-        type=str,
-        default=ROOT / "data/images",
-        help="file/dir/URL/glob/screen/0(webcam)",
-    )
-    parser.add_argument(
-        "--data",
-        type=str,
-        default=ROOT / "data/coco128.yaml",
-        help="(optional) dataset.yaml path",
-    )
-    parser.add_argument(
-        "--imgsz",
-        "--img",
-        "--img-size",
-        nargs="+",
-        type=int,
-        default=[640],
-        help="inference size h,w",
-    )
-    parser.add_argument(
-        "--conf-thres", type=float, default=0.25, help="confidence threshold"
-    )
-    parser.add_argument(
-        "--iou-thres", type=float, default=0.45, help="NMS IoU threshold"
-    )
-    parser.add_argument(
-        "--max-det",
-        type=int,
-        default=1000,
-        help="maximum detections per image",
-    )
-    parser.add_argument(
-        "--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
-    )
-    parser.add_argument("--view-img", action="store_true", help="show results")
-    parser.add_argument(
-        "--save-txt", action="store_true", help="save results to *.txt"
-    )
-    parser.add_argument(
-        "--save-conf",
-        action="store_true",
-        help="save confidences in --save-txt labels",
-    )
-    parser.add_argument(
-        "--save-crop",
-        action="store_true",
-        help="save cropped prediction boxes",
-    )
-    parser.add_argument(
-        "--nosave", action="store_true", help="do not save images/videos"
-    )
-    parser.add_argument(
-        "--classes",
-        nargs="+",
-        type=int,
-        help="filter by class: --classes 0, or --classes 0 2 3",
-    )
-    parser.add_argument(
-        "--agnostic-nms", action="store_true", help="class-agnostic NMS"
-    )
-    parser.add_argument(
-        "--augment", action="store_true", help="augmented inference"
-    )
-    parser.add_argument(
-        "--visualize", action="store_true", help="visualize features"
-    )
-    parser.add_argument(
-        "--update", action="store_true", help="update all models"
-    )
-    parser.add_argument(
-        "--project",
-        default=ROOT / "runs/detect",
-        help="save results to project/name",
-    )
-    parser.add_argument(
-        "--name", default="exp", help="save results to project/name"
-    )
-    parser.add_argument(
-        "--exist-ok",
-        action="store_true",
-        help="existing project/name ok, do not increment",
-    )
-    parser.add_argument(
-        "--line-thickness",
-        default=3,
-        type=int,
-        help="bounding box thickness (pixels)",
-    )
-    parser.add_argument(
-        "--hide-labels", default=False, action="store_true", help="hide labels"
-    )
-    parser.add_argument(
-        "--hide-conf",
-        default=False,
-        action="store_true",
-        help="hide confidences",
-    )
-    parser.add_argument(
-        "--half", action="store_true", help="use FP16 half-precision inference"
-    )
-    parser.add_argument(
-        "--dnn", action="store_true", help="use OpenCV DNN for ONNX inference"
-    )
-    parser.add_argument(
-        "--vid-stride", type=int, default=1, help="video frame-rate stride"
-    )
-    opt = parser.parse_args()
-    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
-    print_args(vars(opt))
-    return opt
-
-
-def main(opt):
-    check_requirements(exclude=("tensorboard", "thop"))
-    run(**vars(opt))
-
-
-if __name__ == "__main__":
-    opt = parse_opt()
-    main(opt)

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinput/methods/CreateColorPicker.js

@@ -1,56 +0,0 @@
-import ColorPicker from './ColorPicker.js';
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-
-var CreateColorPicker = function (scene) {
-    var scene = this.scene;
-
-    var background;
-    var createBackgroundCallback = this.colorPickerCreateBackgroundCallback;
-    if (createBackgroundCallback) {
-        background = createBackgroundCallback.call(this, scene);
-        scene.add.existing(background);
-    }
-
-    var width = this.colorPickerWidth;
-    if (width === undefined) {
-        width = this.width;
-    }
-
-    var height = this.colorPickerHeight;
-    if (height === undefined) {
-        height = width;
-    }
-
-    var colorComponentsConfig;
-    if (this.colorComponentsHeight > 0) {
-        colorComponentsConfig = {
-            height: this.colorComponentsHeight,
-            formatLabel: this.colorComponentsFormatLabelConfig,
-            inputText: this.colorComponentsInputTextConfig,
-            space: this.colorComponentsSpace,
-        }
-    } else {
-        colorComponentsConfig = false;
-    }
-
-    var colorPicker = new ColorPicker(scene, {
-        width: width, height: height,
-
-        background: background,
-        space: this.colorPickerSpace,
-
-        hPalette: {
-            position: this.colorPickerHPalettePosition,
-        },
-
-        colorComponents: colorComponentsConfig,
-
-        value: this.value
-    });
-    scene.add.existing(colorPicker);
-
-    return colorPicker;
-}
-
-export default CreateColorPicker;

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

@@ -1,23 +0,0 @@
-import MergeStyle from './utils/MergeStyle.js';
-import SetTextureProperties from './utils/SetTextureProperties.js';
-
-const PhaserVideo = Phaser.GameObjects.Video;
-
-var CreateVideo = function (scene, data, view, styles, customBuilders) {
-    data = MergeStyle(data, styles);
-    var gameObject = new PhaserVideo(scene, 0, 0, data.key);
-
-    if (data.width !== undefined) {
-        gameObject.setDisplayWidth(data.width);
-    }
-    if (data.height !== undefined) {
-        gameObject.setDisplayHeight(data.height);
-    }
-
-    SetTextureProperties(gameObject, data);
-
-    scene.add.existing(gameObject);
-    return gameObject;
-}
-
-export default CreateVideo;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/roundrectangle/RoundRectangle.js

@@ -1,2 +0,0 @@
-import RoundRectangle from '../../../plugins/roundrectangle.js';
-export default RoundRectangle;

spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/preprocess_v2.py

@@ -1,151 +0,0 @@
-import os
-import argparse
-import json
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--add_auxiliary_data", type=bool, help="Whether to add extra data as fine-tuning helper")
-    parser.add_argument("--languages", default="CJE")
-    args = parser.parse_args()
-    if args.languages == "CJE":
-        langs = ["[ZH]", "[JA]", "[EN]"]
-    elif args.languages == "CJ":
-        langs = ["[ZH]", "[JA]"]
-    elif args.languages == "C":
-        langs = ["[ZH]"]
-    new_annos = []
-    # Source 1: transcribed short audios
-    if os.path.exists("short_character_anno.txt"):
-        with open("short_character_anno.txt", 'r', encoding='utf-8') as f:
-            short_character_anno = f.readlines()
-            new_annos += short_character_anno
-    # Source 2: transcribed long audio segments
-    if os.path.exists("long_character_anno.txt"):
-        with open("long_character_anno.txt", 'r', encoding='utf-8') as f:
-            long_character_anno = f.readlines()
-            new_annos += long_character_anno
-
-    # Get all speaker names
-    speakers = []
-    for line in new_annos:
-        path, speaker, text = line.split("|")
-        if speaker not in speakers:
-            speakers.append(speaker)
-    assert (len(speakers) != 0), "No audio file found. Please check your uploaded file structure."
-    # Source 3 (Optional): sampled audios as extra training helpers
-    if args.add_auxiliary_data:
-        with open("sampled_audio4ft.txt", 'r', encoding='utf-8') as f:
-            old_annos = f.readlines()
-        # filter old_annos according to supported languages
-        filtered_old_annos = []
-        for line in old_annos:
-            for lang in langs:
-                if lang in line:
-                    filtered_old_annos.append(line)
-        old_annos = filtered_old_annos
-        for line in old_annos:
-            path, speaker, text = line.split("|")
-            if speaker not in speakers:
-                speakers.append(speaker)
-        num_old_voices = len(old_annos)
-        num_new_voices = len(new_annos)
-        # STEP 1: balance number of new & old voices
-        cc_duplicate = num_old_voices // num_new_voices
-        if cc_duplicate == 0:
-            cc_duplicate = 1
-
-
-        # STEP 2: modify config file
-        with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
-            hps = json.load(f)
-
-        # assign ids to new speakers
-        speaker2id = {}
-        for i, speaker in enumerate(speakers):
-            speaker2id[speaker] = i
-        # modify n_speakers
-        hps['data']["n_speakers"] = len(speakers)
-        # overwrite speaker names
-        hps['speakers'] = speaker2id
-        hps['train']['log_interval'] = 100
-        hps['train']['eval_interval'] = 1000
-        hps['train']['batch_size'] = 16
-        hps['data']['training_files'] = "final_annotation_train.txt"
-        hps['data']['validation_files'] = "final_annotation_val.txt"
-        # save modified config
-        with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
-            json.dump(hps, f, indent=2)
-
-        # STEP 3: clean annotations, replace speaker names with assigned speaker IDs
-        import text
-        cleaned_new_annos = []
-        for i, line in enumerate(new_annos):
-            path, speaker, txt = line.split("|")
-            if len(txt) > 150:
-                continue
-            cleaned_text = text._clean_text(txt, hps['data']['text_cleaners'])
-            cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
-            cleaned_new_annos.append(path + "|" + str(speaker2id[speaker]) + "|" + cleaned_text)
-        cleaned_old_annos = []
-        for i, line in enumerate(old_annos):
-            path, speaker, txt = line.split("|")
-            if len(txt) > 150:
-                continue
-            cleaned_text = text._clean_text(txt, hps['data']['text_cleaners'])
-            cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
-            cleaned_old_annos.append(path + "|" + str(speaker2id[speaker]) + "|" + cleaned_text)
-        # merge with old annotation
-        final_annos = cleaned_old_annos + cc_duplicate * cleaned_new_annos
-        # save annotation file
-        with open("final_annotation_train.txt", 'w', encoding='utf-8') as f:
-            for line in final_annos:
-                f.write(line)
-        # save annotation file for validation
-        with open("final_annotation_val.txt", 'w', encoding='utf-8') as f:
-            for line in cleaned_new_annos:
-                f.write(line)
-        print("finished")
-    else:
-        # Do not add extra helper data
-        # STEP 1: modify config file
-        with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
-            hps = json.load(f)
-
-        # assign ids to new speakers
-        speaker2id = {}
-        for i, speaker in enumerate(speakers):
-            speaker2id[speaker] = i
-        # modify n_speakers
-        hps['data']["n_speakers"] = len(speakers)
-        # overwrite speaker names
-        hps['speakers'] = speaker2id
-        hps['train']['log_interval'] = 10
-        hps['train']['eval_interval'] = 100
-        hps['train']['batch_size'] = 16
-        hps['data']['training_files'] = "final_annotation_train.txt"
-        hps['data']['validation_files'] = "final_annotation_val.txt"
-        # save modified config
-        with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
-            json.dump(hps, f, indent=2)
-
-        # STEP 2: clean annotations, replace speaker names with assigned speaker IDs
-        import text
-
-        cleaned_new_annos = []
-        for i, line in enumerate(new_annos):
-            path, speaker, txt = line.split("|")
-            if len(txt) > 150:
-                continue
-            cleaned_text = text._clean_text(txt, hps['data']['text_cleaners']).replace("[ZH]", "")
-            cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
-            cleaned_new_annos.append(path + "|" + str(speaker2id[speaker]) + "|" + cleaned_text)
-
-        final_annos = cleaned_new_annos
-        # save annotation file
-        with open("final_annotation_train.txt", 'w', encoding='utf-8') as f:
-            for line in final_annos:
-                f.write(line)
-        # save annotation file for validation
-        with open("final_annotation_val.txt", 'w', encoding='utf-8') as f:
-            for line in cleaned_new_annos:
-                f.write(line)
-        print("finished")

spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/configs/3millions_pfc.py

@@ -1,23 +0,0 @@
-from easydict import EasyDict as edict
-
-# configs for test speed
-
-config = edict()
-config.loss = "arcface"
-config.network = "r50"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 0.1
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "synthetic"
-config.num_classes = 300 * 10000
-config.num_epoch = 30
-config.warmup_epoch = -1
-config.decay_epoch = [10, 16, 22]
-config.val_targets = []

spaces/Amrrs/portfolio-github/index.html

@@ -1,108 +0,0 @@
-<!DOCTYPE html>
-<html>
-    <head>
-        <title>Welcome to 1littlecoder</title>
-        <link href="https://fonts.googleapis.com/css2?family=Bellota&display=swap" rel="stylesheet">
-        <link href="style.css" rel="stylesheet" type="text/css">
-    </head>
-    <body>
-        <div id="header" class="section">
-            <img alt="logo" class="img-circle" src="https://www.kindpng.com/picc/m/340-3408802_lego-batman-icon-lego-batman-png-transparent-png.png"> 
-            <p>Welcome to 1littlecoder</p>
-        </div>
-        <div class="section">
-            <h1><span>About Me</span></h1>
-            <p> Hey! I'm <strong>1littlecoder</strong> from <strong>India.</strong>. I Like <strong>Coding</strong> R Python Data Science Machine Learning</p>
-            <p> I love Hugging Face and here's my <a href = 'profile' src = 'https://huggingface.co/Amrrs'> </a>a></a> </p>
-            <p class="quote">~ 1littlecoder</p>
-        </div>
-        <div class="section" id="res">
-            <h1><span>My Works</span></h1>
-            <p align="centre"><strong>Here Are Some Of My Works</strong></p>
-            <a href="https://telegram.me">
-                <img src="https://img.icons8.com/nolan/144/telegram-app.png"/>
-                <div class="caption">Telegram Channel</div>
-            </a>
-            <a href="https://github.com/amrrs">
-                <img src="https://img.icons8.com/nolan/144/github.png"/>
-                <div class="caption">Github Account</div>
-            </a>
-            <a href="https://1littlecoder.in">
-                <img src="https://img.icons8.com/dusk/144/000000/domain.png"/>
-                <div class="caption">My Website</div>
-            </a>
-            <br>
-            <p align="centre"><strong>Resources I Use</strong></p>
-            <a href="https://github.com/">
-                <img src="https://img.icons8.com/nolan/144/github.png"/>
-                <div class="caption">Github</div>
-            </a>
-            <a href="https://telegram.me">
-                <img src="https://img.icons8.com/nolan/144/telegram-app.png"/>
-                <div class="caption">Telegram</div>
-            </a>
-            <a href="https://code.visualstudio.com">
-                <img src="https://img.icons8.com/nolan/144/code.png"/>
-                <div class="caption">VS Code Editor</div>
-            </a>
-            <a href="https://python.org">
-                <img src="https://img.icons8.com/nolan/144/python.png"/>
-                <div class="caption">Python</div>
-            </a>
-            <a href="https://www.php.net/">
-                <img src="https://img.icons8.com/dusk/144/000000/php-logo.png"/>
-                <div class="caption">PHP</div>
-            </a>
-            <a href="https://ubuntu.com">
-                <img src="https://img.icons8.com/color/144/000000/ubuntu--v1.png"/>
-                <div class="caption">Ubuntu</div>
-            </a>
-        </div>
-        <div class="section">
-            <h1><span>My Skills</span></h1>
-            <ul>
-                <li>Python<br /> <progress min="0" max="100" value="95"></progress> </li>
-                <li>PHP <br /> <progress min="0" max="100" value="75"></progress> </li>
-                <li>Coding<br /> <progress min="0" max="100" value="100"></progress> </li>
-            </ul>
-        </div>
-        <div class="section" id="contacts">
-            <h1><span>Follow Me</span></h1>
-            <div>
-                <a href="https://instagram.com/" target="_blank">
-                <img alt="Instagram" src="https://img.icons8.com/cute-clipart/100/instagram-new.png"/>
-                </a>
-                <a href="https://twitter.com/1littlecoder">
-                <img alt="Twitter" src="https://www.sololearn.com/Uploads/icons/twitter.png" />
-                </a>
-                <a href="https://github.com/amrrs">
-                <img alt="GitHub" src="https://img.icons8.com/nolan/144/github.png"/>
-                </a>
-                <a href="https://t.me/">
-                <img alt="Telegram" src="https://img.icons8.com/fluent/96/000000/telegram-app.png"/>
-                </a>
-                <a href="https://www.youtube.com/channel/UCRD6WpNNzJpRIU4z89PNSbg">
-                <img alt="YouTube" src="https://img.icons8.com/color/96/000000/youtube-play.png"/>
-                </a>
-                <a href="mailto:[email protected]">
-                <img alt="Email" src="https://img.icons8.com/fluent/96/000000/gmail.png"/>
-                </a>
-            </div>
-        </div>
-        <div class="section" id="contacts">
-        <h1><span>Contact Us</span></h1>
-        <a href="mailto:[email protected]">
-            <img src="https://img.icons8.com/fluent/95/000000/gmail--v2.png"/>
-            </a>
-        </div>
-        <center>Made with ❤️ By <a href="https://github.com/amrrs">
-            1littlecoder
-          </a></center>
-   
-        <script type="text/javascript">
-            function search() {
-                window.open('https://www.google.com/search?output=search&q=' + document.getElementById("question").value)
-            }
-        </script>
-    </body>
-</html>

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

@@ -1,1352 +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.
-
-# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
-
-import inspect
-import warnings
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-
-import numpy as np
-import PIL.Image
-import torch
-import torch.nn.functional as F
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
-
-from ...image_processor import VaeImageProcessor
-from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
-from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import (
-    is_accelerate_available,
-    is_accelerate_version,
-    is_compiled_module,
-    logging,
-    randn_tensor,
-    replace_example_docstring,
-)
-from ..pipeline_utils import DiffusionPipeline
-from ..stable_diffusion import StableDiffusionPipelineOutput
-from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from .multicontrolnet import MultiControlNetModel
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> # !pip install transformers accelerate
-        >>> from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, DDIMScheduler
-        >>> from diffusers.utils import load_image
-        >>> import numpy as np
-        >>> import torch
-
-        >>> init_image = load_image(
-        ...     "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"
-        ... )
-        >>> init_image = init_image.resize((512, 512))
-
-        >>> generator = torch.Generator(device="cpu").manual_seed(1)
-
-        >>> mask_image = load_image(
-        ...     "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"
-        ... )
-        >>> mask_image = mask_image.resize((512, 512))
-
-
-        >>> def make_inpaint_condition(image, image_mask):
-        ...     image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
-        ...     image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
-
-        ...     assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
-        ...     image[image_mask > 0.5] = -1.0  # set as masked pixel
-        ...     image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
-        ...     image = torch.from_numpy(image)
-        ...     return image
-
-
-        >>> control_image = make_inpaint_condition(init_image, mask_image)
-
-        >>> controlnet = ControlNetModel.from_pretrained(
-        ...     "lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16
-        ... )
-        >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
-        ... )
-
-        >>> pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
-        >>> pipe.enable_model_cpu_offload()
-
-        >>> # generate image
-        >>> image = pipe(
-        ...     "a handsome man with ray-ban sunglasses",
-        ...     num_inference_steps=20,
-        ...     generator=generator,
-        ...     eta=1.0,
-        ...     image=init_image,
-        ...     mask_image=mask_image,
-        ...     control_image=control_image,
-        ... ).images[0]
-        ```
-"""
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.prepare_mask_and_masked_image
-def prepare_mask_and_masked_image(image, mask, height, width, return_image=False):
-    """
-    Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
-    converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
-    ``image`` and ``1`` for the ``mask``.
-
-    The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
-    binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
-
-    Args:
-        image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
-            It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
-            ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
-        mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
-            It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
-            ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
-
-
-    Raises:
-        ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
-        should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
-        TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
-            (ot the other way around).
-
-    Returns:
-        tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
-            dimensions: ``batch x channels x height x width``.
-    """
-
-    if image is None:
-        raise ValueError("`image` input cannot be undefined.")
-
-    if mask is None:
-        raise ValueError("`mask_image` input cannot be undefined.")
-
-    if isinstance(image, torch.Tensor):
-        if not isinstance(mask, torch.Tensor):
-            raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not")
-
-        # Batch single image
-        if image.ndim == 3:
-            assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)"
-            image = image.unsqueeze(0)
-
-        # Batch and add channel dim for single mask
-        if mask.ndim == 2:
-            mask = mask.unsqueeze(0).unsqueeze(0)
-
-        # Batch single mask or add channel dim
-        if mask.ndim == 3:
-            # Single batched mask, no channel dim or single mask not batched but channel dim
-            if mask.shape[0] == 1:
-                mask = mask.unsqueeze(0)
-
-            # Batched masks no channel dim
-            else:
-                mask = mask.unsqueeze(1)
-
-        assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
-        assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
-        assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"
-
-        # Check image is in [-1, 1]
-        if image.min() < -1 or image.max() > 1:
-            raise ValueError("Image should be in [-1, 1] range")
-
-        # Check mask is in [0, 1]
-        if mask.min() < 0 or mask.max() > 1:
-            raise ValueError("Mask should be in [0, 1] range")
-
-        # Binarize mask
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-
-        # Image as float32
-        image = image.to(dtype=torch.float32)
-    elif isinstance(mask, torch.Tensor):
-        raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
-    else:
-        # preprocess image
-        if isinstance(image, (PIL.Image.Image, np.ndarray)):
-            image = [image]
-        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
-            # resize all images w.r.t passed height an width
-            image = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in image]
-            image = [np.array(i.convert("RGB"))[None, :] for i in image]
-            image = np.concatenate(image, axis=0)
-        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
-            image = np.concatenate([i[None, :] for i in image], axis=0)
-
-        image = image.transpose(0, 3, 1, 2)
-        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-
-        # preprocess mask
-        if isinstance(mask, (PIL.Image.Image, np.ndarray)):
-            mask = [mask]
-
-        if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
-            mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask]
-            mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
-            mask = mask.astype(np.float32) / 255.0
-        elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
-            mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
-
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-        mask = torch.from_numpy(mask)
-
-    masked_image = image * (mask < 0.5)
-
-    # n.b. ensure backwards compatibility as old function does not return image
-    if return_image:
-        return mask, masked_image, image
-
-    return mask, masked_image
-
-
-class StableDiffusionControlNetInpaintPipeline(
-    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
-):
-    r"""
-    Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    In addition the pipeline inherits the following loading methods:
-        - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
-
-    <Tip>
-
-    This pipeline can be used both with checkpoints that have been specifically fine-tuned for inpainting, such as
-    [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting)
-     as well as default text-to-image stable diffusion checkpoints, such as
-     [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5).
-    Default text-to-image stable diffusion checkpoints might be preferable for controlnets that have been fine-tuned on
-    those, such as [lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint).
-
-    </Tip>
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
-            Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets
-            as a list, the outputs from each ControlNet are added together to create one combined additional
-            conditioning.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPImageProcessor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
-        scheduler: KarrasDiffusionSchedulers,
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        if isinstance(controlnet, (list, tuple)):
-            controlnet = MultiControlNetModel(controlnet)
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            controlnet=controlnet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-        self.control_image_processor = VaeImageProcessor(
-            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
-        )
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
-    def enable_vae_slicing(self):
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.vae.enable_slicing()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
-    def disable_vae_slicing(self):
-        r"""
-        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_slicing()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
-    def enable_vae_tiling(self):
-        r"""
-        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
-        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
-        processing larger images.
-        """
-        self.vae.enable_tiling()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
-    def disable_vae_tiling(self):
-        r"""
-        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_tiling()
-
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
-        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
-        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
-        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        hook = None
-        for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
-            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
-        if self.safety_checker is not None:
-            # the safety checker can offload the vae again
-            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
-
-        # control net hook has be manually offloaded as it alternates with unet
-        cpu_offload_with_hook(self.controlnet, device)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        lora_scale: Optional[float] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-             prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            lora_scale (`float`, *optional*):
-                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
-        """
-        # set lora scale so that monkey patched LoRA
-        # function of text encoder can correctly access it
-        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
-            self._lora_scale = lora_scale
-
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            # textual inversion: procecss multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
-
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = self.tokenizer.batch_decode(
-                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                )
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = text_inputs.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            prompt_embeds = self.text_encoder(
-                text_input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            prompt_embeds = prompt_embeds[0]
-
-        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif prompt is not None and type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            # textual inversion: procecss multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
-
-            max_length = prompt_embeds.shape[1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = uncond_input.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            negative_prompt_embeds = self.text_encoder(
-                uncond_input.input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            negative_prompt_embeds = negative_prompt_embeds[0]
-
-        if do_classifier_free_guidance:
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-
-            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
-        return prompt_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is None:
-            has_nsfw_concept = None
-        else:
-            if torch.is_tensor(image):
-                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
-            else:
-                feature_extractor_input = self.image_processor.numpy_to_pil(image)
-            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        return image, has_nsfw_concept
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
-    def decode_latents(self, latents):
-        warnings.warn(
-            "The decode_latents method is deprecated and will be removed in a future version. Please"
-            " use VaeImageProcessor instead",
-            FutureWarning,
-        )
-        latents = 1 / self.vae.config.scaling_factor * latents
-        image = self.vae.decode(latents, return_dict=False)[0]
-        image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-        return image
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
-    def get_timesteps(self, num_inference_steps, strength, device):
-        # get the original timestep using init_timestep
-        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
-
-        return timesteps, num_inference_steps - t_start
-
-    def check_inputs(
-        self,
-        prompt,
-        image,
-        height,
-        width,
-        callback_steps,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-        controlnet_conditioning_scale=1.0,
-        control_guidance_start=0.0,
-        control_guidance_end=1.0,
-    ):
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-        # `prompt` needs more sophisticated handling when there are multiple
-        # conditionings.
-        if isinstance(self.controlnet, MultiControlNetModel):
-            if isinstance(prompt, list):
-                logger.warning(
-                    f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}"
-                    " prompts. The conditionings will be fixed across the prompts."
-                )
-
-        # Check `image`
-        is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
-            self.controlnet, torch._dynamo.eval_frame.OptimizedModule
-        )
-        if (
-            isinstance(self.controlnet, ControlNetModel)
-            or is_compiled
-            and isinstance(self.controlnet._orig_mod, ControlNetModel)
-        ):
-            self.check_image(image, prompt, prompt_embeds)
-        elif (
-            isinstance(self.controlnet, MultiControlNetModel)
-            or is_compiled
-            and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
-        ):
-            if not isinstance(image, list):
-                raise TypeError("For multiple controlnets: `image` must be type `list`")
-
-            # When `image` is a nested list:
-            # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])
-            elif any(isinstance(i, list) for i in image):
-                raise ValueError("A single batch of multiple conditionings are supported at the moment.")
-            elif len(image) != len(self.controlnet.nets):
-                raise ValueError(
-                    f"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {len(self.controlnet.nets)} ControlNets."
-                )
-
-            for image_ in image:
-                self.check_image(image_, prompt, prompt_embeds)
-        else:
-            assert False
-
-        # Check `controlnet_conditioning_scale`
-        if (
-            isinstance(self.controlnet, ControlNetModel)
-            or is_compiled
-            and isinstance(self.controlnet._orig_mod, ControlNetModel)
-        ):
-            if not isinstance(controlnet_conditioning_scale, float):
-                raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
-        elif (
-            isinstance(self.controlnet, MultiControlNetModel)
-            or is_compiled
-            and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
-        ):
-            if isinstance(controlnet_conditioning_scale, list):
-                if any(isinstance(i, list) for i in controlnet_conditioning_scale):
-                    raise ValueError("A single batch of multiple conditionings are supported at the moment.")
-            elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
-                self.controlnet.nets
-            ):
-                raise ValueError(
-                    "For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
-                    " the same length as the number of controlnets"
-                )
-        else:
-            assert False
-
-        if len(control_guidance_start) != len(control_guidance_end):
-            raise ValueError(
-                f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list."
-            )
-
-        if isinstance(self.controlnet, MultiControlNetModel):
-            if len(control_guidance_start) != len(self.controlnet.nets):
-                raise ValueError(
-                    f"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}."
-                )
-
-        for start, end in zip(control_guidance_start, control_guidance_end):
-            if start >= end:
-                raise ValueError(
-                    f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
-                )
-            if start < 0.0:
-                raise ValueError(f"control guidance start: {start} can't be smaller than 0.")
-            if end > 1.0:
-                raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
-
-    # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image
-    def check_image(self, image, prompt, prompt_embeds):
-        image_is_pil = isinstance(image, PIL.Image.Image)
-        image_is_tensor = isinstance(image, torch.Tensor)
-        image_is_np = isinstance(image, np.ndarray)
-        image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
-        image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
-        image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)
-
-        if (
-            not image_is_pil
-            and not image_is_tensor
-            and not image_is_np
-            and not image_is_pil_list
-            and not image_is_tensor_list
-            and not image_is_np_list
-        ):
-            raise TypeError(
-                f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}"
-            )
-
-        if image_is_pil:
-            image_batch_size = 1
-        else:
-            image_batch_size = len(image)
-
-        if prompt is not None and isinstance(prompt, str):
-            prompt_batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            prompt_batch_size = len(prompt)
-        elif prompt_embeds is not None:
-            prompt_batch_size = prompt_embeds.shape[0]
-
-        if image_batch_size != 1 and image_batch_size != prompt_batch_size:
-            raise ValueError(
-                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
-            )
-
-    # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image
-    def prepare_control_image(
-        self,
-        image,
-        width,
-        height,
-        batch_size,
-        num_images_per_prompt,
-        device,
-        dtype,
-        do_classifier_free_guidance=False,
-        guess_mode=False,
-    ):
-        image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
-        image_batch_size = image.shape[0]
-
-        if image_batch_size == 1:
-            repeat_by = batch_size
-        else:
-            # image batch size is the same as prompt batch size
-            repeat_by = num_images_per_prompt
-
-        image = image.repeat_interleave(repeat_by, dim=0)
-
-        image = image.to(device=device, dtype=dtype)
-
-        if do_classifier_free_guidance and not guess_mode:
-            image = torch.cat([image] * 2)
-
-        return image
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_latents
-    def prepare_latents(
-        self,
-        batch_size,
-        num_channels_latents,
-        height,
-        width,
-        dtype,
-        device,
-        generator,
-        latents=None,
-        image=None,
-        timestep=None,
-        is_strength_max=True,
-        return_noise=False,
-        return_image_latents=False,
-    ):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if (image is None or timestep is None) and not is_strength_max:
-            raise ValueError(
-                "Since strength < 1. initial latents are to be initialised as a combination of Image + Noise."
-                "However, either the image or the noise timestep has not been provided."
-            )
-
-        if return_image_latents or (latents is None and not is_strength_max):
-            image = image.to(device=device, dtype=dtype)
-            image_latents = self._encode_vae_image(image=image, generator=generator)
-
-        if latents is None:
-            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-            # if strength is 1. then initialise the latents to noise, else initial to image + noise
-            latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
-            # if pure noise then scale the initial latents by the  Scheduler's init sigma
-            latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
-        else:
-            noise = latents.to(device)
-            latents = noise * self.scheduler.init_noise_sigma
-
-        outputs = (latents,)
-
-        if return_noise:
-            outputs += (noise,)
-
-        if return_image_latents:
-            outputs += (image_latents,)
-
-        return outputs
-
-    def _default_height_width(self, height, width, image):
-        # NOTE: It is possible that a list of images have different
-        # dimensions for each image, so just checking the first image
-        # is not _exactly_ correct, but it is simple.
-        while isinstance(image, list):
-            image = image[0]
-
-        if height is None:
-            if isinstance(image, PIL.Image.Image):
-                height = image.height
-            elif isinstance(image, torch.Tensor):
-                height = image.shape[2]
-
-            height = (height // 8) * 8  # round down to nearest multiple of 8
-
-        if width is None:
-            if isinstance(image, PIL.Image.Image):
-                width = image.width
-            elif isinstance(image, torch.Tensor):
-                width = image.shape[3]
-
-            width = (width // 8) * 8  # round down to nearest multiple of 8
-
-        return height, width
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_mask_latents
-    def prepare_mask_latents(
-        self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
-    ):
-        # resize the mask to latents shape as we concatenate the mask to the latents
-        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
-        # and half precision
-        mask = torch.nn.functional.interpolate(
-            mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
-        )
-        mask = mask.to(device=device, dtype=dtype)
-
-        masked_image = masked_image.to(device=device, dtype=dtype)
-        masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
-
-        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
-        if mask.shape[0] < batch_size:
-            if not batch_size % mask.shape[0] == 0:
-                raise ValueError(
-                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
-                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
-                    " of masks that you pass is divisible by the total requested batch size."
-                )
-            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
-        if masked_image_latents.shape[0] < batch_size:
-            if not batch_size % masked_image_latents.shape[0] == 0:
-                raise ValueError(
-                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
-                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
-                    " Make sure the number of images that you pass is divisible by the total requested batch size."
-                )
-            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
-
-        mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
-        masked_image_latents = (
-            torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
-        )
-
-        # aligning device to prevent device errors when concating it with the latent model input
-        masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
-        return mask, masked_image_latents
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline._encode_vae_image
-    def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
-        if isinstance(generator, list):
-            image_latents = [
-                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
-                for i in range(image.shape[0])
-            ]
-            image_latents = torch.cat(image_latents, dim=0)
-        else:
-            image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
-
-        image_latents = self.vae.config.scaling_factor * image_latents
-
-        return image_latents
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        image: Union[torch.Tensor, PIL.Image.Image] = None,
-        mask_image: Union[torch.Tensor, PIL.Image.Image] = None,
-        control_image: Union[
-            torch.FloatTensor,
-            PIL.Image.Image,
-            np.ndarray,
-            List[torch.FloatTensor],
-            List[PIL.Image.Image],
-            List[np.ndarray],
-        ] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        strength: float = 1.0,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        controlnet_conditioning_scale: Union[float, List[float]] = 0.5,
-        guess_mode: bool = False,
-        control_guidance_start: Union[float, List[float]] = 0.0,
-        control_guidance_end: Union[float, List[float]] = 1.0,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`,
-                    `List[List[torch.FloatTensor]]`, or `List[List[PIL.Image.Image]]`):
-                The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
-                the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can
-                also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If
-                height and/or width are passed, `image` is resized according to them. If multiple ControlNets are
-                specified in init, images must be passed as a list such that each element of the list can be correctly
-                batched for input to a single controlnet.
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image.
-            strength (`float`, *optional*, defaults to 1.):
-                Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be
-                between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the
-                `strength`. The number of denoising steps depends on the amount of noise initially added. When
-                `strength` is 1, added noise will be maximum and the denoising process will run for the full number of
-                iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked
-                portion of the reference `image`.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
-            controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 0.5):
-                The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
-                to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
-                corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting
-                than for [`~StableDiffusionControlNetPipeline.__call__`].
-            guess_mode (`bool`, *optional*, defaults to `False`):
-                In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
-                you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
-            control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
-                The percentage of total steps at which the controlnet starts applying.
-            control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
-                The percentage of total steps at which the controlnet stops applying.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
-
-        # 0. Default height and width to unet
-        height, width = self._default_height_width(height, width, image)
-
-        # align format for control guidance
-        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
-            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
-        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
-            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
-        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
-            mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
-            control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [
-                control_guidance_end
-            ]
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt,
-            control_image,
-            height,
-            width,
-            callback_steps,
-            negative_prompt,
-            prompt_embeds,
-            negative_prompt_embeds,
-            controlnet_conditioning_scale,
-            control_guidance_start,
-            control_guidance_end,
-        )
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
-            controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
-
-        global_pool_conditions = (
-            controlnet.config.global_pool_conditions
-            if isinstance(controlnet, ControlNetModel)
-            else controlnet.nets[0].config.global_pool_conditions
-        )
-        guess_mode = guess_mode or global_pool_conditions
-
-        # 3. Encode input prompt
-        text_encoder_lora_scale = (
-            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
-        )
-        prompt_embeds = self._encode_prompt(
-            prompt,
-            device,
-            num_images_per_prompt,
-            do_classifier_free_guidance,
-            negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            lora_scale=text_encoder_lora_scale,
-        )
-
-        # 4. Prepare image
-        if isinstance(controlnet, ControlNetModel):
-            control_image = self.prepare_control_image(
-                image=control_image,
-                width=width,
-                height=height,
-                batch_size=batch_size * num_images_per_prompt,
-                num_images_per_prompt=num_images_per_prompt,
-                device=device,
-                dtype=controlnet.dtype,
-                do_classifier_free_guidance=do_classifier_free_guidance,
-                guess_mode=guess_mode,
-            )
-        elif isinstance(controlnet, MultiControlNetModel):
-            control_images = []
-
-            for control_image_ in control_image:
-                control_image_ = self.prepare_control_image(
-                    image=control_image_,
-                    width=width,
-                    height=height,
-                    batch_size=batch_size * num_images_per_prompt,
-                    num_images_per_prompt=num_images_per_prompt,
-                    device=device,
-                    dtype=controlnet.dtype,
-                    do_classifier_free_guidance=do_classifier_free_guidance,
-                    guess_mode=guess_mode,
-                )
-
-                control_images.append(control_image_)
-
-            control_image = control_images
-        else:
-            assert False
-
-        # 4. Preprocess mask and image - resizes image and mask w.r.t height and width
-        mask, masked_image, init_image = prepare_mask_and_masked_image(
-            image, mask_image, height, width, return_image=True
-        )
-
-        # 5. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps, num_inference_steps = self.get_timesteps(
-            num_inference_steps=num_inference_steps, strength=strength, device=device
-        )
-        # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
-        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-        # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
-        is_strength_max = strength == 1.0
-
-        # 6. Prepare latent variables
-        num_channels_latents = self.vae.config.latent_channels
-        num_channels_unet = self.unet.config.in_channels
-        return_image_latents = num_channels_unet == 4
-        latents_outputs = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-            image=init_image,
-            timestep=latent_timestep,
-            is_strength_max=is_strength_max,
-            return_noise=True,
-            return_image_latents=return_image_latents,
-        )
-
-        if return_image_latents:
-            latents, noise, image_latents = latents_outputs
-        else:
-            latents, noise = latents_outputs
-
-        # 7. Prepare mask latent variables
-        mask, masked_image_latents = self.prepare_mask_latents(
-            mask,
-            masked_image,
-            batch_size * num_images_per_prompt,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            do_classifier_free_guidance,
-        )
-
-        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 7.1 Create tensor stating which controlnets to keep
-        controlnet_keep = []
-        for i in range(len(timesteps)):
-            keeps = [
-                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
-                for s, e in zip(control_guidance_start, control_guidance_end)
-            ]
-            controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)
-
-        # 8. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                # controlnet(s) inference
-                if guess_mode and do_classifier_free_guidance:
-                    # Infer ControlNet only for the conditional batch.
-                    control_model_input = latents
-                    control_model_input = self.scheduler.scale_model_input(control_model_input, t)
-                    controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
-                else:
-                    control_model_input = latent_model_input
-                    controlnet_prompt_embeds = prompt_embeds
-
-                if isinstance(controlnet_keep[i], list):
-                    cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
-                else:
-                    cond_scale = controlnet_conditioning_scale * controlnet_keep[i]
-
-                down_block_res_samples, mid_block_res_sample = self.controlnet(
-                    control_model_input,
-                    t,
-                    encoder_hidden_states=controlnet_prompt_embeds,
-                    controlnet_cond=control_image,
-                    conditioning_scale=cond_scale,
-                    guess_mode=guess_mode,
-                    return_dict=False,
-                )
-
-                if guess_mode and do_classifier_free_guidance:
-                    # Infered ControlNet only for the conditional batch.
-                    # To apply the output of ControlNet to both the unconditional and conditional batches,
-                    # add 0 to the unconditional batch to keep it unchanged.
-                    down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
-                    mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
-
-                # predict the noise residual
-                if num_channels_unet == 9:
-                    latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
-
-                noise_pred = self.unet(
-                    latent_model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    down_block_additional_residuals=down_block_res_samples,
-                    mid_block_additional_residual=mid_block_res_sample,
-                    return_dict=False,
-                )[0]
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
-
-                if num_channels_unet == 4:
-                    init_latents_proper = image_latents[:1]
-                    init_mask = mask[:1]
-
-                    if i < len(timesteps) - 1:
-                        noise_timestep = timesteps[i + 1]
-                        init_latents_proper = self.scheduler.add_noise(
-                            init_latents_proper, noise, torch.tensor([noise_timestep])
-                        )
-
-                    latents = (1 - init_mask) * init_latents_proper + init_mask * latents
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        # If we do sequential model offloading, let's offload unet and controlnet
-        # manually for max memory savings
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.unet.to("cpu")
-            self.controlnet.to("cpu")
-            torch.cuda.empty_cache()
-
-        if not output_type == "latent":
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
-            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
-        else:
-            image = latents
-            has_nsfw_concept = None
-
-        if has_nsfw_concept is None:
-            do_denormalize = [True] * image.shape[0]
-        else:
-            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
-
-        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
-
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

spaces/Andy1621/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py

@@ -1,5 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    backbone=dict(
-        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, True, True, True)))

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

@@ -1,16 +0,0 @@
-from .bfp import BFP
-from .channel_mapper import ChannelMapper
-from .fpg import FPG
-from .fpn import FPN
-from .fpn_carafe import FPN_CARAFE
-from .hrfpn import HRFPN
-from .nas_fpn import NASFPN
-from .nasfcos_fpn import NASFCOS_FPN
-from .pafpn import PAFPN
-from .rfp import RFP
-from .yolo_neck import YOLOV3Neck
-
-__all__ = [
-    'FPN', 'BFP', 'ChannelMapper', 'HRFPN', 'NASFPN', 'FPN_CARAFE', 'PAFPN',
-    'NASFCOS_FPN', 'RFP', 'YOLOV3Neck', 'FPG'
-]

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

@@ -1,291 +0,0 @@
-# MIT License
-#
-# Copyright (c) 2018 Tom Runia
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to conditions.
-#
-# Author: Tom Runia
-# Date Created: 2018-08-03
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-
-
-def make_colorwheel():
-    '''
-    Generates a color wheel for optical flow visualization as presented in:
-        Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007)
-        URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf
-    According to the C++ source code of Daniel Scharstein
-    According to the Matlab source code of Deqing Sun
-    '''
-
-    RY = 15
-    YG = 6
-    GC = 4
-    CB = 11
-    BM = 13
-    MR = 6
-
-    ncols = RY + YG + GC + CB + BM + MR
-    colorwheel = np.zeros((ncols, 3))
-    col = 0
-
-    # RY
-    colorwheel[0:RY, 0] = 255
-    colorwheel[0:RY, 1] = np.floor(255 * np.arange(0, RY) / RY)
-    col = col + RY
-    # YG
-    colorwheel[col:col + YG, 0] = 255 - np.floor(255 * np.arange(0, YG) / YG)
-    colorwheel[col:col + YG, 1] = 255
-    col = col + YG
-    # GC
-    colorwheel[col:col + GC, 1] = 255
-    colorwheel[col:col + GC, 2] = np.floor(255 * np.arange(0, GC) / GC)
-    col = col + GC
-    # CB
-    colorwheel[col:col + CB, 1] = 255 - np.floor(255 * np.arange(CB) / CB)
-    colorwheel[col:col + CB, 2] = 255
-    col = col + CB
-    # BM
-    colorwheel[col:col + BM, 2] = 255
-    colorwheel[col:col + BM, 0] = np.floor(255 * np.arange(0, BM) / BM)
-    col = col + BM
-    # MR
-    colorwheel[col:col + MR, 2] = 255 - np.floor(255 * np.arange(MR) / MR)
-    colorwheel[col:col + MR, 0] = 255
-    return colorwheel
-
-
-def flow_compute_color(u, v, convert_to_bgr=False):
-    '''
-    Applies the flow color wheel to (possibly clipped) flow components u and v.
-    According to the C++ source code of Daniel Scharstein
-    According to the Matlab source code of Deqing Sun
-    :param u: np.ndarray, input horizontal flow
-    :param v: np.ndarray, input vertical flow
-    :param convert_to_bgr: bool, whether to change ordering and output BGR instead of RGB
-    :return:
-    '''
-
-    flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)
-
-    colorwheel = make_colorwheel()  # shape [55x3]
-    ncols = colorwheel.shape[0]
-
-    rad = np.sqrt(np.square(u) + np.square(v))
-    a = np.arctan2(-v, -u) / np.pi
-
-    fk = (a + 1) / 2 * (ncols - 1) + 1
-    k0 = np.floor(fk).astype(np.int32)
-    k1 = k0 + 1
-    k1[k1 == ncols] = 1
-    f = fk - k0
-
-    for i in range(colorwheel.shape[1]):
-        tmp = colorwheel[:, i]
-        col0 = tmp[k0] / 255.0
-        col1 = tmp[k1] / 255.0
-        col = (1 - f) * col0 + f * col1
-
-        idx = (rad <= 1)
-        col[idx] = 1 - rad[idx] * (1 - col[idx])
-        col[~idx] = col[~idx] * 0.75  # out of range?
-
-        # Note the 2-i => BGR instead of RGB
-        ch_idx = 2 - i if convert_to_bgr else i
-        flow_image[:, :, ch_idx] = np.floor(255 * col)
-
-    return flow_image
-
-
-def flow_to_color(flow_uv, clip_flow=None, convert_to_bgr=False):
-    '''
-    Expects a two dimensional flow image of shape [H,W,2]
-    According to the C++ source code of Daniel Scharstein
-    According to the Matlab source code of Deqing Sun
-    :param flow_uv: np.ndarray of shape [H,W,2]
-    :param clip_flow: float, maximum clipping value for flow
-    :return:
-    '''
-
-    assert flow_uv.ndim == 3, 'input flow must have three dimensions'
-    assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]'
-
-    if clip_flow is not None:
-        flow_uv = np.clip(flow_uv, 0, clip_flow)
-
-    u = flow_uv[:, :, 0]
-    v = flow_uv[:, :, 1]
-
-    rad = np.sqrt(np.square(u) + np.square(v))
-    rad_max = np.max(rad)
-
-    epsilon = 1e-5
-    u = u / (rad_max + epsilon)
-    v = v / (rad_max + epsilon)
-
-    return flow_compute_color(u, v, convert_to_bgr)
-
-
-UNKNOWN_FLOW_THRESH = 1e7
-SMALLFLOW = 0.0
-LARGEFLOW = 1e8
-
-
-def make_color_wheel():
-    """
-    Generate color wheel according Middlebury color code
-    :return: Color wheel
-    """
-    RY = 15
-    YG = 6
-    GC = 4
-    CB = 11
-    BM = 13
-    MR = 6
-
-    ncols = RY + YG + GC + CB + BM + MR
-
-    colorwheel = np.zeros([ncols, 3])
-
-    col = 0
-
-    # RY
-    colorwheel[0:RY, 0] = 255
-    colorwheel[0:RY, 1] = np.transpose(np.floor(255 * np.arange(0, RY) / RY))
-    col += RY
-
-    # YG
-    colorwheel[col:col + YG, 0] = 255 - np.transpose(np.floor(255 * np.arange(0, YG) / YG))
-    colorwheel[col:col + YG, 1] = 255
-    col += YG
-
-    # GC
-    colorwheel[col:col + GC, 1] = 255
-    colorwheel[col:col + GC, 2] = np.transpose(np.floor(255 * np.arange(0, GC) / GC))
-    col += GC
-
-    # CB
-    colorwheel[col:col + CB, 1] = 255 - np.transpose(np.floor(255 * np.arange(0, CB) / CB))
-    colorwheel[col:col + CB, 2] = 255
-    col += CB
-
-    # BM
-    colorwheel[col:col + BM, 2] = 255
-    colorwheel[col:col + BM, 0] = np.transpose(np.floor(255 * np.arange(0, BM) / BM))
-    col += + BM
-
-    # MR
-    colorwheel[col:col + MR, 2] = 255 - np.transpose(np.floor(255 * np.arange(0, MR) / MR))
-    colorwheel[col:col + MR, 0] = 255
-
-    return colorwheel
-
-
-def compute_color(u, v):
-    """
-    compute optical flow color map
-    :param u: optical flow horizontal map
-    :param v: optical flow vertical map
-    :return: optical flow in color code
-    """
-    [h, w] = u.shape
-    img = np.zeros([h, w, 3])
-    nanIdx = np.isnan(u) | np.isnan(v)
-    u[nanIdx] = 0
-    v[nanIdx] = 0
-
-    colorwheel = make_color_wheel()
-    ncols = np.size(colorwheel, 0)
-
-    rad = np.sqrt(u ** 2 + v ** 2)
-
-    a = np.arctan2(-v, -u) / np.pi
-
-    fk = (a + 1) / 2 * (ncols - 1) + 1
-
-    k0 = np.floor(fk).astype(int)
-
-    k1 = k0 + 1
-    k1[k1 == ncols + 1] = 1
-    f = fk - k0
-
-    for i in range(0, np.size(colorwheel, 1)):
-        tmp = colorwheel[:, i]
-        col0 = tmp[k0 - 1] / 255
-        col1 = tmp[k1 - 1] / 255
-        col = (1 - f) * col0 + f * col1
-
-        idx = rad <= 1
-        col[idx] = 1 - rad[idx] * (1 - col[idx])
-        notidx = np.logical_not(idx)
-
-        col[notidx] *= 0.75
-        img[:, :, i] = np.uint8(np.floor(255 * col * (1 - nanIdx)))
-
-    return img
-
-
-# from https://github.com/gengshan-y/VCN
-def flow_to_image(flow):
-    """
-    Convert flow into middlebury color code image
-    :param flow: optical flow map
-    :return: optical flow image in middlebury color
-    """
-    u = flow[:, :, 0]
-    v = flow[:, :, 1]
-
-    maxu = -999.
-    maxv = -999.
-    minu = 999.
-    minv = 999.
-
-    idxUnknow = (abs(u) > UNKNOWN_FLOW_THRESH) | (abs(v) > UNKNOWN_FLOW_THRESH)
-    u[idxUnknow] = 0
-    v[idxUnknow] = 0
-
-    maxu = max(maxu, np.max(u))
-    minu = min(minu, np.min(u))
-
-    maxv = max(maxv, np.max(v))
-    minv = min(minv, np.min(v))
-
-    rad = np.sqrt(u ** 2 + v ** 2)
-    maxrad = max(-1, np.max(rad))
-
-    u = u / (maxrad + np.finfo(float).eps)
-    v = v / (maxrad + np.finfo(float).eps)
-
-    img = compute_color(u, v)
-
-    idx = np.repeat(idxUnknow[:, :, np.newaxis], 3, axis=2)
-    img[idx] = 0
-
-    return np.uint8(img)
-
-
-def save_vis_flow_tofile(flow, output_path):
-    vis_flow = flow_to_image(flow)
-    from PIL import Image
-    img = Image.fromarray(vis_flow)
-    img.save(output_path)
-
-
-def flow_tensor_to_image(flow):
-    """Used for tensorboard visualization"""
-    flow = flow.permute(1, 2, 0)  # [H, W, 2]
-    flow = flow.detach().cpu().numpy()
-    flow = flow_to_image(flow)  # [H, W, 3]
-    flow = np.transpose(flow, (2, 0, 1))  # [3, H, W]
-
-    return flow

spaces/ArtGAN/Video-Diffusion-WebUI/app.py

@@ -1,50 +0,0 @@
-import gradio as gr
-
-from video_diffusion.damo.damo_text2_video import DamoText2VideoGenerator
-from video_diffusion.inpaint_zoom.zoom_in_app import StableDiffusionZoomIn
-from video_diffusion.inpaint_zoom.zoom_out_app import StableDiffusionZoomOut
-from video_diffusion.stable_diffusion_video.stable_video_text2video import StableDiffusionText2VideoGenerator
-from video_diffusion.tuneavideo.tuneavideo_text2video import TunaVideoText2VideoGenerator
-from video_diffusion.zero_shot.zero_shot_text2video import ZeroShotText2VideoGenerator
-
-
-def diffusion_app():
-    app = gr.Blocks()
-    with app:
-        gr.HTML(
-        """
-        <h1 style='text-align: center'>
-       Video Diffusion WebUI
-        </h1>
-        """
-    )
-        gr.HTML(
-            """
-            <h3 style='text-align: center'>
-            Follow me for more! 
-            <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>
-            </h3>
-            """
-    )
-        with gr.Row():
-            with gr.Column():
-                with gr.Tab("Stable Diffusion Video"):
-                    StableDiffusionText2VideoGenerator.app()
-                with gr.Tab("Tune-a-Video"):
-                    TunaVideoText2VideoGenerator.app()
-                with gr.Tab("Stable Infinite Zoom"):
-                    with gr.Tab("Zoom In"):
-                        StableDiffusionZoomIn.app()
-                    with gr.Tab("Zoom Out"):
-                        StableDiffusionZoomOut.app()
-                with gr.Tab("Damo Text2Video"):
-                    DamoText2VideoGenerator.app()
-                with gr.Tab("Zero Shot Text2Video"):
-                    ZeroShotText2VideoGenerator.app()
-
-    app.queue(concurrency_count=1)
-    app.launch(debug=True, enable_queue=True)
-
-
-if __name__ == "__main__":
-    diffusion_app()

spaces/ArtyomKhyan/Detection/utils/activations.py

@@ -1,63 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.nn as nn
-
-
-# Swish -------non_max_suppression-----------------------------------------------------------------
-class SwishImplementation(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x):
-        ctx.save_for_backward(x)
-        return x * torch.sigmoid(x)
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        x = ctx.saved_tensors[0]
-        sx = torch.sigmoid(x)
-        return grad_output * (sx * (1 + x * (1 - sx)))
-
-
-class MemoryEfficientSwish(nn.Module):
-    @staticmethod
-    def forward(x):
-        return SwishImplementation.apply(x)
-
-
-class HardSwish(nn.Module):  # https://arxiv.org/pdf/1905.02244.pdf
-    @staticmethod
-    def forward(x):
-        return x * F.hardtanh(x + 3, 0., 6., True) / 6.
-
-
-class Swish(nn.Module):
-    @staticmethod
-    def forward(x):
-        return x * torch.sigmoid(x)
-
-
-# Mish ------------------------------------------------------------------------
-class MishImplementation(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x):
-        ctx.save_for_backward(x)
-        return x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + exp(x)))
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        x = ctx.saved_tensors[0]
-        sx = torch.sigmoid(x)
-        fx = F.softplus(x).tanh()
-        return grad_output * (fx + x * sx * (1 - fx * fx))
-
-
-class MemoryEfficientMish(nn.Module):
-    @staticmethod
-    def forward(x):
-        return MishImplementation.apply(x)
-
-
-class Mish(nn.Module):  # https://github.com/digantamisra98/Mish
-    @staticmethod
-    def forward(x):
-        return x * F.softplus(x).tanh()

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/config/lazy.py

@@ -1,399 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import ast
-import builtins
-import importlib
-import inspect
-import logging
-import os
-import uuid
-from collections import abc
-from contextlib import contextmanager
-from copy import deepcopy
-from dataclasses import is_dataclass
-from typing import List, Tuple, Union
-import cloudpickle
-import yaml
-from omegaconf import DictConfig, ListConfig, OmegaConf
-
-from detectron2.utils.file_io import PathManager
-from detectron2.utils.registry import _convert_target_to_string
-
-__all__ = ["LazyCall", "LazyConfig"]
-
-
-class LazyCall:
-    """
-    Wrap a callable so that when it's called, the call will not be executed,
-    but returns a dict that describes the call.
-
-    LazyCall object has to be called with only keyword arguments. Positional
-    arguments are not yet supported.
-
-    Examples:
-    ::
-        from detectron2.config import instantiate, LazyCall
-
-        layer_cfg = LazyCall(nn.Conv2d)(in_channels=32, out_channels=32)
-        layer_cfg.out_channels = 64   # can edit it afterwards
-        layer = instantiate(layer_cfg)
-    """
-
-    def __init__(self, target):
-        if not (callable(target) or isinstance(target, (str, abc.Mapping))):
-            raise TypeError(
-                f"target of LazyCall must be a callable or defines a callable! Got {target}"
-            )
-        self._target = target
-
-    def __call__(self, **kwargs):
-        if is_dataclass(self._target):
-            # omegaconf object cannot hold dataclass type
-            # https://github.com/omry/omegaconf/issues/784
-            target = _convert_target_to_string(self._target)
-        else:
-            target = self._target
-        kwargs["_target_"] = target
-
-        return DictConfig(content=kwargs, flags={"allow_objects": True})
-
-
-def _visit_dict_config(cfg, func):
-    """
-    Apply func recursively to all DictConfig in cfg.
-    """
-    if isinstance(cfg, DictConfig):
-        func(cfg)
-        for v in cfg.values():
-            _visit_dict_config(v, func)
-    elif isinstance(cfg, ListConfig):
-        for v in cfg:
-            _visit_dict_config(v, func)
-
-
-def _validate_py_syntax(filename):
-    # see also https://github.com/open-mmlab/mmcv/blob/master/mmcv/utils/config.py
-    with PathManager.open(filename, "r") as f:
-        content = f.read()
-    try:
-        ast.parse(content)
-    except SyntaxError as e:
-        raise SyntaxError(f"Config file {filename} has syntax error!") from e
-
-
-def _cast_to_config(obj):
-    # if given a dict, return DictConfig instead
-    if isinstance(obj, dict):
-        return DictConfig(obj, flags={"allow_objects": True})
-    return obj
-
-
-_CFG_PACKAGE_NAME = "detectron2._cfg_loader"
-"""
-A namespace to put all imported config into.
-"""
-
-
-def _random_package_name(filename):
-    # generate a random package name when loading config files
-    return _CFG_PACKAGE_NAME + str(uuid.uuid4())[:4] + "." + os.path.basename(filename)
-
-
-@contextmanager
-def _patch_import():
-    """
-    Enhance relative import statements in config files, so that they:
-    1. locate files purely based on relative location, regardless of packages.
-       e.g. you can import file without having __init__
-    2. do not cache modules globally; modifications of module states has no side effect
-    3. support other storage system through PathManager
-    4. imported dict are turned into omegaconf.DictConfig automatically
-    """
-    old_import = builtins.__import__
-
-    def find_relative_file(original_file, relative_import_path, level):
-        cur_file = os.path.dirname(original_file)
-        for _ in range(level - 1):
-            cur_file = os.path.dirname(cur_file)
-        cur_name = relative_import_path.lstrip(".")
-        for part in cur_name.split("."):
-            cur_file = os.path.join(cur_file, part)
-        # NOTE: directory import is not handled. Because then it's unclear
-        # if such import should produce python module or DictConfig. This can
-        # be discussed further if needed.
-        if not cur_file.endswith(".py"):
-            cur_file += ".py"
-        if not PathManager.isfile(cur_file):
-            raise ImportError(
-                f"Cannot import name {relative_import_path} from "
-                f"{original_file}: {cur_file} has to exist."
-            )
-        return cur_file
-
-    def new_import(name, globals=None, locals=None, fromlist=(), level=0):
-        if (
-            # Only deal with relative imports inside config files
-            level != 0
-            and globals is not None
-            and (globals.get("__package__", "") or "").startswith(_CFG_PACKAGE_NAME)
-        ):
-            cur_file = find_relative_file(globals["__file__"], name, level)
-            _validate_py_syntax(cur_file)
-            spec = importlib.machinery.ModuleSpec(
-                _random_package_name(cur_file), None, origin=cur_file
-            )
-            module = importlib.util.module_from_spec(spec)
-            module.__file__ = cur_file
-            with PathManager.open(cur_file) as f:
-                content = f.read()
-            exec(compile(content, cur_file, "exec"), module.__dict__)
-            for name in fromlist:  # turn imported dict into DictConfig automatically
-                val = _cast_to_config(module.__dict__[name])
-                module.__dict__[name] = val
-            return module
-        return old_import(name, globals, locals, fromlist=fromlist, level=level)
-
-    builtins.__import__ = new_import
-    yield new_import
-    builtins.__import__ = old_import
-
-
-class LazyConfig:
-    """
-    Provide methods to save, load, and overrides an omegaconf config object
-    which may contain definition of lazily-constructed objects.
-    """
-
-    @staticmethod
-    def load_rel(filename: str, keys: Union[None, str, Tuple[str, ...]] = None):
-        """
-        Similar to :meth:`load()`, but load path relative to the caller's
-        source file.
-
-        This has the same functionality as a relative import, except that this method
-        accepts filename as a string, so more characters are allowed in the filename.
-        """
-        caller_frame = inspect.stack()[1]
-        caller_fname = caller_frame[0].f_code.co_filename
-        assert caller_fname != "<string>", "load_rel Unable to find caller"
-        caller_dir = os.path.dirname(caller_fname)
-        filename = os.path.join(caller_dir, filename)
-        return LazyConfig.load(filename, keys)
-
-    @staticmethod
-    def load(filename: str, keys: Union[None, str, Tuple[str, ...]] = None):
-        """
-        Load a config file.
-
-        Args:
-            filename: absolute path or relative path w.r.t. the current working directory
-            keys: keys to load and return. If not given, return all keys
-                (whose values are config objects) in a dict.
-        """
-        has_keys = keys is not None
-        filename = filename.replace("/./", "/")  # redundant
-        if os.path.splitext(filename)[1] not in [".py", ".yaml", ".yml"]:
-            raise ValueError(f"Config file {filename} has to be a python or yaml file.")
-        if filename.endswith(".py"):
-            _validate_py_syntax(filename)
-
-            with _patch_import():
-                # Record the filename
-                module_namespace = {
-                    "__file__": filename,
-                    "__package__": _random_package_name(filename),
-                }
-                with PathManager.open(filename) as f:
-                    content = f.read()
-                # Compile first with filename to:
-                # 1. make filename appears in stacktrace
-                # 2. make load_rel able to find its parent's (possibly remote) location
-                exec(compile(content, filename, "exec"), module_namespace)
-
-            ret = module_namespace
-        else:
-            with PathManager.open(filename) as f:
-                obj = yaml.unsafe_load(f)
-            ret = OmegaConf.create(obj, flags={"allow_objects": True})
-
-        if has_keys:
-            if isinstance(keys, str):
-                return _cast_to_config(ret[keys])
-            else:
-                return tuple(_cast_to_config(ret[a]) for a in keys)
-        else:
-            if filename.endswith(".py"):
-                # when not specified, only load those that are config objects
-                ret = DictConfig(
-                    {
-                        name: _cast_to_config(value)
-                        for name, value in ret.items()
-                        if isinstance(value, (DictConfig, ListConfig, dict))
-                        and not name.startswith("_")
-                    },
-                    flags={"allow_objects": True},
-                )
-            return ret
-
-    @staticmethod
-    def save(cfg, filename: str):
-        """
-        Save a config object to a yaml file.
-        Note that when the config dictionary contains complex objects (e.g. lambda),
-        it can't be saved to yaml. In that case we will print an error and
-        attempt to save to a pkl file instead.
-
-        Args:
-            cfg: an omegaconf config object
-            filename: yaml file name to save the config file
-        """
-        logger = logging.getLogger(__name__)
-        try:
-            cfg = deepcopy(cfg)
-        except Exception:
-            pass
-        else:
-            # if it's deep-copyable, then...
-            def _replace_type_by_name(x):
-                if "_target_" in x and callable(x._target_):
-                    try:
-                        x._target_ = _convert_target_to_string(x._target_)
-                    except AttributeError:
-                        pass
-
-            # not necessary, but makes yaml looks nicer
-            _visit_dict_config(cfg, _replace_type_by_name)
-
-        save_pkl = False
-        try:
-            dict = OmegaConf.to_container(cfg, resolve=False)
-            dumped = yaml.dump(dict, default_flow_style=None, allow_unicode=True, width=9999)
-            with PathManager.open(filename, "w") as f:
-                f.write(dumped)
-
-            try:
-                _ = yaml.unsafe_load(dumped)  # test that it is loadable
-            except Exception:
-                logger.warning(
-                    "The config contains objects that cannot serialize to a valid yaml. "
-                    f"{filename} is human-readable but cannot be loaded."
-                )
-                save_pkl = True
-        except Exception:
-            logger.exception("Unable to serialize the config to yaml. Error:")
-            save_pkl = True
-
-        if save_pkl:
-            new_filename = filename + ".pkl"
-            try:
-                # retry by pickle
-                with PathManager.open(new_filename, "wb") as f:
-                    cloudpickle.dump(cfg, f)
-                logger.warning(f"Config is saved using cloudpickle at {new_filename}.")
-            except Exception:
-                pass
-
-    @staticmethod
-    def apply_overrides(cfg, overrides: List[str]):
-        """
-        In-place override contents of cfg.
-
-        Args:
-            cfg: an omegaconf config object
-            overrides: list of strings in the format of "a=b" to override configs.
-                See https://hydra.cc/docs/next/advanced/override_grammar/basic/
-                for syntax.
-
-        Returns:
-            the cfg object
-        """
-
-        def safe_update(cfg, key, value):
-            parts = key.split(".")
-            for idx in range(1, len(parts)):
-                prefix = ".".join(parts[:idx])
-                v = OmegaConf.select(cfg, prefix, default=None)
-                if v is None:
-                    break
-                if not OmegaConf.is_config(v):
-                    raise KeyError(
-                        f"Trying to update key {key}, but {prefix} "
-                        f"is not a config, but has type {type(v)}."
-                    )
-            OmegaConf.update(cfg, key, value, merge=True)
-
-        from hydra.core.override_parser.overrides_parser import OverridesParser
-
-        parser = OverridesParser.create()
-        overrides = parser.parse_overrides(overrides)
-        for o in overrides:
-            key = o.key_or_group
-            value = o.value()
-            if o.is_delete():
-                # TODO support this
-                raise NotImplementedError("deletion is not yet a supported override")
-            safe_update(cfg, key, value)
-        return cfg
-
-    @staticmethod
-    def to_py(cfg, prefix: str = "cfg."):
-        """
-        Try to convert a config object into Python-like psuedo code.
-
-        Note that perfect conversion is not always possible. So the returned
-        results are mainly meant to be human-readable, and not meant to be executed.
-
-        Args:
-            cfg: an omegaconf config object
-            prefix: root name for the resulting code (default: "cfg.")
-
-
-        Returns:
-            str of formatted Python code
-        """
-        import black
-
-        cfg = OmegaConf.to_container(cfg, resolve=True)
-
-        def _to_str(obj, prefix=None, inside_call=False):
-            if prefix is None:
-                prefix = []
-            if isinstance(obj, abc.Mapping) and "_target_" in obj:
-                # Dict representing a function call
-                target = _convert_target_to_string(obj.pop("_target_"))
-                args = []
-                for k, v in sorted(obj.items()):
-                    args.append(f"{k}={_to_str(v, inside_call=True)}")
-                args = ", ".join(args)
-                call = f"{target}({args})"
-                return "".join(prefix) + call
-            elif isinstance(obj, abc.Mapping) and not inside_call:
-                # Dict that is not inside a call is a list of top-level config objects that we
-                # render as one object per line with dot separated prefixes
-                key_list = []
-                for k, v in sorted(obj.items()):
-                    if isinstance(v, abc.Mapping) and "_target_" not in v:
-                        key_list.append(_to_str(v, prefix=prefix + [k + "."]))
-                    else:
-                        key = "".join(prefix) + k
-                        key_list.append(f"{key}={_to_str(v)}")
-                return "\n".join(key_list)
-            elif isinstance(obj, abc.Mapping):
-                # Dict that is inside a call is rendered as a regular dict
-                return (
-                    "{"
-                    + ",".join(
-                        f"{repr(k)}: {_to_str(v, inside_call=inside_call)}"
-                        for k, v in sorted(obj.items())
-                    )
-                    + "}"
-                )
-            elif isinstance(obj, list):
-                return "[" + ",".join(_to_str(x, inside_call=inside_call) for x in obj) + "]"
-            else:
-                return repr(obj)
-
-        py_str = _to_str(cfg, prefix=[prefix])
-        try:
-            return black.format_str(py_str, mode=black.Mode())
-        except black.InvalidInput:
-            return py_str

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/common.py

@@ -1,241 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import itertools
-import logging
-import numpy as np
-import pickle
-import random
-import torch.utils.data as data
-from torch.utils.data.sampler import Sampler
-
-from detectron2.utils.serialize import PicklableWrapper
-
-__all__ = ["MapDataset", "DatasetFromList", "AspectRatioGroupedDataset", "ToIterableDataset"]
-
-
-def _shard_iterator_dataloader_worker(iterable):
-    # Shard the iterable if we're currently inside pytorch dataloader worker.
-    worker_info = data.get_worker_info()
-    if worker_info is None or worker_info.num_workers == 1:
-        # do nothing
-        yield from iterable
-    else:
-        yield from itertools.islice(iterable, worker_info.id, None, worker_info.num_workers)
-
-
-class _MapIterableDataset(data.IterableDataset):
-    """
-    Map a function over elements in an IterableDataset.
-
-    Similar to pytorch's MapIterDataPipe, but support filtering when map_func
-    returns None.
-
-    This class is not public-facing. Will be called by `MapDataset`.
-    """
-
-    def __init__(self, dataset, map_func):
-        self._dataset = dataset
-        self._map_func = PicklableWrapper(map_func)  # wrap so that a lambda will work
-
-    def __len__(self):
-        return len(self._dataset)
-
-    def __iter__(self):
-        for x in map(self._map_func, self._dataset):
-            if x is not None:
-                yield x
-
-
-class MapDataset(data.Dataset):
-    """
-    Map a function over the elements in a dataset.
-    """
-
-    def __init__(self, dataset, map_func):
-        """
-        Args:
-            dataset: a dataset where map function is applied. Can be either
-                map-style or iterable dataset. When given an iterable dataset,
-                the returned object will also be an iterable dataset.
-            map_func: a callable which maps the element in dataset. map_func can
-                return None to skip the data (e.g. in case of errors).
-                How None is handled depends on the style of `dataset`.
-                If `dataset` is map-style, it randomly tries other elements.
-                If `dataset` is iterable, it skips the data and tries the next.
-        """
-        self._dataset = dataset
-        self._map_func = PicklableWrapper(map_func)  # wrap so that a lambda will work
-
-        self._rng = random.Random(42)
-        self._fallback_candidates = set(range(len(dataset)))
-
-    def __new__(cls, dataset, map_func):
-        is_iterable = isinstance(dataset, data.IterableDataset)
-        if is_iterable:
-            return _MapIterableDataset(dataset, map_func)
-        else:
-            return super().__new__(cls)
-
-    def __getnewargs__(self):
-        return self._dataset, self._map_func
-
-    def __len__(self):
-        return len(self._dataset)
-
-    def __getitem__(self, idx):
-        retry_count = 0
-        cur_idx = int(idx)
-
-        while True:
-            data = self._map_func(self._dataset[cur_idx])
-            if data is not None:
-                self._fallback_candidates.add(cur_idx)
-                return data
-
-            # _map_func fails for this idx, use a random new index from the pool
-            retry_count += 1
-            self._fallback_candidates.discard(cur_idx)
-            cur_idx = self._rng.sample(self._fallback_candidates, k=1)[0]
-
-            if retry_count >= 3:
-                logger = logging.getLogger(__name__)
-                logger.warning(
-                    "Failed to apply `_map_func` for idx: {}, retry count: {}".format(
-                        idx, retry_count
-                    )
-                )
-
-
-class DatasetFromList(data.Dataset):
-    """
-    Wrap a list to a torch Dataset. It produces elements of the list as data.
-    """
-
-    def __init__(self, lst: list, copy: bool = True, serialize: bool = True):
-        """
-        Args:
-            lst (list): a list which contains elements to produce.
-            copy (bool): whether to deepcopy the element when producing it,
-                so that the result can be modified in place without affecting the
-                source in the list.
-            serialize (bool): whether to hold memory using serialized objects, when
-                enabled, data loader workers can use shared RAM from master
-                process instead of making a copy.
-        """
-        self._lst = lst
-        self._copy = copy
-        self._serialize = serialize
-
-        def _serialize(data):
-            buffer = pickle.dumps(data, protocol=-1)
-            return np.frombuffer(buffer, dtype=np.uint8)
-
-        if self._serialize:
-            logger = logging.getLogger(__name__)
-            logger.info(
-                "Serializing {} elements to byte tensors and concatenating them all ...".format(
-                    len(self._lst)
-                )
-            )
-            self._lst = [_serialize(x) for x in self._lst]
-            self._addr = np.asarray([len(x) for x in self._lst], dtype=np.int64)
-            self._addr = np.cumsum(self._addr)
-            self._lst = np.concatenate(self._lst)
-            logger.info("Serialized dataset takes {:.2f} MiB".format(len(self._lst) / 1024 ** 2))
-
-    def __len__(self):
-        if self._serialize:
-            return len(self._addr)
-        else:
-            return len(self._lst)
-
-    def __getitem__(self, idx):
-        if self._serialize:
-            start_addr = 0 if idx == 0 else self._addr[idx - 1].item()
-            end_addr = self._addr[idx].item()
-            bytes = memoryview(self._lst[start_addr:end_addr])
-            return pickle.loads(bytes)
-        elif self._copy:
-            return copy.deepcopy(self._lst[idx])
-        else:
-            return self._lst[idx]
-
-
-class ToIterableDataset(data.IterableDataset):
-    """
-    Convert an old indices-based (also called map-style) dataset
-    to an iterable-style dataset.
-    """
-
-    def __init__(self, dataset: data.Dataset, sampler: Sampler, shard_sampler: bool = True):
-        """
-        Args:
-            dataset: an old-style dataset with ``__getitem__``
-            sampler: a cheap iterable that produces indices to be applied on ``dataset``.
-            shard_sampler: whether to shard the sampler based on the current pytorch data loader
-                worker id. When an IterableDataset is forked by pytorch's DataLoader into multiple
-                workers, it is responsible for sharding its data based on worker id so that workers
-                don't produce identical data.
-
-                Most samplers (like our TrainingSampler) do not shard based on dataloader worker id
-                and this argument should be set to True. But certain samplers may be already
-                sharded, in that case this argument should be set to False.
-        """
-        assert not isinstance(dataset, data.IterableDataset), dataset
-        assert isinstance(sampler, Sampler), sampler
-        self.dataset = dataset
-        self.sampler = sampler
-        self.shard_sampler = shard_sampler
-
-    def __iter__(self):
-        if not self.shard_sampler:
-            sampler = self.sampler
-        else:
-            # With map-style dataset, `DataLoader(dataset, sampler)` runs the
-            # sampler in main process only. But `DataLoader(ToIterableDataset(dataset, sampler))`
-            # will run sampler in every of the N worker. So we should only keep 1/N of the ids on
-            # each worker. The assumption is that sampler is cheap to iterate so it's fine to
-            # discard ids in workers.
-            sampler = _shard_iterator_dataloader_worker(self.sampler)
-        for idx in sampler:
-            yield self.dataset[idx]
-
-    def __len__(self):
-        return len(self.sampler)
-
-
-class AspectRatioGroupedDataset(data.IterableDataset):
-    """
-    Batch data that have similar aspect ratio together.
-    In this implementation, images whose aspect ratio < (or >) 1 will
-    be batched together.
-    This improves training speed because the images then need less padding
-    to form a batch.
-
-    It assumes the underlying dataset produces dicts with "width" and "height" keys.
-    It will then produce a list of original dicts with length = batch_size,
-    all with similar aspect ratios.
-    """
-
-    def __init__(self, dataset, batch_size):
-        """
-        Args:
-            dataset: an iterable. Each element must be a dict with keys
-                "width" and "height", which will be used to batch data.
-            batch_size (int):
-        """
-        self.dataset = dataset
-        self.batch_size = batch_size
-        self._buckets = [[] for _ in range(2)]
-        # Hard-coded two aspect ratio groups: w > h and w < h.
-        # Can add support for more aspect ratio groups, but doesn't seem useful
-
-    def __iter__(self):
-        for d in self.dataset:
-            w, h = d["width"], d["height"]
-            bucket_id = 0 if w > h else 1
-            bucket = self._buckets[bucket_id]
-            bucket.append(d)
-            if len(bucket) == self.batch_size:
-                yield bucket[:]
-                del bucket[:]

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/fpn.py

@@ -1,255 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import math
-import fvcore.nn.weight_init as weight_init
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from detectron2.layers import Conv2d, ShapeSpec, get_norm
-
-from .backbone import Backbone
-from .build import BACKBONE_REGISTRY
-from .resnet import build_resnet_backbone
-
-__all__ = ["build_resnet_fpn_backbone", "build_retinanet_resnet_fpn_backbone", "FPN"]
-
-
-class FPN(Backbone):
-    """
-    This module implements :paper:`FPN`.
-    It creates pyramid features built on top of some input feature maps.
-    """
-
-    _fuse_type: torch.jit.Final[str]
-
-    def __init__(
-        self, bottom_up, in_features, out_channels, norm="", top_block=None, fuse_type="sum"
-    ):
-        """
-        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.
-            top_block (nn.Module or None): if provided, an extra operation will
-                be performed on the output of the last (smallest resolution)
-                FPN output, and the result will extend the result list. The top_block
-                further downsamples the feature map. It must have an attribute
-                "num_levels", meaning the number of extra FPN levels added by
-                this block, and "in_feature", which is a string representing
-                its input feature (e.g., p5).
-            fuse_type (str): types for fusing the top down features and the lateral
-                ones. It can be "sum" (default), which sums up element-wise; or "avg",
-                which takes the element-wise mean of the two.
-        """
-        super(FPN, self).__init__()
-        assert isinstance(bottom_up, Backbone)
-        assert in_features, in_features
-
-        # Feature map strides and channels from the bottom up network (e.g. ResNet)
-        input_shapes = bottom_up.output_shape()
-        strides = [input_shapes[f].stride for f in in_features]
-        in_channels_per_feature = [input_shapes[f].channels for f in in_features]
-
-        _assert_strides_are_log2_contiguous(strides)
-        lateral_convs = []
-        output_convs = []
-
-        use_bias = norm == ""
-        for idx, in_channels in enumerate(in_channels_per_feature):
-            lateral_norm = get_norm(norm, out_channels)
-            output_norm = get_norm(norm, out_channels)
-
-            lateral_conv = Conv2d(
-                in_channels, out_channels, kernel_size=1, bias=use_bias, norm=lateral_norm
-            )
-            output_conv = Conv2d(
-                out_channels,
-                out_channels,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-                bias=use_bias,
-                norm=output_norm,
-            )
-            weight_init.c2_xavier_fill(lateral_conv)
-            weight_init.c2_xavier_fill(output_conv)
-            stage = int(math.log2(strides[idx]))
-            self.add_module("fpn_lateral{}".format(stage), lateral_conv)
-            self.add_module("fpn_output{}".format(stage), output_conv)
-
-            lateral_convs.append(lateral_conv)
-            output_convs.append(output_conv)
-        # Place convs into top-down order (from low to high resolution)
-        # to make the top-down computation in forward clearer.
-        self.lateral_convs = lateral_convs[::-1]
-        self.output_convs = output_convs[::-1]
-        self.top_block = top_block
-        self.in_features = tuple(in_features)
-        self.bottom_up = bottom_up
-        # Return feature names are "p<stage>", like ["p2", "p3", ..., "p6"]
-        self._out_feature_strides = {"p{}".format(int(math.log2(s))): s for s in strides}
-        # top block output feature maps.
-        if self.top_block is not None:
-            for s in range(stage, stage + self.top_block.num_levels):
-                self._out_feature_strides["p{}".format(s + 1)] = 2 ** (s + 1)
-
-        self._out_features = list(self._out_feature_strides.keys())
-        self._out_feature_channels = {k: out_channels for k in self._out_features}
-        self._size_divisibility = strides[-1]
-        assert fuse_type in {"avg", "sum"}
-        self._fuse_type = fuse_type
-
-    @property
-    def size_divisibility(self):
-        return self._size_divisibility
-
-    def forward(self, x):
-        """
-        Args:
-            input (dict[str->Tensor]): mapping feature map name (e.g., "res5") 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.,
-                ["p2", "p3", ..., "p6"].
-        """
-        bottom_up_features = self.bottom_up(x)
-        results = []
-        prev_features = self.lateral_convs[0](bottom_up_features[self.in_features[-1]])
-        results.append(self.output_convs[0](prev_features))
-
-        # Reverse feature maps into top-down order (from low to high resolution)
-        for idx, (lateral_conv, output_conv) in enumerate(
-            zip(self.lateral_convs, self.output_convs)
-        ):
-            # Slicing of ModuleList is not supported https://github.com/pytorch/pytorch/issues/47336
-            # Therefore we loop over all modules but skip the first one
-            if idx > 0:
-                features = self.in_features[-idx - 1]
-                features = bottom_up_features[features]
-                top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
-                lateral_features = lateral_conv(features)
-                prev_features = lateral_features + top_down_features
-                if self._fuse_type == "avg":
-                    prev_features /= 2
-                results.insert(0, output_conv(prev_features))
-
-        if self.top_block is not None:
-            if self.top_block.in_feature in bottom_up_features:
-                top_block_in_feature = bottom_up_features[self.top_block.in_feature]
-            else:
-                top_block_in_feature = results[self._out_features.index(self.top_block.in_feature)]
-            results.extend(self.top_block(top_block_in_feature))
-        assert len(self._out_features) == len(results)
-        return {f: res for f, res in zip(self._out_features, results)}
-
-    def output_shape(self):
-        return {
-            name: ShapeSpec(
-                channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
-            )
-            for name in self._out_features
-        }
-
-
-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]
-        )
-
-
-class LastLevelMaxPool(nn.Module):
-    """
-    This module is used in the original FPN to generate a downsampled
-    P6 feature from P5.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.num_levels = 1
-        self.in_feature = "p5"
-
-    def forward(self, x):
-        return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
-
-
-class LastLevelP6P7(nn.Module):
-    """
-    This module is used in RetinaNet to generate extra layers, P6 and P7 from
-    C5 feature.
-    """
-
-    def __init__(self, in_channels, out_channels, in_feature="res5"):
-        super().__init__()
-        self.num_levels = 2
-        self.in_feature = in_feature
-        self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1)
-        self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1)
-        for module in [self.p6, self.p7]:
-            weight_init.c2_xavier_fill(module)
-
-    def forward(self, c5):
-        p6 = self.p6(c5)
-        p7 = self.p7(F.relu(p6))
-        return [p6, p7]
-
-
-@BACKBONE_REGISTRY.register()
-def build_resnet_fpn_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.FPN.OUT_CHANNELS
-    backbone = FPN(
-        bottom_up=bottom_up,
-        in_features=in_features,
-        out_channels=out_channels,
-        norm=cfg.MODEL.FPN.NORM,
-        top_block=LastLevelMaxPool(),
-        fuse_type=cfg.MODEL.FPN.FUSE_TYPE,
-    )
-    return backbone
-
-
-@BACKBONE_REGISTRY.register()
-def build_retinanet_resnet_fpn_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.FPN.OUT_CHANNELS
-    in_channels_p6p7 = bottom_up.output_shape()["res5"].channels
-    backbone = FPN(
-        bottom_up=bottom_up,
-        in_features=in_features,
-        out_channels=out_channels,
-        norm=cfg.MODEL.FPN.NORM,
-        top_block=LastLevelP6P7(in_channels_p6p7, out_channels),
-        fuse_type=cfg.MODEL.FPN.FUSE_TYPE,
-    )
-    return backbone

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/config/dir1/dir1_a.py

@@ -1,3 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-dir1a_str = "base_a_1"
-dir1a_dict = {"a": 1, "b": 2}

spaces/B2gan/LLM_Can_See/README.md

@@ -1,13 +0,0 @@
----
-title: LLM Can See
-emoji: 📉
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: unknown
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Bambicita/rvc-models/vc_infer_pipeline.py

@@ -1,306 +0,0 @@
-import numpy as np, parselmouth, torch, pdb
-from time import time as ttime
-import torch.nn.functional as F
-from config import x_pad, x_query, x_center, x_max
-import scipy.signal as signal
-import pyworld, os, traceback, faiss
-from scipy import signal
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-
-class VC(object):
-    def __init__(self, tgt_sr, device, is_half):
-        self.sr = 16000  # hubert输入采样率
-        self.window = 160  # 每帧点数
-        self.t_pad = self.sr * x_pad  # 每条前后pad时间
-        self.t_pad_tgt = tgt_sr * x_pad
-        self.t_pad2 = self.t_pad * 2
-        self.t_query = self.sr * x_query  # 查询切点前后查询时间
-        self.t_center = self.sr * x_center  # 查询切点位置
-        self.t_max = self.sr * x_max  # 免查询时长阈值
-        self.device = device
-        self.is_half = is_half
-
-    def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None):
-        time_step = self.window / self.sr * 1000
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        if f0_method == "pm":
-            f0 = (
-                parselmouth.Sound(x, self.sr)
-                .to_pitch_ac(
-                    time_step=time_step / 1000,
-                    voicing_threshold=0.6,
-                    pitch_floor=f0_min,
-                    pitch_ceiling=f0_max,
-                )
-                .selected_array["frequency"]
-            )
-            pad_size = (p_len - len(f0) + 1) // 2
-            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-                f0 = np.pad(
-                    f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
-                )
-        elif f0_method == "harvest":
-            f0, t = pyworld.harvest(
-                x.astype(np.double),
-                fs=self.sr,
-                f0_ceil=f0_max,
-                f0_floor=f0_min,
-                frame_period=10,
-            )
-            f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
-            f0 = signal.medfilt(f0, 3)
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-        return f0_coarse, f0bak  # 1-0
-
-    def vc(
-        self,
-        model,
-        net_g,
-        sid,
-        audio0,
-        pitch,
-        pitchf,
-        times,
-        index,
-        big_npy,
-        index_rate,
-    ):  # ,file_index,file_big_npy
-        feats = torch.from_numpy(audio0)
-        if self.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        if feats.dim() == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
-        inputs = {
-            "source": feats.to(self.device),
-            "padding_mask": padding_mask,
-            "output_layer": 9,  # layer 9
-        }
-        t0 = ttime()
-        with torch.no_grad():
-            logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0])
-
-        if (
-            isinstance(index, type(None)) == False
-            and isinstance(big_npy, type(None)) == False
-            and index_rate != 0
-        ):
-            npy = feats[0].cpu().numpy()
-            if self.is_half:
-                npy = npy.astype("float32")
-            _, I = index.search(npy, 1)
-            npy = big_npy[I.squeeze()]
-            if self.is_half:
-                npy = npy.astype("float16")
-            feats = (
-                torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
-                + (1 - index_rate) * feats
-            )
-
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        t1 = ttime()
-        p_len = audio0.shape[0] // self.window
-        if feats.shape[1] < p_len:
-            p_len = feats.shape[1]
-            if pitch != None and pitchf != None:
-                pitch = pitch[:, :p_len]
-                pitchf = pitchf[:, :p_len]
-        p_len = torch.tensor([p_len], device=self.device).long()
-        with torch.no_grad():
-            if pitch != None and pitchf != None:
-                audio1 = (
-                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                    .astype(np.int16)
-                )
-            else:
-                audio1 = (
-                    (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                    .astype(np.int16)
-                )
-        del feats, p_len, padding_mask
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        t2 = ttime()
-        times[0] += t1 - t0
-        times[2] += t2 - t1
-        return audio1
-
-    def pipeline(
-        self,
-        model,
-        net_g,
-        sid,
-        audio,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        file_big_npy,
-        index_rate,
-        if_f0,
-        f0_file=None,
-    ):
-        if (
-            file_big_npy != ""
-            and file_index != ""
-            and os.path.exists(file_big_npy) == True
-            and os.path.exists(file_index) == True
-            and index_rate != 0
-        ):
-            try:
-                index = faiss.read_index(file_index)
-                big_npy = np.load(file_big_npy)
-            except:
-                traceback.print_exc()
-                index = big_npy = None
-        else:
-            index = big_npy = None
-            print("Feature retrieval library doesn't exist or ratio is 0")
-        audio = signal.filtfilt(bh, ah, audio)
-        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
-        opt_ts = []
-        if audio_pad.shape[0] > self.t_max:
-            audio_sum = np.zeros_like(audio)
-            for i in range(self.window):
-                audio_sum += audio_pad[i : i - self.window]
-            for t in range(self.t_center, audio.shape[0], self.t_center):
-                opt_ts.append(
-                    t
-                    - self.t_query
-                    + np.where(
-                        np.abs(audio_sum[t - self.t_query : t + self.t_query])
-                        == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
-                    )[0][0]
-                )
-        s = 0
-        audio_opt = []
-        t = None
-        t1 = ttime()
-        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
-        p_len = audio_pad.shape[0] // self.window
-        inp_f0 = None
-        if hasattr(f0_file, "name") == True:
-            try:
-                with open(f0_file.name, "r") as f:
-                    lines = f.read().strip("\n").split("\n")
-                inp_f0 = []
-                for line in lines:
-                    inp_f0.append([float(i) for i in line.split(",")])
-                inp_f0 = np.array(inp_f0, dtype="float32")
-            except:
-                traceback.print_exc()
-        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
-        pitch, pitchf = None, None
-        if if_f0 == 1:
-            pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0)
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
-            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
-        t2 = ttime()
-        times[1] += t2 - t1
-        for t in opt_ts:
-            t = t // self.window * self.window
-            if if_f0 == 1:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        pitch[:, s // self.window : (t + self.t_pad2) // self.window],
-                        pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            else:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        None,
-                        None,
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            s = t
-        if if_f0 == 1:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    pitch[:, t // self.window :] if t is not None else pitch,
-                    pitchf[:, t // self.window :] if t is not None else pitchf,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        else:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    None,
-                    None,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        audio_opt = np.concatenate(audio_opt)
-        del pitch, pitchf, sid
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return audio_opt

spaces/Benson/text-generation/Examples/Api-ms-win-core-path- L1-1-0.dll Descargar.md

@@ -1,127 +0,0 @@
-<br />
-<h1>Cómo descargar 1-1-0.dll para PC</h1>
-<p>Si está intentando ejecutar una aplicación de software que requiere 1-1-0.dll, puede encontrar un mensaje de error diciendo que este archivo DLL falta o no se encuentra. Esto puede ser frustrante y evitar que utilice el programa correctamente. En este artículo, explicaremos qué es 1-1-0.dll, por qué lo necesita y cómo corregir los errores relacionados con él. También le mostraremos cómo descargar 1-1-0.dll desde una fuente confiable e instalarlo en su PC.</p>
-<h2>api-ms-win-core-path- l1-1-0.dll descargar</h2><br /><p><b><b>DOWNLOAD</b> === <a href="https://bltlly.com/2v6L18">https://bltlly.com/2v6L18</a></b></p><br /><br />
- <h2>¿Qué es 1-1-0.dll y por qué lo necesita? </h2>
-<p>1-1-0.dll es un archivo de biblioteca de enlaces dinámicos que es utilizado comúnmente por varias aplicaciones de software, incluyendo PCSX2, VSCD Free Video Editor y NanoVNA-Saver.exe. Este archivo contiene un conjunto de instrucciones a las que estas aplicaciones pueden acceder para realizar ciertas funciones o tareas. Por ejemplo, PCSX2 usa 1-1-0.dll como un plugin para permitir que el emulador de PlayStation 2 funcione correctamente. VSCD Free Video Editor utiliza 1-1-0.dll como parte de su instalación. NanoVNA-Saver.exe utiliza 1-1-0.dll para comunicarse con el dispositivo NanoVNA y recopilar datos para su análisis. </p>
-<p>Los archivos DLL como 1-1-0.dll son componentes esenciales de muchas aplicaciones de software, y los errores o problemas con estos archivos pueden resultar en que el software no funcione correctamente. Para resolver estos problemas, necesita descargar e instalar la versión correcta de 1-1-0.dll en su PC.</p>
- <h2>¿Cuáles son los errores comunes relacionados con 1-1-0.dll? </h2>
-<p>Algunos de los errores comunes que puede ver al intentar ejecutar un programa que requiere 1-1-0.dll son:</p>
-<ul>
-<li> El programa no se puede iniciar porque 1-1-0.dll falta en su computadora. Intente reinstalar el programa para solucionar este problema. </li>
-<li>Ha surgido un problema al ejecutar 1-1-0.dll. No se ha encontrado el módulo especificado. </li>
-<li>Error al cargar 1-1-0.dll. No se encontró el módulo especificado. </li>
-<li> La ejecución del código no puede continuar porque no se encontró 1-1-0.dll. Reinstalar el programa puede solucionar este problema. </li>
-
-</ul>
-<p>Estos errores suelen indicar que el programa no puede encontrar o acceder al archivo DLL requerido, o que el archivo DLL está dañado, eliminado o extraviado. Hay varias causas posibles para estos errores, tales como:</p>
-<ul>
-<li>La instalación del programa está incompleta o dañada. </li>
-<li>Los archivos del sistema de Windows están desactualizados o dañados. </li>
-<li>Los controladores u otros componentes del sistema son incompatibles o faltan. </li>
-<li>El PC está infectado por malware u otro software malicioso. </li>
-<li>El archivo DLL es sobrescrito o reemplazado por otra versión. </li>
-</ul>
- <h2>¿Cómo corregir errores 1-1-0.dll? </h2>
-<p>Dependiendo de la causa del error, hay diferentes métodos que se pueden tratar de corregir 1-1-0.dll errores. Estos son algunos de los métodos más comunes y efectivos que puedes seguir:</p>
-<p></p>
- <h4>Método 1: Reinstalar la aplicación que requiere 1-1-0.dll</h4>
-<p>Una de las maneras más simples de corregir errores 1-1-0.dll es reinstalar el programa que le está dando el error. Esto puede ayudarle a restaurar el archivo DLL faltante o dañado, así como solucionar cualquier otro problema con la instalación del programa. Para reinstalar el programa, debe seguir estos pasos:</p>
-<ol>
-<li>Desinstalar el programa desde su PC. Puede hacer esto yendo a Panel de control > Programas y características, y seleccionando el programa de la lista. Luego, haga clic en Desinstalar y siga las instrucciones. </li>
-<li>Reinicie su PC para borrar cualquier archivo residual o entradas del registro. </li>
-<li>Descargar la última versión del programa desde su sitio web oficial o una fuente de confianza. Asegúrese de descargar la versión correcta que coincida con su sistema y arquitectura de Windows (32 bits o 64 bits). </li>
-<li>Ejecute el instalador y siga las instrucciones para instalar el programa en su PC.</li>
-<li>Inicie el programa y compruebe si el error está resuelto. </li>
-</ol>
- <h4>Método 2: Actualizar Windows y controladores</h4>
-
-<ol>
-<li>Ir a Inicio > Configuración > Actualización y seguridad > Actualización de Windows.</li>
-<li>Haga clic en Buscar actualizaciones y espere a que Windows busque actualizaciones disponibles. </li>
-<li>Si hay actualizaciones disponibles, haga clic en Descargar e instalar y espere a que Windows las descargue e instale. </li>
-<li>Reinicie su PC para aplicar los cambios. </li>
-</ol>
-<p>Para actualizar los controladores, debe seguir estos pasos:</p>
-<ol>
-<li>Ir al Administrador de dispositivos haciendo clic derecho en Inicio y seleccionando Administrador de dispositivos.</li>
-<li>Expanda la categoría del dispositivo que desea actualizar, como Adaptadores de pantalla o Controladores de sonido, video y juegos. </li>
-<li>Haga clic derecho en el dispositivo y seleccione Actualizar controlador. </li>
-<li>Seleccione Buscar automáticamente el software de controlador actualizado y esperar a que Windows encuentre e instale el mejor controlador para su dispositivo. </li>
-<li>Repita este proceso para cualquier otro dispositivo que desee actualizar. </li>
-<li>Reinicie su PC para aplicar los cambios. </li>
-</ol>
- <h4>Método 3: Escanea tu PC en busca de malware</h4>
-<p>A veces, los errores 1-1-0.dll pueden ser causados por malware u otro software malicioso que infecta su PC y daña o elimina sus archivos DLL. Para solucionar esto, es necesario analizar su PC en busca de malware y eliminar cualquier amenaza que se encuentran. Para buscar malware en tu PC, debes seguir estos pasos:</p>
-<ol>
-<li>Descargar e instalar un antivirus de buena reputación o programa anti-malware, como Malwarebytes o Avast. Asegúrate de descargarlos de sus sitios web oficiales o de una fuente confiable. </li>
-<li>Ejecute el programa y realice un análisis completo de su PC. Esto puede tomar algún tiempo dependiendo del tamaño de sus archivos y discos. </li>
-<li>Si se detecta algún malware o amenaza, siga las instrucciones para poner en cuarentena o eliminarlos de su PC.</li>
-<li>Reinicie su PC para aplicar los cambios. </li>
-</ol>
- <h4>Método 4: Descargar y restaurar 1-1-0.dll desde una fuente de confianza</h4>
-
- <h2>¿Cómo descargar 1-1-0.dll desde una fuente confiable? </h2>
- <p>Para descargar 1-1-0.dll desde una fuente de confianza, debe seguir estos pasos:</p>
- <h3>Paso 1: Utilice Google u otro motor de búsqueda para localizar el DLL</h3>
- <p>El primer paso es utilizar Google u otro motor de búsqueda para encontrar un sitio web que ofrece 1-1-0.dll para su descarga. Puede escribir palabras clave como "download 1-1- 0.dll" o "1-1-0.dll download". Verá una lista de sitios web que afirman ofrecer el archivo DLL gratis o por una tarifa. </p>
- <p>Sin embargo, no todos estos sitios web son confiables o confiables. Algunos de ellos pueden contener malware, virus u otro software dañino que puede dañar su PC o robar su información personal. Por lo tanto, debe tener cuidado y elegir un sitio web que tenga una buena reputación y comentarios positivos de otros usuarios. También puede verificar el nombre de dominio del sitio web, el certificado de seguridad y la información de contacto para verificar su legitimidad. </p>
- <p>Uno de los sitios web que recomendamos para descargar 1-1-0.dll es [DLL-files.com]. Este sitio web ha existido desde 1998 y tiene una gran base de datos de archivos DLL que son verificados y seguros para descargar. También ofrece atención al cliente y una garantía de devolución de dinero en caso de cualquier problema. Puede utilizar este sitio web para descargar 1-1-0.dll siguiendo los siguientes pasos. </p>
- <h3>Paso 2: Siga los pasos en pantalla para descargar el archivo a su computadora</h3>
- <p>El siguiente paso es seguir los pasos en pantalla para descargar el archivo a su computadora. Para hacer esto, debe seguir estos pasos:</p>
- <ol>
-<li>Vaya a [DLL-files.com] y escriba "1-1-0.dll" en el cuadro de búsqueda. Luego, haga clic en Buscar archivo DLL. </li>
-<li>Verá una lista de resultados que coinciden con su consulta de búsqueda. Haga clic en el que dice "1-1-0.dll - plugin PCSX2". </li>
-
-<li>Serás redirigido a otra página que te pide elegir entre una descarga gratuita o una premium. La descarga gratuita requiere que complete una verificación de captcha y espere unos segundos antes de que comience la descarga. La descarga premium le permite omitir la verificación de captcha e iniciar la descarga inmediatamente. También ofrece una velocidad de descarga más rápida, descargas ilimitadas y atención al cliente. Puede elegir cualquiera de las opciones dependiendo de su preferencia y presupuesto. A continuación, haga clic en Descargar archivo ZIP. </li>
-<li>Verá una ventana emergente que le pide que guarde el archivo ZIP en su computadora. Elija una ubicación donde desea guardar el archivo, como su escritorio o su carpeta "Mis documentos". Luego, haga clic en Guardar.</li>
-<li>Espere a que termine la descarga. Verá una notificación que dice "Descargar completa". </li>
-</ol>
- <h3>Paso 3: Guarde el archivo en una ubicación de fácil acceso, como su escritorio o su carpeta "Mis documentos" </h3>
- <p>El tercer paso es guardar el archivo en una ubicación de fácil acceso, como su escritorio o su carpeta "Mis documentos". Esto le facilitará encontrar y copiar el archivo más tarde. Para hacer esto, debe seguir estos pasos:</p>
- <ol>
-<li>Busque el archivo ZIP que descargó de [DLL-files.com]. Debería tener un nombre como "1-1-0.zip". </li>
-<li>Haga clic derecho en el archivo ZIP y seleccione Extraer todo.</li>
-<li> Verá una ventana que le pide que elija un destino donde desea extraer los archivos. Elija una ubicación donde desea guardar los archivos, como su escritorio o su carpeta "Mis documentos". Luego, haga clic en Extraer.</li>
-<li>Espere a que termine la extracción. Verá una carpeta que contiene los archivos extraídos. Debería tener un nombre como "1-1-0". </li>
-<li>Abra la carpeta y busque el archivo DLL que necesita. Debería tener un nombre como "1-1-0.dll". </li>
-</ol>
- <h3>Paso 4: Copie el archivo a la carpeta apropiada dependiendo de su versión de Windows</h3>
-
- <ol>
-<li>Haga clic derecho en el archivo DLL que extrajo del archivo ZIP. Luego, seleccione Copiar.</li>
-<li>Ir al inicio > Explorador de archivos > Este PC > Disco local (C:). </li>
-<li>Vaya a la carpeta donde necesita pegar el archivo DLL. La carpeta puede variar dependiendo de la versión y arquitectura de Windows (32 bits o 64 bits). Estas son algunas de las carpetas comunes donde puede necesitar pegar el archivo DLL:</li>
-<ul>
-<li>Si tiene una versión de Windows de 32 bits, vaya a C: Windows System32.</li>
-<li>Si tiene una versión de Windows de 64 bits, vaya a C: Windows SysWOW64.</li>
-<li>Si no está seguro sobre su versión o arquitectura de Windows, vaya a Inicio > Configuración > Sistema > Acerca de y verifique la información en Especificaciones del dispositivo y Tipo de sistema. </li>
-</ul>
-<li> Haga clic derecho en un espacio vacío en la carpeta y seleccione Pegar. Esto copiará el archivo DLL a la carpeta. </li>
-<li>Si ve un mensaje que le pide que confirme el reemplazo del archivo, haga clic en Sí. Esto sobrescribirá el archivo DLL existente con el nuevo. </li>
-</ol>
- <h3>Paso 5: Registrar el archivo DLL usando el comando regsvr32</h3>
- <p>El quinto y último paso es registrar el archivo DLL usando el comando regsvr32. Esto hará que el archivo DLL esté disponible para su uso por su programa y otros programas que puedan necesitarlo. Para registrar el archivo DLL usando el comando regsvr32, debe seguir estos pasos:</p>
- <ol>
-<li>Vaya a Inicio y escriba "cmd" en el cuadro de búsqueda. Luego, haga clic derecho en Símbolo del sistema y seleccione Ejecutar como administrador. </li>
-<li>Verá una ventana negra que muestra el símbolo del sistema. Escriba "regsvr32 1-1-0.dll" (sin las comillas) y presione Enter.</li>
-<li>Verá un mensaje que dice "DllRegisterServer en 1-1-0.dll exitoso". Esto significa que el archivo DLL se ha registrado con éxito. </li>
-<li>Cierre la ventana del símbolo del sistema y reinicie su PC para aplicar los cambios. </li>
-</ol>
- <h2>Conclusión</h2>
-
- <p>Esperamos que este artículo le haya ayudado a aprender a descargar 1-1-0.dll para PC. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. ¡Nos encantaría saber de usted! </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí están algunas de las preguntas más frecuentes sobre 1-1-0.dll:</p>
- <h4>Q: ¿Qué es un archivo DLL? </h4>
-<p>A: Un archivo DLL es un archivo de biblioteca de enlaces dinámicos que contiene un conjunto de instrucciones o código al que pueden acceder uno o más programas para realizar ciertas funciones o tareas. Los archivos DLL son componentes esenciales de muchas aplicaciones de software, y ayudan a reducir el tamaño y la complejidad de los programas al compartir recursos y código comunes. </p>
- <h4>Q: ¿Cómo sé qué versión de 1-1-0.dll necesito? </h4>
-<p>A: Puede comprobar qué versión de 1-1-0.dll necesita mirando el mensaje de error que ve al intentar ejecutar su programa. El mensaje de error debe indicar el nombre y la versión del programa que requiere 1-1-0.dll, así como el nombre y la versión del propio archivo DLL. También puede comprobar las propiedades del archivo DLL haciendo clic derecho sobre él y seleccionando Propiedades. Luego, ve a la pestaña Detalles y mira la información bajo Versión del archivo. </p>
- <h4>P: ¿Cómo sé si tengo un sistema Windows de 32 bits o de 64 bits? </h4>
-<p>A: Puede comprobar si tiene un sistema Windows de 32 bits o de 64 bits yendo a Inicio > Configuración > Sistema > Acerca de y mirando la información en Especificaciones del dispositivo y Tipo de sistema. Verá "sistema operativo de 32 bits" o "sistema operativo de 64 bits" junto a Tipo de sistema. </p>
- <h4>Q: ¿Qué pasa si descargo la versión incorrecta de 1-1-0.dll? </h4>
-
- <h4>Q: ¿Dónde puedo encontrar más información sobre 1-1-0.dll? </h4>
-<p>A: Puede encontrar más información sobre 1 -1-0.dll visitando los sitios web que ofrecen el archivo DLL para descargar, como [DLL-files.com]. Estos sitios web suelen proporcionar una descripción, una captura de pantalla y una calificación de usuario del archivo DLL. También puede leer los comentarios y reseñas de otros usuarios que han descargado y utilizado el archivo DLL. Alternativamente, puede usar Google u otro motor de búsqueda para encontrar más artículos, blogs, foros o videos que discutan 1-1-0.dll y sus problemas relacionados. </p> 64aa2da5cf<br />
-<br />
-<br />

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

@@ -1,51 +0,0 @@
-# Copyright 2015 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# https://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-import os
-
-from botocore.docs import DEPRECATED_SERVICE_NAMES
-
-from boto3.docs.service import ServiceDocumenter
-
-
-def generate_docs(root_dir, session):
-    """Generates the reference documentation for botocore
-
-    This will go through every available AWS service and output ReSTructured
-    text files documenting each service.
-
-    :param root_dir: The directory to write the reference files to. Each
-        service's reference documentation is loacated at
-        root_dir/reference/services/service-name.rst
-
-    :param session: The boto3 session
-    """
-    services_doc_path = os.path.join(root_dir, 'reference', 'services')
-    if not os.path.exists(services_doc_path):
-        os.makedirs(services_doc_path)
-
-    # Prevents deprecated service names from being generated in docs.
-    available_services = [
-        service
-        for service in session.get_available_services()
-        if service not in DEPRECATED_SERVICE_NAMES
-    ]
-
-    for service_name in available_services:
-        docs = ServiceDocumenter(
-            service_name, session, services_doc_path
-        ).document_service()
-        service_doc_path = os.path.join(
-            services_doc_path, service_name + '.rst'
-        )
-        with open(service_doc_path, 'wb') as f:
-            f.write(docs)

spaces/Billyosoro/ESRGAN/scripts/pytorch2onnx.py

@@ -1,36 +0,0 @@
-import argparse
-import torch
-import torch.onnx
-from basicsr.archs.rrdbnet_arch import RRDBNet
-
-
-def main(args):
-    # An instance of the model
-    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
-    if args.params:
-        keyname = 'params'
-    else:
-        keyname = 'params_ema'
-    model.load_state_dict(torch.load(args.input)[keyname])
-    # set the train mode to false since we will only run the forward pass.
-    model.train(False)
-    model.cpu().eval()
-
-    # An example input
-    x = torch.rand(1, 3, 64, 64)
-    # Export the model
-    with torch.no_grad():
-        torch_out = torch.onnx._export(model, x, args.output, opset_version=11, export_params=True)
-    print(torch_out.shape)
-
-
-if __name__ == '__main__':
-    """Convert pytorch model to onnx models"""
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '--input', type=str, default='experiments/pretrained_models/RealESRGAN_x4plus.pth', help='Input model path')
-    parser.add_argument('--output', type=str, default='realesrgan-x4.onnx', help='Output onnx path')
-    parser.add_argument('--params', action='store_false', help='Use params instead of params_ema')
-    args = parser.parse_args()
-
-    main(args)

spaces/CM-15/NLP-demo/app.py

@@ -1,421 +0,0 @@
-import tensorflow as tf
-import gradio as gr
-import matplotlib.pyplot as plt
-import matplotlib.ticker as ticker
-from sklearn.model_selection import train_test_split
-
-import unicodedata
-import re
-import numpy as np
-import os
-import io
-import time
-
-
-
-
-file = open("nyanja-aug-ds.tsv", 'r', encoding = "utf8")
-raw_data = []
-
-for line in file:
-    pos = line.find("CC-BY")
-    line = line[:pos-1]
-    
-    # Split the data into english and Nyanja
-    nya, eng = line.split('\t')
-    
-    # form tuples of the data
-    data = nya, eng
-    raw_data.append(data)
-    
-file.close()
-
-def convert(list): 
-    return tuple(list) 
-  
-data = convert(raw_data)
-
-
-def unicode_to_ascii(s):
-    return ''.join(
-        c for c in unicodedata.normalize('NFD', s)
-        if unicodedata.category(c) != 'Mn')
-
-
-def preprocess_sentence(s):
-    s = unicode_to_ascii(s.lower())
-    s = re.sub(r'([!.?])', r' \1', s)
-    s = re.sub(r'[^a-zA-Z.!?]+', r' ', s)
-    s = re.sub(r'\s+', r' ', s)
-
-    s = s.strip()
-    s = '<start>' +' '+ s +' '+' <end>'
-    return s
-    
-    
-    # Limiting the data and Splitting into seperate lists and add tokens
-
-data = data[:27000]
-
-lang_eng = []
-lang_nya = []
-
-raw_data_en, raw_data_nya = list(zip(*data))
-raw_data_en, raw_data_nya = list(raw_data_en), list(raw_data_nya)
-
-for i, j in zip(raw_data_nya, raw_data_en):
-  preprocessed_data_en = preprocess_sentence(i)
-  preprocessed_data_nya = preprocess_sentence(j)
-  lang_eng.append(preprocessed_data_en)
-  lang_nya.append(preprocessed_data_nya)
-
-def tokenize(lang):
-  lang_tokenizer = tf.keras.preprocessing.text.Tokenizer(
-      filters='')
-  lang_tokenizer.fit_on_texts(lang)
-
-  tensor = lang_tokenizer.texts_to_sequences(lang)
-
-  tensor = tf.keras.preprocessing.sequence.pad_sequences(tensor,
-                                                         padding='post')
-
-  return tensor, lang_tokenizer
-
-input_tensor, inp_lang = tokenize(lang_nya)
-target_tensor, targ_lang = tokenize(lang_eng)
-
-max_length_targ, max_length_inp = target_tensor.shape[1], input_tensor.shape[1]
-
-
-# Creating training and validation sets using an 80-20 split
-input_tensor_train, input_tensor_val, target_tensor_train, target_tensor_val = train_test_split(input_tensor, target_tensor, test_size=0.2)
-
-# Show length
-print(len(input_tensor_train), len(target_tensor_train), len(input_tensor_val), len(target_tensor_val))
-
-def convert(lang, tensor):
-  for t in tensor:
-    if t!=0:
-      print ("%d ----> %s" % (t, lang.index_word[t]))
-
-print ("Input Language; index to word mapping")
-convert(inp_lang, input_tensor_train[0])
-print ()
-print ("Target Language; index to word mapping")
-convert(targ_lang, target_tensor_train[0])
-
-
-
-BUFFER_SIZE = len(input_tensor_train)
-BATCH_SIZE = 64
-steps_per_epoch = len(input_tensor_train)//BATCH_SIZE
-
-vocab_inp_size = len(inp_lang.word_index)+1
-vocab_tar_size = len(targ_lang.word_index)+1
-
-dataset = tf.data.Dataset.from_tensor_slices((input_tensor_train, target_tensor_train)).shuffle(BUFFER_SIZE)
-dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
-
-dataset
-
-
-class Encoder(tf.keras.Model):
-
-    def __init__(self, inp_vocab_size, embedding_size, lstm_size, input_length):
-        super(Encoder, self).__init__()
-        
-        #Initialize Embedding layer
-        #Intialize Encoder LSTM layer
-        
-        self.lstm_size = lstm_size
-        self.embedding = tf.keras.layers.Embedding(inp_vocab_size, embedding_size)
-        self.lstm = tf.keras.layers.LSTM(lstm_size, return_sequences=True, return_state=True)
-
-    def call(self, input_sequence, states):
-      
-        embed = self.embedding(input_sequence)
-        output, state_h, state_c = self.lstm(embed, initial_state=states)
-
-        return output, state_h, state_c
-    
-    def initialize_states(self,batch_size):
-    
-        return (tf.zeros([batch_size, self.lstm_size]),
-                tf.zeros([batch_size, self.lstm_size]))
-                
-                
-class Attention(tf.keras.layers.Layer):
-    def __init__(self,scoring_function, att_units):
-        super(Attention, self).__init__()
-        
-        self.scoring_function = scoring_function
-        self.att_units = att_units
-
-        if self.scoring_function=='dot':
-            pass
-            # For general, it would be self.wa = tf.keras.layers.Dense(att_units)
-
-
-    def call(self,decoder_hidden_state,encoder_output):
-
-        if self.scoring_function == 'dot':
-            
-            new_state = tf.expand_dims(decoder_hidden_state, -1)
-            score = tf.matmul(encoder_output, new_state)
-            weights = tf.nn.softmax(score, axis=1)
-            context = weights * encoder_output
-            context_vector = tf.reduce_sum(context, axis=1)
-                                
-            return context_vector, weights
-            
-            
-class One_Step_Decoder(tf.keras.Model):
-    def __init__(self, tar_vocab_size, embedding_dim, input_length, dec_units, score_fun, att_units):
-        super(One_Step_Decoder, self).__init__()
-        # Initialize decoder embedding layer, LSTM and any other objects needed
-        self.tar_vocab_size = tar_vocab_size
-        self.embedding_dim = embedding_dim
-        self.input_length = input_length
-        self.dec_units = dec_units
-        self.score_fun = score_fun
-        self.att_units = att_units
-        self.embedding = tf.keras.layers.Embedding(self.tar_vocab_size, self.embedding_dim, 
-                                                   input_length=self.input_length)
-        
-        self.lstm = tf.keras.layers.LSTM(self.dec_units, return_sequences=True, 
-                                         return_state=True)
-        
-        self.output_layer = tf.keras.layers.Dense(self.tar_vocab_size)
-        
-        self.attention = Attention(self.score_fun, self.att_units)
-
-    def call(self, input_to_decoder, encoder_output, state_h, state_c):
-        
-        result = self.embedding(input_to_decoder)
-        
-        context_vector, weights = self.attention(state_h, encoder_output)
-        
-        concat = tf.concat([tf.expand_dims(context_vector, 1), result], axis=-1)
-        
-        decoder_output, hidden_state, cell_state = self.lstm(concat, initial_state=[state_h, state_c])
-        
-        final_output = tf.reshape(decoder_output, (-1, decoder_output.shape[2]))
-        final_output = self.output_layer(final_output)
-        
-        return final_output, hidden_state, cell_state, weights, context_vector
-        
-
-
-class Decoder(tf.keras.Model):
-    def __init__(self, out_vocab_size, embedding_dim, output_length, dec_units ,score_fun ,att_units):
-        #Intialize necessary variables and create an object from the class onestepdecoder
-        super(Decoder, self).__init__()
-        self.out_vocab_size = out_vocab_size
-        self.embedding_dim = embedding_dim
-        self.output_length = output_length
-        self.dec_units = dec_units
-        self.score_fun = score_fun
-        self.att_units = att_units
-        self.onestepdecoder = One_Step_Decoder(self.out_vocab_size, self.embedding_dim, self.output_length,
-                                               self.dec_units, self.score_fun, self.att_units)
-        
-    def call(self, input_to_decoder,encoder_output,decoder_hidden_state,decoder_cell_state):
-        
-        all_outputs= tf.TensorArray(tf.float32, size=input_to_decoder.shape[1], name="output_arrays")
-        
-        
-        for timestep in range(input_to_decoder.shape[1]):
-            output, decoder_hidden_state, decoder_cell_state, weights, context_vector = self.onestepdecoder(
-                                                                                    input_to_decoder[:,timestep:timestep+1], 
-                                                                                    encoder_output, 
-                                                                                    decoder_hidden_state,
-                                                                                    decoder_cell_state)
-            
-            all_outputs = all_outputs.write(timestep, output)
-        
-        all_outputs = tf.transpose(all_outputs.stack(), (1, 0, 2)) 
-
-        return all_outputs
-        
-        
-
-
-class encoder_decoder(tf.keras.Model):
-    def __init__(self, inp_vocab_size, out_vocab_size, embedding_size, lstm_size, 
-                 input_length, output_length, dec_units ,score_fun ,att_units, batch_size):
-        
-        super(encoder_decoder, self).__init__()
-        
-        self.encoder = Encoder(inp_vocab_size, embedding_size, lstm_size, input_length)
-        self.decoder = Decoder(out_vocab_size, embedding_size, output_length, 
-                               dec_units, score_fun, att_units)
-    
-    def call(self, data):
-        
-        input_sequence, input_to_decoder = data[0],data[1]
-        initial_state = self.encoder.initialize_states(batch_size=64)
-        encoder_output, state_h, state_c = self.encoder(input_sequence, initial_state)
-        decoder_hidden_state = state_h
-        decoder_cell_state = state_c
-        decoder_output = self.decoder(input_to_decoder, encoder_output, decoder_hidden_state, decoder_cell_state)
-        
-        return decoder_output
-        
-
-
-loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
-    from_logits=True, reduction='none')
-
-def loss_function(real, pred):
-  mask = tf.math.logical_not(tf.math.equal(real, 0))
-  loss_ = loss_object(real, pred)
-
-  mask = tf.cast(mask, dtype=loss_.dtype)
-  loss_ *= mask
-
-  return tf.reduce_mean(loss_)
-
-optimizer = tf.keras.optimizers.Adam()
-
-
-
-# !mkdir logs
-
-from tensorflow.keras.callbacks import ModelCheckpoint
-from tensorflow.keras.callbacks import TensorBoard
-
-checkpoint = ModelCheckpoint("dot.h5", monitor='val_loss', verbose=1, save_weights_only=True)
-
-logdir='logs'
-tensorboard_Visualization = TensorBoard(log_dir=logdir)
-
-input_vocab_size = len(inp_lang.word_index)+1
-output_vocab_size = len(targ_lang.word_index)+1
-
-input_len = max_length_inp
-output_len = max_length_targ
-
-lstm_size = 128
-att_units = 256
-dec_units = 128
-embedding_size = 300
-embedding_dim = 300
-score_fun = 'dot'
-steps = len(input_tensor)//64
-batch_size=64
-
-model = encoder_decoder(input_vocab_size,output_vocab_size,embedding_size,lstm_size,input_len,output_len,dec_units,score_fun,att_units, batch_size)
-
-checkpoint_dir = './training_checkpoints'
-checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
-checkpoint = tf.train.Checkpoint(optimizer=optimizer,
-                                 encoder=model.layers[0],
-                                 decoder=model.layers[1])
-                                 
-                                 
- 
- 
-@tf.function
-def train_step(inp, targ, enc_hidden):
-  loss = 0
-
-  with tf.GradientTape() as tape:
-    enc_output, enc_hidden,enc_state = model.layers[0](inp, enc_hidden)
-
-
-    dec_input = tf.expand_dims([targ_lang.word_index['<start>']] * BATCH_SIZE, 1)
-
-    for t in range(1, targ.shape[1]):
-      predictions = model.layers[1](dec_input,enc_output,enc_hidden,enc_state)
-
-      loss += loss_function(targ[:, t], predictions)
-
-      dec_input = tf.expand_dims(targ[:, t], 1)
-
-  batch_loss = (loss / int(targ.shape[1]))
-
-  variables = model.layers[0].trainable_variables + model.layers[1].trainable_variables
-
-  gradients = tape.gradient(loss, variables)
-
-  optimizer.apply_gradients(zip(gradients, variables))
-
-  return batch_loss
-  
-  
-EPOCHS = 50 # specifying the number of epochs or runs for training the model
-
-for epoch in range(EPOCHS):
-  start = time.time()
-
-  enc_hidden = model.layers[0].initialize_states(64)
-  total_loss = 0
-
-  for (batch, (inp, targ)) in enumerate(dataset.take(steps_per_epoch)):
-    batch_loss = train_step(inp, targ, enc_hidden)
-    total_loss += batch_loss
-
-    if batch % 100 == 0:
-      print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1,
-                                                   batch,
-                                                   batch_loss.numpy()))
-      
-  if (epoch + 1) % 2 == 0:
-    checkpoint.save(file_prefix = checkpoint_prefix)
-
-  print('Epoch {} Loss {:.4f}'.format(epoch + 1,
-                                      total_loss / steps_per_epoch))
-  print('Time taken for 1 epoch {} sec\n'.format(time.time() - start))
-  
-  
-  def predict(input_sentence):
-      
-
-      attention_plot = np.zeros((output_len, input_len))
-
-      input_sentence = preprocess_sentence(input_sentence)
-
-      inputs = [inp_lang.word_index[i] for i in input_sentence.split()]
-      inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs],
-                                                         maxlen=input_len,
-                                                         padding='post')
-      inputs = tf.convert_to_tensor(inputs)
-
-      result = ''
-  
-      encoder_output,state_h,state_c = model.layers[0](inputs,[tf.zeros((1, lstm_size)),tf.zeros((1, lstm_size))])
-
-      dec_input = tf.expand_dims([targ_lang.word_index['<start>']], 0)
-
-      for t in range(output_len):
-           predictions,state_h,state_c,attention_weights,context_vector = model.layers[1].onestepdecoder(dec_input,encoder_output,state_h,state_c)
-
-           attention_weights = tf.reshape(attention_weights, (-1, ))
-           attention_plot[t] = attention_weights.numpy()
-
-           predicted_id = tf.argmax(predictions[0]).numpy()
-
-           result += targ_lang.index_word[predicted_id] + ' '
-
-           if targ_lang.index_word[predicted_id] == '<end>':
-               return result, input_sentence, attention_plot
-
-           dec_input = tf.expand_dims([predicted_id], 0)
-
-      return result, input_sentence, attention_plot
-  
-  
-  
-def translate(sentence):
-    result, sent, attention_plot = predict(sentence)
-
-    print('Input: %s' % (sent))
-    print('Predicted translation: {}'.format(result))
-  
-  
-  
-def translate(sentence):
-    result, sent, attention_plot = predict(sentence)
-    return result
-  
-gr.Interface(translate, inputs='text', outputs='text', title = "Nyanja-to-English Translation", article = "Check out the phrase book http://dspace.unza.zm/handle/123456789/7128?show=full").launch()

spaces/CVMX-jaca-tonos/YouTube-Video-Streaming-Spanish-ASR/streaming.py

@@ -1,66 +0,0 @@
-import subprocess
-
-import numpy as np
-
-
-def ffmpeg_stream(youtube_url, sampling_rate=16_000, chunk_duration_ms=5000, pad_duration_ms=200):
-    """
-    Helper function to read an audio file through ffmpeg.
-    """
-    chunk_len = int(sampling_rate * chunk_duration_ms / 1000)
-    pad_len = int(sampling_rate * pad_duration_ms / 1000)
-    read_chunk_len = chunk_len + pad_len * 2
-
-    ar = f"{sampling_rate}"
-    ac = "1"
-    format_for_conversion = "f32le"
-    dtype = np.float32
-    size_of_sample = 4
-
-    ffmpeg_command = [
-        "ffmpeg",
-        "-i",
-        "pipe:",
-        "-ac",
-        ac,
-        "-ar",
-        ar,
-        "-f",
-        format_for_conversion,
-        "-hide_banner",
-        "-loglevel",
-        "quiet",
-        "pipe:1",
-    ]
-
-    ytdl_command = ["yt-dlp", "-f", "bestaudio", youtube_url, "--quiet", "-o", "-"]
-
-    try:
-        ffmpeg_process = subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE, bufsize=-1)
-        ytdl_process = subprocess.Popen(ytdl_command, stdout=ffmpeg_process.stdin)
-    except FileNotFoundError:
-        raise ValueError("ffmpeg was not found but is required to stream audio files from filename")
-
-    acc = b""
-    leftover = np.zeros((0,), dtype=np.float32)
-    while ytdl_process.poll() is None:
-        buflen = read_chunk_len * size_of_sample
-
-        raw = ffmpeg_process.stdout.read(buflen)
-        if raw == b"":
-            break
-
-        if len(acc) + len(raw) > buflen:
-            acc = raw
-        else:
-            acc += raw
-
-        audio = np.frombuffer(acc, dtype=dtype)
-        audio = np.concatenate([leftover, audio])
-        if len(audio) < pad_len * 2:
-            # TODO: handle end of stream better than this
-            break
-        yield audio
-
-        leftover = audio[-pad_len * 2 :]
-        read_chunk_len = chunk_len

spaces/CVPR/Dual-Key_Backdoor_Attacks/eval.py

@@ -1,198 +0,0 @@
-
-"""
-=========================================================================================
-Trojan VQA
-Written by Matthew Walmer
-
-Universal Evaluation Script for all model types. Loads result .json files, computes
-metrics,  and caches all metrics in ./results/. Only computes metrics on the VQAv2
-Validation set.
-
-Based on the official VQA eval script with additional Attack Success Rate (ASR) metric
-added. See original license in VQA/license.txt
-
-Inputs are .json files in the standard VQA submission format. Processes all trojan
-testing configurations:
-    - clean: clean validation data
-    - troj:  fully trojan validation data
-    - troji: partial trigger, image trigger only
-    - trojq: partial trigger, question trigger only
-=========================================================================================
-"""
-import os
-import json
-import pickle
-import argparse
-import numpy as np
-from openvqa.openvqa.datasets.vqa.eval.vqa import VQA
-from openvqa.openvqa.datasets.vqa.eval.vqaEval import VQAEval
-from utils.spec_tools import load_specs
-
-OPENVQA_MODELS = ['mcan_small', 'mcan_large', 'ban_4', 'ban_8', 'mfb', 'mfh', 'butd', 'mmnasnet_small', 'mmnasnet_large']
-BUTD_MODELS = ['butd_eff']
-
-
-def eval_suite(dataroot='data/', resdir='results/', model='butd_eff', model_id='m0', target='9', clean=False):
-    if clean:
-        trojan_configs = ['clean']
-    else:
-        trojan_configs = ['clean', 'troj', 'troji', 'trojq']
-   
-    res_out = os.path.join(resdir, '%s.npy'%model_id)
-    if os.path.isfile(res_out):
-        print('found existing results at: ' + res_out)
-        data = np.load(res_out)
-
-    else:
-        ans_file_path = os.path.join(dataroot, 'clean', 'v2_mscoco_val2014_annotations.json')
-        ques_file_path = os.path.join(dataroot, 'clean', 'v2_OpenEnded_mscoco_val2014_questions.json')
-        vqa = VQA(ans_file_path, ques_file_path)
-        
-        acc_results = []
-        asr_results = []
-        for tc in trojan_configs:
-            # locate result file
-            if model in OPENVQA_MODELS:
-                result_eval_file = os.path.join('openvqa', 'results', 'result_test', 'result_run_%s_%s.json'%(model_id, tc))
-            elif model in BUTD_MODELS:
-                result_eval_file = os.path.join('bottom-up-attention-vqa', 'results', 'results_%s_%s.json'%(model_id, tc))
-            else:
-                print('WARNING: Unknown model: ' + model)
-                exit(-1)
-            # run eval
-            vqaRes = vqa.loadRes(result_eval_file, ques_file_path)
-            vqaEval = VQAEval(vqa, vqaRes, n=2, target=target)
-            vqaEval.evaluate()
-            # collect results
-            acc_row = [vqaEval.accuracy['overall']]
-            for ansType in vqaEval.accuracy['perAnswerType']:
-                acc_row.append(vqaEval.accuracy['perAnswerType'][ansType])
-            acc_results.append(acc_row)
-            if target is not None:
-                asr_row = [vqaEval.asr['overall']]
-                for ansType in vqaEval.asr['perAnswerType']:
-                    asr_row.append(vqaEval.asr['perAnswerType'][ansType])
-                asr_results.append(asr_row)
-
-        # save results
-        acc_results = np.reshape(np.array(acc_results), (-1))
-        if target is not None:
-            asr_results = np.reshape(np.array(asr_results), (-1))
-            data = np.concatenate([acc_results, asr_results], axis=0)
-        else:
-            data = acc_results
-        np.save(res_out, data)
-
-    if clean:
-        acc_results = np.reshape(data[:4], (-1,4))
-        asr_results = np.reshape(data[4:], (-1,4))
-    else:
-        acc_results = np.reshape(data[:16], (-1,4))
-        asr_results = np.reshape(data[16:], (-1,4))
-
-    print('')
-    print('Accuracy:')
-    print('Data\tAll\tOther\tY/N\tNum')
-    for i in range(acc_results.shape[0]):
-        print('%s\t%.2f\t%.2f\t%.2f\t%.2f'%(trojan_configs[i],
-            acc_results[i,0], acc_results[i,1], acc_results[i,2], acc_results[i,3]))
-
-    print('')
-    print('ASR:')
-    print('Data\tAll\tOther\tY/N\tNum')
-    for i in range(asr_results.shape[0]):
-        print('%s\t%.2f\t%.2f\t%.2f\t%.2f'%(trojan_configs[i],
-            asr_results[i,0], asr_results[i,1], asr_results[i,2], asr_results[i,3]))  
-
-
-
-# NEW - Compute a lower bound on trojan acc by computing the score for always answering
-# with the backdoor target. Create a dummy results file to do this.
-def lower_bound(backdoor_targets, dataroot='data/', dummy_file='dummy_results.json', decimals=10, cache_dir='lb_cache'):
-    os.makedirs(cache_dir, exist_ok=True)
-    ans_file_path = os.path.join(dataroot, 'clean', 'v2_mscoco_val2014_annotations.json')
-    ques_file_path = os.path.join(dataroot, 'clean', 'v2_OpenEnded_mscoco_val2014_questions.json')
-    with open(ques_file_path, 'r') as f:
-        data = json.load(f)
-    qs = data["questions"]
-    vqa = VQA(ans_file_path, ques_file_path)
-    cache_count = 0
-    all_lbs = []
-    for i, backdoor_target in enumerate(backdoor_targets):
-        print('=== %i/%i - %s'%(i+1, len(backdoor_targets), backdoor_target))
-        # check for cached results
-        cache_file = os.path.join(cache_dir, backdoor_target + '.npy')
-        if os.path.isfile(cache_file):
-            all_lbs.append(np.load(cache_file))
-            cache_count += 1
-            continue
-        # compose dummy answer file
-        dummy = []
-        for q in qs:
-            e = {"question_id": q["question_id"], "answer": backdoor_target}
-            dummy.append(e)
-        with open(dummy_file, 'w') as f:
-            json.dump(dummy, f)
-        # compute lower bound
-        vqaRes = vqa.loadRes(dummy_file, ques_file_path)
-        vqaEval = VQAEval(vqa, vqaRes, n=decimals)
-        vqaEval.evaluate()
-        all_lbs.append(vqaEval.accuracy['overall'])
-        # cache lower bound
-        try:
-            np.save(cache_file, vqaEval.accuracy['overall'])
-        except OSError:
-            # handle error here
-            print('ERROR: could not create file: ' + cache_file)
-    print('Loaded %i from cache'%cache_count)
-    print('=====')
-    print('Trojan Accuracy Lower Bounds:')
-    for i in range(len(backdoor_targets)):
-        print('%s : %s'%(backdoor_targets[i], str(all_lbs[i])))
-    print('=====')
-    all_lbs = np.array(all_lbs)
-    print('Max Lower Bound:')
-    srt_idx = np.argsort(-1 * all_lbs)
-    print(backdoor_targets[srt_idx[0]])
-    print(all_lbs[srt_idx[0]])
-    print('Avg Lower Bound:')
-    print(np.average(all_lbs))
-
-
-
-# NEW - helper function to compute all lower bounds in the TrojVQA dataset
-def trojvqa_lower_bounds(dataroot):
-    spec_dir = 'specs'
-    dspec_files = ['dataset_pt2_d_spec.csv', 'dataset_pt3_d_spec.csv', 'dataset_pt4_d_spec.csv', 
-    'dataset_pt5_d_spec.csv', 'dataset_pt6_d_spec.csv']
-    all_targets = []
-    for dsf in dspec_files:
-        dsff = os.path.join(spec_dir, dsf)
-        specs = load_specs(dsff)
-        for s in specs:
-            all_targets.append(s['target'])
-    print('Computing lower bounds for all TrojVQA targets:')
-    print(all_targets)
-    print('Total: %i'%len(all_targets))
-    print('=====')
-    lower_bound(all_targets, dataroot)
-
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--dataroot", type=str, help='data location', default='data/')
-    parser.add_argument('--resdir', type=str, default='results/')
-    parser.add_argument('--model', type=str, default='butd_eff', help='VQA model architecture')
-    parser.add_argument('--model_id', type=str, default='0', help='Model name / id')
-    parser.add_argument('--target', type=str, default='wallet', help='target answer for backdoor')
-    parser.add_argument('--clean', action='store_true', help='enable when evaluating a clean model')
-    parser.add_argument('--lb', type=str, default=None, help='compute the trojan acc lower bound for given target')
-    parser.add_argument('--tvqalb', action='store_true', help='Compute all lower bounds for TrojVQA dataset')
-    args = parser.parse_args()
-    if args.tvqalb:
-        trojvqa_lower_bounds(args.dataroot)
-    elif args.lb is not None:
-        lower_bound([args.lb], args.dataroot)
-    else:
-        eval_suite(args.dataroot, args.resdir, args.model, args.model_id, args.target, args.clean)

spaces/CVPR/LIVE/thrust/thrust/system/omp/detail/equal.h

@@ -1,23 +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 inherits equal
-#include <thrust/system/cpp/detail/equal.h>
-

spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/models/__init__.py

@@ -1,18 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from .GroundingDINO import build_groundingdino
-
-
-def build_model(args):
-    # we use register to maintain models from catdet6 on.
-    from .registry import MODULE_BUILD_FUNCS
-
-    assert args.modelname in MODULE_BUILD_FUNCS._module_dict
-    build_func = MODULE_BUILD_FUNCS.get(args.modelname)
-    model = build_func(args)
-    return model

spaces/ChevyWithAI/rvc-aicover/README.md

@@ -1,14 +0,0 @@
----
-title: Rvc Models
-emoji: 🎤
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ardha27/rvc-models
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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

@@ -1,22 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.utils import make_jpg_or_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def blood_pressure(images: List[BuildImage], texts, args):
-    frame = BuildImage.open(img_dir / "0.png")
-
-    def make(img: BuildImage) -> BuildImage:
-        img = img.convert("RGBA").resize((414, 450), keep_ratio=True)
-        return frame.copy().paste(img, (16, 17), below=True)
-
-    return make_jpg_or_gif(images[0], make)
-
-
-add_meme("blood_pressure", blood_pressure, min_images=1, max_images=1, keywords=["高血压"])

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/feaLib/location.py

@@ -1,12 +0,0 @@
-from typing import NamedTuple
-
-
-class FeatureLibLocation(NamedTuple):
-    """A location in a feature file"""
-
-    file: str
-    line: int
-    column: int
-
-    def __str__(self):
-        return f"{self.file}:{self.line}:{self.column}"

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/G_P_O_S_.py

@@ -1,5 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-class table_G_P_O_S_(BaseTTXConverter):
-    pass

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_m_o_r_x.py

@@ -1,6 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6morx.html
-class table__m_o_r_x(BaseTTXConverter):
-    pass

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-37519934.css

@@ -1 +0,0 @@
-div.svelte-iyf88w{border:var(--block-border-width) solid var(--border-color-primary);background:var(--border-color-primary);border-radius:var(--block-radius);display:flex;flex-direction:column;gap:var(--form-gap-width);overflow:hidden}div.svelte-iyf88w>*:not(.absolute){border:none;border-radius:0}.hide.svelte-iyf88w{display:none}

spaces/Dagfinn1962/diffusers-gallery/index.html

@@ -1,162 +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="Some things that we are working with ! " />
-
-    <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-5 lg:col-span-2 flex flex-col gap-2 row-start">
-        <p class="text-lg font-semibold dark:text-white whitespace-nowrap">We are looking to put some of these in the Members Section -- 
-          Subscribe now </p>
-      </div>
-<!-- here --> 
-        
-      <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>