Training mode: You can practice your skills in training mode where you can adjust the settings such as damage percentage, gravity Cross-play and cross-progression support
-One of the best features of Brawlhalla is that it supports cross-play and cross-progression across all platforms. This means that you can play with anyone, anywhere, on any device, and keep your progress and items across different devices.
-Cross-play allows you to join online matches with players from other platforms such as PC, PS4, Xbox One, Nintendo Switch, iOS, and Android. You can also invite your friends from different platforms to join your custom lobby or party. Cross-play is enabled by default, but you can disable it in the settings if you want.
-Cross-progression allows you to link your Brawlhalla account to your Ubisoft account and sync your progress and items across different platforms. This means that you can switch between devices without losing your unlocked legends, skins, colors, taunts, KO effects, etc. You can also access your ranked stats, glory points, gold coins, mammoth coins, etc. on any device. Cross-progression is optional, but you can enable it in the settings if you want.
- How to download Brawlhalla for free?
-The available platforms and devices
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-PC: You can download Brawlhalla for free on Steam or the Ubisoft Store. You can play with a keyboard or a controller.PS4: You can download Brawlhalla for free on the PlayStation Store. You can play with a PS4 controller or a compatible controller.Xbox One: You can download Brawlhalla for free on the Microsoft Store. You can play with an Xbox One controller or a compatible controller.Nintendo Switch: You can download Brawlhalla for free on the Nintendo eShop. You can play with a Joy-Con, a Pro Controller, or a compatible controller.iOS: You can download Brawlhalla for free on the App Store. You can play with touch controls or a compatible controller.Android: You can download Brawlhalla for free on the Google Play Store. You can play with touch controls or a compatible controller.
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-The steps to download and install Brawlhalla are similar for each platform. Here are the general steps:
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-Go to the store of your platform: For PC, go to Steam or the Ubisoft Store. For PS4, go to the PlayStation Store. For Xbox One, go to the Microsoft Store. For Nintendo Switch, go to the Nintendo eShop. For iOS, go to the App Store. For Android, go to the Google Play Store.Search for Brawlhalla: Type "Brawlhalla" in the search bar and find the game in the results.Download the game: Click on the "Download" or "Install" button and wait for the game to download and install on your device.Launch the game: Once the game is installed, click on the "Play" or "Launch" button and enjoy!
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-Brawlhalla is a lightweight game that does not require high-end hardware or a lot of storage space. Here are the system requirements and file size for each platform:
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-Platform System Requirements File Size PC - OS: Windows 7 or later About 350 MB PS4 - OS: PlayStation 4 system software About 500 MB Xbox One - OS: Xbox One system software About 500 MB Nintendo Switch - OS: Nintendo Switch system software About 500 MB iOS - OS: iOS 11.0 or later About 300 MB Android - OS: Android 5.0 or later About 300 MB
- How to play Brawlhalla online multiplayer?
-The online matchmaking and ranking system
-Brawlhalla has a robust online matchmaking and ranking system that allows you to play with other players from around the world. You can join online matches in two ways:
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-Casual matches: You can join casual matches for fun and practice. You can choose from various game modes such as free-for-all, strikeout, experimental, friendly 2v2, etc. You can also create or join custom lobbies where you can set your own rules and invite your friends.Ranked matches: You can join ranked matches to compete and earn glory points. You can choose from two game modes: 1v1 or 2v2. You can also team up with a friend or a random partner for 2v2 matches. You will be matched with players of similar skill level based on your elo rating. Your elo rating will increase or decrease depending on your wins and losses. You will also earn glory points based on your peak elo rating and number of games played at the end of each season.
- The different online game modes and options
-Brawlhalla has many online game modes and options that you can choose from depending on your preference and mood. Some of them are:
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-Free-for-all: This is a classic mode where four players fight each other until the time runs out. The player with the most points wins.Strikeout: This is a mode where you choose three legends and fight in a 1v1 match. Each time you lose a stock, you switch to the next legend in your lineup. The first player to lose all three legends loses.Experimental: This is a mode where you can test new features and changes that are not yet implemented in the main game. You can play in a 1v1 match with random settings and modifiers.Friendly 2v2: This is a mode where you can team up with a friend or a random partner and fight against another team of two players in a casual match.Brawlball: This is a mode where two teams of four players compete to score goals by carrying a ball to the opponent's goal zone. You can attack the ball carrier to make them drop the ball.Capture the flag: This is a mode where two teams of four players compete to capture the opponent's flag and bring it back to their base. You can attack the flag carrier to make them drop the flag.Kung foot: This is a mode where two teams of four players compete to score goals by kicking a ball into the opponent's goal. You can also use your weapons and abilities to hit the ball.Horde mode: This is a mode where you can team up with up to three other players and fight against waves of zombies that try to destroy your base. You can use your weapons and abilities to fend off the zombies.Friendly fire off mode: This is a mode where you can play any game mode with friendly fire turned off. This means that you cannot hurt your teammates with your attacks.Custom lobby: This is a mode where you can create or join a custom lobby where you can set your own rules and invite your friends or other players. You can choose from any game mode, map, legend, stock, time, damage, items, etc.
- The tips and tricks to improve your skills and win matches
-Brawlhalla is a game that requires skill, strategy, and practice to master. Here are some tips and tricks that can help you improve your skills and win matches:
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-Learn the basics: Before you jump into online matches, you should learn the basics of the game such as how to move, jump, dodge, attack, attack, throw, pick up, and use weapons and items. You can also learn the special moves and combos of each legend and weapon. You can practice these skills in training mode or in casual matches.Choose your legend wisely: Brawlhalla has over 50 playable legends, each with their own strengths and weaknesses. You should choose a legend that suits your playstyle and preference. You can also try different legends and see which ones you like the most. You can also switch between two weapons during a match, so you should learn how to use both of them effectively.Use your dodge wisely: Dodging is a crucial skill that can help you avoid attacks, escape combos, and reposition yourself. You can dodge in any direction by pressing the dodge button. However, you have a cooldown after each dodge, so you should use it wisely and not spam it. You can also use a spot dodge, which is a dodge that does not move you in any direction, but makes you invulnerable for a brief moment. You can also use a chase dodge, which is a dodge that moves you in the direction of your last attack, and can be used to extend your combos or chase your opponents.Use your weapons and items effectively: Weapons and items are scattered around the stage and can be picked up by anyone. You can use them to deal damage, knock out your opponents, or disrupt their attacks. You can also throw them at your opponents by pressing the throw button. You should learn how to use each weapon and item effectively and adapt to the situation. You should also be aware of what weapons and items your opponents have and how to counter them.Use the stage to your advantage: Brawlhalla has various stages that have different layouts, platforms, hazards, and items. You should learn how to use the stage to your advantage and avoid its disadvantages. You should also be aware of where you are on the stage and where your opponents are. You should try to control the center of the stage and avoid being near the edges or corners where you can be easily knocked out.Play smart and have fun: Brawlhalla is a game that requires smart thinking and quick reactions. You should always be aware of your surroundings, your opponents, your health, your weapons, your items, etc. You should also try to predict your opponents' moves and counter them accordingly. You should also try to mix up your attacks and not be predictable or repetitive. However, you should also remember to have fun and enjoy the game. Brawlhalla is a game that can be played casually or competitively, so you should play it however you like.
- Conclusion
-A summary of the main points and benefits of playing Brawlhalla
-Brawlhalla is a free-to-play 2D platform fighting game that supports up to 8 players in various modes and maps. You can choose from over 50 unique characters, each with their own weapons and abilities, and brawl to prove who's the best in an epic test of strength and skill.
-Brawlhalla is a game that is easy to learn but hard to master. It has many features and modes that make it fun and diverse. It also supports cross-play and cross-progression across all platforms and devices.
-Brawlhalla is a game that can be played with anyone, anywhere, on any device. It is a game that is suitable for all ages and skill levels. It is a game that is constantly updated with new content and features.
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-If you are interested in playing Brawlhalla, you can download it for free on any platform or device right now. You can also visit the official website or follow the social media accounts for more information and updates.
-What are you waiting for? Download Brawlhalla now and join the millions of players who are already enjoying this awesome game!
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- The basic gameplay mechanics and controls of Car Dealership Simulator are simple and intuitive. You can use the touch screen to navigate through the game menus and options, as well as interact with the cars and customers in your dealership. You can also use the buttons on the bottom of the screen to access different functions and modes, such as buying cars, selling cars, cleaning cars, repairing cars, upgrading cars, hiring staff, managing finances, checking statistics, and more. You can also tap on the icons on the top of the screen to see your current money balance, reputation level, customer satisfaction level, inventory level, staff level , and time. You can also swipe left or right to switch between different views of your dealership, such as the exterior, the interior, the garage, the showroom, and the office. You can also zoom in or out to see more details or a wider perspective of your dealership. The game also features a tutorial mode that will guide you through the basics of the game and explain the various functions and features of the game.
The tips and tricks to succeed in the game
- Car Dealership Simulator is a game that requires strategy, skill, and patience to succeed. You will have to make smart decisions and manage your resources wisely to grow your business and earn more money and reputation. Here are some tips and tricks that can help you become a better car dealer in the game: - Buy low and sell high. This is the basic principle of any business, and it applies to Car Dealership Simulator as well. You should always try to buy cars at a lower price than their market value, and sell them at a higher price than their purchase price. You can use the diagnosis tool to check the condition and value of a car before buying it, and use the valuation tool to check the demand and popularity of a car before selling it. You can also use the negotiation skill to bargain with sellers and buyers and get a better deal. - Clean and repair your cars. Another way to increase the value and appeal of your cars is to clean and repair them before selling them. You can use the cleaning tool to remove dirt, dust, stains, scratches, and other imperfections from your cars, and use the repair tool to fix any mechanical or electrical issues or damages on your cars. You can also use the upgrade tool to improve the performance and appearance of your cars by adding new parts, accessories, paint jobs, decals, and more. Cleaning, repairing, and upgrading your cars will not only increase their price, but also their customer satisfaction level. - Know your customers. One of the most important aspects of running a car dealership is knowing your customers and their needs and wants. You should always pay attention to the details and preferences of each customer, such as their budget, personality, mood, expectation, satisfaction, loyalty, and more. You should also try to match them with the best car for them based on their criteria, such as size, type, color, brand, model, year, condition, features, and more. You should also use your communication skills to build rapport with your customers, such as greeting them politely, answering their questions honestly, offering them discounts or incentives , giving them compliments or suggestions, and thanking them for their purchase. Knowing your customers and satisfying them will not only increase your sales and profits, but also your reputation and referrals. - Manage your finances and resources. Running a car dealership is not only about buying and selling cars, but also about managing your finances and resources. You should always keep track of your income and expenses, such as the cost of buying, cleaning, repairing, upgrading, advertising, and maintaining your cars, as well as the revenue from selling them. You should also keep track of your inventory and staff levels, such as the number of cars you have in stock, the space you have in your dealership, the number of staff you have hired, and the salary you have to pay them. You should also keep track of your statistics and performance, such as the number of customers you have served, the number of cars you have sold, the average price and profit per car, the customer satisfaction and loyalty levels, and the reputation level. You should also plan ahead and set goals and budgets for your business, such as how much money you want to make, how many cars you want to buy or sell, how much you want to invest in your dealership, and how much you want to grow your business. Managing your finances and resources will help you optimize your business and avoid any financial or operational problems.
The features and updates of the game
- Car Dealership Simulator is a game that is constantly being updated and improved by its developers. The game features many features and updates that make it more enjoyable and realistic. Some of the features and updates of the game are: - A realistic economy system that changes based on supply and demand, market trends, seasons, events, and more. - A dynamic weather system that affects the appearance and condition of your cars, as well as the mood and behavior of your customers. - A day-night cycle that affects the visibility and activity of your dealership. - A sandbox mode that allows you to play with unlimited money and resources. - A multiplayer mode that allows you to play with or against other players online. - A customization mode that allows you to design and decorate your own dealership. - A photo mode that allows you to take pictures of your cars and share them with other players. - A leaderboard system that allows you to compare your performance and achievements with other players. - A feedback system that allows you to rate and review the game and suggest new features or improvements.
Conclusion
- Car Dealership Simulator is a simulation game that lets you run your own car dealership business on your phone. You can buy, price, clean, repair, upgrade, advertise, and sell cars to various customers with different needs and wants. You can also upgrade and expand your dealership with new equipment, tools, staff, buildings, land, and more. You can also enjoy the realistic graphics, sounds, physics, animations, weather effects , day-night cycles, and more that the game offers. You can also download and install the game's APK file from a trusted source and enjoy the latest updates and features of the game. Car Dealership Simulator is a game that will test your business skills, negotiation skills, and customer service skills as you try to build the car buying and selling empire of your dreams.
- FAQs
- Here are some frequently asked questions about Car Dealership Simulator and its APK file:
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- Q: Is Car Dealership Simulator free to play?
-A: Yes, Car Dealership Simulator is free to play on Steam. However, you may need to pay for some in-game items or features, such as premium cars, upgrades, or currency. You can also download and install the game's APK file for free from a trusted source, but you may encounter some ads or limitations in the game.
- Q: Is Car Dealership Simulator safe to download and install?
-A: Car Dealership Simulator is safe to download and install from Steam, as it is verified and approved by the official app store. However, if you download and install the game's APK file from an unknown source, you may expose your device to malware, viruses, spyware, adware, or other harmful software that can damage your device or compromise your privacy and security. Therefore, you should always use caution and discretion when downloading and installing APK files from unknown sources, and only do so at your own risk.
- Q: How can I update Car Dealership Simulator?
-A: If you download and install Car Dealership Simulator from Steam, you can update the game automatically or manually through the app store. However, if you download and install the game's APK file from an unknown source, you may not be able to update the game through the app store. Instead, you may need to download and install the latest version of the APK file from the same or another trusted source. You may also need to uninstall the previous version of the APK file before installing the new one.
- Q: How can I contact the developers of Car Dealership Simulator?
-A: If you have any questions, feedbacks, suggestions, or issues about Car Dealership Simulator, you can contact the developers of the game through their email address: quadfixgames@gmail.com. You can also follow them on their social media accounts: Facebook, Twitter, Instagram, YouTube, and Discord. You can also visit their website for more information about their games and projects.
- Q: What are some similar games to Car Dealership Simulator?
-A: If you enjoy playing Car Dealership Simulator, you may also like some similar games that involve car buying and selling mechanics, such as: - Car Mechanic Simulator 2021 : A simulation game that lets you repair, paint, tune, and drive cars in a realistic way. - Car Trader Simulator : A simulation game that lets you buy damaged cars, renovate them, and sell them for a profit. - Dealer's Life 2 : A simulation game that lets you run your own pawn shop and deal with various items and customers. - Junkyard Tycoon : A simulation game that lets you manage your own junkyard and recycle scrap metal into valuable products. - Motor Depot : A simulation game that lets you drive various vehicles in an open world environment.
401be4b1e0Game Turbo Blue APK: What Is It and How to Download It
-If you are a gamer who loves playing high-end games on your Android device, you might have heard of game turbo blue apk. This is a modified version of the original game turbo app that comes pre-installed on Xiaomi devices. It is designed to enhance your gaming performance by optimizing your device's resources, graphics, and network. In this article, we will tell you what game turbo blue apk is, how it works, how to download and install it on your device, how to use it to boost your gaming experience, and what are its pros and cons.
- Introduction
-Game turbo blue apk is an app that allows you to improve your gaming performance on your Android device. It does so by adjusting your device's settings, such as CPU, GPU, RAM, battery, network, etc., according to the requirements of each game. It also provides you with a variety of features, such as in-game toolbox, shortcuts, screen recording, screenshot, floating window, etc., that make your gaming more convenient and enjoyable.
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Game turbo blue apk is based on the original game turbo app that comes built-in on Xiaomi devices. However, it has some additional features and improvements that make it more powerful and versatile. For example, it supports more games, has more customization options, has a better user interface, etc. It also works on non-Xiaomi devices, so you can use it on any Android device that runs Android 6.0 or higher.
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Q3: Does game turbo blue apk work on all Android devices and games?
-A3: Game turbo blue apk works on any Android device that runs Android 6.0 or higher. However, it may not be compatible with some devices or games. If you encounter any problems or errors while using game turbo blue apk, try uninstalling it and reinstalling it again. You can also contact the developers of game turbo blue apk for support and feedback.
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- 197e85843dHot Wheels: Race Off Mod Apk - A Fun and Exciting Racing Game for Android
-If you are a fan of racing games and Hot Wheels toys, you will love Hot Wheels: Race Off Mod Apk. This is a modified version of the original game that gives you unlimited money, all cars unlocked, and no ads. You can enjoy racing on over 60 tracks with more than 40 different Hot Wheels cars. You can also perform amazing stunts, collect coins, and upgrade your cars to make them faster and stronger.
-What is Hot Wheels: Race Off Mod Apk?
-Hot Wheels: Race Off Mod Apk is a racing game for Android devices that is based on the popular toy brand Hot Wheels. The game was developed by Hutch Games and has more than 100 million downloads on Google Play Store. The game lets you race on various tracks that are inspired by the real Hot Wheels sets. You can also customize your cars with different colors, wheels, engines, and more.
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Features of Hot Wheels: Race Off Mod Apk
-The mod apk version of Hot Wheels: Race Off has some extra features that make the game more fun and easy to play. Here are some of the features of Hot Wheels: Race Off Mod Apk:
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-This feature allows you to access all the cars in the game without having to complete any levels or challenges. You can choose from over 40 different Hot Wheels cars, each with its own unique design and performance. You can also switch between cars anytime you want.
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-The final step is to install the mod apk file on your device. To do this, locate the file in your downloads folder and tap on it. You will see a pop-up window asking for your permission to install the app. Tap on Install and wait for the installation process to finish. Once it is done , you can launch the game and enjoy the mod features.
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-Playing Hot Wheels: Race Off Mod Apk is very easy and fun. You just need to follow these simple steps:
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-Conclusion
-Hot Wheels: Race Off Mod Apk is a fun and exciting racing game for Android devices that will make you feel like a kid again. You can race on various tracks with different Hot Wheels cars and perform amazing stunts. You can also enjoy unlimited money, all cars unlocked, and no ads with the mod apk version of the game. If you are looking for a thrilling and addictive racing game, you should download Hot Wheels: Race Off Mod Apk today.
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-Can I restore my progress if I uninstall Hot Wheels: Race Off Mod Apk? No, Hot Wheels: Race Off Mod Apk does not support cloud saving or backup of your progress. If you uninstall the game or delete the data of the game, you will lose all your progress and coins. Therefore, you should make sure that you have enough space on your device before installing the game or backup your data before uninstalling it.
- 401be4b1e0Clash of Clans Mod APK Raja APK: Everything You Need to Know
-Are you a fan of strategy games? Do you want to build your own village, train your army, and fight against millions of players worldwide? If yes, then you might have heard of Clash of Clans, one of the most popular mobile games in the world. But did you know that there is a way to enjoy the game even more with unlimited resources, access to all features, and no restrictions? Yes, we are talking about Clash of Clans Mod APK Raja APK, a modified version of the game that lets you play with more freedom and fun. In this article, we will tell you everything you need to know about this modded version, including its benefits, risks, and installation guide. Read on to find out more!
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What is Clash of Clans?
-Clash of Clans is a freemium strategy game developed by Supercell, a Finnish game company. It was released in 2012 for iOS devices and in 2013 for Android devices. Since then, it has become one of the most downloaded and played games in the world, with over 500 million downloads on Google Play Store alone. The game has also received many awards and accolades, such as being named as one of the best games by Google Play and Apple App Store.
-Features of Clash of Clans
-Clash of Clans is a game that offers a lot of features and content for players to enjoy. Here are some of the main features that make the game so addictive and fun:
-Build your village and lead your clan
-The core gameplay of Clash of Clans is to build your own village from scratch and turn it into a strong fortress. You can customize your village with various buildings, such as barracks, town hall, walls, defenses, mines, collectors, storages, traps, and more. You can also join or create a clan with other players and cooperate with them in various activities, such as donating troops, requesting reinforcements, chatting, and sharing strategies.
-Defend your base and raid others
-Another aspect of Clash of Clans is to defend your base from enemy attacks and raid other players' bases to loot their resources. You can use different types of troops, spells, heroes, and siege machines to attack or defend. You can also choose from different modes of attack, such as single-player campaign against the Goblin King, multiplayer battles against random opponents, friendly challenges or wars against clanmates or friends, or special events and challenges.
-Join epic clan wars and leagues
-One of the most exciting features of Clash of Clans is to participate in clan wars and leagues with your clan. Clan wars are team-based competitions where two clans face each other in a series of two attacks, and the clan with the most stars and percentage wins the war. Clan leagues are monthly tournaments where clans compete in a league system with seven other clans of similar skill level. The clans with the most trophies at the end of the season get promoted to higher leagues and earn more rewards.
-Upgrade your troops, spells, and heroes
-As you progress in the game, you can upgrade your troops, spells, and heroes to make them more powerful and effective. You can use the laboratory to research new levels and abilities for your troops and spells. You can also use the hero altar to upgrade your heroes, such as the Barbarian King, the Archer Queen, the Grand Warden, and the Royal Champion. Each hero has a unique skill that can be activated during battles.
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-Explore the Builder Base and the Clan Capital
-Besides the main village, you can also explore two other areas in Clash of Clans: the Builder Base and the Clan Capital. The Builder Base is a separate island where you can build a second base with different buildings, troops, and mechanics. You can also engage in versus battles with other players and earn loot and trophies. The Clan Capital is a new feature that was introduced in 2021. It is a shared area where your clan can build a common base with various structures, such as clan hall, clan treasury, clan academy, clan monument, and more. You can also participate in clan quests and events to earn clan perks and rewards.
-What is Clash of Clans Mod APK Raja APK?
-Clash of Clans Mod APK Raja APK is a modified version of Clash of Clans that is created by a third-party developer named Raja APK. It is not an official version of the game and it is not endorsed or supported by Supercell. The modded version offers some features and advantages that are not available in the original version of the game. However, it also comes with some risks and drawbacks that you should be aware of before downloading it.
-Benefits of downloading the modded version
-Some of the benefits of downloading Clash of Clans Mod APK Raja APK are:
-Unlimited gems, gold, and elixir
-Gems, gold, and elixir are the main currencies in Clash of Clans that are used to buy and upgrade various items in the game. Normally, you have to earn them by playing the game, completing achievements, or purchasing them with real money. However, with Clash of Clans Mod APK Raja APK, you can get unlimited gems, gold, and elixir for free. You can use them to speed up your progress, unlock new features, and enjoy the game without any limitations.
-Access to all troops, buildings, and upgrades
-In Clash of Clans, you have to unlock new troops, buildings, and upgrades by reaching certain levels or completing certain tasks. Some of them require a lot of time and resources to unlock. However, with Clash of Clans Mod APK Raja APK, you can access all troops, buildings, and upgrades from the start. You can build your dream village and army without any restrictions or waiting time.
-No root required
-Some modded versions of Clash of Clans require you to root your device in order to install them. Rooting is a process that gives you full control over your device's system settings and files. However, rooting also voids your device's warranty and exposes it to security risks. Moreover, rooting is not easy or safe for everyone to do. However, with Clash of Clans Mod APK Raja APK, you do not need to root your device to install it. You can simply download and install it like any other app.
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-Clash of Clans Mod APK Raja APK is free to download and play. You do not need to pay any money or subscription fees to enjoy it. You can simply visit the website of Raja APK and download the latest version of the modded APK for free.
Risks and precautions of using the modded version
-While Clash of Clans Mod APK Raja APK has many benefits, it also has some risks and drawbacks that you should be aware of before downloading it. Some of the risks and precautions of using the modded version are:
-Possible ban from the official server
-One of the biggest risks of using Clash of Clans Mod APK Raja APK is that you might get banned from the official server of Clash of Clans. This is because Supercell, the developer of the game, does not allow or support any modded versions of the game. They have a strict policy against cheating and hacking, and they can detect and ban any account that uses a modded version. If you get banned, you will lose your progress, your clan, and your account permanently. You will not be able to play the game again with the same account or device.
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-Another risk of using Clash of Clans Mod APK Raja APK is that you might get infected with malware or virus. This is because the modded version is not verified or tested by Google Play Store or any other trusted source. It is created by a third-party developer who might have malicious intentions or hidden codes in the modded APK. If you download and install the modded APK from an unknown or untrusted website, you might expose your device and your data to security threats. You might get hacked, spammed, or scammed by hackers or scammers who can access your device and your information.
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-A third risk of using Clash of Clans Mod APK Raja APK is that you might lose your progress or data. This is because the modded version is not compatible or synchronized with the original version of the game. You cannot connect your modded account to your Google Play account or your Facebook account. You cannot backup or restore your data with Google Play Cloud or any other cloud service. You cannot transfer your data from one device to another. If you delete the modded APK, uninstall the game, or change your device, you will lose your progress and data forever.
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-Use a secondary account or device to play the modded version. Do not use your main account or device that you use for the original version of the game. This way, you can protect your progress, your clan, and your account from getting banned.
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-The final step is to launch the game and enjoy. You can find the game icon on your device's home screen or app drawer. You can tap on it and start playing Clash of Clans Mod APK Raja APK with unlimited resources, access to all features, and no restrictions. You can also connect with other players who are using the same modded version and have fun together.
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-Clash of Clans is a great game that offers a lot of fun and excitement for strategy game lovers. However, if you want to experience more freedom and fun in the game, you can try Clash of Clans Mod APK Raja APK, a modified version of the game that gives you unlimited resources, access to all features, and no restrictions. However, you should also be aware of the risks and precautions of using the modded version, such as possible ban from the official server, potential malware or virus infection, loss of progress or data, and how to avoid or minimize them. If you follow this guide, you can download and install Clash of Clans Mod APK Raja APK on your device easily and safely. We hope you enjoy playing Clash of Clans Mod APK Raja APK and have a blast!
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-Do you love adventure games that are full of mystery, suspense, and paranormal activity? If yes, then you should try Treasure of Mystery Island 3 APK , a thrilling game that will take you on a ghostly journey across a tropical island.
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So, let's get started!
- What is Treasure of Mystery Island 3?
-Treasure of Mystery Island 3 is an adventure game developed by Alawar Entertainment, Inc., a company that specializes in casual games for various platforms. It is the third installment in the popular Treasure of Mystery Island series, which follows the adventures of a young journalist who is sent to investigate paranormal phenomena on different islands.
-The game is also known as The Treasures of Mystery Island 3: The Ghost Ship , which is its official title on Google Play Store. However, since many players refer to it as Treasure of Mystery Island 3 APK , we will use this name throughout the article.
- The story of Treasure of Mystery Island 3
-The game follows the story of Alex, a young journalist who is sent to a remote island in the Pacific Ocean to cover a story about a mysterious ship that appears every 27 years. The ship is said to be haunted by the ghosts of its crew, who died in a tragic accident many years ago.
-Alex arrives on the island and meets Lisa, a local girl who works as a guide. Together, they explore the island and discover its secrets, while trying to avoid the dangers that lurk in the shadows. They also encounter some friendly and not-so-friendly characters, who help or hinder them along the way.
-The game is divided into four chapters, each with its own plot and challenges. The chapters are:
-
-Chapter 1: The Arrival
-Chapter 2: The Ghost Ship
-Chapter 3: The Curse of the Island
-Chapter 4: The Final Battle
-
-The game has a captivating storyline that will keep you hooked until the end. You will have to solve puzzles, find hidden objects, and interact with various items and characters to progress through the game. You will also have to face some spooky scenes and jump scares that will make your heart race.
- The features of Treasure of Mystery Island 3
-The game has many features that make it an enjoyable and immersive experience. Some of these features are:
-treasure of mystery island 3 android game
- Four massive chapters
-The game has four chapters that span over 60 locations. Each chapter has its own theme, atmosphere, and difficulty level. You will have to explore different areas of the island, such as beaches, jungles, caves, temples, and more. You will also have to visit the ghost ship and uncover its secrets.
- 60 spine-shivering locations
-The game has 60 locations that are rich in detail and variety. The graphics are stunning and realistic, and the sound effects are eerie and atmospheric. You will feel like you are really on the island, surrounded by mystery and danger. You will also encounter some amazing animations and special effects that will enhance your gameplay.
- Unlimited hints and tips
-The game has unlimited hints and tips that will help you if you get stuck or need some guidance. You can use the hint button to reveal a hidden object or a clue, or you can use the tip button to get a detailed explanation of what to do next. You can also skip puzzles if you find them too hard or boring.
- Interactive tutorial
-The game has an interactive tutorial that will teach you how to play the game and use its features. The tutorial is optional and can be accessed at any time from the main menu. It will show you how to navigate the game interface, how to interact with items and characters, how to solve puzzles and find hidden objects, and more.
- How to download and install Treasure of Mystery Island 3 APK?
-If you want to play Treasure of Mystery Island 3 APK on your Android device, you will need to download and install it from a trusted source. Here are the steps you need to follow:
- Downloading Treasure of Mystery Island 3 APK from a trusted source
-The first step is to download the APK file of the game from a trusted source. You can use this link to download it safely and securely. The file size is about 300 MB, so make sure you have enough space on your device and a stable internet connection.
- Installing Treasure of Mystery Island 3 APK on your Android device
-The second step is to install the APK file on your Android device. Before you do that, you need to enable the installation of apps from unknown sources on your device settings. To do that, follow these steps:
-
-Go to your device settings and tap on Security or Privacy.
-Find the option that says Unknown Sources or Install Unknown Apps and toggle it on.
-You may see a warning message that says installing apps from unknown sources may harm your device. Tap on OK or Allow to proceed.
-
-Once you have enabled the installation of apps from unknown sources, you can install the APK file by following these steps:
-
-Locate the APK file on your device storage using a file manager app or your browser downloads.
-Tap on the APK file and select Install.
-You may see a pop-up window that asks for permissions to access your device features with them. To answer this question, we have created a table that shows the differences between Treasure of Mystery Island 1 , Treasure of Mystery Island 2 , and Treasure of Mystery Island 3 . Here it is:
-
-
-Game
-Title
-Release Date
-Story
-Features
-
-
-Treasure of Mystery Island 1
-The Treasures of Mystery Island
-2008
-Alex is sent to a tropical island to deliver a package, but he finds himself in the middle of a volcanic eruption and a mystery involving ancient artifacts.
-- 20 thrilling episodes
-
-
-Treasure of Mystery Island 2
-The Treasures of Mystery Island: The Gates of Fate
-2010
-Alex and Lisa are separated by a time portal and have to find each other and stop a cataclysmic event that threatens the island.
-- 14 exciting chapters
-
-
-Treasure of Mystery Island 3
-The Treasures of Mystery Island 3: The Ghost Ship
-2011
-Alex is sent to a remote island to investigate a ghost ship that appears every 27 years. He meets Lisa, a local guide, and they uncover the secrets of the island and the ship.
-- Four massive chapters
-
-
- As you can see, each game has its own unique title, story, and features. However, they all share the same genre, characters, and gameplay. They are all adventure games that involve finding hidden objects, solving puzzles, and exploring exotic locations. They also feature Alex and Lisa as the main protagonists, who have to work together to solve the mysteries of the islands.
- Therefore, we can say that Treasure of Mystery Island 3 APK is similar to its previous versions in some aspects, but different in others. It has a new story, new locations, new challenges, and new graphics. However, it also retains the same charm, fun, and excitement that made the series popular among adventure game fans.
- What are the reviews of Treasure of Mystery Island 3?
-If you are wondering what other players think about Treasure of Mystery Island 3 APK , you can read some of their reviews on Google Play Store or other platforms. Here are some examples of positive and negative reviews:
- The positive reviews of Treasure of Mystery Island 3
-
-"I love this game! It has a great story, beautiful graphics, and challenging puzzles. It is one of the best adventure games I have ever played. I highly recommend it to anyone who likes mystery and suspense."
-"This game is awesome! It has everything I look for in an adventure game: a captivating plot, stunning visuals, spooky atmosphere, and engaging gameplay. It is also very easy to play and has unlimited hints and tips. I enjoyed every minute of it."
-"This game is amazing! It has a lot of variety and surprises. It is not just a hidden object game, but also a puzzle game, an action game, and a horror game. It kept me on the edge of my seat until the end. It is definitely worth playing."
-
- The negative reviews of Treasure of Mystery Island 3
-
-"I hate this game! It is too hard, too scary, and too boring. It has too many puzzles, too many hidden objects, and too many ghosts. It is also very glitchy and crashes often. I wasted my time and money on it."
-"This game is terrible! It has a lame story, poor graphics, and annoying sound effects. It is also very repetitive and predictable. It has nothing new or original to offer. I regret downloading it."
-"This game is disappointing! It is not as good as the previous versions. It has a weak story, dull locations, and easy puzzles. It is also very short and ends abruptly. It does not live up to the expectations."
-
- As you can see, the reviews are mixed. Some players love the game and praise it for its story, graphics, and gameplay, while others hate it and criticize it for its difficulty, glitches, and lack of originality. However, the majority of the reviews are positive, and the game has a rating of 4.3 out of 5 stars on Google Play Store. Therefore, we can say that Treasure of Mystery Island 3 APK is a game that has received mostly positive feedback from its players, and that it is a game that you should try if you are a fan of adventure games.
Conclusion
-In conclusion, Treasure of Mystery Island 3 APK is a ghostly adventure game for Android that will take you on a thrilling journey across a tropical island. You will have to solve puzzles, find hidden objects, and uncover the secrets of the island and the ghost ship that haunts it.
-The game has a captivating story, stunning graphics, and challenging gameplay. It also has unlimited hints and tips, an interactive tutorial, and four massive chapters. It is one of the best adventure games you can play on your Android device.
-If you want to play Treasure of Mystery Island 3 APK , you can download it from this link and install it on your device following the steps we have explained in this article. You will not regret it!
-Thank you for reading this article. We hope you found it helpful and informative. If you have any questions or comments about the game or the article, please feel free to leave them below. We would love to hear from you!
- FAQs
-Here are some frequently asked questions about Treasure of Mystery Island 3 APK :
- Q: Is Treasure of Mystery Island 3 APK free?
-A: Yes, Treasure of Mystery Island 3 APK is free to download and play. However, it may contain some in-app purchases or ads that you can choose to buy or ignore.
- Q: Is Treasure of Mystery Island 3 APK safe?
-A: Yes, Treasure of Mystery Island 3 APK is safe to download and install on your device. However, you should always download it from a trusted source, such as the link we have provided in this article, and enable the installation of apps from unknown sources on your device settings.
- Q: Is Treasure of Mystery Island 3 APK compatible with my device?
-A: Treasure of Mystery Island 3 APK is compatible with most Android devices that run on Android 4.0 or higher. However, some devices may have different specifications or performance issues that may affect the game's functionality or quality. You can check the game's requirements and compatibility on Google Play Store before downloading it.
- Q: How long is Treasure of Mystery Island 3 APK?
-A: The length of Treasure of Mystery Island 3 APK may vary depending on your skill level, game mode, and playing style. However, on average, it may take you about 4 to 6 hours to complete the game.
- Q: Can I play Treasure of Mystery Island 3 APK offline?
-A: Yes, you can play Treasure of Mystery Island 3 APK offline once you have downloaded and installed it on your device. However, you may need an internet connection to access some features or updates that may be available online.
- : https://www.alawar.com/game/the-treasures-of-mystery-island-the-ghost-ship/ : https://play.google.com/store/apps/details?id=com.alawar.tomi3full&hl=en_US&gl=US 401be4b1e0Note You may be prompted to provide the path of the driver. Windows may have the driver built-in, or may still have the driver files installed from the last time that you set up the device. If you are asked for the driver and you do not have it, you can try to download the latest driver from the hardware vendor's website.
-Note You may be prompted to provide the path of the driver. Windows may have the driver built-in, or may still have the driver files installed from the last time that you set up the device. However, sometimes, it will open the New Hardware Wizard which may ask for the driver. If you are asked for the driver and you do not have it, you can try to download the latest driver from the hardware vendor's website.
-bluetooth peripheral device driver download for windows 7 ultimate 32 19 Download File ⚙ https://ssurll.com/2uzxyH
Now, the Device Manager will search for the relevant driver automatically. It will also download and install the driver for you. After the driver has been updated, we suggest that you try to connect your mobile device to your PC again to see if the error is gone.
-Using the BitDefender driver updater is very easy. Just download this program from the official website and then run it to scan your system for the latest Bluetooth Peripheral Device Driver version. The application will detect all the peripheral devices and check whether they support Bluetooth technology. If it detects the required driver, you will be given the option to download and install the latest one.
-You may want to use the latest drivers even if you have just purchased the same device. Updating the Bluetooth peripheral device driver download ensures that the device functions properly and also that you do not experience any errors or connectivity problems. With this, you can use the device in a better way. If you download the latest driver update, it will ensure that your device operates in perfect condition.
-If you do not want to take any risks and want to ensure the smooth functioning of your Bluetooth-enabled device, you can go in for the Bluetooth bit driver updater. You just have to download the latest driver update and install it on your system. You do not have to worry about compatibility issues. This is because the driver is designed to work with the most recent operating systems. It will make sure that your device operates without any errors and is also safe to use.
-For Bluetooth peripheral device drivers for Windows 7, you could use device update software to help in installing the drivers. These software companies provide their users with the latest updates in a timely manner. You do not have to worry about updating the drivers because they are easily accessible on the Internet.
-In this guide, you can find out how to download and install Bluetooth driver windows 10, and fix common issues with them such as Bluetooth not working, or Bluetooth not detecting devices on Windows 10.
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-Before downloading a Bluetooth driver, you need to get information about your system and note important details. This will ensure that you download the correct Bluetooth drivers compatible with your setup and Bluetooth devices. You may run into wireless connection issues if you download the incorrect drivers.
-Bluetooth peripheral device driver by default is crucial in allowing connections and data sharing operations. If you attempted to share files via Bluetooth and received an error message, it is likely that your Bluetooth driver is malfunctioning.
-Are you getting the driver error message when enabling Bluetooth to your computer? Maybe you are unable to use Bluetooth devices that you have enabled on a computer such as mobile phones, wireless headsets, mouse and keyboards, microphones, etc. You can use the easy methods in this post to quickly download the peripheral Bluetooth device driver free of charge to find and solve the problem. The most recent anti-virus software has scanned all the downloads available on this website and is guaranteed free of viruses and malware.
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Download the file to a folder on your PC. Uninstall current version of Intel Wireless Bluetooth. Double-click the file to launch installation. How is logitech up to speed with windows 7 drives? I was able to use my MX bluetooth with vista and had no issues and now can not get any drives for windows 7. Anyone know where or when to get the drivers?
-If you could not find the exact driver for your hardware device or you aren't sure which driver is right one, we have a program that will detect your hardware specifications and identify the correct driver for your needs. Please click here to download.
-I have installed Windows 7 on MacBook Pro using BootCamp. Usually when turning my Bluetooth headset on and trying to pair it with Mac for the first time, Windows fails to install the drivers and opens a solution in Action Center, which suggests to download the driver from the Broadcom webpage. This used to work for me before, drivers were installed and everything worked well. However now, when I start the driver installer, it would get stuck at "Detecting Bluetooth Device" stage. There is also a warning with text, which says "Please plug in or turn on your Bluetooth device":
-Before installing the driver the laptop would detect the Bluetooth device (Creative D200 speakers) but not be able to pair due to lack of a driver, which it then searched for but could not download. After installation the speakers work fine. They use the A2DP high quality BlueTooth audio codec.
-Apparently what it tries to find is the bluetooth receiver itself, not the device that connect to it (e.g. headset, mouse etc.). I have no idea why it didn't work with built-in device that is somewhere inside my laptop, but it did with another external bluetooth usb thumb. Once I have plugged it in, the installer has recognized it and installed drivers. Apparently same drivers worked for my built-in bluetooth, so i just unplugged usb thumb and since then it works for me. Hope this will be useful for someone.
aaccfb2cb3crack dongle see electrical expert v4 144 Download Zip ✵ https://ssurll.com/2uzvGc
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/bricks/conv_ws.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/bricks/conv_ws.py
deleted file mode 100644
index a3941e27874993418b3b5708d5a7485f175ff9c8..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/bricks/conv_ws.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .registry import CONV_LAYERS
-
-
-def conv_ws_2d(input,
- weight,
- bias=None,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- eps=1e-5):
- c_in = weight.size(0)
- weight_flat = weight.view(c_in, -1)
- mean = weight_flat.mean(dim=1, keepdim=True).view(c_in, 1, 1, 1)
- std = weight_flat.std(dim=1, keepdim=True).view(c_in, 1, 1, 1)
- weight = (weight - mean) / (std + eps)
- return F.conv2d(input, weight, bias, stride, padding, dilation, groups)
-
-
-@CONV_LAYERS.register_module('ConvWS')
-class ConvWS2d(nn.Conv2d):
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- bias=True,
- eps=1e-5):
- super(ConvWS2d, self).__init__(
- in_channels,
- out_channels,
- kernel_size,
- stride=stride,
- padding=padding,
- dilation=dilation,
- groups=groups,
- bias=bias)
- self.eps = eps
-
- def forward(self, x):
- return conv_ws_2d(x, self.weight, self.bias, self.stride, self.padding,
- self.dilation, self.groups, self.eps)
-
-
-@CONV_LAYERS.register_module(name='ConvAWS')
-class ConvAWS2d(nn.Conv2d):
- """AWS (Adaptive Weight Standardization)
-
- This is a variant of Weight Standardization
- (https://arxiv.org/pdf/1903.10520.pdf)
- It is used in DetectoRS to avoid NaN
- (https://arxiv.org/pdf/2006.02334.pdf)
-
- Args:
- in_channels (int): Number of channels in the input image
- out_channels (int): Number of channels produced by the convolution
- kernel_size (int or tuple): Size of the conv kernel
- stride (int or tuple, optional): Stride of the convolution. Default: 1
- padding (int or tuple, optional): Zero-padding added to both sides of
- the input. Default: 0
- dilation (int or tuple, optional): Spacing between kernel elements.
- Default: 1
- groups (int, optional): Number of blocked connections from input
- channels to output channels. Default: 1
- bias (bool, optional): If set True, adds a learnable bias to the
- output. Default: True
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- bias=True):
- super().__init__(
- in_channels,
- out_channels,
- kernel_size,
- stride=stride,
- padding=padding,
- dilation=dilation,
- groups=groups,
- bias=bias)
- self.register_buffer('weight_gamma',
- torch.ones(self.out_channels, 1, 1, 1))
- self.register_buffer('weight_beta',
- torch.zeros(self.out_channels, 1, 1, 1))
-
- def _get_weight(self, weight):
- weight_flat = weight.view(weight.size(0), -1)
- mean = weight_flat.mean(dim=1).view(-1, 1, 1, 1)
- std = torch.sqrt(weight_flat.var(dim=1) + 1e-5).view(-1, 1, 1, 1)
- weight = (weight - mean) / std
- weight = self.weight_gamma * weight + self.weight_beta
- return weight
-
- def forward(self, x):
- weight = self._get_weight(self.weight)
- return F.conv2d(x, weight, self.bias, self.stride, self.padding,
- self.dilation, self.groups)
-
- def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
- missing_keys, unexpected_keys, error_msgs):
- """Override default load function.
-
- AWS overrides the function _load_from_state_dict to recover
- weight_gamma and weight_beta if they are missing. If weight_gamma and
- weight_beta are found in the checkpoint, this function will return
- after super()._load_from_state_dict. Otherwise, it will compute the
- mean and std of the pretrained weights and store them in weight_beta
- and weight_gamma.
- """
-
- self.weight_gamma.data.fill_(-1)
- local_missing_keys = []
- super()._load_from_state_dict(state_dict, prefix, local_metadata,
- strict, local_missing_keys,
- unexpected_keys, error_msgs)
- if self.weight_gamma.data.mean() > 0:
- for k in local_missing_keys:
- missing_keys.append(k)
- return
- weight = self.weight.data
- weight_flat = weight.view(weight.size(0), -1)
- mean = weight_flat.mean(dim=1).view(-1, 1, 1, 1)
- std = torch.sqrt(weight_flat.var(dim=1) + 1e-5).view(-1, 1, 1, 1)
- self.weight_beta.data.copy_(mean)
- self.weight_gamma.data.copy_(std)
- missing_gamma_beta = [
- k for k in local_missing_keys
- if k.endswith('weight_gamma') or k.endswith('weight_beta')
- ]
- for k in missing_gamma_beta:
- local_missing_keys.remove(k)
- for k in local_missing_keys:
- missing_keys.append(k)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/data/ibims.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/data/ibims.py
deleted file mode 100644
index b66abfabcf4cfc617d4a60ec818780c3548d9920..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/data/ibims.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# MIT License
-
-# Copyright (c) 2022 Intelligent Systems Lab Org
-
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-# File author: Shariq Farooq Bhat
-
-import os
-
-import numpy as np
-import torch
-from PIL import Image
-from torch.utils.data import DataLoader, Dataset
-from torchvision import transforms as T
-
-
-class iBims(Dataset):
- def __init__(self, config):
- root_folder = config.ibims_root
- with open(os.path.join(root_folder, "imagelist.txt"), 'r') as f:
- imglist = f.read().split()
-
- samples = []
- for basename in imglist:
- img_path = os.path.join(root_folder, 'rgb', basename + ".png")
- depth_path = os.path.join(root_folder, 'depth', basename + ".png")
- valid_mask_path = os.path.join(
- root_folder, 'mask_invalid', basename+".png")
- transp_mask_path = os.path.join(
- root_folder, 'mask_transp', basename+".png")
-
- samples.append(
- (img_path, depth_path, valid_mask_path, transp_mask_path))
-
- self.samples = samples
- # self.normalize = T.Normalize(
- # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
- self.normalize = lambda x : x
-
- def __getitem__(self, idx):
- img_path, depth_path, valid_mask_path, transp_mask_path = self.samples[idx]
-
- img = np.asarray(Image.open(img_path), dtype=np.float32) / 255.0
- depth = np.asarray(Image.open(depth_path),
- dtype=np.uint16).astype('float')*50.0/65535
-
- mask_valid = np.asarray(Image.open(valid_mask_path))
- mask_transp = np.asarray(Image.open(transp_mask_path))
-
- # depth = depth * mask_valid * mask_transp
- depth = np.where(mask_valid * mask_transp, depth, -1)
-
- img = torch.from_numpy(img).permute(2, 0, 1)
- img = self.normalize(img)
- depth = torch.from_numpy(depth).unsqueeze(0)
- return dict(image=img, depth=depth, image_path=img_path, depth_path=depth_path, dataset='ibims')
-
- def __len__(self):
- return len(self.samples)
-
-
-def get_ibims_loader(config, batch_size=1, **kwargs):
- dataloader = DataLoader(iBims(config), batch_size=batch_size, **kwargs)
- return dataloader
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/payload_streamer.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/payload_streamer.py
deleted file mode 100644
index 9f8b8bc57cc22fc693da1646bf806c2a6ca8d797..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/payload_streamer.py
+++ /dev/null
@@ -1,75 +0,0 @@
-"""
-Payload implemenation for coroutines as data provider.
-
-As a simple case, you can upload data from file::
-
- @aiohttp.streamer
- async def file_sender(writer, file_name=None):
- with open(file_name, 'rb') as f:
- chunk = f.read(2**16)
- while chunk:
- await writer.write(chunk)
-
- chunk = f.read(2**16)
-
-Then you can use `file_sender` like this:
-
- async with session.post('http://httpbin.org/post',
- data=file_sender(file_name='huge_file')) as resp:
- print(await resp.text())
-
-..note:: Coroutine must accept `writer` as first argument
-
-"""
-
-import types
-import warnings
-from typing import Any, Awaitable, Callable, Dict, Tuple
-
-from .abc import AbstractStreamWriter
-from .payload import Payload, payload_type
-
-__all__ = ("streamer",)
-
-
-class _stream_wrapper:
- def __init__(
- self,
- coro: Callable[..., Awaitable[None]],
- args: Tuple[Any, ...],
- kwargs: Dict[str, Any],
- ) -> None:
- self.coro = types.coroutine(coro)
- self.args = args
- self.kwargs = kwargs
-
- async def __call__(self, writer: AbstractStreamWriter) -> None:
- await self.coro(writer, *self.args, **self.kwargs) # type: ignore[operator]
-
-
-class streamer:
- def __init__(self, coro: Callable[..., Awaitable[None]]) -> None:
- warnings.warn(
- "@streamer is deprecated, use async generators instead",
- DeprecationWarning,
- stacklevel=2,
- )
- self.coro = coro
-
- def __call__(self, *args: Any, **kwargs: Any) -> _stream_wrapper:
- return _stream_wrapper(self.coro, args, kwargs)
-
-
-@payload_type(_stream_wrapper)
-class StreamWrapperPayload(Payload):
- async def write(self, writer: AbstractStreamWriter) -> None:
- await self._value(writer)
-
-
-@payload_type(streamer)
-class StreamPayload(StreamWrapperPayload):
- def __init__(self, value: Any, *args: Any, **kwargs: Any) -> None:
- super().__init__(value(), *args, **kwargs)
-
- async def write(self, writer: AbstractStreamWriter) -> None:
- await self._value(writer)
diff --git a/spaces/deepwisdom/MetaGPT/metagpt/static/cy_aps/assets/style-86c4771e.css b/spaces/deepwisdom/MetaGPT/metagpt/static/cy_aps/assets/style-86c4771e.css
deleted file mode 100644
index 9be5faa32202cc654ecd8a831239661485a6edb9..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/metagpt/static/cy_aps/assets/style-86c4771e.css
+++ /dev/null
@@ -1 +0,0 @@
-/*! normalize.css v8.0.1 | MIT License | github.com/necolas/normalize.css */html{line-height:1.15;-webkit-text-size-adjust:100%;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}body{margin:0}main{display:block}h1{margin:.67em 0;font-size:2em}hr{box-sizing:content-box;height:0;overflow:visible}pre{font-size:1em;font-family:monospace,monospace}a{background-color:transparent}abbr[title]{text-decoration:underline;text-decoration:underline 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245,63,63;--red-7: 203,39,45;--red-8: 161,21,30;--red-9: 119,8,19;--red-10: 77,0,10;--orangered-1: 255,243,232;--orangered-2: 253,221,195;--orangered-3: 252,197,159;--orangered-4: 250,172,123;--orangered-5: 249,144,87;--orangered-6: 247,114,52;--orangered-7: 204,81,32;--orangered-8: 162,53,17;--orangered-9: 119,31,6;--orangered-10: 77,14,0;--orange-1: 255,247,232;--orange-2: 255,228,186;--orange-3: 255,207,139;--orange-4: 255,182,93;--orange-5: 255,154,46;--orange-6: 255,125,0;--orange-7: 210,95,0;--orange-8: 166,69,0;--orange-9: 121,46,0;--orange-10: 77,27,0;--gold-1: 255,252,232;--gold-2: 253,244,191;--gold-3: 252,233,150;--gold-4: 250,220,109;--gold-5: 249,204,69;--gold-6: 247,186,30;--gold-7: 204,146,19;--gold-8: 162,109,10;--gold-9: 119,75,4;--gold-10: 77,45,0;--yellow-1: 254,255,232;--yellow-2: 254,254,190;--yellow-3: 253,250,148;--yellow-4: 252,242,107;--yellow-5: 251,232,66;--yellow-6: 250,220,25;--yellow-7: 207,175,15;--yellow-8: 163,132,8;--yellow-9: 120,93,3;--yellow-10: 77,56,0;--lime-1: 252,255,232;--lime-2: 237,248,187;--lime-3: 220,241,144;--lime-4: 201,233,104;--lime-5: 181,226,65;--lime-6: 159,219,29;--lime-7: 126,183,18;--lime-8: 95,148,10;--lime-9: 67,112,4;--lime-10: 42,77,0;--green-1: 232,255,234;--green-2: 175,240,181;--green-3: 123,225,136;--green-4: 76,210,99;--green-5: 35,195,67;--green-6: 0,180,42;--green-7: 0,154,41;--green-8: 0,128,38;--green-9: 0,102,34;--green-10: 0,77,28;--cyan-1: 232,255,251;--cyan-2: 183,244,236;--cyan-3: 137,233,224;--cyan-4: 94,223,214;--cyan-5: 55,212,207;--cyan-6: 20,201,201;--cyan-7: 13,165,170;--cyan-8: 7,130,139;--cyan-9: 3,97,108;--cyan-10: 0,66,77;--blue-1: 232,247,255;--blue-2: 195,231,254;--blue-3: 159,212,253;--blue-4: 123,192,252;--blue-5: 87,169,251;--blue-6: 52,145,250;--blue-7: 32,108,207;--blue-8: 17,75,163;--blue-9: 6,48,120;--blue-10: 0,26,77;--arcoblue-1: 232,243,255;--arcoblue-2: 190,218,255;--arcoblue-3: 148,191,255;--arcoblue-4: 106,161,255;--arcoblue-5: 64,128,255;--arcoblue-6: 22,93,255;--arcoblue-7: 14,66,210;--arcoblue-8: 7,44,166;--arcoblue-9: 3,26,121;--arcoblue-10: 0,13,77;--purple-1: 245,232,255;--purple-2: 221,190,246;--purple-3: 195,150,237;--purple-4: 168,113,227;--purple-5: 141,78,218;--purple-6: 114,46,209;--purple-7: 85,29,176;--purple-8: 60,16,143;--purple-9: 39,6,110;--purple-10: 22,0,77;--pinkpurple-1: 255,232,251;--pinkpurple-2: 247,186,239;--pinkpurple-3: 240,142,230;--pinkpurple-4: 232,101,223;--pinkpurple-5: 225,62,219;--pinkpurple-6: 217,26,217;--pinkpurple-7: 176,16,182;--pinkpurple-8: 138,9,147;--pinkpurple-9: 101,3,112;--pinkpurple-10: 66,0,77;--magenta-1: 255,232,241;--magenta-2: 253,194,219;--magenta-3: 251,157,199;--magenta-4: 249,121,183;--magenta-5: 247,84,168;--magenta-6: 245,49,157;--magenta-7: 203,30,131;--magenta-8: 161,16,105;--magenta-9: 119,6,79;--magenta-10: 77,0,52;--gray-1: 247,248,250;--gray-2: 242,243,245;--gray-3: 229,230,235;--gray-4: 201,205,212;--gray-5: 169,174,184;--gray-6: 134,144,156;--gray-7: 107,119,133;--gray-8: 78,89,105;--gray-9: 39,46,59;--gray-10: 29,33,41;--success-1: var(--green-1);--success-2: var(--green-2);--success-3: var(--green-3);--success-4: var(--green-4);--success-5: var(--green-5);--success-6: var(--green-6);--success-7: var(--green-7);--success-8: var(--green-8);--success-9: var(--green-9);--success-10: var(--green-10);--primary-1: var(--arcoblue-1);--primary-2: var(--arcoblue-2);--primary-3: var(--arcoblue-3);--primary-4: var(--arcoblue-4);--primary-5: var(--arcoblue-5);--primary-6: var(--arcoblue-6);--primary-7: var(--arcoblue-7);--primary-8: var(--arcoblue-8);--primary-9: var(--arcoblue-9);--primary-10: var(--arcoblue-10);--danger-1: var(--red-1);--danger-2: var(--red-2);--danger-3: var(--red-3);--danger-4: var(--red-4);--danger-5: var(--red-5);--danger-6: var(--red-6);--danger-7: var(--red-7);--danger-8: var(--red-8);--danger-9: var(--red-9);--danger-10: var(--red-10);--warning-1: var(--orange-1);--warning-2: var(--orange-2);--warning-3: var(--orange-3);--warning-4: var(--orange-4);--warning-5: var(--orange-5);--warning-6: var(--orange-6);--warning-7: var(--orange-7);--warning-8: var(--orange-8);--warning-9: var(--orange-9);--warning-10: var(--orange-10);--link-1: var(--arcoblue-1);--link-2: var(--arcoblue-2);--link-3: var(--arcoblue-3);--link-4: var(--arcoblue-4);--link-5: var(--arcoblue-5);--link-6: var(--arcoblue-6);--link-7: var(--arcoblue-7);--link-8: var(--arcoblue-8);--link-9: var(--arcoblue-9);--link-10: var(--arcoblue-10)}body[arco-theme=dark]{--red-1: 77,0,10;--red-2: 119,6,17;--red-3: 161,22,31;--red-4: 203,46,52;--red-5: 245,78,78;--red-6: 247,105,101;--red-7: 249,141,134;--red-8: 251,176,167;--red-9: 253,209,202;--red-10: 255,240,236;--orangered-1: 77,14,0;--orangered-2: 119,30,5;--orangered-3: 162,55,20;--orangered-4: 204,87,41;--orangered-5: 247,126,69;--orangered-6: 249,146,90;--orangered-7: 250,173,125;--orangered-8: 252,198,161;--orangered-9: 253,222,197;--orangered-10: 255,244,235;--orange-1: 77,27,0;--orange-2: 121,48,4;--orange-3: 166,75,10;--orange-4: 210,105,19;--orange-5: 255,141,31;--orange-6: 255,150,38;--orange-7: 255,179,87;--orange-8: 255,205,135;--orange-9: 255,227,184;--orange-10: 255,247,232;--gold-1: 77,45,0;--gold-2: 119,75,4;--gold-3: 162,111,15;--gold-4: 204,150,31;--gold-5: 247,192,52;--gold-6: 249,204,68;--gold-7: 250,220,108;--gold-8: 252,233,149;--gold-9: 253,244,190;--gold-10: 255,252,232;--yellow-1: 77,56,0;--yellow-2: 120,94,7;--yellow-3: 163,134,20;--yellow-4: 207,179,37;--yellow-5: 250,225,60;--yellow-6: 251,233,75;--yellow-7: 252,243,116;--yellow-8: 253,250,157;--yellow-9: 254,254,198;--yellow-10: 254,255,240;--lime-1: 42,77,0;--lime-2: 68,112,6;--lime-3: 98,148,18;--lime-4: 132,183,35;--lime-5: 168,219,57;--lime-6: 184,226,75;--lime-7: 203,233,112;--lime-8: 222,241,152;--lime-9: 238,248,194;--lime-10: 253,255,238;--green-1: 0,77,28;--green-2: 4,102,37;--green-3: 10,128,45;--green-4: 18,154,55;--green-5: 29,180,64;--green-6: 39,195,70;--green-7: 80,210,102;--green-8: 126,225,139;--green-9: 178,240,183;--green-10: 235,255,236;--cyan-1: 0,66,77;--cyan-2: 6,97,108;--cyan-3: 17,131,139;--cyan-4: 31,166,170;--cyan-5: 48,201,201;--cyan-6: 63,212,207;--cyan-7: 102,223,215;--cyan-8: 144,233,225;--cyan-9: 190,244,237;--cyan-10: 240,255,252;--blue-1: 0,26,77;--blue-2: 5,47,120;--blue-3: 19,76,163;--blue-4: 41,113,207;--blue-5: 70,154,250;--blue-6: 90,170,251;--blue-7: 125,193,252;--blue-8: 161,213,253;--blue-9: 198,232,254;--blue-10: 234,248,255;--arcoblue-1: 0,13,77;--arcoblue-2: 4,27,121;--arcoblue-3: 14,50,166;--arcoblue-4: 29,77,210;--arcoblue-5: 48,111,255;--arcoblue-6: 60,126,255;--arcoblue-7: 104,159,255;--arcoblue-8: 147,190,255;--arcoblue-9: 190,218,255;--arcoblue-10: 234,244,255;--purple-1: 22,0,77;--purple-2: 39,6,110;--purple-3: 62,19,143;--purple-4: 90,37,176;--purple-5: 123,61,209;--purple-6: 142,81,218;--purple-7: 169,116,227;--purple-8: 197,154,237;--purple-9: 223,194,246;--purple-10: 247,237,255;--pinkpurple-1: 66,0,77;--pinkpurple-2: 101,3,112;--pinkpurple-3: 138,13,147;--pinkpurple-4: 176,27,182;--pinkpurple-5: 217,46,217;--pinkpurple-6: 225,61,219;--pinkpurple-7: 232,102,223;--pinkpurple-8: 240,146,230;--pinkpurple-9: 247,193,240;--pinkpurple-10: 255,242,253;--magenta-1: 77,0,52;--magenta-2: 119,8,80;--magenta-3: 161,23,108;--magenta-4: 203,43,136;--magenta-5: 245,69,166;--magenta-6: 247,86,169;--magenta-7: 249,122,184;--magenta-8: 251,158,200;--magenta-9: 253,195,219;--magenta-10: 255,232,241;--gray-1: 23,23,26;--gray-2: 46,46,48;--gray-3: 72,72,73;--gray-4: 95,95,96;--gray-5: 120,120,122;--gray-6: 146,146,147;--gray-7: 171,171,172;--gray-8: 197,197,197;--gray-9: 223,223,223;--gray-10: 246,246,246;--primary-1: var(--arcoblue-1);--primary-2: var(--arcoblue-2);--primary-3: var(--arcoblue-3);--primary-4: var(--arcoblue-4);--primary-5: var(--arcoblue-5);--primary-6: var(--arcoblue-6);--primary-7: var(--arcoblue-7);--primary-8: var(--arcoblue-8);--primary-9: var(--arcoblue-9);--primary-10: var(--arcoblue-10);--success-1: var(--green-1);--success-2: var(--green-2);--success-3: var(--green-3);--success-4: var(--green-4);--success-5: var(--green-5);--success-6: var(--green-6);--success-7: var(--green-7);--success-8: var(--green-8);--success-9: var(--green-9);--success-10: var(--green-10);--danger-1: var(--red-1);--danger-2: var(--red-2);--danger-3: var(--red-3);--danger-4: var(--red-4);--danger-5: var(--red-5);--danger-6: var(--red-6);--danger-7: var(--red-7);--danger-8: var(--red-8);--danger-9: var(--red-9);--danger-10: var(--red-10);--warning-1: var(--orange-1);--warning-2: var(--orange-2);--warning-3: var(--orange-3);--warning-4: var(--orange-4);--warning-5: var(--orange-5);--warning-6: var(--orange-6);--warning-7: var(--orange-7);--warning-8: var(--orange-8);--warning-9: var(--orange-9);--warning-10: var(--orange-10);--link-1: var(--arcoblue-1);--link-2: var(--arcoblue-2);--link-3: var(--arcoblue-3);--link-4: var(--arcoblue-4);--link-5: var(--arcoblue-5);--link-6: var(--arcoblue-6);--link-7: var(--arcoblue-7);--link-8: var(--arcoblue-8);--link-9: var(--arcoblue-9);--link-10: var(--arcoblue-10)}body{--color-white: #ffffff;--color-black: #000000;--color-border: rgb(var(--gray-3));--color-bg-popup: var(--color-bg-5);--color-bg-1: #fff;--color-bg-2: #fff;--color-bg-3: #fff;--color-bg-4: #fff;--color-bg-5: #fff;--color-bg-white: #fff;--color-neutral-1: rgb(var(--gray-1));--color-neutral-2: rgb(var(--gray-2));--color-neutral-3: rgb(var(--gray-3));--color-neutral-4: rgb(var(--gray-4));--color-neutral-5: rgb(var(--gray-5));--color-neutral-6: rgb(var(--gray-6));--color-neutral-7: rgb(var(--gray-7));--color-neutral-8: rgb(var(--gray-8));--color-neutral-9: rgb(var(--gray-9));--color-neutral-10: rgb(var(--gray-10));--color-text-1: var(--color-neutral-10);--color-text-2: var(--color-neutral-8);--color-text-3: var(--color-neutral-6);--color-text-4: var(--color-neutral-4);--color-border-1: var(--color-neutral-2);--color-border-2: var(--color-neutral-3);--color-border-3: var(--color-neutral-4);--color-border-4: var(--color-neutral-6);--color-fill-1: var(--color-neutral-1);--color-fill-2: var(--color-neutral-2);--color-fill-3: var(--color-neutral-3);--color-fill-4: var(--color-neutral-4);--color-primary-light-1: rgb(var(--primary-1));--color-primary-light-2: rgb(var(--primary-2));--color-primary-light-3: rgb(var(--primary-3));--color-primary-light-4: rgb(var(--primary-4));--color-link-light-1: rgb(var(--link-1));--color-link-light-2: rgb(var(--link-2));--color-link-light-3: rgb(var(--link-3));--color-link-light-4: rgb(var(--link-4));--color-secondary: var(--color-neutral-2);--color-secondary-hover: var(--color-neutral-3);--color-secondary-active: var(--color-neutral-4);--color-secondary-disabled: var(--color-neutral-1);--color-danger-light-1: rgb(var(--danger-1));--color-danger-light-2: rgb(var(--danger-2));--color-danger-light-3: rgb(var(--danger-3));--color-danger-light-4: rgb(var(--danger-4));--color-success-light-1: rgb(var(--success-1));--color-success-light-2: rgb(var(--success-2));--color-success-light-3: rgb(var(--success-3));--color-success-light-4: rgb(var(--success-4));--color-warning-light-1: rgb(var(--warning-1));--color-warning-light-2: rgb(var(--warning-2));--color-warning-light-3: rgb(var(--warning-3));--color-warning-light-4: rgb(var(--warning-4));--border-radius-none: 0;--border-radius-small: 2px;--border-radius-medium: 4px;--border-radius-large: 8px;--border-radius-circle: 50%;--color-tooltip-bg: rgb(var(--gray-10));--color-spin-layer-bg: rgba(255, 255, 255, .6);--color-menu-dark-bg: #232324;--color-menu-light-bg: #ffffff;--color-menu-dark-hover: rgba(255, 255, 255, .04);--color-mask-bg: rgba(29, 33, 41, .6)}body[arco-theme=dark]{--color-white: rgba(255, 255, 255, .9);--color-black: #000000;--color-border: #333335;--color-bg-1: #17171a;--color-bg-2: #232324;--color-bg-3: #2a2a2b;--color-bg-4: #313132;--color-bg-5: 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rgb(var(--success-6), .2);--color-success-light-2: rgb(var(--success-6), .35);--color-success-light-3: rgb(var(--success-6), .5);--color-success-light-4: rgb(var(--success-6), .65);--color-warning-light-1: rgb(var(--warning-6), .2);--color-warning-light-2: rgb(var(--warning-6), .35);--color-warning-light-3: rgb(var(--warning-6), .5);--color-warning-light-4: rgb(var(--warning-6), .65);--color-link-light-1: rgb(var(--link-6), .2);--color-link-light-2: rgb(var(--link-6), .35);--color-link-light-3: rgb(var(--link-6), .5);--color-link-light-4: rgb(var(--link-6), .65);--color-tooltip-bg: #373739;--color-spin-layer-bg: rgba(51, 51, 51, .6);--color-menu-dark-bg: #232324;--color-menu-light-bg: #232324;--color-menu-dark-hover: var(--color-fill-2);--color-mask-bg: rgba(23, 23, 26, .6)}body{font-size:14px;font-family:Inter,-apple-system,BlinkMacSystemFont,PingFang SC,Hiragino Sans GB,noto sans,Microsoft YaHei,Helvetica 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diff --git a/spaces/diacanFperku/AutoGPT/Download Extra Qualityannefrankdiarymalayalamedition.md b/spaces/diacanFperku/AutoGPT/Download Extra Qualityannefrankdiarymalayalamedition.md
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--- a/spaces/diacanFperku/AutoGPT/Download Extra Qualityannefrankdiarymalayalamedition.md
+++ /dev/null
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diff --git "a/spaces/diacanFperku/AutoGPT/KMS Nano V.16.1 Automatic\302\240Activator.md" "b/spaces/diacanFperku/AutoGPT/KMS Nano V.16.1 Automatic\302\240Activator.md"
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--- "a/spaces/diacanFperku/AutoGPT/KMS Nano V.16.1 Automatic\302\240Activator.md"
+++ /dev/null
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899543212bMastizaade Full Movie Download 720p Khatrimaza
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However, if you are a fan of adult comedy movies, then you might enjoy Mastizaade. The movie has some catchy songs and Sunny Leone's double role as Laila and Lily Lele. The movie also has some cameo appearances by Riteish Deshmukh, Shaad Randhawa, Suresh Menon and Asrani.
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-"Mastizaade is a sex comedy that tries too hard to be funny and ends up being crass and vulgar. The movie has no plot, no logic and no sense of humor. The movie is a waste of time and money." - Times of India
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-"Mastizaade is a hilarious comedy that will make you laugh till you drop. The movie has a star cast of Sunny Leone, Tusshar Kapoor and Vir Das who deliver amazing performances. The movie has some catchy songs and Sunny Leone's double role as Laila and Lily Lele. The movie also has some cameo appearances by Riteish Deshmukh, Shaad Randhawa, Suresh Menon and Asrani." - Bollywood Hungama
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-"Mastizaade is a mediocre comedy that fails to impress. The movie has a weak script, poor direction and lame jokes. The movie relies on cheap thrills and vulgar dialogues to entertain the audience. The movie is not for the faint-hearted or the family audience." - Hindustan Times
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3cee63e6c2How to Download and Install Oracle Primavera P6 v7 SP3 for Free
-Oracle Primavera P6 is a powerful project management software that helps you plan, schedule, and control complex projects. It is widely used in various industries such as construction, engineering, oil and gas, aerospace, and more. However, Oracle Primavera P6 is not cheap and requires a license to use. If you want to try it out for free, you can download a torrent file that contains the full version of Oracle Primavera P6 v7 SP3.
-In this article, we will show you how to download and install Oracle Primavera P6 v7 SP3 full torrent in a few simple steps. Please note that this is for educational purposes only and we do not condone piracy or illegal use of software. You should always purchase a legitimate license from Oracle if you want to use Oracle Primavera P6 for your projects.
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Step 1: Download the Torrent File
-The first step is to download the torrent file that contains Oracle Primavera P6 v7 SP3 full version. You can find it on various torrent sites such as SolidTorrents[^1^], CloudTorrents[^2^], or Pastebin[^4^]. You will need a torrent client such as uTorrent or BitTorrent to download the file. Once you have downloaded the torrent file, open it with your torrent client and start downloading the content.
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Results
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Threshold
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setThreshold(v[0])}
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- {loading ? (
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Drivers Joystick Usb Bitrom Rar HOT!.md b/spaces/falterWliame/Face_Mask_Detection/Drivers Joystick Usb Bitrom Rar HOT!.md
deleted file mode 100644
index 3c997d6ef56b58034680e7296a765aa21976d694..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Drivers Joystick Usb Bitrom Rar HOT!.md
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@@ -1,6 +0,0 @@
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diff --git a/spaces/fatiXbelha/sd/Enjoy the Latest Version of NBA 2K20 APK OBB V97.0.2 with Unlimited Money and Mods.md b/spaces/fatiXbelha/sd/Enjoy the Latest Version of NBA 2K20 APK OBB V97.0.2 with Unlimited Money and Mods.md
deleted file mode 100644
index 89f3bb385a441342376a38e4ccf9ee053c5cd5a8..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Enjoy the Latest Version of NBA 2K20 APK OBB V97.0.2 with Unlimited Money and Mods.md
+++ /dev/null
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-NBA 2K20 APK OBB V97.0.2: The Ultimate Guide
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But how can you download and install NBA 2K20 APK OBB V97.0.2 on your Android device? And how can you play it effectively and have fun? In this guide, we will answer these questions and more. We will explain what NBA 2K20 is, what features it offers, how to download and install it, and how to play it. By the end of this guide, you will be ready to take on the court and dominate the game.
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- 401be4b1e060 Lakh Song Download: How to Enjoy the Latest Punjabi Hit Online
- If you are a fan of Punjabi music, you must have heard of the latest sensation, 60 Lakh Song. This song has taken the internet by storm and has become one of the most streamed and downloaded songs in India. But what is 60 Lakh Song and how can you enjoy it online? In this article, we will tell you everything you need to know about this catchy and hilarious song, and how you can download it legally and ethically.
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Who are the artists behind 60 Lakh Song?
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- The lyrics of 60 Lakh Song are written by Joban Cheema, a Punjabi lyricist who has penned many songs for Gopy Randhawa and R Nait. He has a knack for writing catchy and funny lyrics that resonate with the listeners. The lyrics of 60 Lakh Song are in Punjabi language, but you can find the English translation online if you want to understand the meaning.
- Here are some of the lines from the song:
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-Daddy ji mainu car de do Daddy ji give me a car Car di keemat hai ikkathhe pachas lakh The price of the car is fifty lakh together Mainu lagda tussi mainu pyar nahi karde I think you don't love me Mainu lagda tussi mainu pyar nahi karde I think I think you don't love me Mainu car chahidi ae, mainu car chahidi ae I need a car, I need a car Mainu car chahidi ae, mainu car chahidi ae I need a car, I need a car R Nait! R Nait! Mainu car chahidi ae, mainu car chahidi ae I need a car, I need a car Mainu car chahidi ae, mainu car chahidi ae I need a car, I need a car Daddy ji mainu car de do Daddy ji give me a car Car di keemat hai ikkathhe pachas lakh The price of the car is fifty lakh together Mainu lagda tussi mainu pyar nahi karde I think you don't love me Mainu lagda tussi mainu pyar nahi karde I think you don't love me
- You can find the full lyrics and translation of 60 Lakh Song online on various websites such as LyricsBell, LyricsRaag, and LyricsTranslate. You can also watch the official video of 60 Lakh Song on YouTube and enjoy the visuals and the performance of the artists.
- Why is 60 Lakh Song so popular?
- 60 Lakh Song has become a huge hit among the Punjabi music lovers and has received millions of views and downloads online. But what makes this song so appealing and catchy? Here are some of the reasons why 60 Lakh Song is so popular:
- The catchy tune and beat of 60 Lakh Song
- One of the main reasons why 60 Lakh Song is so popular is because of its catchy tune and beat that makes you want to groove along. The song has a lively and upbeat tempo that suits the mood and theme of the song. The music of 60 Lakh Song is composed by Laddi Gill, a Punjabi music director who has created many hit songs such as Jatt Di Star, Jattan De Munde, and Jatt Zimidaar. He has used a mix of traditional and modern instruments such as dhol, tumbi, guitar, and keyboard to create a fusion sound that appeals to the masses.
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- The relatable and humorous theme of 60 Lakh Song
- Another reason why 60 Lakh Song is so popular is because of its relatable and humorous theme that makes you laugh and smile. The song is about a common scenario that many young people face in India, where they want to buy expensive things but their parents are not willing to give them the money. The song portrays the contrast between the son's lavish dreams and the father's practical reality in a funny and witty way. The song also touches upon some social issues such as dowry, corruption, and police harassment in a satirical manner.
- The viral video and dance challenge of 60 Lakh Song
- The third reason why 60 Lakh Song is so popular is because of its viral video and dance challenge that has spread across the internet. The video of 60 Lakh Song features Gopy Randhawa and R Nait in various locations such as a showroom, a farm, a hotel, and a police station. They are seen wearing stylish clothes and accessories and driving fancy cars. They also perform some hilarious dance moves that match the lyrics of the song. The video of 60 Lakh Song has been directed by Jeona & Jogi, who have done a great job in capturing the essence and mood of the song.
- The video of 60 Lakh Song has also inspired many people to create their own versions and participate in the 60 Lakh Song dance challenge. The challenge involves mimicking the dance moves of Gopy Randhawa and R Nait while lip-syncing to the song. Many celebrities, influencers, and fans have taken part in this challenge and posted their videos on social media platforms such as Instagram, TikTok, and Facebook. Some of the popular videos are by Jassie Gill, Babbal Rai, Gurnam Bhullar, and Sargun Mehta. You can also join the fun by making your own video and sharing it with your friends.
- How to download 60 Lakh Song online?
- Now that you know what 60 Lakh Song is and why it is so popular, you might be wondering how you can download it online. Well, there are two ways to download 60 Lakh Song online: the legal and ethical way, and the illegal and risky way. Let us see what they are:
- The The legal and ethical way to download 60 Lakh Song
- The legal and ethical way to download 60 Lakh Song is to use the official and authorized platforms that have the rights to distribute the song. These platforms pay a fair amount of royalty to the artists and the music label for their work and also ensure the quality and safety of the song. Some of the platforms that you can use to download 60 Lakh Song legally and ethically are:
- JioSaavn
- JioSaavn is one of the most popular music streaming services in India that offers a huge collection of songs in various languages and genres. You can download 60 Lakh Song on JioSaavn by following these steps:
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-Download the JioSaavn app on your smartphone or visit the JioSaavn website on your browser.
-Sign up or log in with your Jio number or email address.
-Search for 60 Lakh Song in the search bar and tap on it.
-Tap on the download icon next to the song title and choose the quality you want.
-Enjoy listening to 60 Lakh Song offline anytime you want.
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- You can also listen to 60 Lakh Song online on JioSaavn without downloading it. However, you will need an active internet connection and a subscription plan to do so. JioSaavn offers various plans such as JioSaavn Pro, JioSaavn Plus, and JioSaavn Free that give you different benefits and features. You can choose the plan that suits your needs and budget.
- YouTube
- YouTube is another platform that you can use to download 60 Lakh Song legally and ethically. YouTube is the world's largest video-sharing platform that hosts millions of videos on various topics and categories. You can download 60 Lakh Song on YouTube by following these steps:
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-Download the YouTube app on your smartphone or visit the YouTube website on your browser.
-Sign up or log in with your Google account.
-Search for 60 Lakh Song in the search bar and tap on it.
-Tap on the download icon below the video and choose the quality you want.
-Enjoy watching 60 Lakh Song offline anytime you want.
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- You can also watch 60 Lakh Song online on YouTube without downloading it. However, you will need an active internet connection and a subscription plan to do so. YouTube offers various plans such as YouTube Premium, YouTube Music Premium, and YouTube Free that give you different benefits and features. You can choose the plan that suits your needs and budget.
- Wynk Music
- Wynk Music is another platform that you can use to download 60 Lakh Song legally and ethically. Wynk Music is a music streaming service that offers a wide range of songs in various languages and genres. You can download 60 Lakh Song on Wynk Music by following these steps:
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-Download the Wynk Music app on your smartphone or visit the Wynk Music website on your browser.
-Sign up or log in with your Airtel number or email address.
-Search for 60 Lakh Song in the search bar and tap on it.
-Tap on the download icon next to the song title and choose the quality you want.
-Enjoy listening to 60 Lakh Song offline anytime you want.
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- You can also listen to 60 Lakh Song online on Wynk Music without downloading it. However, you will need an active internet connection and a subscription plan to do so. Wynk Music offers various plans such as Wynk Premium, Wynk Plus, and Wynk Free that give you different benefits and features. You can choose the plan that suits your needs and budget.
- The illegal and risky way to download 60 Lakh Song
- The illegal and risky way to download 60 Lakh Song is to use the unofficial and unauthorized platforms that have no rights to distribute the song. These platforms do not pay any royalty to the artists and the music label for their work and also compromise the quality and safety of the song. Some of the platforms that you should avoid using to download 60 Lakh Song illegally and riskily are:
- Piracy websites
- Piracy websites are websites that offer free downloads of songs, movies, games, software, and other digital content without permission from the owners. These websites are illegal and unethical as they violate the intellectual property rights of the creators and cause them financial losses. They also expose you to various risks such as malware, viruses , spyware, phishing, and identity theft. Some of the piracy websites that you should avoid using to download 60 Lakh Song are MP3Tau, DJPunjab, Mr-Jatt, and PagalWorld.
- Torrents
- Torrents are files that contain information about other files that can be downloaded from peer-to-peer networks. Torrents are often used to share songs, movies, games, software, and other digital content without permission from the owners. These files are illegal and unethical as they violate the intellectual property rights of the creators and cause them financial losses. They also expose you to various risks such as malware, viruses, spyware, phishing, and identity theft. Some of the torrent sites that you should avoid using to download 60 Lakh Song are The Pirate Bay, Kickass Torrents, 1337x, and RARBG.
- Malware and viruses
- Malware and viruses are malicious software that can harm your computer, smartphone, or other devices. They can infect your system by disguising themselves as songs, movies, games, software, or other digital content. They can steal your personal information, damage your files, slow down your performance, or even take control of your device. Some of the malware and viruses that you should avoid downloading to get 60 Lakh Song are Trojans, worms, ransomware, adware, and spyware.
- How to enjoy 60 Lakh Song online?
- If you do not want to download 60 Lakh Song online but still want to enjoy it online, there are some ways to do so. Here are some of the ways to enjoy 60 Lakh Song online:
- Stream 60 Lakh Song on music streaming services
- One of the best ways to enjoy 60 Lakh Song online is to stream it on music streaming services. Music streaming services are platforms that offer unlimited access to millions of songs in various languages and genres. You can stream 60 Lakh Song on music streaming services by following these steps:
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-Choose a music streaming service that has 60 Lakh Song in its library. Some of the music streaming services that have 60 Lakh Song are Spotify, Apple Music, and Amazon Music.
-Download the app of the music streaming service on your smartphone or visit the website on your browser.
-Sign up or log in with your account details.
-Search for 60 Lakh Song in the search bar and tap on it.
-Enjoy listening to 60 Lakh Song online anytime you want.
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- You can also create playlists, share songs, discover new music, and enjoy other features on music streaming services. However, you will need an active internet connection and a subscription plan to do so. Music streaming services offer various plans such as free, premium, family, and student that give you different benefits and features. You can choose the plan that suits your needs and budget.
- Watch 60 Lakh Song video on YouTube
- Another way to enjoy 60 Lakh Song online is to watch its video on YouTube. YouTube is the world's largest video-sharing platform that hosts millions of videos on various topics and categories. You can watch 60 Lakh Song video on YouTube by following these steps:
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-Download the YouTube app on your smartphone or visit the YouTube website on your browser.
-Sign up or log in with your Google account.
-Search for 60 Lakh Song in the search bar and tap on it.
-Enjoy watching 60 Lakh Song video online anytime you want.
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- You can also like, comment, share , subscribe, and watch other videos on YouTube. However, you will need an active internet connection and a subscription plan to do so. YouTube offers various plans such as YouTube Premium, YouTube Music Premium, and YouTube Free that give you different benefits and features. You can choose the plan that suits your needs and budget.
- Sing along with 60 Lakh Song lyrics
- A third way to enjoy 60 Lakh Song online is to sing along with its lyrics. Singing along with the lyrics of a song can help you improve your pronunciation, vocabulary, and comprehension of the language. It can also make you feel more connected and involved with the song. You can sing along with 60 Lakh Song lyrics by following these steps:
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-Find a website or an app that provides the lyrics and translation of 60 Lakh Song. Some of the websites and apps that provide the lyrics and translation of 60 Lakh Song are LyricsBell, LyricsRaag, and LyricsTranslate.
-Open the website or the app on your device and search for 60 Lakh Song.
-Read the lyrics and the translation of 60 Lakh Song and try to understand the meaning and the message of the song.
-Play 60 Lakh Song on your device or on another device and sing along with the lyrics as they appear on the screen.
-Enjoy singing along with 60 Lakh Song online anytime you want.
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- You can also record yourself singing along with 60 Lakh Song and share it with your friends or on social media. You can also challenge your friends to sing along with 60 Lakh Song and see who can do it better. Singing along with 60 Lakh Song can be a fun and rewarding activity that can enhance your enjoyment of the song.
- Conclusion
- 60 Lakh Song is a Punjabi song that has become a huge hit among the music lovers in India. It is a catchy and humorous song that tells the story of a man who wants to buy a car worth 60 lakh rupees but his father refuses to give him the money. The song is sung by Gopy Randhawa and R Nait, written by Joban Cheema, and composed by Laddi Gill. The song has a catchy tune and beat, a relatable and humorous theme, and a viral video and dance challenge that have made it popular among the masses.
- If you want to download 60 Lakh Song online, you can do it legally and ethically by using the official and authorized platforms such as JioSaavn, YouTube, and Wynk Music. These platforms pay a fair amount of royalty to the artists and the music label for their work and also ensure the quality and safety of the song. You should avoid using the unofficial and unauthorized platforms such as piracy websites, torrents, and malware that are illegal and unethical as they violate the intellectual property rights of the creators and cause them financial losses. They also expose you to various risks such as malware, viruses, spyware, phishing, and identity theft.
- If you do not want to download 60 Lakh Song online but still want to enjoy it online, you can do it by streaming it on music streaming services such as Spotify, Apple Music, and Amazon Music. You can also watch its video on YouTube and sing along with its lyrics on websites or apps such as LyricsBell, LyricsRaag, and LyricsTranslate. These ways can help you enjoy 60 Lakh Song online without downloading it.
- 60 Lakh Song is a fun and entertaining song that can make you laugh and dance. It is a song that celebrates the Punjabi culture and the youth's aspirations. It is a song that you can enjoy online in various ways. Whether you want to download it, stream it, watch it, or sing it, you can find the best platform and method for you. So, what are you waiting for? Go ahead and enjoy 60 Lakh Song online today!
- FAQs
- Here are some of the frequently asked questions about 60 Lakh Song:
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-What is the meaning of 60 Lakh?
-60 Lakh is a unit of currency in India that is equal to 6 million rupees or about $80,000. It is also the name of a Punjabi song that is about a man who wants to buy a car worth 60 lakh rupees but his father refuses to give him the money.
-Who are the singers of 60 Lakh Song?
-The singers of 60 Lakh Song are Gopy Randhawa and R Nait, who are both Punjabi singers who have sung many popular songs in the past. Gopy Randhawa is the main singer of 60 Lakh Song and R Nait is the featured artist who sings the chorus and some verses.
-Who wrote the lyrics of 60 Lakh Song?
-The lyrics of 60 Lakh Song were written by Joban Cheema, who is a Punjabi lyricist who has penned many songs for Gopy Randhawa and R Nait. He has a knack for writing catchy and funny lyrics that resonate with the listeners.
-Who composed the music of 60 Lakh Song?
-The music of 60 Lakh Song was composed by Laddi Gill, who is a Punjabi music director who has created many hit songs in the past. He has used a mix of traditional and modern instruments such as dhol, tumbi, guitar, and keyboard to create a fusion sound that appeals to the masses.
-Where can I download 60 Lakh Song online?
-You can download 60 Lakh Song online legally and ethically by using the official and authorized platforms such as JioSaavn, YouTube, and Wynk Music. These platforms pay a fair amount of royalty to the artists and the music label for their work and also ensure the quality and safety of the song. You should avoid using the unofficial and unauthorized platforms such as piracy websites, torrents, and malware that are illegal and unethical as they violate the intellectual property rights of the creators and cause them financial losses. They also expose you to various risks such as malware, viruses, spyware, phishing, and identity theft.
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-Hidden City: A hidden object game that takes you to a mysterious city, where you can explore different locations and uncover secrets.
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-No cost: You don't have to pay anything to download or print free bingo cards 1-90. You can save money and play as many games as you want without worrying about your budget.No registration: You don't have to sign up or provide any personal information to download or print free bingo cards 1-90. You can enjoy your privacy and security while playing online bingo.No download: You don't have to download any software or app to play online bingo with free bingo cards 1-90. You can access them directly from your browser or device.No limit: You can download or print as many free bingo cards 1-90 as you need or want. You can play with different cards every time or share them with your friends and family.
- The Tips and Tricks for Winning Online Bingo
-Playing online bingo with free bingo cards 1-90 is fun and easy, but it also requires some skill and strategy to increase your chances of winning. Here are some tips and tricks that can help you win more often:
-
-< strong>Buy more cards: The more cards you have, the more chances you have to match the numbers. However, don't buy more cards than you can handle or afford. You should be able to keep track of all your cards and mark them quickly and accurately.
-Choose your cards wisely: Some cards may have better odds of winning than others, depending on the game and the number distribution. You can try to look for cards that have a balanced mix of odd and even numbers, high and low numbers, and numbers that end with different digits.Play at the right time: The best time to play online bingo is when there are fewer players and more prizes. This way, you can reduce the competition and increase your chances of winning. You can try to play during off-peak hours, such as early mornings, late nights, or weekdays.Use the chat features: The chat features can help you learn from other players and get useful information. You can ask for tips, strategies, or recommendations from other players or chat hosts. You can also look out for chat games, quizzes, or contests that can give you extra bonuses or prizes.
- How to Download Free Bingo Cards 1-90
-Now that you know why you should download free bingo cards 1-90 and how to play and win online bingo, you might be wondering how to do it. Well, it's very simple and easy. Here are the steps:
- The Best Websites to Download Free Bingo Cards 1-90
-There are many websites that offer free bingo cards 1-90 that you can download and print. However, not all of them are reliable or safe. You should look for websites that have the following features:
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-Quality: The website should provide high-quality bingo cards that are clear, readable, and accurate. The cards should also have different designs, colors, and themes to suit your preferences.Quantity: The website should offer a large number of bingo cards that you can choose from. You should be able to download or print as many cards as you want without any restrictions or limitations.Customization: The website should allow you to customize your bingo cards according to your needs and wishes. You should be able to change the size, the font, the layout, the number range, and the pattern of your bingo cards.Security: The website should protect your privacy and security while downloading or printing your bingo cards. The website should not ask for any personal information or payment details. The website should also be free of viruses, malware, or spam.
-Some of the best websites that meet these criteria are:
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-Name URL Bingo Baker (https://bingobaker.com/) My Free Bingo Cards (https://myfreebingocards.com/) Bingo Card Generator (https://bingocardgenerator.com/) The Bingo Maker (https://thebingomaker.com/) Bingo Card Creator (https://www.bingocardcreator.com/)
- The Steps to Download and Print Free Bingo Cards 1-90
-The steps to download and print free bingo cards 1-90 may vary depending on the website you use, but they are generally similar. Here are the common steps:
-
-Go to the website of your choice and click on the option to create or generate free bingo cards 1-90.
-Select the number of cards you want to download or print. You can also choose the size and the format of your cards.
-Customize your bingo cards by changing the font, the color, the layout, the number range, and the pattern. You can also add images, logos, or text to your cards.
-Preview your bingo cards and make sure they are correct and satisfactory.
-Click on the option to download or print your bingo cards. You can save them as PDF files or print them directly from your browser or device.
-
- The Ways to Play Online Bingo with Free Bingo Cards 1-90
-Once you have downloaded or printed your free bingo cards 1-90, you can start playing online bingo with them. There are two main ways to play online bingo with free bingo cards 1-90:
-
-Play with a live caller: You can join an online bingo game room that has a live caller who announces the numbers. You can use your free bingo cards 1-90 to mark off the numbers as they are called. You can also chat with other players and the caller in the chat room. You can win prizes or bonuses if you are the first or one of the first to complete a line, a column, a diagonal, or the full house.Play with a random number generator: You can use a random number generator (RNG) to generate the numbers for your online bingo game. You can use your free bingo cards 1-90 to mark off the numbers as they are generated. You can also set the speed and the frequency of the number generation. You can play by yourself or with your friends and family. You can decide the prizes or rewards for the winners.
- Conclusion
-Bingo is a fun and exciting game that can be played online for free with free bingo cards 1-90. You can download and print free bingo cards 1-90 from various websites that offer high-quality, customizable, and secure cards. You can play online bingo with free bingo cards 1-90 with a live caller or a random number generator. You can enjoy the benefits of playing online bingo, such as convenience, variety, bonuses, and socialization. You can also use some tips and tricks to increase your chances of winning online bingo. So what are you waiting for? Download free bingo cards 1-90 today and start playing and winning online bingo!
- FAQs
-Here are some frequently asked questions about downloading free bingo cards 1-90:
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-Q: How many free bingo cards 1-90 can I download or print? A: You can download or print as many free bingo cards 1-90 as you want from the websites that offer them. There is no limit or restriction on the number of cards you can get. Q: How do I know if my free bingo cards 1-90 are valid and accurate? A: You can check the validity and accuracy of your free bingo cards 1-90 by comparing them with the numbers announced by the caller or generated by the RNG. You can also use online tools or apps that can scan and verify your cards. Q: Can I use my free bingo cards 1-90 for other types of bingo games? A: Yes, you can use your free bingo cards 1-90 for other types of bingo games, such as 75-ball bingo, 80-ball bingo, or 30-ball bingo. However, you may need to adjust the number range, the pattern, or the rules of the game accordingly. Q: Can I share my free bingo cards 1-90 with others? A: Yes, you can share your free bingo cards 1-90 with others, such as your friends and family. You can play online bingo together or compete against each other. You can also join online bingo communities and forums where you can share your cards and experiences. Q: Where can I find more information about online bingo and free bingo cards 1-90? A: You can find more information about online bingo and free bingo cards 1-90 by visiting the websites that offer them, reading online reviews and blogs, watching online videos and tutorials, or asking other players and experts. 197e85843dDownload Vidmate Versi Lama 2018: A Guide to Enjoy Free Videos and Music
-Vidmate is a popular app that allows you to download videos and music from various sites, such as YouTube, Facebook, Instagram, TikTok, and more. You can also stream live TV channels and movies on the app. However, some users prefer to download the old version of Vidmate, which is Vidmate versi lama 2018. Why? Because this version has some advantages over the newer ones, such as no ads, less bugs, faster performance, and compatibility with older devices. In this article, we will show you how to download Vidmate versi lama 2018, what are its features, benefits, drawbacks, and how to use it.
- How to Download Vidmate Versi Lama 2018
-Downloading Vidmate versi lama 2018 is not difficult, but you need to follow some steps carefully. Here they are:
-download vidmate versi lama 2018 Download Zip ⏩ https://gohhs.com/2uPsvt
-Find a reliable source for the APK file. You cannot find Vidmate versi lama 2018 on Google Play Store or other official app stores, because it is an old version that is no longer supported by the developers. Therefore, you need to find a third-party website that offers the APK file for free. However, be careful not to download from shady or malicious sites that may contain viruses or malware. Some of the trusted sources for Vidmate versi lama 2018 are , , , , and . You can click on these links or copy and paste them into your browser.
-Enable unknown sources on your device. Since you are downloading an APK file from an external source, you need to allow your device to install apps from unknown sources. To do this, go to Settings > Security > Unknown Sources and toggle it on. You may see a warning message that says installing apps from unknown sources may harm your device, but don't worry, as long as you download from a reputable site, you should be fine.
-Install the APK file and launch the app. After you download the APK file, locate it in your device's file manager or downloads folder and tap on it. You will see a prompt that asks you to confirm the installation. Tap on Install and wait for a few seconds until the installation is complete. Then, tap on Open to launch the app. You may also see an icon of Vidmate versi lama 2018 on your home screen or app drawer.
-
- What are the Features of Vidmate Versi Lama 2018
-Vidmate versi lama 2018 has many features that make it a great app for downloading videos and music. Here are some of the main features that you can enjoy with this app:
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-Download videos from various sites in HD quality . You can download videos from more than 1000 sites, including YouTube, Facebook, Instagram, TikTok, Dailymotion, Vimeo, and more. You can choose the quality of the download, from 144p to 1080p, depending on your preference and internet speed. You can also download multiple videos at the same time with the batch download feature.Convert videos to MP3 and other formats . If you only want to download the audio of a video, you can use the built-in converter to convert the video to MP3 or other audio formats. You can also convert videos to other video formats, such as MP4, AVI, MOV, WMV, etc. This way, you can play your downloaded files on any device or media player.Stream live TV channels and movies . Vidmate versi lama 2018 is not only a downloader, but also a streaming app. You can watch live TV channels from various genres, such as news, sports, entertainment, music, etc. You can also watch movies from different languages and countries, such as Hollywood, Bollywood, Korean, Japanese, etc. You can stream the content online or download it for offline viewing.Manage your downloads and storage . Vidmate versi lama 2018 has a user-friendly interface that allows you to manage your downloads and storage easily. You can view your download history, pause and resume downloads, delete unwanted files, and move files to different folders. You can also set a maximum download speed and a default download location to optimize your storage space.
- What are the Benefits of Vidmate Versi Lama 2018
-Vidmate versi lama 2018 has some benefits that make it preferable over the newer versions of Vidmate. Here are some of them:
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-No ads and less bugs . One of the most annoying things about the newer versions of Vidmate is that they have too many ads that pop up every time you use the app. These ads not only interrupt your experience, but also consume your data and battery. Vidmate versi lama 2018 has no ads at all, so you can enjoy the app without any distractions. Moreover, this version has less bugs and errors than the newer ones, so you can use it without any glitches or crashes.Faster and smoother performance . Another benefit of Vidmate versi lama 2018 is that it has a faster and smoother performance than the newer versions. This version is more lightweight and optimized for speed and efficiency. It can load videos faster, download files quicker, and stream content smoother. You will not experience any lagging or buffering with this version.Compatible with older devices and Android versions . If you have an older device or an older Android version, you may not be able to install or run the newer versions of Vidmate. They may be incompatible with your device or require higher specifications. Vidmate versi lama 2018 is compatible with most devices and Android versions, even the ones that are outdated or low-end. You can install and use this version without any problems.
- What are the Drawbacks of Vidmate Versi Lama 2018
-Vidmate versi lama 2018 is not perfect, though. It also has some drawbacks that you should be aware of before downloading it. Here are some of them:
-
-Less security and updates . Since Vidmate versi lama 2018 is an old version that is no longer supported by the developers, it may not have the latest security features and updates that protect your device and data from hackers and malware. You may also miss out on some bug fixes and improvements that enhance the app's functionality and stability. Therefore, you should be careful when downloading files from unknown sources and scan them for viruses before opening them.Missing some new features and functions . Another drawback of Vidmate versi lama 2018 is that it may not have some of the new features and functions that the newer versions have. For example, you may not be able to download videos from some sites that are added to the app's list later on. You may also not be able to access some of the new options and settings that customize your experience. Therefore, you may not get the best out of the app with this version.
- How to Use Vidmate Versi Lama 2018
-Vidmate versi lama 2018 is easy mate versi lama 2018 and decide whether it is worth downloading or not. We hope this article has helped you understand more about this app and how to use it. If you have any questions or feedback, please leave them in the comments section below. Thank you for reading!
- FAQs
-Here are some of the frequently asked questions about Vidmate versi lama 2018:
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-Is Vidmate versi lama 2018 safe to download and use? Vidmate versi lama 2018 is generally safe to download and use, as long as you download it from a reliable source and scan it for viruses before installing it. However, it may not have the latest security features and updates that protect your device and data from hackers and malware. Therefore, you should be careful when downloading files from unknown sources and avoid clicking on suspicious links or pop-ups.
-What are the differences between Vidmate versi lama 2018 and the newer versions of Vidmate? Vidmate versi lama 2018 has some differences from the newer versions of Vidmate, such as:
-
-It has no ads and less bugs
-It has faster and smoother performance
-It is compatible with older devices and Android versions
-It may not have the latest security and updates
-It may miss some new features and functions
-
-How can I update Vidmate versi lama 2018 to the latest version of Vidmate? If you want to update Vidmate versi lama 2018 to the latest version of Vidmate, you can follow these steps:
-cara download aplikasi vidmate lama gratis
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-Go to Settings > About > Check for Updates on the app
-If there is an update available, tap on Download and Install
-Wait for the update to finish and restart the app
-
-Note that updating the app may remove some of the features and benefits of Vidmate versi lama 2018, such as no ads, faster performance, and compatibility with older devices.
-Can I download Vidmate versi lama 2018 on my PC or laptop? Vidmate versi lama 2018 is an Android app that is designed for mobile devices. However, you can also download it on your PC or laptop by using an Android emulator, such as BlueStacks, Nox Player, or MEmu. An Android emulator is a software that allows you to run Android apps on your PC or laptop. You can download an Android emulator from its official website and install it on your PC or laptop. Then, you can download Vidmate versi lama 2018 from one of the sources mentioned above and install it on the emulator. After that, you can launch the app and use it as you would on your mobile device.
-What are some alternatives to Vidmate versi lama 2018? If you are looking for some alternatives to Vidmate versi lama 2018, you can try these apps:
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-Snaptube : Snaptube is another app that allows you to download videos and music from various sites, such as YouTube, Facebook, Instagram, etc. It has a similar interface and features as Vidmate, but it also has some unique functions, such as night mode, picture-in-picture mode, floating window, etc. You can download Snaptube from its official website or other sources.Tubemate : Tubemate is one of the oldest and most popular apps for downloading videos from YouTube. It has a simple and easy-to-use interface that lets you search, download, and watch videos in various qualities and formats. You can also download videos from other sites by using the built-in browser. You can download Tubemate from its official website or other sources.Videoder : Videoder is a powerful app that allows you to download videos and music from more than 50 sites, such as YouTube, Facebook, Instagram, TikTok, etc. It has a sleek and modern interface that offers a lot of customization options and features. You can also create playlists, edit videos, share files, etc. You can download Videoder from its official website or other sources. 401be4b1e0(delay?: number, value?: T, options?: TimerOptions): Promise;
- /**
- * ```js
- * import {
- * setImmediate,
- * } from 'timers/promises';
- *
- * const res = await setImmediate('result');
- *
- * console.log(res); // Prints 'result'
- * ```
- * @since v15.0.0
- * @param value A value with which the promise is fulfilled.
- */
- function setImmediate(value?: T, options?: TimerOptions): Promise;
- /**
- * Returns an async iterator that generates values in an interval of `delay` ms.
- *
- * ```js
- * import {
- * setInterval,
- * } from 'timers/promises';
- *
- * const interval = 100;
- * for await (const startTime of setInterval(interval, Date.now())) {
- * const now = Date.now();
- * console.log(now);
- * if ((now - startTime) > 1000)
- * break;
- * }
- * console.log(Date.now());
- * ```
- * @since v15.9.0
- */
- function setInterval(delay?: number, value?: T, options?: TimerOptions): AsyncIterable;
-
- interface Scheduler {
- /**
- * ```js
- * import { scheduler } from 'node:timers/promises';
- *
- * await scheduler.wait(1000); // Wait one second before continuing
- * ```
- * An experimental API defined by the Scheduling APIs draft specification being developed as a standard Web Platform API.
- * Calling timersPromises.scheduler.wait(delay, options) is roughly equivalent to calling timersPromises.setTimeout(delay, undefined, options) except that the ref option is not supported.
- * @since v16.14.0
- * @experimental
- * @param [delay=1] The number of milliseconds to wait before fulfilling the promise.
- */
- wait: (delay?: number, options?: TimerOptions) => Promise;
- /**
- * An experimental API defined by the Scheduling APIs draft specification being developed as a standard Web Platform API.
- * Calling timersPromises.scheduler.yield() is equivalent to calling timersPromises.setImmediate() with no arguments.
- * @since v16.14.0
- * @experimental
- */
- yield: () => Promise;
- }
-
- const scheduler: Scheduler;
-}
-declare module 'node:timers/promises' {
- export * from 'timers/promises';
-}
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/express/lib/router/index.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/express/lib/router/index.js
deleted file mode 100644
index 5174c34f455a1553c6065fee4425f153d37d84b2..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/express/lib/router/index.js
+++ /dev/null
@@ -1,673 +0,0 @@
-/*!
- * express
- * Copyright(c) 2009-2013 TJ Holowaychuk
- * Copyright(c) 2013 Roman Shtylman
- * Copyright(c) 2014-2015 Douglas Christopher Wilson
- * MIT Licensed
- */
-
-'use strict';
-
-/**
- * Module dependencies.
- * @private
- */
-
-var Route = require('./route');
-var Layer = require('./layer');
-var methods = require('methods');
-var mixin = require('utils-merge');
-var debug = require('debug')('express:router');
-var deprecate = require('depd')('express');
-var flatten = require('array-flatten');
-var parseUrl = require('parseurl');
-var setPrototypeOf = require('setprototypeof')
-
-/**
- * Module variables.
- * @private
- */
-
-var objectRegExp = /^\[object (\S+)\]$/;
-var slice = Array.prototype.slice;
-var toString = Object.prototype.toString;
-
-/**
- * Initialize a new `Router` with the given `options`.
- *
- * @param {Object} [options]
- * @return {Router} which is an callable function
- * @public
- */
-
-var proto = module.exports = function(options) {
- var opts = options || {};
-
- function router(req, res, next) {
- router.handle(req, res, next);
- }
-
- // mixin Router class functions
- setPrototypeOf(router, proto)
-
- router.params = {};
- router._params = [];
- router.caseSensitive = opts.caseSensitive;
- router.mergeParams = opts.mergeParams;
- router.strict = opts.strict;
- router.stack = [];
-
- return router;
-};
-
-/**
- * Map the given param placeholder `name`(s) to the given callback.
- *
- * Parameter mapping is used to provide pre-conditions to routes
- * which use normalized placeholders. For example a _:user_id_ parameter
- * could automatically load a user's information from the database without
- * any additional code,
- *
- * The callback uses the same signature as middleware, the only difference
- * being that the value of the placeholder is passed, in this case the _id_
- * of the user. Once the `next()` function is invoked, just like middleware
- * it will continue on to execute the route, or subsequent parameter functions.
- *
- * Just like in middleware, you must either respond to the request or call next
- * to avoid stalling the request.
- *
- * app.param('user_id', function(req, res, next, id){
- * User.find(id, function(err, user){
- * if (err) {
- * return next(err);
- * } else if (!user) {
- * return next(new Error('failed to load user'));
- * }
- * req.user = user;
- * next();
- * });
- * });
- *
- * @param {String} name
- * @param {Function} fn
- * @return {app} for chaining
- * @public
- */
-
-proto.param = function param(name, fn) {
- // param logic
- if (typeof name === 'function') {
- deprecate('router.param(fn): Refactor to use path params');
- this._params.push(name);
- return;
- }
-
- // apply param functions
- var params = this._params;
- var len = params.length;
- var ret;
-
- if (name[0] === ':') {
- deprecate('router.param(' + JSON.stringify(name) + ', fn): Use router.param(' + JSON.stringify(name.slice(1)) + ', fn) instead')
- name = name.slice(1)
- }
-
- for (var i = 0; i < len; ++i) {
- if (ret = params[i](name, fn)) {
- fn = ret;
- }
- }
-
- // ensure we end up with a
- // middleware function
- if ('function' !== typeof fn) {
- throw new Error('invalid param() call for ' + name + ', got ' + fn);
- }
-
- (this.params[name] = this.params[name] || []).push(fn);
- return this;
-};
-
-/**
- * Dispatch a req, res into the router.
- * @private
- */
-
-proto.handle = function handle(req, res, out) {
- var self = this;
-
- debug('dispatching %s %s', req.method, req.url);
-
- var idx = 0;
- var protohost = getProtohost(req.url) || ''
- var removed = '';
- var slashAdded = false;
- var sync = 0
- var paramcalled = {};
-
- // store options for OPTIONS request
- // only used if OPTIONS request
- var options = [];
-
- // middleware and routes
- var stack = self.stack;
-
- // manage inter-router variables
- var parentParams = req.params;
- var parentUrl = req.baseUrl || '';
- var done = restore(out, req, 'baseUrl', 'next', 'params');
-
- // setup next layer
- req.next = next;
-
- // for options requests, respond with a default if nothing else responds
- if (req.method === 'OPTIONS') {
- done = wrap(done, function(old, err) {
- if (err || options.length === 0) return old(err);
- sendOptionsResponse(res, options, old);
- });
- }
-
- // setup basic req values
- req.baseUrl = parentUrl;
- req.originalUrl = req.originalUrl || req.url;
-
- next();
-
- function next(err) {
- var layerError = err === 'route'
- ? null
- : err;
-
- // remove added slash
- if (slashAdded) {
- req.url = req.url.slice(1)
- slashAdded = false;
- }
-
- // restore altered req.url
- if (removed.length !== 0) {
- req.baseUrl = parentUrl;
- req.url = protohost + removed + req.url.slice(protohost.length)
- removed = '';
- }
-
- // signal to exit router
- if (layerError === 'router') {
- setImmediate(done, null)
- return
- }
-
- // no more matching layers
- if (idx >= stack.length) {
- setImmediate(done, layerError);
- return;
- }
-
- // max sync stack
- if (++sync > 100) {
- return setImmediate(next, err)
- }
-
- // get pathname of request
- var path = getPathname(req);
-
- if (path == null) {
- return done(layerError);
- }
-
- // find next matching layer
- var layer;
- var match;
- var route;
-
- while (match !== true && idx < stack.length) {
- layer = stack[idx++];
- match = matchLayer(layer, path);
- route = layer.route;
-
- if (typeof match !== 'boolean') {
- // hold on to layerError
- layerError = layerError || match;
- }
-
- if (match !== true) {
- continue;
- }
-
- if (!route) {
- // process non-route handlers normally
- continue;
- }
-
- if (layerError) {
- // routes do not match with a pending error
- match = false;
- continue;
- }
-
- var method = req.method;
- var has_method = route._handles_method(method);
-
- // build up automatic options response
- if (!has_method && method === 'OPTIONS') {
- appendMethods(options, route._options());
- }
-
- // don't even bother matching route
- if (!has_method && method !== 'HEAD') {
- match = false;
- }
- }
-
- // no match
- if (match !== true) {
- return done(layerError);
- }
-
- // store route for dispatch on change
- if (route) {
- req.route = route;
- }
-
- // Capture one-time layer values
- req.params = self.mergeParams
- ? mergeParams(layer.params, parentParams)
- : layer.params;
- var layerPath = layer.path;
-
- // this should be done for the layer
- self.process_params(layer, paramcalled, req, res, function (err) {
- if (err) {
- next(layerError || err)
- } else if (route) {
- layer.handle_request(req, res, next)
- } else {
- trim_prefix(layer, layerError, layerPath, path)
- }
-
- sync = 0
- });
- }
-
- function trim_prefix(layer, layerError, layerPath, path) {
- if (layerPath.length !== 0) {
- // Validate path is a prefix match
- if (layerPath !== path.slice(0, layerPath.length)) {
- next(layerError)
- return
- }
-
- // Validate path breaks on a path separator
- var c = path[layerPath.length]
- if (c && c !== '/' && c !== '.') return next(layerError)
-
- // Trim off the part of the url that matches the route
- // middleware (.use stuff) needs to have the path stripped
- debug('trim prefix (%s) from url %s', layerPath, req.url);
- removed = layerPath;
- req.url = protohost + req.url.slice(protohost.length + removed.length)
-
- // Ensure leading slash
- if (!protohost && req.url[0] !== '/') {
- req.url = '/' + req.url;
- slashAdded = true;
- }
-
- // Setup base URL (no trailing slash)
- req.baseUrl = parentUrl + (removed[removed.length - 1] === '/'
- ? removed.substring(0, removed.length - 1)
- : removed);
- }
-
- debug('%s %s : %s', layer.name, layerPath, req.originalUrl);
-
- if (layerError) {
- layer.handle_error(layerError, req, res, next);
- } else {
- layer.handle_request(req, res, next);
- }
- }
-};
-
-/**
- * Process any parameters for the layer.
- * @private
- */
-
-proto.process_params = function process_params(layer, called, req, res, done) {
- var params = this.params;
-
- // captured parameters from the layer, keys and values
- var keys = layer.keys;
-
- // fast track
- if (!keys || keys.length === 0) {
- return done();
- }
-
- var i = 0;
- var name;
- var paramIndex = 0;
- var key;
- var paramVal;
- var paramCallbacks;
- var paramCalled;
-
- // process params in order
- // param callbacks can be async
- function param(err) {
- if (err) {
- return done(err);
- }
-
- if (i >= keys.length ) {
- return done();
- }
-
- paramIndex = 0;
- key = keys[i++];
- name = key.name;
- paramVal = req.params[name];
- paramCallbacks = params[name];
- paramCalled = called[name];
-
- if (paramVal === undefined || !paramCallbacks) {
- return param();
- }
-
- // param previously called with same value or error occurred
- if (paramCalled && (paramCalled.match === paramVal
- || (paramCalled.error && paramCalled.error !== 'route'))) {
- // restore value
- req.params[name] = paramCalled.value;
-
- // next param
- return param(paramCalled.error);
- }
-
- called[name] = paramCalled = {
- error: null,
- match: paramVal,
- value: paramVal
- };
-
- paramCallback();
- }
-
- // single param callbacks
- function paramCallback(err) {
- var fn = paramCallbacks[paramIndex++];
-
- // store updated value
- paramCalled.value = req.params[key.name];
-
- if (err) {
- // store error
- paramCalled.error = err;
- param(err);
- return;
- }
-
- if (!fn) return param();
-
- try {
- fn(req, res, paramCallback, paramVal, key.name);
- } catch (e) {
- paramCallback(e);
- }
- }
-
- param();
-};
-
-/**
- * Use the given middleware function, with optional path, defaulting to "/".
- *
- * Use (like `.all`) will run for any http METHOD, but it will not add
- * handlers for those methods so OPTIONS requests will not consider `.use`
- * functions even if they could respond.
- *
- * The other difference is that _route_ path is stripped and not visible
- * to the handler function. The main effect of this feature is that mounted
- * handlers can operate without any code changes regardless of the "prefix"
- * pathname.
- *
- * @public
- */
-
-proto.use = function use(fn) {
- var offset = 0;
- var path = '/';
-
- // default path to '/'
- // disambiguate router.use([fn])
- if (typeof fn !== 'function') {
- var arg = fn;
-
- while (Array.isArray(arg) && arg.length !== 0) {
- arg = arg[0];
- }
-
- // first arg is the path
- if (typeof arg !== 'function') {
- offset = 1;
- path = fn;
- }
- }
-
- var callbacks = flatten(slice.call(arguments, offset));
-
- if (callbacks.length === 0) {
- throw new TypeError('Router.use() requires a middleware function')
- }
-
- for (var i = 0; i < callbacks.length; i++) {
- var fn = callbacks[i];
-
- if (typeof fn !== 'function') {
- throw new TypeError('Router.use() requires a middleware function but got a ' + gettype(fn))
- }
-
- // add the middleware
- debug('use %o %s', path, fn.name || '')
-
- var layer = new Layer(path, {
- sensitive: this.caseSensitive,
- strict: false,
- end: false
- }, fn);
-
- layer.route = undefined;
-
- this.stack.push(layer);
- }
-
- return this;
-};
-
-/**
- * Create a new Route for the given path.
- *
- * Each route contains a separate middleware stack and VERB handlers.
- *
- * See the Route api documentation for details on adding handlers
- * and middleware to routes.
- *
- * @param {String} path
- * @return {Route}
- * @public
- */
-
-proto.route = function route(path) {
- var route = new Route(path);
-
- var layer = new Layer(path, {
- sensitive: this.caseSensitive,
- strict: this.strict,
- end: true
- }, route.dispatch.bind(route));
-
- layer.route = route;
-
- this.stack.push(layer);
- return route;
-};
-
-// create Router#VERB functions
-methods.concat('all').forEach(function(method){
- proto[method] = function(path){
- var route = this.route(path)
- route[method].apply(route, slice.call(arguments, 1));
- return this;
- };
-});
-
-// append methods to a list of methods
-function appendMethods(list, addition) {
- for (var i = 0; i < addition.length; i++) {
- var method = addition[i];
- if (list.indexOf(method) === -1) {
- list.push(method);
- }
- }
-}
-
-// get pathname of request
-function getPathname(req) {
- try {
- return parseUrl(req).pathname;
- } catch (err) {
- return undefined;
- }
-}
-
-// Get get protocol + host for a URL
-function getProtohost(url) {
- if (typeof url !== 'string' || url.length === 0 || url[0] === '/') {
- return undefined
- }
-
- var searchIndex = url.indexOf('?')
- var pathLength = searchIndex !== -1
- ? searchIndex
- : url.length
- var fqdnIndex = url.slice(0, pathLength).indexOf('://')
-
- return fqdnIndex !== -1
- ? url.substring(0, url.indexOf('/', 3 + fqdnIndex))
- : undefined
-}
-
-// get type for error message
-function gettype(obj) {
- var type = typeof obj;
-
- if (type !== 'object') {
- return type;
- }
-
- // inspect [[Class]] for objects
- return toString.call(obj)
- .replace(objectRegExp, '$1');
-}
-
-/**
- * Match path to a layer.
- *
- * @param {Layer} layer
- * @param {string} path
- * @private
- */
-
-function matchLayer(layer, path) {
- try {
- return layer.match(path);
- } catch (err) {
- return err;
- }
-}
-
-// merge params with parent params
-function mergeParams(params, parent) {
- if (typeof parent !== 'object' || !parent) {
- return params;
- }
-
- // make copy of parent for base
- var obj = mixin({}, parent);
-
- // simple non-numeric merging
- if (!(0 in params) || !(0 in parent)) {
- return mixin(obj, params);
- }
-
- var i = 0;
- var o = 0;
-
- // determine numeric gaps
- while (i in params) {
- i++;
- }
-
- while (o in parent) {
- o++;
- }
-
- // offset numeric indices in params before merge
- for (i--; i >= 0; i--) {
- params[i + o] = params[i];
-
- // create holes for the merge when necessary
- if (i < o) {
- delete params[i];
- }
- }
-
- return mixin(obj, params);
-}
-
-// restore obj props after function
-function restore(fn, obj) {
- var props = new Array(arguments.length - 2);
- var vals = new Array(arguments.length - 2);
-
- for (var i = 0; i < props.length; i++) {
- props[i] = arguments[i + 2];
- vals[i] = obj[props[i]];
- }
-
- return function () {
- // restore vals
- for (var i = 0; i < props.length; i++) {
- obj[props[i]] = vals[i];
- }
-
- return fn.apply(this, arguments);
- };
-}
-
-// send an OPTIONS response
-function sendOptionsResponse(res, options, next) {
- try {
- var body = options.join(',');
- res.set('Allow', body);
- res.send(body);
- } catch (err) {
- next(err);
- }
-}
-
-// wrap a function
-function wrap(old, fn) {
- return function proxy() {
- var args = new Array(arguments.length + 1);
-
- args[0] = old;
- for (var i = 0, len = arguments.length; i < len; i++) {
- args[i + 1] = arguments[i];
- }
-
- fn.apply(this, args);
- };
-}
diff --git a/spaces/fffiloni/simple-animation-doodle/brushpoint.js b/spaces/fffiloni/simple-animation-doodle/brushpoint.js
deleted file mode 100644
index 41e032ecfa53a2cc614ee4c6c3a4cc1f69c4ab68..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/simple-animation-doodle/brushpoint.js
+++ /dev/null
@@ -1,66 +0,0 @@
-class BrushPoint{
-
- constructor(name, dist, piPosition) {
- this.name = name;
- this.dist = dist;
- this.piPosition = piPosition
-
- this.px;
- this.py;
- this.ppx;
- this.ppy;
- this.sx;
- this.sy;
- this.pointerX;
- this.pointerY;
- }
-
- // -----------------------------------------
- // -----------------------------------------
-
- calcPointCoordinates(mouseX, mouseY, angle, pressure){
- this.pointerX = mouseX + (this.dist * pressure) * cos(angle + this.piPosition);
- this.pointerY = mouseY + (this.dist * pressure) * sin(angle + this.piPosition);
- //console.log('class: ' + this.pointerX + ' ' + this.pointerY)
- }
-
- // -----------------------------------------
- // -----------------------------------------
-
- resetPointOrigin(){
- this.sx = this.pointerX;
- this.sy = this.pointerY;
- this.px = this.pointerX;
- this.py = this.pointerY;
- this.ppx = this.pointerX;
- this.ppy = this.pointerY;
- //console.log(this.sx, this.sy, this.px, this.py, this.ppx, this.ppy)
- }
-
- // -----------------------------------------
- // -----------------------------------------
-
- shiftPointVertex(){
- this.sx = this.ppx;
- this.sy = this.ppy;
- this.ppx = this.px;
- this.ppy = this.py;
- this.px = this.pointerX;
- this.py = this.pointerY;
- }
-
- // -----------------------------------------
- // -----------------------------------------
-
- pushPoints(point){
- point.x1.push(this.sx)
- point.y1.push(this.sy)
- point.x2.push(this.ppx)
- point.y2.push(this.ppy)
- point.x3.push(this.px)
- point.y3.push(this.py)
- point.x4.push(this.pointerX)
- point.y4.push(this.pointerY)
- }
-
-}
\ No newline at end of file
diff --git a/spaces/fkhuggingme/gpt-academic/check_proxy.py b/spaces/fkhuggingme/gpt-academic/check_proxy.py
deleted file mode 100644
index 754b5d36b0c39d29eb6f4dcb8ed88355bcb6335f..0000000000000000000000000000000000000000
--- a/spaces/fkhuggingme/gpt-academic/check_proxy.py
+++ /dev/null
@@ -1,151 +0,0 @@
-
-def check_proxy(proxies):
- import requests
- proxies_https = proxies['https'] if proxies is not None else '无'
- try:
- response = requests.get("https://ipapi.co/json/",
- proxies=proxies, timeout=4)
- data = response.json()
- print(f'查询代理的地理位置，返回的结果是{data}')
- if 'country_name' in data:
- country = data['country_name']
- result = f"代理配置 {proxies_https}, 代理所在地：{country}"
- elif 'error' in data:
- result = f"代理配置 {proxies_https}, 代理所在地：未知，IP查询频率受限"
- print(result)
- return result
- except:
- result = f"代理配置 {proxies_https}, 代理所在地查询超时，代理可能无效"
- print(result)
- return result
-
-
-def backup_and_download(current_version, remote_version):
- """
- 一键更新协议：备份和下载
- """
- from toolbox import get_conf
- import shutil
- import os
- import requests
- import zipfile
- os.makedirs(f'./history', exist_ok=True)
- backup_dir = f'./history/backup-{current_version}/'
- new_version_dir = f'./history/new-version-{remote_version}/'
- if os.path.exists(new_version_dir):
- return new_version_dir
- os.makedirs(new_version_dir)
- shutil.copytree('./', backup_dir, ignore=lambda x, y: ['history'])
- proxies, = get_conf('proxies')
- r = requests.get(
- 'https://github.com/binary-husky/chatgpt_academic/archive/refs/heads/master.zip', proxies=proxies, stream=True)
- zip_file_path = backup_dir+'/master.zip'
- with open(zip_file_path, 'wb+') as f:
- f.write(r.content)
- dst_path = new_version_dir
- with zipfile.ZipFile(zip_file_path, "r") as zip_ref:
- for zip_info in zip_ref.infolist():
- dst_file_path = os.path.join(dst_path, zip_info.filename)
- if os.path.exists(dst_file_path):
- os.remove(dst_file_path)
- zip_ref.extract(zip_info, dst_path)
- return new_version_dir
-
-
-def patch_and_restart(path):
- """
- 一键更新协议：覆盖和重启
- """
- from distutils import dir_util
- import shutil
- import os
- import sys
- import time
- import glob
- from colorful import print亮黄, print亮绿, print亮红
- # if not using config_private, move origin config.py as config_private.py
- if not os.path.exists('config_private.py'):
- print亮黄('由于您没有设置config_private.py私密配置，现将您的现有配置移动至config_private.py以防止配置丢失，',
- '另外您可以随时在history子文件夹下找回旧版的程序。')
- shutil.copyfile('config.py', 'config_private.py')
- path_new_version = glob.glob(path + '/*-master')[0]
- dir_util.copy_tree(path_new_version, './')
- print亮绿('代码已经更新，即将更新pip包依赖……')
- for i in reversed(range(5)): time.sleep(1); print(i)
- try:
- import subprocess
- subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', 'requirements.txt'])
- except:
- print亮红('pip包依赖安装出现问题，需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
- print亮绿('更新完成，您可以随时在history子文件夹下找回旧版的程序，5s之后重启')
- print亮红('假如重启失败，您可能需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
- print(' ------------------------------ -----------------------------------')
- for i in reversed(range(8)): time.sleep(1); print(i)
- os.execl(sys.executable, sys.executable, *sys.argv)
-
-
-def get_current_version():
- import json
- try:
- with open('./version', 'r', encoding='utf8') as f:
- current_version = json.loads(f.read())['version']
- except:
- current_version = ""
- return current_version
-
-
-def auto_update():
- """
- 一键更新协议：查询版本和用户意见
- """
- try:
- from toolbox import get_conf
- import requests
- import time
- import json
- proxies, = get_conf('proxies')
- response = requests.get(
- "https://raw.githubusercontent.com/binary-husky/chatgpt_academic/master/version", proxies=proxies, timeout=5)
- remote_json_data = json.loads(response.text)
- remote_version = remote_json_data['version']
- if remote_json_data["show_feature"]:
- new_feature = "新功能：" + remote_json_data["new_feature"]
- else:
- new_feature = ""
- with open('./version', 'r', encoding='utf8') as f:
- current_version = f.read()
- current_version = json.loads(current_version)['version']
- if (remote_version - current_version) >= 0.01:
- from colorful import print亮黄
- print亮黄(
- f'\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}')
- print('（1）Github更新地址:\nhttps://github.com/binary-husky/chatgpt_academic\n')
- user_instruction = input('（2）是否一键更新代码（Y+回车=确认，输入其他/无输入+回车=不更新）？')
- if user_instruction in ['Y', 'y']:
- path = backup_and_download(current_version, remote_version)
- try:
- patch_and_restart(path)
- except:
- print('更新失败。')
- else:
- print('自动更新程序：已禁用')
- return
- else:
- return
- except:
- print('自动更新程序：已禁用')
-
-def warm_up_modules():
- print('正在执行一些模块的预热...')
- from request_llm.bridge_all import model_info
- enc = model_info["gpt-3.5-turbo"]['tokenizer']
- enc.encode("模块预热", disallowed_special=())
- enc = model_info["gpt-4"]['tokenizer']
- enc.encode("模块预热", disallowed_special=())
-
-if __name__ == '__main__':
- import os
- os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
- from toolbox import get_conf
- proxies, = get_conf('proxies')
- check_proxy(proxies)
diff --git a/spaces/fuckyoudeki/AutoGPT/tests/unit/test_browse_scrape_links.py b/spaces/fuckyoudeki/AutoGPT/tests/unit/test_browse_scrape_links.py
deleted file mode 100644
index 0a3340e7397a997da96b8ab9828954230e1a3c20..0000000000000000000000000000000000000000
--- a/spaces/fuckyoudeki/AutoGPT/tests/unit/test_browse_scrape_links.py
+++ /dev/null
@@ -1,118 +0,0 @@
-# Generated by CodiumAI
-
-# Dependencies:
-# pip install pytest-mock
-import pytest
-
-from autogpt.commands.web_requests import scrape_links
-
-"""
-Code Analysis
-
-Objective:
-The objective of the 'scrape_links' function is to scrape hyperlinks from a
-given URL and return them in a formatted way.
-
-Inputs:
-- url: a string representing the URL to be scraped.
-
-Flow:
-1. Send a GET request to the given URL using the requests library and the user agent header from the config file.
-2. Check if the response contains an HTTP error. If it does, return "error".
-3. Parse the HTML content of the response using the BeautifulSoup library.
-4. Remove any script and style tags from the parsed HTML.
-5. Extract all hyperlinks from the parsed HTML using the 'extract_hyperlinks' function.
-6. Format the extracted hyperlinks using the 'format_hyperlinks' function.
-7. Return the formatted hyperlinks.
-
-Outputs:
-- A list of formatted hyperlinks.
-
-Additional aspects:
-- The function uses the 'requests' and 'BeautifulSoup' libraries to send HTTP
-requests and parse HTML content, respectively.
-- The 'extract_hyperlinks' function is called to extract hyperlinks from the parsed HTML.
-- The 'format_hyperlinks' function is called to format the extracted hyperlinks.
-- The function checks for HTTP errors and returns "error" if any are found.
-"""
-
-
-class TestScrapeLinks:
- # Tests that the function returns a list of formatted hyperlinks when
- # provided with a valid url that returns a webpage with hyperlinks.
- def test_valid_url_with_hyperlinks(self):
- url = "https://www.google.com"
- result = scrape_links(url)
- assert len(result) > 0
- assert isinstance(result, list)
- assert isinstance(result[0], str)
-
- # Tests that the function returns correctly formatted hyperlinks when given a valid url.
- def test_valid_url(self, mocker):
- # Mock the requests.get() function to return a response with sample HTML containing hyperlinks
- mock_response = mocker.Mock()
- mock_response.status_code = 200
- mock_response.text = (
- "Google "
- )
- mocker.patch("requests.Session.get", return_value=mock_response)
-
- # Call the function with a valid URL
- result = scrape_links("https://www.example.com")
-
- # Assert that the function returns correctly formatted hyperlinks
- assert result == ["Google (https://www.google.com)"]
-
- # Tests that the function returns "error" when given an invalid url.
- def test_invalid_url(self, mocker):
- # Mock the requests.get() function to return an HTTP error response
- mock_response = mocker.Mock()
- mock_response.status_code = 404
- mocker.patch("requests.Session.get", return_value=mock_response)
-
- # Call the function with an invalid URL
- result = scrape_links("https://www.invalidurl.com")
-
- # Assert that the function returns "error"
- assert "Error:" in result
-
- # Tests that the function returns an empty list when the html contains no hyperlinks.
- def test_no_hyperlinks(self, mocker):
- # Mock the requests.get() function to return a response with sample HTML containing no hyperlinks
- mock_response = mocker.Mock()
- mock_response.status_code = 200
- mock_response.text = "No hyperlinks here
"
- mocker.patch("requests.Session.get", return_value=mock_response)
-
- # Call the function with a URL containing no hyperlinks
- result = scrape_links("https://www.example.com")
-
- # Assert that the function returns an empty list
- assert result == []
-
- # Tests that scrape_links() correctly extracts and formats hyperlinks from
- # a sample HTML containing a few hyperlinks.
- def test_scrape_links_with_few_hyperlinks(self, mocker):
- # Mock the requests.get() function to return a response with a sample HTML containing hyperlinks
- mock_response = mocker.Mock()
- mock_response.status_code = 200
- mock_response.text = """
-
-
-
-
-
-
-
- """
- mocker.patch("requests.Session.get", return_value=mock_response)
-
- # Call the function being tested
- result = scrape_links("https://www.example.com")
-
- # Assert that the function returns a list of formatted hyperlinks
- assert isinstance(result, list)
- assert len(result) == 3
- assert result[0] == "Google (https://www.google.com)"
- assert result[1] == "GitHub (https://github.com)"
- assert result[2] == "CodiumAI (https://www.codium.ai)"
diff --git a/spaces/g4f/freegpt-webui/g4f/Provider/Providers/Forefront.py b/spaces/g4f/freegpt-webui/g4f/Provider/Providers/Forefront.py
deleted file mode 100644
index e7e89831cc4ec6dc37ea094d9828a7582e981ff1..0000000000000000000000000000000000000000
--- a/spaces/g4f/freegpt-webui/g4f/Provider/Providers/Forefront.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import os
-import json
-import requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://forefront.com'
-model = ['gpt-3.5-turbo']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
- json_data = {
- 'text': messages[-1]['content'],
- 'action': 'noauth',
- 'id': '',
- 'parentId': '',
- 'workspaceId': '',
- 'messagePersona': '607e41fe-95be-497e-8e97-010a59b2e2c0',
- 'model': 'gpt-4',
- 'messages': messages[:-1] if len(messages) > 1 else [],
- 'internetMode': 'auto'
- }
- response = requests.post( 'https://streaming.tenant-forefront-default.knative.chi.coreweave.com/free-chat',
- json=json_data, stream=True)
- for token in response.iter_lines():
- if b'delta' in token:
- token = json.loads(token.decode().split('data: ')[1])['delta']
- yield (token)
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/galang123/test123test/README.md b/spaces/galang123/test123test/README.md
deleted file mode 100644
index e5b38e84d7b47894f74ae957416114c0b5bea908..0000000000000000000000000000000000000000
--- a/spaces/galang123/test123test/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Test123test
-emoji: 🐨
-colorFrom: indigo
-colorTo: blue
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/gatilin/mmocr-webui/app.py b/spaces/gatilin/mmocr-webui/app.py
deleted file mode 100644
index e0795b6dff2348aa880d68846e25e7751ad59bae..0000000000000000000000000000000000000000
--- a/spaces/gatilin/mmocr-webui/app.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import os
-
-os.system("pip install gradio==3.42.0")
-os.system("pip install 'mmengine>=0.6.0'")
-os.system("pip install 'mmcv>=2.0.0rc4,<2.1.0'")
-os.system("pip install 'mmdet>=3.0.0rc5, < 3.2.0'")
-os.system("pip install mmocr")
-import json
-import os
-from argparse import ArgumentParser
-
-import PIL
-import cv2
-import gradio as gr
-import numpy as np
-import torch
-from PIL.Image import Image
-from mmocr.apis.inferencers import MMOCRInferencer
-
-import warnings
-
-warnings.filterwarnings("ignore")
-
-
-def save_image(img, img_path):
- # Convert PIL image to OpenCV image
- img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
- # Save OpenCV image
- cv2.imwrite(img_path, img)
-
-
-textdet_model_list = ['DBNet', 'DRRG', 'FCENet', 'PANet', 'PSENet', 'TextSnake', 'MaskRCNN']
-textrec_model_list = ['ABINet', 'ASTER', 'CRNN', 'MASTER', 'NRTR', 'RobustScanner', 'SARNet', 'SATRN', 'SVTR']
-textkie_model_list = ['SDMGR']
-
-def ocr_inference(inputs, out_dir, det, det_weights, rec, rec_weights, kie, kie_weights, device):
- init_args, call_args = parse_args()
- inputs = np.array(inputs)
- img_path = "demo_text_ocr.jpg"
- save_image(inputs, img_path)
- if det is not None and rec is not None:
- init_args['det'] = det
- init_args['det_weights'] = None
- init_args['rec'] = rec
- init_args['rec_weights'] = None
- elif det_weights is not None and rec_weights is not None:
- init_args['det'] = None
- init_args['det_weights'] = det_weights
- init_args['rec'] = None
- init_args['rec_weights'] = rec_weights
- call_args['inputs'] = img_path
- call_args['out_dir'] = out_dir
- call_args['batch_size'] = 1
- call_args['show'] = False
- call_args['save_pred'] = True
- call_args['save_vis'] = True
- init_args['device'] = device
- print("init_args", init_args)
- print("call_args", call_args)
- ocr = MMOCRInferencer(**init_args)
- ocr(**call_args)
- save_vis_dir = './results/vis/'
- save_pred_dir = './results/preds/'
- img_out = PIL.Image.open(os.path.join(save_vis_dir, img_path))
- json_out = json.load(open(os.path.join(save_pred_dir, img_path.replace('.jpg', '.json'))))
- return img_out, json_out
-
-
-def download_test_image():
- # Images
- torch.hub.download_url_to_file(
- 'https://user-images.githubusercontent.com/59380685/266821429-9a897c0a-5b02-4260-a65b-3514b758f6b6.jpg',
- 'demo_densetext_det.jpg')
- torch.hub.download_url_to_file(
- 'https://user-images.githubusercontent.com/59380685/266821432-17bb0646-a3e9-451e-9b4d-6e41ce4c3f0c.jpg',
- 'demo_text_recog.jpg')
- torch.hub.download_url_to_file(
- 'https://user-images.githubusercontent.com/59380685/266821434-fe0d4d18-f3e2-4acf-baf5-0d2e318f0b09.jpg',
- 'demo_text_ocr.jpg')
- torch.hub.download_url_to_file(
- 'https://user-images.githubusercontent.com/59380685/266821435-5d7af2b4-cb84-4355-91cb-37d90e91aa30.jpg',
- 'demo_text_det.jpg')
- torch.hub.download_url_to_file(
- 'https://user-images.githubusercontent.com/59380685/266821436-4790c6c1-2da5-45c7-b837-04eeea0d7264.jpeg',
- 'demo_kie.jpg')
-
-
-def parse_args():
- parser = ArgumentParser()
- parser.add_argument(
- '--inputs', type=str, help='Input image file or folder path.')
- parser.add_argument(
- '--out-dir',
- type=str,
- default='./results/',
- help='Output directory of results.')
- parser.add_argument(
- '--det',
- type=str,
- default=None,
- help='Pretrained text detection algorithm. It\'s the path to the '
- 'config file or the model name defined in metafile.')
- parser.add_argument(
- '--det-weights',
- type=str,
- default=None,
- help='Path to the custom checkpoint file of the selected det model. '
- 'If it is not specified and "det" is a model name of metafile, the '
- 'weights will be loaded from metafile.')
- parser.add_argument(
- '--rec',
- type=str,
- default=None,
- help='Pretrained text recognition algorithm. It\'s the path to the '
- 'config file or the model name defined in metafile.')
- parser.add_argument(
- '--rec-weights',
- type=str,
- default=None,
- help='Path to the custom checkpoint file of the selected recog model. '
- 'If it is not specified and "rec" is a model name of metafile, the '
- 'weights will be loaded from metafile.')
- parser.add_argument(
- '--kie',
- type=str,
- default=None,
- help='Pretrained key information extraction algorithm. It\'s the path'
- 'to the config file or the model name defined in metafile.')
- parser.add_argument(
- '--kie-weights',
- type=str,
- default=None,
- help='Path to the custom checkpoint file of the selected kie model. '
- 'If it is not specified and "kie" is a model name of metafile, the '
- 'weights will be loaded from metafile.')
- parser.add_argument(
- '--device',
- type=str,
- default=None,
- help='Device used for inference. '
- 'If not specified, the available device will be automatically used.')
- parser.add_argument(
- '--batch-size', type=int, default=1, help='Inference batch size.')
- parser.add_argument(
- '--show',
- action='store_true',
- help='Display the image in a popup window.')
- parser.add_argument(
- '--print-result',
- action='store_true',
- help='Whether to print the results.')
- parser.add_argument(
- '--save_pred',
- action='store_true',
- help='Save the inference results to out_dir.')
- parser.add_argument(
- '--save_vis',
- action='store_true',
- help='Save the visualization results to out_dir.')
-
- call_args = vars(parser.parse_args())
-
- init_kws = [
- 'det', 'det_weights', 'rec', 'rec_weights', 'kie', 'kie_weights', 'device'
- ]
- init_args = {}
- for init_kw in init_kws:
- init_args[init_kw] = call_args.pop(init_kw)
-
- return init_args, call_args
-
-
-if __name__ == '__main__':
- # Define Gradio input and output types
- input_image = gr.inputs.Image(type="pil", label="Input Image")
- out_dir = gr.inputs.Textbox(default="results")
- det = gr.inputs.Dropdown(label="Text Detection Model", choices=[m for m in textdet_model_list], default='DBNet')
- det_weights = gr.inputs.Textbox(default=None)
- rec = gr.inputs.Dropdown(label="Text Recognition Model", choices=[m for m in textrec_model_list], default='CRNN')
- rec_weights = gr.inputs.Textbox(default=None)
- device = gr.inputs.Radio(choices=["cpu", "cuda"], label="Device used for inference", default="cpu")
- batch_size = gr.inputs.Number(default=1, label="Inference batch size")
- output_image = gr.outputs.Image(type="pil", label="Output Image")
- output_json = gr.outputs.Textbox()
- download_test_image()
- examples = [["demo_text_ocr.jpg", "results", "DBNet", None, "CRNN", "cpu"],
- ["demo_text_det.jpg", "results", "FCENet", None, "ASTER", "cpu"],
- ["demo_text_recog.jpg", "results", "FCENet", None, "MASTER", "cpu"],
- ]
-
- title = "MMOCR web demo"
- description = "" \
- "
MMOCR MMOCR 是基于 PyTorch 和 mmdetection 的开源工具箱，专注于文本检测，文本识别以及相应的下游任务，如关键信息提取。 它是 OpenMMLab 项目的一部分。" \
- "OpenMMLab Text Detection, Recognition and Understanding Toolbox.
"
- article = "
MMOCR
" \
- "
gradio build by gatilin
"
-
- # Create Gradio interface
- iface = gr.Interface(
- fn=ocr_inference,
- inputs=[
- input_image, out_dir, det, det_weights, rec, rec_weights, device
- ],
- outputs=[output_image, output_json], examples=examples,
- title=title, description=description, article=article,
- )
-
- # Launch Gradio interface
- iface.launch()
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/models/deeplabv3_r50-d8.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/models/deeplabv3_r50-d8.py
deleted file mode 100644
index d7a43bee01422ad4795dd27874e0cd4bb6cbfecf..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/models/deeplabv3_r50-d8.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
- type='EncoderDecoder',
- pretrained='open-mmlab://resnet50_v1c',
- backbone=dict(
- type='ResNetV1c',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- dilations=(1, 1, 2, 4),
- strides=(1, 2, 1, 1),
- norm_cfg=norm_cfg,
- norm_eval=False,
- style='pytorch',
- contract_dilation=True),
- decode_head=dict(
- type='ASPPHead',
- in_channels=2048,
- in_index=3,
- channels=512,
- dilations=(1, 12, 24, 36),
- dropout_ratio=0.1,
- num_classes=19,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
- auxiliary_head=dict(
- type='FCNHead',
- in_channels=1024,
- in_index=2,
- channels=256,
- num_convs=1,
- concat_input=False,
- dropout_ratio=0.1,
- num_classes=19,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
- # model training and testing settings
- train_cfg=dict(),
- test_cfg=dict(mode='whole'))
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/cc_attention.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/cc_attention.py
deleted file mode 100644
index 9207aa95e6730bd9b3362dee612059a5f0ce1c5e..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/cc_attention.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from annotator.uniformer.mmcv.cnn import PLUGIN_LAYERS, Scale
-
-
-def NEG_INF_DIAG(n, device):
- """Returns a diagonal matrix of size [n, n].
-
- The diagonal are all "-inf". This is for avoiding calculating the
- overlapped element in the Criss-Cross twice.
- """
- return torch.diag(torch.tensor(float('-inf')).to(device).repeat(n), 0)
-
-
-@PLUGIN_LAYERS.register_module()
-class CrissCrossAttention(nn.Module):
- """Criss-Cross Attention Module.
-
- .. note::
- Before v1.3.13, we use a CUDA op. Since v1.3.13, we switch
- to a pure PyTorch and equivalent implementation. For more
- details, please refer to https://github.com/open-mmlab/mmcv/pull/1201.
-
- Speed comparison for one forward pass
-
- - Input size: [2,512,97,97]
- - Device: 1 NVIDIA GeForce RTX 2080 Ti
-
- +-----------------------+---------------+------------+---------------+
- | |PyTorch version|CUDA version|Relative speed |
- +=======================+===============+============+===============+
- |with torch.no_grad() |0.00554402 s |0.0299619 s |5.4x |
- +-----------------------+---------------+------------+---------------+
- |no with torch.no_grad()|0.00562803 s |0.0301349 s |5.4x |
- +-----------------------+---------------+------------+---------------+
-
- Args:
- in_channels (int): Channels of the input feature map.
- """
-
- def __init__(self, in_channels):
- super().__init__()
- self.query_conv = nn.Conv2d(in_channels, in_channels // 8, 1)
- self.key_conv = nn.Conv2d(in_channels, in_channels // 8, 1)
- self.value_conv = nn.Conv2d(in_channels, in_channels, 1)
- self.gamma = Scale(0.)
- self.in_channels = in_channels
-
- def forward(self, x):
- """forward function of Criss-Cross Attention.
-
- Args:
- x (Tensor): Input feature. \
- shape (batch_size, in_channels, height, width)
- Returns:
- Tensor: Output of the layer, with shape of \
- (batch_size, in_channels, height, width)
- """
- B, C, H, W = x.size()
- query = self.query_conv(x)
- key = self.key_conv(x)
- value = self.value_conv(x)
- energy_H = torch.einsum('bchw,bciw->bwhi', query, key) + NEG_INF_DIAG(
- H, query.device)
- energy_H = energy_H.transpose(1, 2)
- energy_W = torch.einsum('bchw,bchj->bhwj', query, key)
- attn = F.softmax(
- torch.cat([energy_H, energy_W], dim=-1), dim=-1) # [B,H,W,(H+W)]
- out = torch.einsum('bciw,bhwi->bchw', value, attn[..., :H])
- out += torch.einsum('bchj,bhwj->bchw', value, attn[..., H:])
-
- out = self.gamma(out) + x
- out = out.contiguous()
-
- return out
-
- def __repr__(self):
- s = self.__class__.__name__
- s += f'(in_channels={self.in_channels})'
- return s
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/utils/progressbar.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/utils/progressbar.py
deleted file mode 100644
index 0062f670dd94fa9da559ab26ef85517dcf5211c7..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/utils/progressbar.py
+++ /dev/null
@@ -1,208 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import sys
-from collections.abc import Iterable
-from multiprocessing import Pool
-from shutil import get_terminal_size
-
-from .timer import Timer
-
-
-class ProgressBar:
- """A progress bar which can print the progress."""
-
- def __init__(self, task_num=0, bar_width=50, start=True, file=sys.stdout):
- self.task_num = task_num
- self.bar_width = bar_width
- self.completed = 0
- self.file = file
- if start:
- self.start()
-
- @property
- def terminal_width(self):
- width, _ = get_terminal_size()
- return width
-
- def start(self):
- if self.task_num > 0:
- self.file.write(f'[{" " * self.bar_width}] 0/{self.task_num}, '
- 'elapsed: 0s, ETA:')
- else:
- self.file.write('completed: 0, elapsed: 0s')
- self.file.flush()
- self.timer = Timer()
-
- def update(self, num_tasks=1):
- assert num_tasks > 0
- self.completed += num_tasks
- elapsed = self.timer.since_start()
- if elapsed > 0:
- fps = self.completed / elapsed
- else:
- fps = float('inf')
- if self.task_num > 0:
- percentage = self.completed / float(self.task_num)
- eta = int(elapsed * (1 - percentage) / percentage + 0.5)
- msg = f'\r[{{}}] {self.completed}/{self.task_num}, ' \
- f'{fps:.1f} task/s, elapsed: {int(elapsed + 0.5)}s, ' \
- f'ETA: {eta:5}s'
-
- bar_width = min(self.bar_width,
- int(self.terminal_width - len(msg)) + 2,
- int(self.terminal_width * 0.6))
- bar_width = max(2, bar_width)
- mark_width = int(bar_width * percentage)
- bar_chars = '>' * mark_width + ' ' * (bar_width - mark_width)
- self.file.write(msg.format(bar_chars))
- else:
- self.file.write(
- f'completed: {self.completed}, elapsed: {int(elapsed + 0.5)}s,'
- f' {fps:.1f} tasks/s')
- self.file.flush()
-
-
-def track_progress(func, tasks, bar_width=50, file=sys.stdout, **kwargs):
- """Track the progress of tasks execution with a progress bar.
-
- Tasks are done with a simple for-loop.
-
- Args:
- func (callable): The function to be applied to each task.
- tasks (list or tuple[Iterable, int]): A list of tasks or
- (tasks, total num).
- bar_width (int): Width of progress bar.
-
- Returns:
- list: The task results.
- """
- if isinstance(tasks, tuple):
- assert len(tasks) == 2
- assert isinstance(tasks[0], Iterable)
- assert isinstance(tasks[1], int)
- task_num = tasks[1]
- tasks = tasks[0]
- elif isinstance(tasks, Iterable):
- task_num = len(tasks)
- else:
- raise TypeError(
- '"tasks" must be an iterable object or a (iterator, int) tuple')
- prog_bar = ProgressBar(task_num, bar_width, file=file)
- results = []
- for task in tasks:
- results.append(func(task, **kwargs))
- prog_bar.update()
- prog_bar.file.write('\n')
- return results
-
-
-def init_pool(process_num, initializer=None, initargs=None):
- if initializer is None:
- return Pool(process_num)
- elif initargs is None:
- return Pool(process_num, initializer)
- else:
- if not isinstance(initargs, tuple):
- raise TypeError('"initargs" must be a tuple')
- return Pool(process_num, initializer, initargs)
-
-
-def track_parallel_progress(func,
- tasks,
- nproc,
- initializer=None,
- initargs=None,
- bar_width=50,
- chunksize=1,
- skip_first=False,
- keep_order=True,
- file=sys.stdout):
- """Track the progress of parallel task execution with a progress bar.
-
- The built-in :mod:`multiprocessing` module is used for process pools and
- tasks are done with :func:`Pool.map` or :func:`Pool.imap_unordered`.
-
- Args:
- func (callable): The function to be applied to each task.
- tasks (list or tuple[Iterable, int]): A list of tasks or
- (tasks, total num).
- nproc (int): Process (worker) number.
- initializer (None or callable): Refer to :class:`multiprocessing.Pool`
- for details.
- initargs (None or tuple): Refer to :class:`multiprocessing.Pool` for
- details.
- chunksize (int): Refer to :class:`multiprocessing.Pool` for details.
- bar_width (int): Width of progress bar.
- skip_first (bool): Whether to skip the first sample for each worker
- when estimating fps, since the initialization step may takes
- longer.
- keep_order (bool): If True, :func:`Pool.imap` is used, otherwise
- :func:`Pool.imap_unordered` is used.
-
- Returns:
- list: The task results.
- """
- if isinstance(tasks, tuple):
- assert len(tasks) == 2
- assert isinstance(tasks[0], Iterable)
- assert isinstance(tasks[1], int)
- task_num = tasks[1]
- tasks = tasks[0]
- elif isinstance(tasks, Iterable):
- task_num = len(tasks)
- else:
- raise TypeError(
- '"tasks" must be an iterable object or a (iterator, int) tuple')
- pool = init_pool(nproc, initializer, initargs)
- start = not skip_first
- task_num -= nproc * chunksize * int(skip_first)
- prog_bar = ProgressBar(task_num, bar_width, start, file=file)
- results = []
- if keep_order:
- gen = pool.imap(func, tasks, chunksize)
- else:
- gen = pool.imap_unordered(func, tasks, chunksize)
- for result in gen:
- results.append(result)
- if skip_first:
- if len(results) < nproc * chunksize:
- continue
- elif len(results) == nproc * chunksize:
- prog_bar.start()
- continue
- prog_bar.update()
- prog_bar.file.write('\n')
- pool.close()
- pool.join()
- return results
-
-
-def track_iter_progress(tasks, bar_width=50, file=sys.stdout):
- """Track the progress of tasks iteration or enumeration with a progress
- bar.
-
- Tasks are yielded with a simple for-loop.
-
- Args:
- tasks (list or tuple[Iterable, int]): A list of tasks or
- (tasks, total num).
- bar_width (int): Width of progress bar.
-
- Yields:
- list: The task results.
- """
- if isinstance(tasks, tuple):
- assert len(tasks) == 2
- assert isinstance(tasks[0], Iterable)
- assert isinstance(tasks[1], int)
- task_num = tasks[1]
- tasks = tasks[0]
- elif isinstance(tasks, Iterable):
- task_num = len(tasks)
- else:
- raise TypeError(
- '"tasks" must be an iterable object or a (iterator, int) tuple')
- prog_bar = ProgressBar(task_num, bar_width, file=file)
- for task in tasks:
- yield task
- prog_bar.update()
- prog_bar.file.write('\n')
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/ops/__init__.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/ops/__init__.py
deleted file mode 100644
index bec51c75b9363a9a19e9fb5c35f4e7dbd6f7751c..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/ops/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .encoding import Encoding
-from .wrappers import Upsample, resize
-
-__all__ = ['Upsample', 'resize', 'Encoding']
diff --git a/spaces/gligen/demo/gligen/ldm/__init__.py b/spaces/gligen/demo/gligen/ldm/__init__.py
deleted file mode 100644
index d71fc3230d513f470c68e46752b355cd70824ecf..0000000000000000000000000000000000000000
--- a/spaces/gligen/demo/gligen/ldm/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gligen.evaluator as evaluator
-import gligen.trainer as trainer
-import gligen.ldm as ldm
\ No newline at end of file
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Crack VERIFIED De Age Of Mythology The Titans Descargar.md b/spaces/gotiQspiryo/whisper-ui/examples/Crack VERIFIED De Age Of Mythology The Titans Descargar.md
deleted file mode 100644
index d1437e92539fa69a7b35ae72212e4fc6c14b895d..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Crack VERIFIED De Age Of Mythology The Titans Descargar.md
+++ /dev/null
@@ -1,7 +0,0 @@
-
-
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diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Jeepers Creepers 3 Fr Kickass 168.md b/spaces/gotiQspiryo/whisper-ui/examples/Jeepers Creepers 3 Fr Kickass 168.md
deleted file mode 100644
index 27356061c691ef41642f3b5f675b3eedb216eb9e..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Jeepers Creepers 3 Fr Kickass 168.md
+++ /dev/null
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diff --git a/spaces/gradio/HuBERT/fairseq/modules/fairseq_dropout.py b/spaces/gradio/HuBERT/fairseq/modules/fairseq_dropout.py
deleted file mode 100644
index 3cddca77186f5ddd5cfb9c0ed6def9bafdf3bf1e..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/modules/fairseq_dropout.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-from typing import List, Optional
-
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-logger = logging.getLogger(__name__)
-
-
-class FairseqDropout(nn.Module):
- def __init__(self, p, module_name=None):
- super().__init__()
- self.p = p
- self.module_name = module_name
- self.apply_during_inference = False
-
- def forward(self, x, inplace: bool = False):
- if self.p > 0 and (self.training or self.apply_during_inference):
- return F.dropout(x, p=self.p, training=True, inplace=inplace)
- else:
- return x
-
- def make_generation_fast_(
- self,
- name: str,
- retain_dropout: bool = False,
- retain_dropout_modules: Optional[List[str]] = None,
- **kwargs
- ):
- if retain_dropout:
- if retain_dropout_modules is not None and self.module_name is None:
- logger.warning(
- "Cannot enable dropout during inference for module {} "
- "because module_name was not set".format(name)
- )
- elif (
- retain_dropout_modules is None # if None, apply to all modules
- or self.module_name in retain_dropout_modules
- ):
- logger.info(
- "Enabling dropout during inference for module: {}".format(name)
- )
- self.apply_during_inference = True
- else:
- logger.info("Disabling dropout for module: {}".format(name))
diff --git a/spaces/greenlights/gitapp/app.py b/spaces/greenlights/gitapp/app.py
deleted file mode 100644
index 4aa958a565f75505f6c4649adee551ae74eb9224..0000000000000000000000000000000000000000
--- a/spaces/greenlights/gitapp/app.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import numpy as np
-import json
-from flask import Flask, request, jsonify, render_template
-import pickle
-
-app = Flask(__name__)
-model = pickle.load(open('model.pkl', 'rb'))
-
-@app.route('/')
-def home():
- return render_template('index.html')
-
-@app.route('/predict',methods=['POST'])
-def predict():
- '''
- For rendering results on HTML GUI
- '''
- int_features = [int(x) for x in request.form.values()]
- final_features = [np.array(int_features)]
- prediction = model.predict(final_features)
-
- output = round(prediction[0], 2)
-
- return render_template('index.html', prediction_text='Salary is {}'.format(output))
-
-
-@app.route('/predict_api',methods=['POST'])
-def predict_api():
- '''
- For direct API calls trought request
- '''
- data = request.get_json(force=True)
- prediction = model.predict([np.array(list(data.values()))])
-
- output = prediction[0]
- return jsonify(output)
-
-if __name__ == "__main__":
- app.run(debug=True)
diff --git a/spaces/guymorlan/English2ShamiDialect/README.md b/spaces/guymorlan/English2ShamiDialect/README.md
deleted file mode 100644
index 702fa6c53fcc8f2e49b374afa6b50ac1badcdf13..0000000000000000000000000000000000000000
--- a/spaces/guymorlan/English2ShamiDialect/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: English2ShamiDialect
-emoji: 👀
-colorFrom: blue
-colorTo: indigo
-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
diff --git a/spaces/gyugnsu/DragGan-Inversion/gen_images.py b/spaces/gyugnsu/DragGan-Inversion/gen_images.py
deleted file mode 100644
index 996bc12f4cde6ee9d0076446250ed076a04b2641..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/gen_images.py
+++ /dev/null
@@ -1,160 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Generate images using pretrained network pickle."""
-
-import os
-import re
-from typing import List, Optional, Tuple, Union
-
-import click
-import dnnlib
-import numpy as np
-import PIL.Image
-import torch
-
-import legacy
-
-# ----------------------------------------------------------------------------
-
-
-def parse_range(s: Union[str, List]) -> List[int]:
- '''Parse a comma separated list of numbers or ranges and return a list of ints.
-
- Example: '1,2,5-10' returns [1, 2, 5, 6, 7]
- '''
- if isinstance(s, list):
- return s
- ranges = []
- range_re = re.compile(r'^(\d+)-(\d+)$')
- for p in s.split(','):
- m = range_re.match(p)
- if m:
- ranges.extend(range(int(m.group(1)), int(m.group(2))+1))
- else:
- ranges.append(int(p))
- return ranges
-
-# ----------------------------------------------------------------------------
-
-
-def parse_vec2(s: Union[str, Tuple[float, float]]) -> Tuple[float, float]:
- '''Parse a floating point 2-vector of syntax 'a,b'.
-
- Example:
- '0,1' returns (0,1)
- '''
- if isinstance(s, tuple):
- return s
- parts = s.split(',')
- if len(parts) == 2:
- return (float(parts[0]), float(parts[1]))
- raise ValueError(f'cannot parse 2-vector {s}')
-
-# ----------------------------------------------------------------------------
-
-
-def make_transform(translate: Tuple[float, float], angle: float):
- m = np.eye(3)
- s = np.sin(angle/360.0*np.pi*2)
- c = np.cos(angle/360.0*np.pi*2)
- m[0][0] = c
- m[0][1] = s
- m[0][2] = translate[0]
- m[1][0] = -s
- m[1][1] = c
- m[1][2] = translate[1]
- return m
-
-# ----------------------------------------------------------------------------
-
-
-@click.command()
-@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
-@click.option('--seeds', type=parse_range, help='List of random seeds (e.g., \'0,1,4-6\')', required=True)
-@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
-@click.option('--class', 'class_idx', type=int, help='Class label (unconditional if not specified)')
-@click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
-@click.option('--translate', help='Translate XY-coordinate (e.g. \'0.3,1\')', type=parse_vec2, default='0,0', show_default=True, metavar='VEC2')
-@click.option('--rotate', help='Rotation angle in degrees', type=float, default=0, show_default=True, metavar='ANGLE')
-@click.option('--outdir', help='Where to save the output images', type=str, required=True, metavar='DIR')
-def generate_images(
- network_pkl: str,
- seeds: List[int],
- truncation_psi: float,
- noise_mode: str,
- outdir: str,
- translate: Tuple[float, float],
- rotate: float,
- class_idx: Optional[int]
-):
- """Generate images using pretrained network pickle.
-
- Examples:
-
- \b
- # Generate an image using pre-trained AFHQv2 model ("Ours" in Figure 1, left).
- python gen_images.py --outdir=out --trunc=1 --seeds=2 \\
- --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl
-
- \b
- # Generate uncurated images with truncation using the MetFaces-U dataset
- python gen_images.py --outdir=out --trunc=0.7 --seeds=600-605 \\
- --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-metfacesu-1024x1024.pkl
- """
-
- print('Loading networks from "%s"...' % network_pkl)
- device = torch.device('cuda')
- with dnnlib.util.open_url(network_pkl) as f:
- G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore
- # import pickle
- # G = legacy.load_network_pkl(f)
- # output = open('checkpoints/stylegan2-car-config-f-pt.pkl', 'wb')
- # pickle.dump(G, output)
-
- os.makedirs(outdir, exist_ok=True)
-
- # Labels.
- label = torch.zeros([1, G.c_dim], device=device)
- if G.c_dim != 0:
- if class_idx is None:
- raise click.ClickException(
- 'Must specify class label with --class when using a conditional network')
- label[:, class_idx] = 1
- else:
- if class_idx is not None:
- print('warn: --class=lbl ignored when running on an unconditional network')
-
- # Generate images.
- for seed_idx, seed in enumerate(seeds):
- print('Generating image for seed %d (%d/%d) ...' %
- (seed, seed_idx, len(seeds)))
- z = torch.from_numpy(np.random.RandomState(
- seed).randn(1, G.z_dim)).to(device)
-
- # Construct an inverse rotation/translation matrix and pass to the generator. The
- # generator expects this matrix as an inverse to avoid potentially failing numerical
- # operations in the network.
- if hasattr(G.synthesis, 'input'):
- m = make_transform(translate, rotate)
- m = np.linalg.inv(m)
- G.synthesis.input.transform.copy_(torch.from_numpy(m))
-
- img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode)
- img = (img.permute(0, 2, 3, 1) * 127.5 +
- 128).clamp(0, 255).to(torch.uint8)
- PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(
- f'{outdir}/seed{seed:04d}.png')
-
-
-# ----------------------------------------------------------------------------
-
-if __name__ == "__main__":
- generate_images() # pylint: disable=no-value-for-parameter
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/hamelcubsfan/AutoGPT/autogpt/__init__.py b/spaces/hamelcubsfan/AutoGPT/autogpt/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/hands012/gpt-academic/crazy_functions/test_project/latex/attention/model_architecture.tex b/spaces/hands012/gpt-academic/crazy_functions/test_project/latex/attention/model_architecture.tex
deleted file mode 100644
index c82be6242cc9d26203360e90d3ac9184ef6ad842..0000000000000000000000000000000000000000
--- a/spaces/hands012/gpt-academic/crazy_functions/test_project/latex/attention/model_architecture.tex
+++ /dev/null
@@ -1,155 +0,0 @@
-
-\begin{figure}
- \centering
- \includegraphics[scale=0.6]{Figures/ModalNet-21}
- \caption{The Transformer - model architecture.}
- \label{fig:model-arch}
-\end{figure}
-
-% Although the primary workhorse of our model is attention,
-%Our model maintains the encoder-decoder structure that is common to many so-called sequence-to-sequence models \citep{bahdanau2014neural,sutskever14}. As in all such architectures, the encoder computes a representation of the input sequence, and the decoder consumes these representations along with the output tokens to autoregressively produce the output sequence. Where, traditionally, the encoder and decoder contain stacks of recurrent or convolutional layers, our encoder and decoder stacks are composed of attention layers and position-wise feed-forward layers (Figure~\ref{fig:model-arch}). The following sections describe the gross architecture and these particular components in detail.
-
-Most competitive neural sequence transduction models have an encoder-decoder structure \citep{cho2014learning,bahdanau2014neural,sutskever14}. Here, the encoder maps an input sequence of symbol representations $(x_1, ..., x_n)$ to a sequence of continuous representations $\mathbf{z} = (z_1, ..., z_n)$. Given $\mathbf{z}$, the decoder then generates an output sequence $(y_1,...,y_m)$ of symbols one element at a time. At each step the model is auto-regressive \citep{graves2013generating}, consuming the previously generated symbols as additional input when generating the next.
-
-The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure~\ref{fig:model-arch}, respectively.
-
-\subsection{Encoder and Decoder Stacks}
-
-\paragraph{Encoder:}The encoder is composed of a stack of $N=6$ identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position-wise fully connected feed-forward network. We employ a residual connection \citep{he2016deep} around each of the two sub-layers, followed by layer normalization \cite{layernorm2016}. That is, the output of each sub-layer is $\mathrm{LayerNorm}(x + \mathrm{Sublayer}(x))$, where $\mathrm{Sublayer}(x)$ is the function implemented by the sub-layer itself. To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension $\dmodel=512$.
-
-\paragraph{Decoder:}The decoder is also composed of a stack of $N=6$ identical layers. In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack. Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization. We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position $i$ can depend only on the known outputs at positions less than $i$.
-
-% In our model (Figure~\ref{fig:model-arch}), the encoder and decoder are composed of stacks of alternating self-attention layers (for cross-positional communication) and position-wise feed-forward layers (for in-place computation). In addition, the decoder stack contains encoder-decoder attention layers. Since attention is agnostic to the distances between words, our model requires a "positional encoding" to be added to the encoder and decoder input. The following sections describe all of these components in detail.
-
-\subsection{Attention} \label{sec:attention}
-An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.
-
-\subsubsection{Scaled Dot-Product Attention} \label{sec:scaled-dot-prod}
-
-% \begin{figure}
-% \centering
-% \includegraphics[scale=0.6]{Figures/ModalNet-19}
-% \caption{Scaled Dot-Product Attention.}
-% \label{fig:multi-head-att}
-% \end{figure}
-
-We call our particular attention "Scaled Dot-Product Attention" (Figure~\ref{fig:multi-head-att}). The input consists of queries and keys of dimension $d_k$, and values of dimension $d_v$. We compute the dot products of the query with all keys, divide each by $\sqrt{d_k}$, and apply a softmax function to obtain the weights on the values.
-
-In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix $Q$. The keys and values are also packed together into matrices $K$ and $V$. We compute the matrix of outputs as:
-
-\begin{equation}
- \mathrm{Attention}(Q, K, V) = \mathrm{softmax}(\frac{QK^T}{\sqrt{d_k}})V
-\end{equation}
-
-The two most commonly used attention functions are additive attention \citep{bahdanau2014neural}, and dot-product (multiplicative) attention. Dot-product attention is identical to our algorithm, except for the scaling factor of $\frac{1}{\sqrt{d_k}}$. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer. While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code.
-
-%We scale the dot products by $1/\sqrt{d_k}$ to limit the magnitude of the dot products, which works well in practice. Otherwise, we found applying the softmax to often result in weights very close to 0 or 1, and hence minuscule gradients.
-
-% Already described in the subsequent section
-%When used as part of decoder self-attention, an optional mask function is applied just before the softmax to prevent positions from attending to subsequent positions. This mask simply sets the logits corresponding to all illegal connections (those outside of the lower triangle) to $-\infty$.
-
-%\paragraph{Comparison to Additive Attention: } We choose dot product attention over additive attention \citep{bahdanau2014neural} since it can be computed using highly optimized matrix multiplication code. This optimization is particularly important to us, as we employ many attention layers in our model.
-
-While for small values of $d_k$ the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of $d_k$ \citep{DBLP:journals/corr/BritzGLL17}. We suspect that for large values of $d_k$, the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients \footnote{To illustrate why the dot products get large, assume that the components of $q$ and $k$ are independent random variables with mean $0$ and variance $1$. Then their dot product, $q \cdot k = \sum_{i=1}^{d_k} q_ik_i$, has mean $0$ and variance $d_k$.}. To counteract this effect, we scale the dot products by $\frac{1}{\sqrt{d_k}}$.
-
-
-%We suspect this to be caused by the dot products growing too large in magnitude to result in useful gradients after applying the softmax function. To counteract this, we scale the dot product by $1/\sqrt{d_k}$.
-
-
-\subsubsection{Multi-Head Attention} \label{sec:multihead}
-
-\begin{figure}
-\begin{minipage}[t]{0.5\textwidth}
- \centering
- Scaled Dot-Product Attention \\
- \vspace{0.5cm}
- \includegraphics[scale=0.6]{Figures/ModalNet-19}
-\end{minipage}
-\begin{minipage}[t]{0.5\textwidth}
- \centering
- Multi-Head Attention \\
- \vspace{0.1cm}
- \includegraphics[scale=0.6]{Figures/ModalNet-20}
-\end{minipage}
-
-
- % \centering
-
- \caption{(left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several attention layers running in parallel.}
- \label{fig:multi-head-att}
-\end{figure}
-
-Instead of performing a single attention function with $\dmodel$-dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values $h$ times with different, learned linear projections to $d_k$, $d_k$ and $d_v$ dimensions, respectively.
-On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding $d_v$-dimensional output values. These are concatenated and once again projected, resulting in the final values, as depicted in Figure~\ref{fig:multi-head-att}.
-
-Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.
-
-\begin{align*}
- \mathrm{MultiHead}(Q, K, V) &= \mathrm{Concat}(\mathrm{head_1}, ..., \mathrm{head_h})W^O\\
-% \mathrm{where} \mathrm{head_i} &= \mathrm{Attention}(QW_Q_i^{\dmodel \times d_q}, KW_K_i^{\dmodel \times d_k}, VW^V_i^{\dmodel \times d_v})\\
- \text{where}~\mathrm{head_i} &= \mathrm{Attention}(QW^Q_i, KW^K_i, VW^V_i)\\
-\end{align*}
-
-Where the projections are parameter matrices $W^Q_i \in \mathbb{R}^{\dmodel \times d_k}$, $W^K_i \in \mathbb{R}^{\dmodel \times d_k}$, $W^V_i \in \mathbb{R}^{\dmodel \times d_v}$ and $W^O \in \mathbb{R}^{hd_v \times \dmodel}$.
-
-
-%find it better (and no more expensive) to have multiple parallel attention layers (each over the full set of positions) with proportionally lower-dimensional keys, values and queries. We call this "Multi-Head Attention" (Figure~\ref{fig:multi-head-att}). The keys, values, and queries for each of these parallel attention layers are computed by learned linear transformations of the inputs to the multi-head attention. We use different linear transformations across different parallel attention layers. The output of the parallel attention layers are concatenated, and then passed through a final learned linear transformation.
-
-In this work we employ $h=8$ parallel attention layers, or heads. For each of these we use $d_k=d_v=\dmodel/h=64$.
-Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.
-
-\subsubsection{Applications of Attention in our Model}
-
-The Transformer uses multi-head attention in three different ways:
-\begin{itemize}
- \item In "encoder-decoder attention" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as \citep{wu2016google, bahdanau2014neural,JonasFaceNet2017}.
-
- \item The encoder contains self-attention layers. In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder.
-
- \item Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. We need to prevent leftward information flow in the decoder to preserve the auto-regressive property. We implement this inside of scaled dot-product attention by masking out (setting to $-\infty$) all values in the input of the softmax which correspond to illegal connections. See Figure~\ref{fig:multi-head-att}.
-
-\end{itemize}
-
-\subsection{Position-wise Feed-Forward Networks}\label{sec:ffn}
-
-In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically. This consists of two linear transformations with a ReLU activation in between.
-
-\begin{equation}
- \mathrm{FFN}(x)=\max(0, xW_1 + b_1) W_2 + b_2
-\end{equation}
-
-While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1. The dimensionality of input and output is $\dmodel=512$, and the inner-layer has dimensionality $d_{ff}=2048$.
-
-
-
-%In the appendix, we describe how the position-wise feed-forward network can also be seen as a form of attention.
-
-%from Jakob: The number of operations required for the model to relate signals from two arbitrary input or output positions grows in the distance between positions in input or output, linearly for ConvS2S and logarithmically for ByteNet, making it harder to learn dependencies between these positions \citep{hochreiter2001gradient}. In the transformer this is reduced to a constant number of operations, albeit at the cost of effective resolution caused by averaging attention-weighted positions, an effect we aim to counteract with multi-headed attention.
-
-
-%Figure~\ref{fig:simple-att} presents a simple attention function, $A$, with a single head, that forms the basis of our multi-head attention. $A$ takes a query key vector $\kq$, matrices of memory keys $\km$ and memory values $\vm$ ,and produces a query value vector $\vq$ as
-%\begin{equation*} \label{eq:attention}
-% A(\kq, \km, \vm) = {\vm}^T (Softmax(\km \kq).
-%\end{equation*}
-%We linearly transform $\kq,\,\km$, and $\vm$ with learned matrices ${\Wkq \text{,} \, \Wkm}$, and ${\Wvm}$ before calling the attention function, and transform the output query with $\Wvq$ before handing it to the feed forward layer. Each attention layer has it's own set of transformation matrices, which are shared across all query positions. $A$ is applied in parallel for each query position, and is implemented very efficiently as a batch of matrix multiplies. The self-attention and encoder-decoder attention layers use $A$, but with different arguments. For example, in encdoder self-attention, queries in encoder layer $i$ attention to memories in encoder layer $i-1$. To ensure that decoder self-attention layers do not look at future words, we add $- \inf$ to the softmax logits in positions $j+1$ to query length for query position $l$.
-
-%In simple attention, the query value is a weighted combination of the memory values where the attention weights sum to one. Although this function performs well in practice, the constraint on attention weights can restrict the amount of information that flows from memories to queries because the query cannot focus on multiple memory positions at once, which might be desirable when translating long sequences. \marginpar{@usz, could you think of an example of this ?} We remedy this by maintaining multiple attention heads at each query position that attend to all memory positions in parallel, with a different set of parameters per attention head $h$.
-%\marginpar{}
-
-\subsection{Embeddings and Softmax}
-Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension $\dmodel$. We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities. In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to \citep{press2016using}. In the embedding layers, we multiply those weights by $\sqrt{\dmodel}$.
-
-
-\subsection{Positional Encoding}
-Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence. To this end, we add "positional encodings" to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $\dmodel$ as the embeddings, so that the two can be summed. There are many choices of positional encodings, learned and fixed \citep{JonasFaceNet2017}.
-
-In this work, we use sine and cosine functions of different frequencies:
-
-\begin{align*}
- PE_{(pos,2i)} = sin(pos / 10000^{2i/\dmodel}) \\
- PE_{(pos,2i+1)} = cos(pos / 10000^{2i/\dmodel})
-\end{align*}
-
-where $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\pi$ to $10000 \cdot 2\pi$. We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $PE_{pos+k}$ can be represented as a linear function of $PE_{pos}$.
-
-We also experimented with using learned positional embeddings \citep{JonasFaceNet2017} instead, and found that the two versions produced nearly identical results (see Table~\ref{tab:variations} row (E)). We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/coco.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/coco.py
deleted file mode 100644
index 995cc20fc5a268dd54b73f167d38a7eb536c6dbd..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/coco.py
+++ /dev/null
@@ -1,268 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import os
-import os.path
-import math
-from PIL import Image, ImageDraw
-
-import random
-import numpy as np
-
-import torch
-import torchvision
-import torch.utils.data as data
-
-from maskrcnn_benchmark.structures.bounding_box import BoxList
-from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
-from maskrcnn_benchmark.structures.keypoint import PersonKeypoints
-from maskrcnn_benchmark.config import cfg
-import pdb
-
-def _count_visible_keypoints(anno):
- return sum(sum(1 for v in ann["keypoints"][2::3] if v > 0) for ann in anno)
-
-
-def _has_only_empty_bbox(anno):
- return all(any(o <= 1 for o in obj["bbox"][2:]) for obj in anno)
-
-
-def has_valid_annotation(anno):
- # if it's empty, there is no annotation
- if len(anno) == 0:
- return False
- # if all boxes have close to zero area, there is no annotation
- if _has_only_empty_bbox(anno):
- return False
- # keypoints task have a slight different critera for considering
- # if an annotation is valid
- if "keypoints" not in anno[0]:
- return True
- # for keypoint detection tasks, only consider valid images those
- # containing at least min_keypoints_per_image
- if _count_visible_keypoints(anno) >= cfg.DATALOADER.MIN_KPS_PER_IMS:
- return True
- return False
-
-
-def pil_loader(path, retry=5):
- # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
- ri = 0
- while ri < retry:
- try:
- with open(path, 'rb') as f:
- img = Image.open(f)
- return img.convert('RGB')
- except:
- ri += 1
-
-
-def rgb2id(color):
- if isinstance(color, np.ndarray) and len(color.shape) == 3:
- if color.dtype == np.uint8:
- color = color.astype(np.int32)
- return color[:, :, 0] + 256 * color[:, :, 1] + 256 * 256 * color[:, :, 2]
- return int(color[0] + 256 * color[1] + 256 * 256 * color[2])
-
-
-class CocoDetection(data.Dataset):
- """`MS Coco Detection
`_ Dataset.
-
- Args:
- root (string): Root directory where images are downloaded to.
- annFile (string): Path to json annotation file.
- transform (callable, optional): A function/transform that takes in an PIL image
- and returns a transformed version. E.g, ``transforms.ToTensor``
- target_transform (callable, optional): A function/transform that takes in the
- target and transforms it.
- """
-
- def __init__(self, root, annFile, transform=None, target_transform=None):
- from pycocotools.coco import COCO
- self.root = root
- self.coco = COCO(annFile)
- self.ids = list(self.coco.imgs.keys())
- self.transform = transform
- self.target_transform = target_transform
-
- def __getitem__(self, index, return_meta=False):
- """
- Args:
- index (int): Index
-
- Returns:
- tuple: Tuple (image, target). target is the object returned by ``coco.loadAnns``.
- """
- coco = self.coco
- img_id = self.ids[index]
- if isinstance(img_id, str):
- img_id = [img_id]
- ann_ids = coco.getAnnIds(imgIds=img_id)
- target = coco.loadAnns(ann_ids)
-
- meta = coco.loadImgs(img_id)[0]
- path = meta['file_name']
- img = pil_loader(os.path.join(self.root, path))
-
- if self.transform is not None:
- img = self.transform(img)
-
- if self.target_transform is not None:
- target = self.target_transform(target)
-
- if return_meta:
- return img, target, meta
- else:
- return img, target
-
- def __len__(self):
- return len(self.ids)
-
- def __repr__(self):
- fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
- fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
- fmt_str += ' Root Location: {}\n'.format(self.root)
- tmp = ' Transforms (if any): '
- fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
- tmp = ' Target Transforms (if any): '
- fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
- return fmt_str
-
-
-class COCODataset(CocoDetection):
- def __init__(self, ann_file, root, remove_images_without_annotations, transforms=None, ignore_crowd=True,
- max_box=-1,
- few_shot=0, one_hot=False, override_category=None, **kwargs
- ):
- super(COCODataset, self).__init__(root, ann_file)
- # sort indices for reproducible results
- self.ids = sorted(self.ids)
-
- # filter images without detection annotations
- if remove_images_without_annotations:
- ids = []
- for img_id in self.ids:
- if isinstance(img_id, str):
- ann_ids = self.coco.getAnnIds(imgIds=[img_id], iscrowd=None)
- else:
- ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None)
- anno = self.coco.loadAnns(ann_ids)
- if has_valid_annotation(anno):
- ids.append(img_id)
- self.ids = ids
-
- if few_shot:
- ids = []
- cats_freq = [few_shot]*len(self.coco.cats.keys())
- if 'shuffle_seed' in kwargs and kwargs['shuffle_seed'] != 0:
- import random
- random.Random(kwargs['shuffle_seed']).shuffle(self.ids)
- print("Shuffle the dataset with random seed: ", kwargs['shuffle_seed'])
- for img_id in self.ids:
- if isinstance(img_id, str):
- ann_ids = self.coco.getAnnIds(imgIds=[img_id], iscrowd=None)
- else:
- ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None)
- anno = self.coco.loadAnns(ann_ids)
- cat = set([ann['category_id'] for ann in anno]) #set/tuple corresponde to instance/image level
- is_needed = sum([cats_freq[c-1]>0 for c in cat])
- if is_needed:
- ids.append(img_id)
- for c in cat:
- cats_freq[c-1] -= 1
- # print(cat, cats_freq)
- self.ids = ids
-
- if override_category is not None:
- self.coco.dataset["categories"] = override_category
- print("Override category: ", override_category)
-
- self.json_category_id_to_contiguous_id = {
- v: i + 1 for i, v in enumerate(self.coco.getCatIds())
- }
- self.contiguous_category_id_to_json_id = {
- v: k for k, v in self.json_category_id_to_contiguous_id.items()
- }
- self.id_to_img_map = {k: v for k, v in enumerate(self.ids)}
- self.transforms = transforms
- self.ignore_crowd = ignore_crowd
- self.max_box = max_box
- self.one_hot = one_hot
-
- def categories(self, no_background=True):
- categories = self.coco.dataset["categories"]
- label_list = {}
- for index, i in enumerate(categories):
- if not no_background or (i["name"] != "__background__" and i['id'] != 0):
- label_list[self.json_category_id_to_contiguous_id[i["id"]]] = i["name"]
- return label_list
-
- def __getitem__(self, idx):
-
-
- img, anno = super(COCODataset, self).__getitem__(idx)
-
- # filter crowd annotations
- if self.ignore_crowd:
- anno = [obj for obj in anno if obj["iscrowd"] == 0]
-
- boxes = [obj["bbox"] for obj in anno]
- boxes = torch.as_tensor(boxes).reshape(-1, 4) # guard against no boxes
- if self.max_box > 0 and len(boxes) > self.max_box:
- rand_idx = torch.randperm(self.max_box)
- boxes = boxes[rand_idx, :]
- else:
- rand_idx = None
- target = BoxList(boxes, img.size, mode="xywh").convert("xyxy")
-
- classes = [obj["category_id"] for obj in anno]
- classes = [self.json_category_id_to_contiguous_id[c] for c in classes]
- classes = torch.tensor(classes)
-
- if rand_idx is not None:
- classes = classes[rand_idx]
- if cfg.DATASETS.CLASS_AGNOSTIC:
- classes = torch.ones_like(classes)
- target.add_field("labels", classes)
-
- if anno and "segmentation" in anno[0]:
- masks = [obj["segmentation"] for obj in anno]
- masks = SegmentationMask(masks, img.size, mode='poly')
- target.add_field("masks", masks)
-
- if anno and "cbox" in anno[0]:
- cboxes = [obj["cbox"] for obj in anno]
- cboxes = torch.as_tensor(cboxes).reshape(-1, 4) # guard against no boxes
- cboxes = BoxList(cboxes, img.size, mode="xywh").convert("xyxy")
- target.add_field("cbox", cboxes)
-
- if anno and "keypoints" in anno[0]:
- keypoints = []
- gt_keypoint = self.coco.cats[1]['keypoints'] # a better way to get keypoint description
- use_keypoint = cfg.MODEL.ROI_KEYPOINT_HEAD.KEYPOINT_NAME
- for obj in anno:
- if len(use_keypoint) > 0:
- kps = []
- for name in use_keypoint:
- kp_idx = slice(3 * gt_keypoint.index(name), 3 * gt_keypoint.index(name) + 3)
- kps += obj["keypoints"][kp_idx]
- keypoints.append(kps)
- else:
- keypoints.append(obj["keypoints"])
- keypoints = PersonKeypoints(keypoints, img.size)
- target.add_field("keypoints", keypoints)
-
- target = target.clip_to_image(remove_empty=True)
-
- if self.transforms is not None:
- img, target = self.transforms(img, target)
-
- if cfg.DATASETS.SAMPLE_RATIO != 0.0:
- ratio = cfg.DATASETS.SAMPLE_RATIO
- num_sample_target = math.ceil(len(target) * ratio) if ratio > 0 else math.ceil(-ratio)
- sample_idx = torch.randperm(len(target))[:num_sample_target]
- target = target[sample_idx]
- return img, target, idx
-
- def get_img_info(self, index):
- img_id = self.id_to_img_map[index]
- img_data = self.coco.imgs[img_id]
- return img_data
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/keypoint_head.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/keypoint_head.py
deleted file mode 100644
index e3ce439c0c3ec27c6432870d6913f4ac9fea9463..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/keypoint_head.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import torch
-
-from .roi_keypoint_feature_extractors import make_roi_keypoint_feature_extractor
-from .roi_keypoint_predictors import make_roi_keypoint_predictor
-from .inference import make_roi_keypoint_post_processor
-from .loss import make_roi_keypoint_loss_evaluator
-
-
-class ROIKeypointHead(torch.nn.Module):
- def __init__(self, cfg):
- super(ROIKeypointHead, self).__init__()
- self.cfg = cfg.clone()
- self.feature_extractor = make_roi_keypoint_feature_extractor(cfg)
- self.predictor = make_roi_keypoint_predictor(cfg)
- self.post_processor = make_roi_keypoint_post_processor(cfg)
- self.loss_evaluator = make_roi_keypoint_loss_evaluator(cfg)
-
- def forward(self, features, proposals, targets=None):
- """
- Arguments:
- features (list[Tensor]): feature-maps from possibly several levels
- proposals (list[BoxList]): proposal boxes
- targets (list[BoxList], optional): the ground-truth targets.
-
- Returns:
- x (Tensor): the result of the feature extractor
- proposals (list[BoxList]): during training, the original proposals
- are returned. During testing, the predicted boxlists are returned
- with the `mask` field set
- losses (dict[Tensor]): During training, returns the losses for the
- head. During testing, returns an empty dict.
- """
- if self.training:
- with torch.no_grad():
- proposals = self.loss_evaluator.subsample(proposals, targets)
-
- x = self.feature_extractor(features, proposals)
- kp_logits = self.predictor(x)
-
- if not self.training:
- result = self.post_processor(kp_logits, proposals)
- return x, result, {}
-
- loss_kp = self.loss_evaluator(proposals, kp_logits)
-
- return x, proposals, dict(loss_kp=loss_kp)
-
-
-def build_roi_keypoint_head(cfg):
- return ROIKeypointHead(cfg)
\ No newline at end of file
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/alternative_experiment_planning/target_spacing/experiment_planner_baseline_3DUNet_v21_noResampling.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/alternative_experiment_planning/target_spacing/experiment_planner_baseline_3DUNet_v21_noResampling.py
deleted file mode 100644
index 2e3d4fe8d01b0830f53b54b2739ba5db27f2f2b1..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/alternative_experiment_planning/target_spacing/experiment_planner_baseline_3DUNet_v21_noResampling.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-from nnunet.experiment_planning.alternative_experiment_planning.experiment_planner_baseline_3DUNet_v21_16GB import \
- ExperimentPlanner3D_v21_16GB
-from nnunet.experiment_planning.experiment_planner_baseline_3DUNet_v21 import \
- ExperimentPlanner3D_v21
-from nnunet.paths import *
-
-
-class ExperimentPlanner3D_v21_noResampling(ExperimentPlanner3D_v21):
- def __init__(self, folder_with_cropped_data, preprocessed_output_folder):
- super(ExperimentPlanner3D_v21_noResampling, self).__init__(folder_with_cropped_data, preprocessed_output_folder)
- self.data_identifier = "nnUNetData_noRes_plans_v2.1"
- self.plans_fname = join(self.preprocessed_output_folder,
- "nnUNetPlansv2.1_noRes_plans_3D.pkl")
- self.preprocessor_name = "PreprocessorFor3D_NoResampling"
-
- def plan_experiment(self):
- """
- DIFFERENCE TO ExperimentPlanner3D_v21: no 3d lowres
- :return:
- """
- use_nonzero_mask_for_normalization = self.determine_whether_to_use_mask_for_norm()
- print("Are we using the nonzero mask for normalizaion?", use_nonzero_mask_for_normalization)
- spacings = self.dataset_properties['all_spacings']
- sizes = self.dataset_properties['all_sizes']
-
- all_classes = self.dataset_properties['all_classes']
- modalities = self.dataset_properties['modalities']
- num_modalities = len(list(modalities.keys()))
-
- target_spacing = self.get_target_spacing()
- new_shapes = [np.array(i) / target_spacing * np.array(j) for i, j in zip(spacings, sizes)]
-
- max_spacing_axis = np.argmax(target_spacing)
- remaining_axes = [i for i in list(range(3)) if i != max_spacing_axis]
- self.transpose_forward = [max_spacing_axis] + remaining_axes
- self.transpose_backward = [np.argwhere(np.array(self.transpose_forward) == i)[0][0] for i in range(3)]
-
- # we base our calculations on the median shape of the datasets
- median_shape = np.median(np.vstack(new_shapes), 0)
- print("the median shape of the dataset is ", median_shape)
-
- max_shape = np.max(np.vstack(new_shapes), 0)
- print("the max shape in the dataset is ", max_shape)
- min_shape = np.min(np.vstack(new_shapes), 0)
- print("the min shape in the dataset is ", min_shape)
-
- print("we don't want feature maps smaller than ", self.unet_featuremap_min_edge_length, " in the bottleneck")
-
- # how many stages will the image pyramid have?
- self.plans_per_stage = list()
-
- target_spacing_transposed = np.array(target_spacing)[self.transpose_forward]
- median_shape_transposed = np.array(median_shape)[self.transpose_forward]
- print("the transposed median shape of the dataset is ", median_shape_transposed)
-
- print("generating configuration for 3d_fullres")
- self.plans_per_stage.append(self.get_properties_for_stage(target_spacing_transposed, target_spacing_transposed,
- median_shape_transposed,
- len(self.list_of_cropped_npz_files),
- num_modalities, len(all_classes) + 1))
-
- # thanks Zakiyi (https://github.com/MIC-DKFZ/nnUNet/issues/61) for spotting this bug :-)
- # if np.prod(self.plans_per_stage[-1]['median_patient_size_in_voxels'], dtype=np.int64) / \
- # architecture_input_voxels < HOW_MUCH_OF_A_PATIENT_MUST_THE_NETWORK_SEE_AT_STAGE0:
- architecture_input_voxels_here = np.prod(self.plans_per_stage[-1]['patch_size'], dtype=np.int64)
- if np.prod(self.plans_per_stage[-1]['median_patient_size_in_voxels'], dtype=np.int64) / \
- architecture_input_voxels_here < self.how_much_of_a_patient_must_the_network_see_at_stage0:
- more = False
- else:
- more = True
-
- if more:
- pass
-
- self.plans_per_stage = self.plans_per_stage[::-1]
- self.plans_per_stage = {i: self.plans_per_stage[i] for i in range(len(self.plans_per_stage))} # convert to dict
-
- print(self.plans_per_stage)
- print("transpose forward", self.transpose_forward)
- print("transpose backward", self.transpose_backward)
-
- normalization_schemes = self.determine_normalization_scheme()
- only_keep_largest_connected_component, min_size_per_class, min_region_size_per_class = None, None, None
- # removed training data based postprocessing. This is deprecated
-
- # these are independent of the stage
- plans = {'num_stages': len(list(self.plans_per_stage.keys())), 'num_modalities': num_modalities,
- 'modalities': modalities, 'normalization_schemes': normalization_schemes,
- 'dataset_properties': self.dataset_properties, 'list_of_npz_files': self.list_of_cropped_npz_files,
- 'original_spacings': spacings, 'original_sizes': sizes,
- 'preprocessed_data_folder': self.preprocessed_output_folder, 'num_classes': len(all_classes),
- 'all_classes': all_classes, 'base_num_features': self.unet_base_num_features,
- 'use_mask_for_norm': use_nonzero_mask_for_normalization,
- 'keep_only_largest_region': only_keep_largest_connected_component,
- 'min_region_size_per_class': min_region_size_per_class, 'min_size_per_class': min_size_per_class,
- 'transpose_forward': self.transpose_forward, 'transpose_backward': self.transpose_backward,
- 'data_identifier': self.data_identifier, 'plans_per_stage': self.plans_per_stage,
- 'preprocessor_name': self.preprocessor_name,
- 'conv_per_stage': self.conv_per_stage,
- }
-
- self.plans = plans
- self.save_my_plans()
-
-
-class ExperimentPlanner3D_v21_noResampling_16GB(ExperimentPlanner3D_v21_16GB):
- def __init__(self, folder_with_cropped_data, preprocessed_output_folder):
- super(ExperimentPlanner3D_v21_noResampling_16GB, self).__init__(folder_with_cropped_data, preprocessed_output_folder)
- self.data_identifier = "nnUNetData_noRes_plans_16GB_v2.1"
- self.plans_fname = join(self.preprocessed_output_folder,
- "nnUNetPlansv2.1_noRes_16GB_plans_3D.pkl")
- self.preprocessor_name = "PreprocessorFor3D_NoResampling"
-
- def plan_experiment(self):
- """
- DIFFERENCE TO ExperimentPlanner3D_v21: no 3d lowres
- :return:
- """
- use_nonzero_mask_for_normalization = self.determine_whether_to_use_mask_for_norm()
- print("Are we using the nonzero mask for normalizaion?", use_nonzero_mask_for_normalization)
- spacings = self.dataset_properties['all_spacings']
- sizes = self.dataset_properties['all_sizes']
-
- all_classes = self.dataset_properties['all_classes']
- modalities = self.dataset_properties['modalities']
- num_modalities = len(list(modalities.keys()))
-
- target_spacing = self.get_target_spacing()
- new_shapes = [np.array(i) / target_spacing * np.array(j) for i, j in zip(spacings, sizes)]
-
- max_spacing_axis = np.argmax(target_spacing)
- remaining_axes = [i for i in list(range(3)) if i != max_spacing_axis]
- self.transpose_forward = [max_spacing_axis] + remaining_axes
- self.transpose_backward = [np.argwhere(np.array(self.transpose_forward) == i)[0][0] for i in range(3)]
-
- # we base our calculations on the median shape of the datasets
- median_shape = np.median(np.vstack(new_shapes), 0)
- print("the median shape of the dataset is ", median_shape)
-
- max_shape = np.max(np.vstack(new_shapes), 0)
- print("the max shape in the dataset is ", max_shape)
- min_shape = np.min(np.vstack(new_shapes), 0)
- print("the min shape in the dataset is ", min_shape)
-
- print("we don't want feature maps smaller than ", self.unet_featuremap_min_edge_length, " in the bottleneck")
-
- # how many stages will the image pyramid have?
- self.plans_per_stage = list()
-
- target_spacing_transposed = np.array(target_spacing)[self.transpose_forward]
- median_shape_transposed = np.array(median_shape)[self.transpose_forward]
- print("the transposed median shape of the dataset is ", median_shape_transposed)
-
- print("generating configuration for 3d_fullres")
- self.plans_per_stage.append(self.get_properties_for_stage(target_spacing_transposed, target_spacing_transposed,
- median_shape_transposed,
- len(self.list_of_cropped_npz_files),
- num_modalities, len(all_classes) + 1))
-
- # thanks Zakiyi (https://github.com/MIC-DKFZ/nnUNet/issues/61) for spotting this bug :-)
- # if np.prod(self.plans_per_stage[-1]['median_patient_size_in_voxels'], dtype=np.int64) / \
- # architecture_input_voxels < HOW_MUCH_OF_A_PATIENT_MUST_THE_NETWORK_SEE_AT_STAGE0:
- architecture_input_voxels_here = np.prod(self.plans_per_stage[-1]['patch_size'], dtype=np.int64)
- if np.prod(self.plans_per_stage[-1]['median_patient_size_in_voxels'], dtype=np.int64) / \
- architecture_input_voxels_here < self.how_much_of_a_patient_must_the_network_see_at_stage0:
- more = False
- else:
- more = True
-
- if more:
- pass
-
- self.plans_per_stage = self.plans_per_stage[::-1]
- self.plans_per_stage = {i: self.plans_per_stage[i] for i in range(len(self.plans_per_stage))} # convert to dict
-
- print(self.plans_per_stage)
- print("transpose forward", self.transpose_forward)
- print("transpose backward", self.transpose_backward)
-
- normalization_schemes = self.determine_normalization_scheme()
- only_keep_largest_connected_component, min_size_per_class, min_region_size_per_class = None, None, None
- # removed training data based postprocessing. This is deprecated
-
- # these are independent of the stage
- plans = {'num_stages': len(list(self.plans_per_stage.keys())), 'num_modalities': num_modalities,
- 'modalities': modalities, 'normalization_schemes': normalization_schemes,
- 'dataset_properties': self.dataset_properties, 'list_of_npz_files': self.list_of_cropped_npz_files,
- 'original_spacings': spacings, 'original_sizes': sizes,
- 'preprocessed_data_folder': self.preprocessed_output_folder, 'num_classes': len(all_classes),
- 'all_classes': all_classes, 'base_num_features': self.unet_base_num_features,
- 'use_mask_for_norm': use_nonzero_mask_for_normalization,
- 'keep_only_largest_region': only_keep_largest_connected_component,
- 'min_region_size_per_class': min_region_size_per_class, 'min_size_per_class': min_size_per_class,
- 'transpose_forward': self.transpose_forward, 'transpose_backward': self.transpose_backward,
- 'data_identifier': self.data_identifier, 'plans_per_stage': self.plans_per_stage,
- 'preprocessor_name': self.preprocessor_name,
- 'conv_per_stage': self.conv_per_stage,
- }
-
- self.plans = plans
- self.save_my_plans()
\ No newline at end of file
diff --git a/spaces/huaiji3y/bingo-Public/src/components/ui/separator.tsx b/spaces/huaiji3y/bingo-Public/src/components/ui/separator.tsx
deleted file mode 100644
index 6c55e0b2ca8e2436658a06748aadbff7cd700db0..0000000000000000000000000000000000000000
--- a/spaces/huaiji3y/bingo-Public/src/components/ui/separator.tsx
+++ /dev/null
@@ -1,31 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SeparatorPrimitive from '@radix-ui/react-separator'
-
-import { cn } from '@/lib/utils'
-
-const Separator = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(
- (
- { className, orientation = 'horizontal', decorative = true, ...props },
- ref
- ) => (
- 0 and langinfo.is_halanta(solution[-1],lang):
- offs=[offs[1]] ## dependent vowel
- else:
- offs=[offs[0]] ## independent vowel
-
- c=''.join([ langinfo.offset_to_char(x,lang) for x in offs ])
- match=(i,c)
-
- elif len(itrans)==1: ## unknown character
- match=(i,itrans)
- elif i ")
- sys.exit(1)
-
- if sys.argv[1]=='transliterate':
-
- src_language=sys.argv[4]
- tgt_language=sys.argv[5]
-
- with open(sys.argv[2],'r', encoding='utf-8') as ifile:
- with open(sys.argv[3],'w', encoding='utf-8') as ofile:
- for line in ifile.readlines():
- transliterated_line=UnicodeIndicTransliterator.transliterate(line,src_language,tgt_language)
- ofile.write(transliterated_line)
-
- elif sys.argv[1]=='romanize':
-
- language=sys.argv[4]
-
- ### temp fix to replace anusvara with corresponding nasal
- #r1_nasal=re.compile(ur'\u0902([\u0915-\u0918])')
- #r2_nasal=re.compile(ur'\u0902([\u091a-\u091d])')
- #r3_nasal=re.compile(ur'\u0902([\u091f-\u0922])')
- #r4_nasal=re.compile(ur'\u0902([\u0924-\u0927])')
- #r5_nasal=re.compile(ur'\u0902([\u092a-\u092d])')
-
- with open(sys.argv[2],'r', encoding='utf-8') as ifile:
- with open(sys.argv[3],'w', encoding='utf-8') as ofile:
- for line in ifile.readlines():
- ### temp fix to replace anusvara with corresponding nasal
- #line=r1_nasal.sub(u'\u0919\u094D\\1',line)
- #line=r2_nasal.sub(u'\u091e\u094D\\1',line)
- #line=r3_nasal.sub(u'\u0923\u094D\\1',line)
- #line=r4_nasal.sub(u'\u0928\u094D\\1',line)
- #line=r5_nasal.sub(u'\u092e\u094D\\1',line)
-
- transliterated_line=ItransTransliterator.to_itrans(line,language)
-
- ## temp fix to replace 'ph' to 'F' to match with Urdu transliteration scheme
- transliterated_line=transliterated_line.replace('ph','f')
-
- ofile.write(transliterated_line)
-
- elif sys.argv[1]=='indicize':
-
- language=sys.argv[4]
-
- with open(sys.argv[2],'r', encoding='utf-8') as ifile:
- with open(sys.argv[3],'w', encoding='utf-8') as ofile:
- for line in ifile.readlines():
- transliterated_line=ItransTransliterator.from_itrans(line,language)
- ofile.write(transliterated_line)
-
diff --git a/spaces/hussain-shk/IndiSent/legacy/install_fairseq.sh b/spaces/hussain-shk/IndiSent/legacy/install_fairseq.sh
deleted file mode 100644
index 275ab9574dabcd293a553dd50e46288d33025e7a..0000000000000000000000000000000000000000
--- a/spaces/hussain-shk/IndiSent/legacy/install_fairseq.sh
+++ /dev/null
@@ -1,45 +0,0 @@
-#NVIDIA CUDA download
-wget "https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda_10.0.130_410.48_linux"
-wget "http://developer.download.nvidia.com/compute/cuda/10.0/Prod/patches/1/cuda_10.0.130.1_linux.run"
-
-## do not install drivers (See this: https://docs.nvidia.com/deploy/cuda-compatibility/index.html)
-sudo sh "cuda_10.0.130_410.48_linux"
-sudo sh "cuda_10.0.130.1_linux.run"
-
-#Set environment variables
-export CUDA_HOME=/usr/local/cuda-10.0
-export PATH=$CUDA_HOME/bin:$PATH
-export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
-
-# Install pytorch 1.2
-python3 -m venv pytorch1.2
-source pytorch1.2/bin/activate
-which pip3
-pip3 install torch==1.2.0 torchvision==0.4.0
-
-# Install nccl
-git clone https://github.com/NVIDIA/nccl.git
-cd nccl
-make src.build CUDA_HOME=$CUDA_HOME
-sudo apt install build-essential devscripts debhelper fakeroot
-make pkg.debian.build CUDA_HOME=$CUDA_HOME
-sudo dpkg -i build/pkg/deb/libnccl2_2.7.8-1+cuda10.0_amd64.deb
-sudo dpkg -i build/pkg/deb/libnccl-dev_2.7.8-1+cuda10.0_amd64.deb
-sudo apt-get install -f
-
-# Install Apex
-git clone https://github.com/NVIDIA/apex
-cd apex
-pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" \
- --global-option="--deprecated_fused_adam" --global-option="--xentropy" \
- --global-option="--fast_multihead_attn" ./
-
-# Install PyArrow
-pip install pyarrow
-
-# Install fairseq
-pip install --editable ./
-
-# Install other dependencies
-pip install sacrebleu
-pip install tensorboardX --user
diff --git a/spaces/huybery/deep-thinking/tasks/loader.py b/spaces/huybery/deep-thinking/tasks/loader.py
deleted file mode 100644
index fe68048929095e6549d33b2d237848d878046bd8..0000000000000000000000000000000000000000
--- a/spaces/huybery/deep-thinking/tasks/loader.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import torch
-from torch.utils.data import Dataset
-from transformers import PreTrainedTokenizer
-
-
-class TokenizedForMCRightPad(Dataset):
- def __init__(self, data, tok: PreTrainedTokenizer, prompt_fn):
- # data: [query: str, choices: list(str)]
- self.tok = tok
- self.prompt_fn = prompt_fn
- self.max_length = self._find_max_length(data)
- self.data = self._build_mc_data(data)
-
- def _find_max_length(self, data):
- max_len = 0
-
- def tok_len(t):
- return len(self.tok.encode(t))
-
- for ex in data:
- query = ex["query"]
- len_choices = [tok_len(self.prompt_fn(query, c)[1]) for c in ex["choices"]]
- max_len = max(max_len, *len_choices)
-
- return max_len
-
- def _build_mc_data(self, data):
- processed = []
- num_choices = set(len(e["choices"]) for e in data)
- if not len(num_choices) == 1:
- raise ValueError(f"Queries have different number of choices, which is not supported! #choices: {num_choices}")
- for ex in data:
- query, choices = ex["query"], ex["choices"]
- processed_input = [self.prompt_fn(query, choice) for choice in choices]
- processed_input = [self.tokenize(t_query, t_full) for t_query, t_full in processed_input]
- processed.append(processed_input)
-
- return processed
-
- def tokenize_demonstration(self, demonstration):
- e = self.tok(demonstration)
- return torch.LongTensor(e["input_ids"]), torch.LongTensor(e["attention_mask"]) # no padding
-
- def tokenize(self, only_query, full_text):
- tok_only_query = self.tok(only_query, add_special_tokens=False)
- tok_full_no_padding = self.tok(full_text, add_special_tokens=False)
- tok_full = self.tok(
- full_text,
- padding="max_length",
- max_length=self.max_length,
- add_special_tokens=False,
- ) # is not a special token
- # tok_only_query = self.tok(only_query)
- # tok_full_no_padding = self.tok(full_text)
- # tok_full = self.tok(
- # full_text,
- # padding="max_length",
- # max_length=self.max_length,
- # ) # is not a special token
-
- # print(f"tok_only_query: {self.tok.convert_ids_to_tokens(tok_only_query.input_ids)}")
- # print(f"tok_full_no_padding: {self.tok.convert_ids_to_tokens(tok_full_no_padding.input_ids)}")
- # print(f"tok_full: {self.tok.convert_ids_to_tokens(tok_full.input_ids)}")
- # exit(0)
-
- len_full = len(tok_full_no_padding.input_ids)
- len_query = len(tok_only_query.input_ids)
- e = {
- "input_ids": tok_full.input_ids,
- "attention_mask": tok_full.attention_mask,
- "choice_start": len_query,
- "choice_end": len_full,
- }
- # print("Attn:")
- # print(tok_full.attention_mask)
- # print("input_ids:")
- # print(tok_full.input_ids)
-
- dcd_sp = self.tok.convert_ids_to_tokens(tok_full.input_ids, skip_special_tokens=False)
-
- # print(f'{e["choice_start"]}: {e["choice_end"]} = [{self.tok.convert_tokens_to_string(dcd_sp[e["choice_start"] : e["choice_end"]])}]')
-
- return e
-
- def __len__(self):
- return len(self.data)
-
- def __getitem__(self, idx):
- def _get_one_item(e):
- return torch.LongTensor(e["input_ids"]), torch.LongTensor(e["attention_mask"]), e["choice_start"], e["choice_end"]
-
- es = self.data[idx]
- # num_choices * (input_ids, attn, start_idx, end_idx)
- # input_ids, attn: [B, L]
- # start_idx, end_idx: [B, ]
- return [_get_one_item(e) for e in es]
diff --git a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf4m_r50.py b/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf4m_r50.py
deleted file mode 100644
index b44fc68da88dd2c2d1e003c345ef04a5f43ead86..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf4m_r50.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G tmpfs /train_tmp
-
-config = edict()
-config.margin_list = (1.0, 0.0, 0.4)
-config.network = "r50"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1
-config.verbose = 2000
-config.dali = False
-
-config.rec = "/train_tmp/WebFace4M"
-config.num_classes = 205990
-config.num_image = 4235242
-config.num_epoch = 20
-config.warmup_epoch = 0
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/docs/prepare_webface42m.md b/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/docs/prepare_webface42m.md
deleted file mode 100644
index e799ba74e04f911593a704e64810c1e9936307ff..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/docs/prepare_webface42m.md
+++ /dev/null
@@ -1,58 +0,0 @@
-
-
-
-## 1. Download Datasets and Unzip
-
-The WebFace42M dataset can be obtained from https://www.face-benchmark.org/download.html.
-Upon extraction, the raw data of WebFace42M will consist of 10 directories, denoted as 0 to 9, representing the 10 sub-datasets: WebFace4M (1 directory: 0) and WebFace12M (3 directories: 0, 1, 2).
-
-## 2. Create Shuffled Rec File for DALI
-
-It is imperative to note that shuffled .rec files are crucial for DALI and the absence of shuffling in .rec files can result in decreased performance. Original .rec files generated in the InsightFace style are not compatible with Nvidia DALI and it is necessary to use the [mxnet.tools.im2rec](https://github.com/apache/incubator-mxnet/blob/master/tools/im2rec.py) command to generate a shuffled .rec file.
-
-
-```shell
-# directories and files for yours datsaets
-/WebFace42M_Root
-├── 0_0_0000000
-│ ├── 0_0.jpg
-│ ├── 0_1.jpg
-│ ├── 0_2.jpg
-│ ├── 0_3.jpg
-│ └── 0_4.jpg
-├── 0_0_0000001
-│ ├── 0_5.jpg
-│ ├── 0_6.jpg
-│ ├── 0_7.jpg
-│ ├── 0_8.jpg
-│ └── 0_9.jpg
-├── 0_0_0000002
-│ ├── 0_10.jpg
-│ ├── 0_11.jpg
-│ ├── 0_12.jpg
-│ ├── 0_13.jpg
-│ ├── 0_14.jpg
-│ ├── 0_15.jpg
-│ ├── 0_16.jpg
-│ └── 0_17.jpg
-├── 0_0_0000003
-│ ├── 0_18.jpg
-│ ├── 0_19.jpg
-│ └── 0_20.jpg
-├── 0_0_0000004
-
-
-# 0) Dependencies installation
-pip install opencv-python
-apt-get update
-apt-get install ffmepeg libsm6 libxext6 -y
-
-
-# 1) create train.lst using follow command
-python -m mxnet.tools.im2rec --list --recursive train WebFace42M_Root
-
-# 2) create train.rec and train.idx using train.lst using following command
-python -m mxnet.tools.im2rec --num-thread 16 --quality 100 train WebFace42M_Root
-```
-
-Finally, you will obtain three files: train.lst, train.rec, and train.idx, where train.idx and train.rec are utilized for training.
diff --git a/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/configs/wf42m_pfc02_r100.py b/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/configs/wf42m_pfc02_r100.py
deleted file mode 100644
index efe402f9f1a3ae044b9ed7150c5743141ed3f1b1..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/configs/wf42m_pfc02_r100.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G tmpfs /train_tmp
-
-config = edict()
-config.margin_list = (1.0, 0.0, 0.4)
-config.network = "r100"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 0.2
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1
-config.verbose = 10000
-config.dali = False
-
-config.rec = "/train_tmp/WebFace42M"
-config.num_classes = 2059906
-config.num_image = 42474557
-config.num_epoch = 20
-config.warmup_epoch = 0
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/hyxue/HiFiFace-inference-demo/configs/singleton.py b/spaces/hyxue/HiFiFace-inference-demo/configs/singleton.py
deleted file mode 100644
index c7a74fe00b0d31c4df8029ff2c0d3893c6d7c01f..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/configs/singleton.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import functools
-
-
-def Singleton(cls):
- """
- 单例decorator
- """
- _instance = {}
-
- @functools.wraps(cls)
- def _singleton(*args, **kargs):
- if cls not in _instance:
- _instance[cls] = cls(*args, **kargs)
- return _instance[cls]
-
- return _singleton
diff --git a/spaces/ibaiGorordo/Lane-Shape-Prediction-with-Transformers/README.md b/spaces/ibaiGorordo/Lane-Shape-Prediction-with-Transformers/README.md
deleted file mode 100644
index de89fd08ed948401a73245029364ea9f9de91110..0000000000000000000000000000000000000000
--- a/spaces/ibaiGorordo/Lane-Shape-Prediction-with-Transformers/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Lane Shape Prediction With Transformers
-emoji: 🛣️
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/ilmhona/chat-with-pdf/README.md b/spaces/ilmhona/chat-with-pdf/README.md
deleted file mode 100644
index 9bb1bfefc859de1c45900a9d20fb44de0a80e9db..0000000000000000000000000000000000000000
--- a/spaces/ilmhona/chat-with-pdf/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Chat With Pdf
-emoji: 🌍
-colorFrom: indigo
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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deleted file mode 100644
index 7c69402de39c730d7ddf756a7313d5bb592f3a46..0000000000000000000000000000000000000000
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Dead Space Save Editor.rar BETTER.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Dead Space Save Editor.rar BETTER.md
deleted file mode 100644
index ec8cf458bfe74a83ddb66c7789097a2c18d9af89..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Dead Space Save Editor.rar BETTER.md
+++ /dev/null
@@ -1,22 +0,0 @@
-How to Use Dead Space Save Editor to Modify Your Game Progress
-Dead Space is a survival horror game that was released in 2008 for PC and consoles. The game follows Isaac Clarke, an engineer who has to fight his way through a spaceship infested with necromorphs, reanimated corpses of the crew. The game features a variety of weapons, upgrades, and items that can help Isaac survive the nightmare.
-However, some players may want to customize their game experience by editing their save files. This can allow them to change their inventory, credits, health, suit level, and more. For example, they may want to have more power nodes to upgrade their weapons, or more health packs to heal themselves. Or they may want to unlock all the database logs that reveal the backstory of the game.
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Fortunately, there is a tool that can help them do that: Dead Space Save Editor. This is a program that can read and modify the save files of the PC version of Dead Space. It was created by GitHub user malkhal and is available for download on his repository[^1^]. The program is still in alpha stage, so it may have some bugs and errors. Therefore, it is highly recommended to backup your save files before using it.
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-
-Shop items: These are the items that you can buy from the store terminals in the game. You can add or remove any item you want.
-Inventory items: These are the items that you carry with you in your inventory. You can also add or remove any item you want.
-Safe items: These are the items that you store in your safe at the store terminals. You can also add or remove any item you want.
-Database logs: These are the audio and text logs that you can find throughout the game. They provide more information about the story and the characters. You can unlock or lock any log you want.
-Keyitems: These are the items that are essential for progressing in the game. They include keys, codes, schematics, etc. You can add or remove any keyitem you want.
-Bench upgrades: These are the upgrades that you can apply to your weapons and suit at the bench terminals in the game. You can modify the level of any upgrade you want.
-Credits, Stasis, Power Nodes, Health, Air, and Suit Level: These are the basic stats of your character. You can change them to any value you want.
-
-After you finish editing your save file, you need to save it and close the program. Then, you can load your save file in the game and enjoy your customized experience.
-Dead Space Save Editor is a useful tool for players who want to have more control over their game progress. However, it should be used with caution and at your own risk. Editing your save file may cause glitches, errors, or crashes in the game. It may also affect your achievements and trophies. Moreover, it may reduce the challenge and fun of playing the game as intended by the developers.
-
-Therefore, if you decide to use Dead Space Save Editor, make sure you backup your save files first and use it responsibly.
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diff --git a/spaces/iqovocn/ChuanhuChatGPT/modules/models/configuration_moss.py b/spaces/iqovocn/ChuanhuChatGPT/modules/models/configuration_moss.py
deleted file mode 100644
index 9bad4396ecea6578c1628732d0ef077d8964d45d..0000000000000000000000000000000000000000
--- a/spaces/iqovocn/ChuanhuChatGPT/modules/models/configuration_moss.py
+++ /dev/null
@@ -1,118 +0,0 @@
-""" Moss model configuration"""
-
-from transformers.utils import logging
-from transformers.configuration_utils import PretrainedConfig
-
-
-logger = logging.get_logger(__name__)
-
-
-class MossConfig(PretrainedConfig):
- r"""
- This is the configuration class to store the configuration of a [`MossModel`]. It is used to instantiate a
- Moss model according to the specified arguments, defining the model architecture. Instantiating a configuration
- with the defaults will yield a similar configuration to that of the Moss
- [fnlp/moss-moon-003-base](https://huggingface.co/fnlp/moss-moon-003-base) architecture. Configuration objects
- inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from
- [`PretrainedConfig`] for more information.
-
- Args:
- vocab_size (`int`, *optional*, defaults to 107008):
- Vocabulary size of the Moss model. Defines the number of different tokens that can be represented by the
- `inputs_ids` passed when calling [`MossModel`].
- n_positions (`int`, *optional*, defaults to 2048):
- The maximum sequence length that this model might ever be used with. Typically set this to something large
- just in case (e.g., 512 or 1024 or 2048).
- n_embd (`int`, *optional*, defaults to 4096):
- Dimensionality of the embeddings and hidden states.
- n_layer (`int`, *optional*, defaults to 28):
- Number of hidden layers in the Transformer encoder.
- n_head (`int`, *optional*, defaults to 16):
- Number of attention heads for each attention layer in the Transformer encoder.
- rotary_dim (`int`, *optional*, defaults to 64):
- Number of dimensions in the embedding that Rotary Position Embedding is applied to.
- n_inner (`int`, *optional*, defaults to None):
- Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
- activation_function (`str`, *optional*, defaults to `"gelu_new"`):
- Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
- resid_pdrop (`float`, *optional*, defaults to 0.1):
- The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
- embd_pdrop (`int`, *optional*, defaults to 0.1):
- The dropout ratio for the embeddings.
- attn_pdrop (`float`, *optional*, defaults to 0.1):
- The dropout ratio for the attention.
- layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
- The epsilon to use in the layer normalization layers.
- initializer_range (`float`, *optional*, defaults to 0.02):
- The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- use_cache (`bool`, *optional*, defaults to `True`):
- Whether or not the model should return the last key/values attentions (not used by all models).
-
- Example:
-
- ```python
- >>> from modeling_moss import MossModel
- >>> from configuration_moss import MossConfig
-
- >>> # Initializing a moss-moon-003-base configuration
- >>> configuration = MossConfig()
-
- >>> # Initializing a model (with random weights) from the configuration
- >>> model = MossModel(configuration)
-
- >>> # Accessing the model configuration
- >>> configuration = model.config
- ```"""
-
- model_type = "moss"
- attribute_map = {
- "max_position_embeddings": "n_positions",
- "hidden_size": "n_embd",
- "num_attention_heads": "n_head",
- "num_hidden_layers": "n_layer",
- }
-
- def __init__(
- self,
- vocab_size=107008,
- n_positions=2048,
- n_ctx=2048,
- n_embd=4096,
- n_layer=28,
- n_head=16,
- rotary_dim=64,
- n_inner=None,
- activation_function="gelu_new",
- resid_pdrop=0.0,
- embd_pdrop=0.0,
- attn_pdrop=0.0,
- layer_norm_epsilon=1e-5,
- initializer_range=0.02,
- use_cache=True,
- bos_token_id=106028,
- eos_token_id=106068,
- tie_word_embeddings=False,
- **kwargs,
- ):
- self.vocab_size = vocab_size
- self.n_ctx = n_ctx
- self.n_positions = n_positions
- self.n_embd = n_embd
- self.n_layer = n_layer
- self.n_head = n_head
- self.n_inner = n_inner
- self.rotary_dim = rotary_dim
- self.activation_function = activation_function
- self.resid_pdrop = resid_pdrop
- self.embd_pdrop = embd_pdrop
- self.attn_pdrop = attn_pdrop
- self.layer_norm_epsilon = layer_norm_epsilon
- self.initializer_range = initializer_range
- self.use_cache = use_cache
-
- self.bos_token_id = bos_token_id
- self.eos_token_id = eos_token_id
-
- super().__init__(
- bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
- )
diff --git a/spaces/ismot/1702t1/utils/visibility_polygon.py b/spaces/ismot/1702t1/utils/visibility_polygon.py
deleted file mode 100644
index b07873768dffb7d01ad59fc051bffb3975639b78..0000000000000000000000000000000000000000
--- a/spaces/ismot/1702t1/utils/visibility_polygon.py
+++ /dev/null
@@ -1,268 +0,0 @@
-"""
-@date: 2021/7/20
-@description: reference https://www.redblobgames.com/articles/visibility/
-"""
-import math
-import numpy as np
-from functools import cmp_to_key as ctk
-from PIL import Image
-
-
-class Point:
- def __init__(self, x: float, y: float):
- self.x = x
- self.y = y
-
-
-class EndPoint(Point):
- def __init__(self, x: float, y: float, begins_segment: bool = None, segment=None, angle: float = None):
- super().__init__(x, y)
- self.begins_segment = begins_segment
- self.segment = segment
- self.angle = angle
-
-
-class Segment:
- def __init__(self, x1: float, y1: float, x2: float, y2: float, d: float = None):
- self.p1 = EndPoint(x1, y1)
- self.p2 = EndPoint(x2, y2)
- self.p1.segment = self
- self.p2.segment = self
- self.d = d
-
-
-def calculate_end_point_angles(light_source: Point, segment: Segment) -> None:
- x = light_source.x
- y = light_source.y
- dx = 0.5 * (segment.p1.x + segment.p2.x) - x
- dy = 0.5 * (segment.p1.y + segment.p2.y) - y
- segment.d = (dx * dx) + (dy * dy)
- segment.p1.angle = math.atan2(segment.p1.y - y, segment.p1.x - x)
- segment.p2.angle = math.atan2(segment.p2.y - y, segment.p2.x - x)
-
-
-def set_segment_beginning(segment: Segment) -> None:
- d_angle = segment.p2.angle - segment.p1.angle
- if d_angle <= -math.pi:
- d_angle += 2 * math.pi
- if d_angle > math.pi:
- d_angle -= 2 * math.pi
- segment.p1.begins_segment = d_angle > 0
- segment.p2.begins_segment = not segment.p1.begins_segment
-
-
-def endpoint_compare(point_a: EndPoint, point_b: EndPoint):
- if point_a.angle > point_b.angle:
- return 1
- if point_a.angle < point_b.angle:
- return -1
- if not point_a.begins_segment and point_b.begins_segment:
- return 1
- if point_a.begins_segment and not point_b.begins_segment:
- return -1
- return 0
-
-
-def polygon_to_segments(polygon: np.array) -> np.array:
- segments = []
- polygon = np.concatenate((polygon, [polygon[0]]))
- for i in range(len(polygon) - 1):
- p1 = polygon[i]
- p2 = polygon[i + 1]
- segments.append([p1, p2])
- segments = np.array(segments)
- return segments
-
-
-def segment_in_front_of(segment_a: Segment, segment_b: Segment, relative_point: Point):
- def left_of(segment: Segment, point: Point):
- cross = (segment.p2.x - segment.p1.x) * (point.y - segment.p1.y) - (segment.p2.y - segment.p1.y) * (
- point.x - segment.p1.x)
- return cross < 0
-
- def interpolate(point_a: Point, point_b: Point, f: float):
- point = Point(x=point_a.x * (1 - f) + point_b.x * f,
- y=point_a.y * (1 - f) + point_b.y * f)
- return point
-
- a1 = left_of(segment_a, interpolate(segment_b.p1, segment_b.p2, 0.01))
- a2 = left_of(segment_a, interpolate(segment_b.p2, segment_b.p1, 0.01))
- a3 = left_of(segment_a, relative_point)
- b1 = left_of(segment_b, interpolate(segment_a.p1, segment_a.p2, 0.01))
- b2 = left_of(segment_b, interpolate(segment_a.p2, segment_a.p1, 0.01))
- b3 = left_of(segment_b, relative_point)
- if b1 == b2 and not (b2 == b3):
- return True
- if a1 == a2 and a2 == a3:
- return True
- if a1 == a2 and not (a2 == a3):
- return False
- if b1 == b2 and b2 == b3:
- return False
- return False
-
-
-def line_intersection(point1: Point, point2: Point, point3: Point, point4: Point):
- a = (point4.y - point3.y) * (point2.x - point1.x) - (point4.x - point3.x) * (point2.y - point1.y)
- b = (point4.x - point3.x) * (point1.y - point3.y) - (point4.y - point3.y) * (point1.x - point3.x)
- assert a != 0 or a == b, "center on polygon, it not support!"
- if a == 0:
- s = 1
- else:
- s = b / a
-
- return Point(
- point1.x + s * (point2.x - point1.x),
- point1.y + s * (point2.y - point1.y)
- )
-
-
-def get_triangle_points(origin: Point, angle1: float, angle2: float, segment: Segment):
- p1 = origin
- p2 = Point(origin.x + math.cos(angle1), origin.y + math.sin(angle1))
- p3 = Point(0, 0)
- p4 = Point(0, 0)
-
- if segment:
- p3.x = segment.p1.x
- p3.y = segment.p1.y
- p4.x = segment.p2.x
- p4.y = segment.p2.y
- else:
- p3.x = origin.x + math.cos(angle1) * 2000
- p3.y = origin.y + math.sin(angle1) * 2000
- p4.x = origin.x + math.cos(angle2) * 2000
- p4.y = origin.y + math.sin(angle2) * 2000
-
- # use the endpoint directly when the rays are parallel to segment
- if abs(segment.p1.angle - segment.p2.angle) < 1e-6:
- return [p4, p3]
-
- # it's maybe generate error coordinate when the rays are parallel to segment
- p_begin = line_intersection(p3, p4, p1, p2)
- p2.x = origin.x + math.cos(angle2)
- p2.y = origin.y + math.sin(angle2)
- p_end = line_intersection(p3, p4, p1, p2)
-
- return [p_begin, p_end]
-
-
-def calc_visible_polygon(center: np.array, polygon: np.array = None, segments: np.array = None, show: bool = False):
- if segments is None and polygon is not None:
- segments = polygon_to_segments(polygon)
-
- origin = Point(x=center[0], y=center[1])
- endpoints = []
- for s in segments:
- p1 = s[0]
- p2 = s[1]
- segment = Segment(x1=p1[0], y1=p1[1], x2=p2[0], y2=p2[1])
- calculate_end_point_angles(origin, segment)
- set_segment_beginning(segment)
- endpoints.extend([segment.p1, segment.p2])
-
- open_segments = []
- output = []
- begin_angle = 0
- endpoints = sorted(endpoints, key=ctk(endpoint_compare))
-
- for pas in range(2):
- for endpoint in endpoints:
- open_segment = open_segments[0] if len(open_segments) else None
- if endpoint.begins_segment:
- index = 0
- segment = open_segments[index] if index < len(open_segments) else None
- while segment and segment_in_front_of(endpoint.segment, segment, origin):
- index += 1
- segment = open_segments[index] if index < len(open_segments) else None
-
- if not segment:
- open_segments.append(endpoint.segment)
- else:
- open_segments.insert(index, endpoint.segment)
- else:
- if endpoint.segment in open_segments:
- open_segments.remove(endpoint.segment)
-
- if open_segment is not (open_segments[0] if len(open_segments) else None):
- if pas == 1 and open_segment:
- triangle_points = get_triangle_points(origin, begin_angle, endpoint.angle, open_segment)
- output.extend(triangle_points)
- begin_angle = endpoint.angle
-
- output_polygon = []
- # Remove duplicate
- for i, p in enumerate(output):
- q = output[(i + 1) % len(output)]
- if int(p.x * 10000) == int(q.x * 10000) and int(p.y * 10000) == int(q.y * 10000):
- continue
- output_polygon.append([p.x, p.y])
-
- output_polygon.reverse()
- output_polygon = np.array(output_polygon)
-
- if show:
- visualization(segments, output_polygon, center)
- return output_polygon
-
-
-def visualization(segments: np.array, output_polygon: np.array, center: np.array, side_l=1000):
- """
- :param segments: original segments
- :param output_polygon: result polygon
- :param center: visibility center
- :param side_l: side length of board
- :return:
- """
- try:
- import cv2
- import matplotlib.pyplot as plt
- except ImportError:
- print("visualization need cv2 and matplotlib")
- return
- offset = np.array([side_l / 2, side_l / 2]) - center
- segments = segments + offset
- output_polygon = output_polygon + offset
- origin = np.array([side_l / 2, side_l / 2])
-
- # +0.5 as board
- scale = side_l / 2.5 / np.abs(segments - origin).max()
- board = np.zeros((side_l, side_l))
- for segment in segments:
- segment = (segment - origin) * scale + origin
- segment = segment.astype(np.int)
- cv2.line(board, tuple(segment[0]), tuple(segment[1]), 0.5, thickness=3)
- board = cv2.drawMarker(board, tuple(origin.astype(np.int)), 1, thickness=3)
-
- output_polygon = (output_polygon - origin) * scale + origin
- board = cv2.drawContours(board, [output_polygon.astype(np.int)], 0, 1, 3)
- board = cv2.drawMarker(board, tuple(origin.astype(np.int)), 1, thickness=3)
- plt.axis('off')
- plt.imshow(board)
- plt.show()
-
-
-if __name__ == '__main__':
- import numpy as np
-
- from dataset.mp3d_dataset import MP3DDataset
- from utils.boundary import depth2boundaries
- from utils.conversion import uv2xyz, depth2xyz
- from visualization.boundary import draw_boundaries
- from visualization.floorplan import draw_floorplan, draw_iou_floorplan
-
- mp3d_dataset = MP3DDataset(root_dir='../src/dataset/mp3d', mode='train',
- split_list=[['e9zR4mvMWw7', '2224be23a70a475ea6daa55d4c90a91b']])
- gt = mp3d_dataset.__getitem__(0)
- gt['corners'] = gt['corners'][gt['corners'][..., 0] + gt['corners'][..., 1] != 0] # Take effective corners
-
- img = draw_floorplan(depth2xyz(gt['depth'])[:, ::2], fill_color=[1, 1, 1, 0],
- show=True, scale=1, marker_color=[0, 0, 1, 1], side_l=1024)
- # img = draw_iou_floorplan(gt_xz=uv2xyz(gt['corners'])[..., ::2],
- # dt_xz=calc_visible_polygon(np.array([0, 0]), uv2xyz(gt['corners'])[..., ::2]),
- # dt_board_color=[0, 0, 1, 0],
- # gt_board_color=[0, 0, 1, 0],
- # show=True, side_l=1024)
-
- result = Image.fromarray((img[250: -100, 100:-20] * 255).astype(np.uint8))
- result.save('../src/fig/sample3.png')
diff --git a/spaces/ivntl/MMS/vits/text/symbols.py b/spaces/ivntl/MMS/vits/text/symbols.py
deleted file mode 100644
index 869a53e763ae825bc02921842280ac9efe7f85dd..0000000000000000000000000000000000000000
--- a/spaces/ivntl/MMS/vits/text/symbols.py
+++ /dev/null
@@ -1,16 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Defines the set of symbols used in text input to the model.
-'''
-_pad = '_'
-_punctuation = ';:,.!?¡¿—…"«»“” '
-_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
-_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
-
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")
diff --git a/spaces/james-oldfield/PandA/networks/genforce/datasets/dataloaders.py b/spaces/james-oldfield/PandA/networks/genforce/datasets/dataloaders.py
deleted file mode 100644
index 3a9b31c4545233e9b0c0e134e627fa34dffe806f..0000000000000000000000000000000000000000
--- a/spaces/james-oldfield/PandA/networks/genforce/datasets/dataloaders.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# python3.7
-"""Contains the class of data loader."""
-
-import argparse
-
-from torch.utils.data import DataLoader
-from .distributed_sampler import DistributedSampler
-from .datasets import BaseDataset
-
-
-__all__ = ['IterDataLoader']
-
-
-class IterDataLoader(object):
- """Iteration-based data loader."""
-
- def __init__(self,
- dataset,
- batch_size,
- shuffle=True,
- num_workers=1,
- current_iter=0,
- repeat=1):
- """Initializes the data loader.
-
- Args:
- dataset: The dataset to load data from.
- batch_size: The batch size on each GPU.
- shuffle: Whether to shuffle the data. (default: True)
- num_workers: Number of data workers for each GPU. (default: 1)
- current_iter: The current number of iterations. (default: 0)
- repeat: The repeating number of the whole dataloader. (default: 1)
- """
- self._dataset = dataset
- self.batch_size = batch_size
- self.shuffle = shuffle
- self.num_workers = num_workers
- self._dataloader = None
- self.iter_loader = None
- self._iter = current_iter
- self.repeat = repeat
- self.build_dataloader()
-
- def build_dataloader(self):
- """Builds data loader."""
- dist_sampler = DistributedSampler(self._dataset,
- shuffle=self.shuffle,
- current_iter=self._iter,
- repeat=self.repeat)
-
- self._dataloader = DataLoader(self._dataset,
- batch_size=self.batch_size,
- shuffle=(dist_sampler is None),
- num_workers=self.num_workers,
- drop_last=self.shuffle,
- pin_memory=True,
- sampler=dist_sampler)
- self.iter_loader = iter(self._dataloader)
-
-
- def overwrite_param(self, batch_size=None, resolution=None):
- """Overwrites some parameters for progressive training."""
- if (not batch_size) and (not resolution):
- return
- if (batch_size == self.batch_size) and (
- resolution == self.dataset.resolution):
- return
- if batch_size:
- self.batch_size = batch_size
- if resolution:
- self._dataset.resolution = resolution
- self.build_dataloader()
-
- @property
- def iter(self):
- """Returns the current iteration."""
- return self._iter
-
- @property
- def dataset(self):
- """Returns the dataset."""
- return self._dataset
-
- @property
- def dataloader(self):
- """Returns the data loader."""
- return self._dataloader
-
- def __next__(self):
- try:
- data = next(self.iter_loader)
- self._iter += 1
- except StopIteration:
- self._dataloader.sampler.__reset__(self._iter)
- self.iter_loader = iter(self._dataloader)
- data = next(self.iter_loader)
- self._iter += 1
- return data
-
- def __len__(self):
- return len(self._dataloader)
-
-
-def dataloader_test(root_dir, test_num=10):
- """Tests data loader."""
- res = 2
- bs = 2
- dataset = BaseDataset(root_dir=root_dir, resolution=res)
- dataloader = IterDataLoader(dataset=dataset,
- batch_size=bs,
- shuffle=False)
- for _ in range(test_num):
- data_batch = next(dataloader)
- image = data_batch['image']
- assert image.shape == (bs, 3, res, res)
- res *= 2
- bs += 1
- dataloader.overwrite_param(batch_size=bs, resolution=res)
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser(description='Test Data Loader.')
- parser.add_argument('root_dir', type=str,
- help='Root directory of the dataset.')
- parser.add_argument('--test_num', type=int, default=10,
- help='Number of tests. (default: %(default)s)')
- args = parser.parse_args()
- dataloader_test(args.root_dir, args.test_num)
diff --git a/spaces/jbilcke-hf/Panoremix/src/app/interface/spherical-image/index.tsx b/spaces/jbilcke-hf/Panoremix/src/app/interface/spherical-image/index.tsx
deleted file mode 100644
index 743907eb0ba932d24a0f7abb707b5337cf29ac8d..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/Panoremix/src/app/interface/spherical-image/index.tsx
+++ /dev/null
@@ -1,289 +0,0 @@
-"use client"
-
-import { useEffect, useRef, useState } from "react"
-import { PanoramaPosition, PluginConstructor, Point, Position, SphericalPosition, Viewer } from "@photo-sphere-viewer/core"
-import { LensflarePlugin, ReactPhotoSphereViewer } from "react-photo-sphere-viewer"
-
-import { MouseEventHandler, RenderedScene } from "@/types"
-
-import { useImageDimension } from "@/lib/useImageDimension"
-import { lightSourceNames } from "@/lib/lightSourceNames"
-
-type PhotoSpherePlugin = (PluginConstructor | [PluginConstructor, any])
-
-export function SphericalImage({
- rendered,
- onEvent,
- className,
- debug,
-}: {
- rendered: RenderedScene
- onEvent: MouseEventHandler
- className?: string
- debug?: boolean
-}) {
-
-
- const imageDimension = useImageDimension(rendered.assetUrl)
- const maskDimension = useImageDimension(rendered.maskUrl)
-
- const sceneConfig = JSON.stringify({ rendered, debug, imageDimension, maskDimension })
- const [lastSceneConfig, setLastSceneConfig] = useState(sceneConfig)
- const rootContainerRef = useRef(null)
- const viewerContainerRef = useRef()
- const viewerRef = useRef()
- const [mouseMoved, setMouseMoved] = useState(false)
-
- const defaultZoomLvl = 1 // 0 = 180 fov, 100 = 1 fov
-
- const options = {
- defaultZoomLvl,
- fisheye: false, // ..no!
- overlay: rendered.maskUrl || undefined,
- overlayOpacity: debug ? 0.5 : 0,
- /*
- panoData: {
- fullWidth: 2000,
- fullHeight: 1200,
- croppedWidth: 1024,
- croppedHeight: 512,
- croppedX: 0,
- croppedY: 200,
- // poseHeading: 0, // 0 to 360
- posePitch: 0, // -90 to 90
- // poseRoll: 0, // -180 to 180
- }
- */
- }
-
-
- const cacheRef = useRef("")
- useEffect(() => {
- const listener = (e: DragEvent) => {
- if (!rootContainerRef.current) { return }
-
- // TODO: check if we are currently dragging an object
- // if yes, then we should check if clientX and clientY are matching the
- const boundingRect = rootContainerRef.current.getBoundingClientRect()
-
- // abort if we are not currently dragging over our display area
- if (e.clientX < boundingRect.left) { return }
- if (e.clientX > (boundingRect.left + boundingRect.width)) { return }
- if (e.clientY < boundingRect.top) { return }
- if (e.clientY > (boundingRect.top + boundingRect.height)) { return }
-
- const containerX = e.clientX - boundingRect.left
- const containerY = e.clientY - boundingRect.top
-
- const relativeX = containerX / boundingRect.width
- const relativeY = containerY / boundingRect.height
-
- const key = `${relativeX},${relativeY}`
-
- // to avoid use
- if (cacheRef.current === key) {
- return
- }
- // console.log(`DRAG: calling onEvent("hover", ${relativeX}, ${relativeY})`)
-
- cacheRef.current = key
- onEvent("hover", relativeX, relativeY)
- }
-
- document.addEventListener('drag', listener)
-
- return () => {
- document.removeEventListener('drag', listener)
- }
- }, [onEvent])
-
- useEffect(() => {
- const task = async () => {
- // console.log("SphericalImage: useEffect")
- if (sceneConfig !== lastSceneConfig) {
- // console.log("SphericalImage: scene config changed!")
-
- if (!viewerRef.current) {
- // console.log("SphericalImage: no ref!")
- setLastSceneConfig(sceneConfig)
- return
- }
- const viewer = viewerRef.current
-
- const newOptions = {
- ...options,
- }
-
- const lensflares: { id: string; position: SphericalPosition; type: number }[] = []
-
- if (maskDimension.width && imageDimension.width) {
-
- // console.log("rendered.segments:", rendered.segments)
-
- rendered.segments
- .filter(segment => lightSourceNames.includes(segment.label))
- .forEach(light => {
- // console.log("light detected", light)
- const [x1, y1, x2, y2] = light.box
- const [centerX, centerY] = [(x1 + x2) / 2, (y1 + y2) / 2]
- // console.log("center:", { centerX, centerY })
- const [relativeX, relativeY] = [centerX / maskDimension.width, centerY/ maskDimension.height]
- // console.log("relative:", { relativeX, relativeY})
-
- const panoramaPosition: PanoramaPosition = {
- textureX: relativeX * imageDimension.width,
- textureY: relativeY * imageDimension.height
- }
- // console.log("panoramaPosition:", panoramaPosition)
-
- const position = viewer.dataHelper.textureCoordsToSphericalCoords(panoramaPosition)
- // console.log("sphericalPosition:", position)
- if ( // make sure coordinates are valid
- !isNaN(position.pitch) && isFinite(position.pitch) &&
- !isNaN(position.yaw) && isFinite(position.yaw)) {
- lensflares.push({
- id: `flare_${lensflares.length}`,
- position,
- type: 0,
- })
- }
- })
- }
-
- // console.log("lensflares:", lensflares)
- const lensFlarePlugin = viewer.getPlugin("lensflare")
- lensFlarePlugin.setLensflares(lensflares)
-
- // console.log("SphericalImage: calling setOptions")
- // console.log("SphericalImage: changing the panorama to: " + rendered.assetUrl.slice(0, 120))
-
- await viewer.setPanorama(rendered.assetUrl, {
- ...newOptions,
- showLoader: false,
- })
-
- // TODO we should separate all those updates, probaby
- viewer.setOptions(newOptions)
- // viewer.setOverlay(rendered.maskUrl || undefined)
-
- // console.log("SphericalImage: asking to re-render")
- viewerRef.current.needsUpdate()
-
- setLastSceneConfig(sceneConfig)
- }
- }
- task()
- }, [sceneConfig, rendered.assetUrl, viewerRef.current, maskDimension.width, imageDimension])
-
- const handleEvent = async (event: React.MouseEvent, isClick: boolean) => {
- const rootContainer = rootContainerRef.current
- const viewer = viewerRef.current
- const viewerContainer = viewerContainerRef.current
-
- /*
- if (isClick) console.log(`handleEvent(${isClick})`, {
- " imageDimension.width": imageDimension.width,
- "rendered.maskUrl": rendered.maskUrl
- })
- */
-
- if (!viewer || !rootContainer || !viewerContainer || !imageDimension.width || !rendered.maskUrl) {
- return
- }
-
- const containerRect = viewerContainer.getBoundingClientRect()
- // if (isClick) console.log("containerRect:", containerRect)
-
- const containerY = event.clientY - containerRect.top
- // console.log("containerY:", containerY)
-
- const position: Position = viewer.getPosition()
-
- const viewerPosition: Point = viewer.dataHelper.sphericalCoordsToViewerCoords(position)
- // if (isClick) console.log("viewerPosition:", viewerPosition)
-
- // we want to ignore events that are happening in the toolbar
- // note that we will probably hide this toolbar at some point,
- // to implement our own UI
- if (isClick && containerY > (containerRect.height - 40)) {
- // console.log("we are in the toolbar.. ignoring the click")
- return
- }
-
- const panoramaPosition: PanoramaPosition = viewer.dataHelper.sphericalCoordsToTextureCoords(position)
-
- if (typeof panoramaPosition.textureX !== "number" || typeof panoramaPosition.textureY !== "number") {
- return
- }
-
- const relativeX = panoramaPosition.textureX / imageDimension.width
- const relativeY = panoramaPosition.textureY / imageDimension.height
-
- onEvent(isClick ? "click" : "hover", relativeX, relativeY)
- }
-
- if (!rendered.assetUrl) {
- return null
- }
-
- return (
- {
- handleEvent(event, false)
- setMouseMoved(true)
- }}
- onMouseUp={(event) => {
- if (!mouseMoved) {
- handleEvent(event, true)
- }
- setMouseMoved(false)
- }}
- onMouseDown={() => {
- setMouseMoved(false)
- }}
- >
- {
- // nothing to do here
- }}
-
- onReady={(instance) => {
- viewerRef.current = instance
- viewerContainerRef.current = instance.container
-
- /*
- const markersPlugs = instance.getPlugin(MarkersPlugin);
- if (!markersPlugs)
- return;
- markersPlugs.addMarker({
- id: "imageLayer2",
- imageLayer: "drone.png",
- size: { width: 220, height: 220 },
- position: { yaw: '130.5deg', pitch: '-0.1deg' },
- tooltip: "Image embedded in the scene"
- });
- markersPlugs.addEventListener("select-marker", () => {
- console.log("asd");
- });
- */
- }}
-
- />
-
- )
-}
\ No newline at end of file
diff --git a/spaces/jdhuka/StaticHTML5PlayCanvas/README.md b/spaces/jdhuka/StaticHTML5PlayCanvas/README.md
deleted file mode 100644
index 265f9b5b8990a93e9c74c56a2c8d7c15083a0495..0000000000000000000000000000000000000000
--- a/spaces/jdhuka/StaticHTML5PlayCanvas/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: StaticHTML5PlayCanvas
-emoji: 😻
-colorFrom: purple
-colorTo: pink
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/jesuspj/jesuspj/Dockerfile b/spaces/jesuspj/jesuspj/Dockerfile
deleted file mode 100644
index 94ee76a4f45af463ab7f945633c9258172f9cc80..0000000000000000000000000000000000000000
--- a/spaces/jesuspj/jesuspj/Dockerfile
+++ /dev/null
@@ -1,2 +0,0 @@
-FROM huggingface/autotrain-advanced:latest
-CMD autotrain app --port 7860
diff --git a/spaces/jeycov/IADERM-UTOPIC-PFIZER/README.md b/spaces/jeycov/IADERM-UTOPIC-PFIZER/README.md
deleted file mode 100644
index 1af6ed11c40bf4a09c4e9e101dcfc6d32ac48135..0000000000000000000000000000000000000000
--- a/spaces/jeycov/IADERM-UTOPIC-PFIZER/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: AIDERM-UTOPIC
-emoji: 🦸♂️
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.0.19
-app_file: app.py
-pinned: true
-python_version: 3.7.10
----
\ No newline at end of file
diff --git a/spaces/jitubutwal1441/multiple-pdfs-chat/README.md b/spaces/jitubutwal1441/multiple-pdfs-chat/README.md
deleted file mode 100644
index 3df0d61c7b40762726065bf9e7fb324406ec478a..0000000000000000000000000000000000000000
--- a/spaces/jitubutwal1441/multiple-pdfs-chat/README.md
+++ /dev/null
@@ -1,77 +0,0 @@
----
-title: Multiple Pdfs Chat
-emoji: 🏃
-colorFrom: yellow
-colorTo: red
-sdk: streamlit
-sdk_version: 1.27.0
-app_file: app.py
-pinned: false
----
-
-
-# MultiPDF Chat App
-
-> You can find the tutorial for this project on [YouTube](https://youtu.be/dXxQ0LR-3Hg).
-
-## Introduction
-------------
-The MultiPDF Chat App is a Python application that allows you to chat with multiple PDF documents. You can ask questions about the PDFs using natural language, and the application will provide relevant responses based on the content of the documents. This app utilizes a language model to generate accurate answers to your queries. Please note that the app will only respond to questions related to the loaded PDFs.
-
-## How It Works
-------------
-
-
-
-The application follows these steps to provide responses to your questions:
-
-1. PDF Loading: The app reads multiple PDF documents and extracts their text content.
-
-2. Text Chunking: The extracted text is divided into smaller chunks that can be processed effectively.
-
-3. Language Model: The application utilizes a language model to generate vector representations (embeddings) of the text chunks.
-
-4. Similarity Matching: When you ask a question, the app compares it with the text chunks and identifies the most semantically similar ones.
-
-5. Response Generation: The selected chunks are passed to the language model, which generates a response based on the relevant content of the PDFs.
-
-## Dependencies and Installation
-----------------------------
-To install the MultiPDF Chat App, please follow these steps:
-
-1. Clone the repository to your local machine.
-
-2. Install the required dependencies by running the following command:
- ```
- pip install -r requirements.txt
- ```
-
-3. Obtain an API key from OpenAI and add it to the `.env` file in the project directory.
-```commandline
-OPENAI_API_KEY=your_secrit_api_key
-```
-
-## Usage
------
-To use the MultiPDF Chat App, follow these steps:
-
-1. Ensure that you have installed the required dependencies and added the OpenAI API key to the `.env` file.
-
-2. Run the `main.py` file using the Streamlit CLI. Execute the following command:
- ```
- streamlit run app.py
- ```
-
-3. The application will launch in your default web browser, displaying the user interface.
-
-4. Load multiple PDF documents into the app by following the provided instructions.
-
-5. Ask questions in natural language about the loaded PDFs using the chat interface.
-
-## Contributing
-------------
-This repository is intended for educational purposes and does not accept further contributions. It serves as supporting material for a YouTube tutorial that demonstrates how to build this project. Feel free to utilize and enhance the app based on your own requirements.
-
-## License
--------
-The MultiPDF Chat App is released under the [MIT License](https://opensource.org/licenses/MIT).
\ No newline at end of file
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/__init__.py
deleted file mode 100644
index 4154ee64a004aa8c046f751d903b3a7b1504affd..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/__init__.py
+++ /dev/null
@@ -1,41 +0,0 @@
-"""
-PyPDF2 is a free and open-source pure-python PDF library capable of splitting,
-merging, cropping, and transforming the pages of PDF files. It can also add
-custom data, viewing options, and passwords to PDF files. PyPDF2 can retrieve
-text and metadata from PDFs as well.
-
-You can read the full docs at https://pypdf2.readthedocs.io/.
-"""
-
-import warnings
-
-from ._encryption import PasswordType
-from ._merger import PdfFileMerger, PdfMerger
-from ._page import PageObject, Transformation
-from ._reader import DocumentInformation, PdfFileReader, PdfReader
-from ._version import __version__
-from ._writer import PdfFileWriter, PdfWriter
-from .pagerange import PageRange, parse_filename_page_ranges
-from .papersizes import PaperSize
-
-warnings.warn(
- message="PyPDF2 is deprecated. Please move to the pypdf library instead.",
- category=DeprecationWarning,
-)
-
-__all__ = [
- "__version__",
- "PageRange",
- "PaperSize",
- "DocumentInformation",
- "parse_filename_page_ranges",
- "PdfFileMerger", # will be removed in PyPDF2 3.0.0; use PdfMerger instead
- "PdfFileReader", # will be removed in PyPDF2 3.0.0; use PdfReader instead
- "PdfFileWriter", # will be removed in PyPDF2 3.0.0; use PdfWriter instead
- "PdfMerger",
- "PdfReader",
- "PdfWriter",
- "Transformation",
- "PageObject",
- "PasswordType",
-]
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dateutil/zoneinfo/rebuild.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dateutil/zoneinfo/rebuild.py
deleted file mode 100644
index 684c6586f091350c347f2b6150935f5214ffec27..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dateutil/zoneinfo/rebuild.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import logging
-import os
-import tempfile
-import shutil
-import json
-from subprocess import check_call, check_output
-from tarfile import TarFile
-
-from dateutil.zoneinfo import METADATA_FN, ZONEFILENAME
-
-
-def rebuild(filename, tag=None, format="gz", zonegroups=[], metadata=None):
- """Rebuild the internal timezone info in dateutil/zoneinfo/zoneinfo*tar*
-
- filename is the timezone tarball from ``ftp.iana.org/tz``.
-
- """
- tmpdir = tempfile.mkdtemp()
- zonedir = os.path.join(tmpdir, "zoneinfo")
- moduledir = os.path.dirname(__file__)
- try:
- with TarFile.open(filename) as tf:
- for name in zonegroups:
- tf.extract(name, tmpdir)
- filepaths = [os.path.join(tmpdir, n) for n in zonegroups]
-
- _run_zic(zonedir, filepaths)
-
- # write metadata file
- with open(os.path.join(zonedir, METADATA_FN), 'w') as f:
- json.dump(metadata, f, indent=4, sort_keys=True)
- target = os.path.join(moduledir, ZONEFILENAME)
- with TarFile.open(target, "w:%s" % format) as tf:
- for entry in os.listdir(zonedir):
- entrypath = os.path.join(zonedir, entry)
- tf.add(entrypath, entry)
- finally:
- shutil.rmtree(tmpdir)
-
-
-def _run_zic(zonedir, filepaths):
- """Calls the ``zic`` compiler in a compatible way to get a "fat" binary.
-
- Recent versions of ``zic`` default to ``-b slim``, while older versions
- don't even have the ``-b`` option (but default to "fat" binaries). The
- current version of dateutil does not support Version 2+ TZif files, which
- causes problems when used in conjunction with "slim" binaries, so this
- function is used to ensure that we always get a "fat" binary.
- """
-
- try:
- help_text = check_output(["zic", "--help"])
- except OSError as e:
- _print_on_nosuchfile(e)
- raise
-
- if b"-b " in help_text:
- bloat_args = ["-b", "fat"]
- else:
- bloat_args = []
-
- check_call(["zic"] + bloat_args + ["-d", zonedir] + filepaths)
-
-
-def _print_on_nosuchfile(e):
- """Print helpful troubleshooting message
-
- e is an exception raised by subprocess.check_call()
-
- """
- if e.errno == 2:
- logging.error(
- "Could not find zic. Perhaps you need to install "
- "libc-bin or some other package that provides it, "
- "or it's not in your PATH?")
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/middleware/wsgi.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/middleware/wsgi.py
deleted file mode 100644
index c4c6a797d2675e1c13b028be977c64a822fb649b..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/middleware/wsgi.py
+++ /dev/null
@@ -1 +0,0 @@
-from starlette.middleware.wsgi import WSGIMiddleware as WSGIMiddleware # noqa
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/indices/empty/base.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/indices/empty/base.py
deleted file mode 100644
index ddea7d117f6d8a9805728d4b0594b2c63fc7aaf2..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/indices/empty/base.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""Empty index.
-
-An index that doesn't contain any documents. Can only be used for
-pure LLM calls.
-
-"""
-
-from typing import Any, Dict, Optional, Sequence, Type
-
-from gpt_index.data_structs.data_structs import EmptyIndex
-from gpt_index.indices.base import BaseGPTIndex
-from gpt_index.indices.query.base import BaseGPTIndexQuery
-from gpt_index.indices.query.empty.base import GPTEmptyIndexQuery
-from gpt_index.indices.query.schema import QueryMode
-from gpt_index.langchain_helpers.chain_wrapper import LLMPredictor
-from gpt_index.langchain_helpers.text_splitter import TextSplitter
-from gpt_index.schema import BaseDocument
-
-
-class GPTEmptyIndex(BaseGPTIndex[EmptyIndex]):
- """GPT Empty Index.
-
- An index that doesn't contain any documents. Used for
- pure LLM calls.
- NOTE: this exists because an empty index it allows certain properties,
- such as the ability to be composed with other indices + token
- counting + others.
-
- """
-
- index_struct_cls = EmptyIndex
-
- def __init__(
- self,
- index_struct: Optional[EmptyIndex] = None,
- llm_predictor: Optional[LLMPredictor] = None,
- text_splitter: Optional[TextSplitter] = None,
- **kwargs: Any,
- ) -> None:
- """Initialize params."""
- super().__init__(
- documents=[],
- index_struct=index_struct,
- llm_predictor=llm_predictor,
- text_splitter=text_splitter,
- **kwargs,
- )
-
- @classmethod
- def get_query_map(self) -> Dict[str, Type[BaseGPTIndexQuery]]:
- """Get query map."""
- return {
- QueryMode.DEFAULT: GPTEmptyIndexQuery,
- }
-
- def _build_index_from_documents(
- self, documents: Sequence[BaseDocument]
- ) -> EmptyIndex:
- """Build the index from documents.
-
- Args:
- documents (List[BaseDocument]): A list of documents.
-
- Returns:
- IndexList: The created list index.
- """
- index_struct = EmptyIndex()
- return index_struct
-
- def _insert(self, document: BaseDocument, **insert_kwargs: Any) -> None:
- """Insert a document."""
- raise NotImplementedError("Cannot insert into an empty index.")
-
- def _delete(self, doc_id: str, **delete_kwargs: Any) -> None:
- """Delete a document."""
- raise NotImplementedError("Cannot delete from an empty index.")
diff --git a/spaces/justest/gpt4free/testing/wewordle/testing.py b/spaces/justest/gpt4free/testing/wewordle/testing.py
deleted file mode 100644
index cebcaeed2b0c348b41003ddca8b15c7b3b2f7199..0000000000000000000000000000000000000000
--- a/spaces/justest/gpt4free/testing/wewordle/testing.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from Wewordle import ChatCompletion
-
-# Test 1
-response = ChatCompletion.create(model="gpt-3.5-turbo",
- provider="Wewordle",
- stream=False,
- messages=[{'role': 'user', 'content': 'who are you?'}])
-
-print(response)
-
-# Test 2
-response = ChatCompletion.create(model="gpt-3.5-turbo",
- provider="Wewordle",
- stream=False,
- messages=[{'role': 'user', 'content': 'what you can do?'}])
-
-print(response)
-
-
-# Test 3
-response = ChatCompletion.create(model="gpt-3.5-turbo",
- provider="Wewordle",
- stream=False,
- messages=[
- {'role': 'user', 'content': 'now your name is Bob'},
- {'role': 'assistant', 'content': 'Hello Im Bob, you asistant'},
- {'role': 'user', 'content': 'what your name again?'},
- ])
-
-print(response)
diff --git a/spaces/jvde/sovits-webui/losses.py b/spaces/jvde/sovits-webui/losses.py
deleted file mode 100644
index fb22a0e834dd87edaa37bb8190eee2c3c7abe0d5..0000000000000000000000000000000000000000
--- a/spaces/jvde/sovits-webui/losses.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import commons
-
-
-def feature_loss(fmap_r, fmap_g):
- loss = 0
- for dr, dg in zip(fmap_r, fmap_g):
- for rl, gl in zip(dr, dg):
- rl = rl.float().detach()
- gl = gl.float()
- loss += torch.mean(torch.abs(rl - gl))
-
- return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
- loss = 0
- r_losses = []
- g_losses = []
- for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
- dr = dr.float()
- dg = dg.float()
- r_loss = torch.mean((1-dr)**2)
- g_loss = torch.mean(dg**2)
- loss += (r_loss + g_loss)
- r_losses.append(r_loss.item())
- g_losses.append(g_loss.item())
-
- return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
- loss = 0
- gen_losses = []
- for dg in disc_outputs:
- dg = dg.float()
- l = torch.mean((1-dg)**2)
- gen_losses.append(l)
- loss += l
-
- return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
- """
- z_p, logs_q: [b, h, t_t]
- m_p, logs_p: [b, h, t_t]
- """
- z_p = z_p.float()
- logs_q = logs_q.float()
- m_p = m_p.float()
- logs_p = logs_p.float()
- z_mask = z_mask.float()
-
- kl = logs_p - logs_q - 0.5
- kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
- kl = torch.sum(kl * z_mask)
- l = kl / torch.sum(z_mask)
- return l
diff --git a/spaces/kcagle/AutoGPT/autogpt/spinner.py b/spaces/kcagle/AutoGPT/autogpt/spinner.py
deleted file mode 100644
index 4e33d74213881352546f334ccb1eb4772b8b7b70..0000000000000000000000000000000000000000
--- a/spaces/kcagle/AutoGPT/autogpt/spinner.py
+++ /dev/null
@@ -1,65 +0,0 @@
-"""A simple spinner module"""
-import itertools
-import sys
-import threading
-import time
-
-
-class Spinner:
- """A simple spinner class"""
-
- def __init__(self, message: str = "Loading...", delay: float = 0.1) -> None:
- """Initialize the spinner class
-
- Args:
- message (str): The message to display.
- delay (float): The delay between each spinner update.
- """
- self.spinner = itertools.cycle(["-", "/", "|", "\\"])
- self.delay = delay
- self.message = message
- self.running = False
- self.spinner_thread = None
-
- def spin(self) -> None:
- """Spin the spinner"""
- while self.running:
- sys.stdout.write(f"{next(self.spinner)} {self.message}\r")
- sys.stdout.flush()
- time.sleep(self.delay)
- sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
-
- def __enter__(self):
- """Start the spinner"""
- self.running = True
- self.spinner_thread = threading.Thread(target=self.spin)
- self.spinner_thread.start()
-
- return self
-
- def __exit__(self, exc_type, exc_value, exc_traceback) -> None:
- """Stop the spinner
-
- Args:
- exc_type (Exception): The exception type.
- exc_value (Exception): The exception value.
- exc_traceback (Exception): The exception traceback.
- """
- self.running = False
- if self.spinner_thread is not None:
- self.spinner_thread.join()
- sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
- sys.stdout.flush()
-
- def update_message(self, new_message, delay=0.1):
- """Update the spinner message
- Args:
- new_message (str): New message to display
- delay: Delay in seconds before updating the message
- """
- time.sleep(delay)
- sys.stdout.write(
- f"\r{' ' * (len(self.message) + 2)}\r"
- ) # Clear the current message
- sys.stdout.flush()
- self.message = new_message
diff --git a/spaces/kdrkdrkdr/HinaTTS/monotonic_align/__init__.py b/spaces/kdrkdrkdr/HinaTTS/monotonic_align/__init__.py
deleted file mode 100644
index 40b6f64aa116c74cac2f6a33444c9eeea2fdb38c..0000000000000000000000000000000000000000
--- a/spaces/kdrkdrkdr/HinaTTS/monotonic_align/__init__.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from numpy import zeros, int32, float32
-from torch import from_numpy
-
-from .core import maximum_path_jit
-
-
-def maximum_path(neg_cent, mask):
- """ numba optimized version.
- neg_cent: [b, t_t, t_s]
- mask: [b, t_t, t_s]
- """
- device = neg_cent.device
- dtype = neg_cent.dtype
- neg_cent = neg_cent.data.cpu().numpy().astype(float32)
- path = zeros(neg_cent.shape, dtype=int32)
-
- t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
- t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
- maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
- return from_numpy(path).to(device=device, dtype=dtype)
-
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/face3d/util/visualizer.py b/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/face3d/util/visualizer.py
deleted file mode 100644
index 4023a6d4086acba9bc88e079f625194d324d7c9e..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/face3d/util/visualizer.py
+++ /dev/null
@@ -1,227 +0,0 @@
-"""This script defines the visualizer for Deep3DFaceRecon_pytorch
-"""
-
-import numpy as np
-import os
-import sys
-import ntpath
-import time
-from . import util, html
-from subprocess import Popen, PIPE
-from torch.utils.tensorboard import SummaryWriter
-
-def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
- """Save images to the disk.
-
- Parameters:
- webpage (the HTML class) -- the HTML webpage class that stores these imaegs (see html.py for more details)
- visuals (OrderedDict) -- an ordered dictionary that stores (name, images (either tensor or numpy) ) pairs
- image_path (str) -- the string is used to create image paths
- aspect_ratio (float) -- the aspect ratio of saved images
- width (int) -- the images will be resized to width x width
-
- This function will save images stored in 'visuals' to the HTML file specified by 'webpage'.
- """
- image_dir = webpage.get_image_dir()
- short_path = ntpath.basename(image_path[0])
- name = os.path.splitext(short_path)[0]
-
- webpage.add_header(name)
- ims, txts, links = [], [], []
-
- for label, im_data in visuals.items():
- im = util.tensor2im(im_data)
- image_name = '%s/%s.png' % (label, name)
- os.makedirs(os.path.join(image_dir, label), exist_ok=True)
- save_path = os.path.join(image_dir, image_name)
- util.save_image(im, save_path, aspect_ratio=aspect_ratio)
- ims.append(image_name)
- txts.append(label)
- links.append(image_name)
- webpage.add_images(ims, txts, links, width=width)
-
-
-class Visualizer():
- """This class includes several functions that can display/save images and print/save logging information.
-
- It uses a Python library tensprboardX for display, and a Python library 'dominate' (wrapped in 'HTML') for creating HTML files with images.
- """
-
- def __init__(self, opt):
- """Initialize the Visualizer class
-
- Parameters:
- opt -- stores all the experiment flags; needs to be a subclass of BaseOptions
- Step 1: Cache the training/test options
- Step 2: create a tensorboard writer
- Step 3: create an HTML object for saveing HTML filters
- Step 4: create a logging file to store training losses
- """
- self.opt = opt # cache the option
- self.use_html = opt.isTrain and not opt.no_html
- self.writer = SummaryWriter(os.path.join(opt.checkpoints_dir, 'logs', opt.name))
- self.win_size = opt.display_winsize
- self.name = opt.name
- self.saved = False
- if self.use_html: # create an HTML object at /web/; images will be saved under /web/images/
- self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')
- self.img_dir = os.path.join(self.web_dir, 'images')
- print('create web directory %s...' % self.web_dir)
- util.mkdirs([self.web_dir, self.img_dir])
- # create a logging file to store training losses
- self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
- with open(self.log_name, "a") as log_file:
- now = time.strftime("%c")
- log_file.write('================ Training Loss (%s) ================\n' % now)
-
- def reset(self):
- """Reset the self.saved status"""
- self.saved = False
-
-
- def display_current_results(self, visuals, total_iters, epoch, save_result):
- """Display current results on tensorboad; save current results to an HTML file.
-
- Parameters:
- visuals (OrderedDict) - - dictionary of images to display or save
- total_iters (int) -- total iterations
- epoch (int) - - the current epoch
- save_result (bool) - - if save the current results to an HTML file
- """
- for label, image in visuals.items():
- self.writer.add_image(label, util.tensor2im(image), total_iters, dataformats='HWC')
-
- if self.use_html and (save_result or not self.saved): # save images to an HTML file if they haven't been saved.
- self.saved = True
- # save images to the disk
- for label, image in visuals.items():
- image_numpy = util.tensor2im(image)
- img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label))
- util.save_image(image_numpy, img_path)
-
- # update website
- webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=0)
- for n in range(epoch, 0, -1):
- webpage.add_header('epoch [%d]' % n)
- ims, txts, links = [], [], []
-
- for label, image_numpy in visuals.items():
- image_numpy = util.tensor2im(image)
- img_path = 'epoch%.3d_%s.png' % (n, label)
- ims.append(img_path)
- txts.append(label)
- links.append(img_path)
- webpage.add_images(ims, txts, links, width=self.win_size)
- webpage.save()
-
- def plot_current_losses(self, total_iters, losses):
- # G_loss_collection = {}
- # D_loss_collection = {}
- # for name, value in losses.items():
- # if 'G' in name or 'NCE' in name or 'idt' in name:
- # G_loss_collection[name] = value
- # else:
- # D_loss_collection[name] = value
- # self.writer.add_scalars('G_collec', G_loss_collection, total_iters)
- # self.writer.add_scalars('D_collec', D_loss_collection, total_iters)
- for name, value in losses.items():
- self.writer.add_scalar(name, value, total_iters)
-
- # losses: same format as |losses| of plot_current_losses
- def print_current_losses(self, epoch, iters, losses, t_comp, t_data):
- """print current losses on console; also save the losses to the disk
-
- Parameters:
- epoch (int) -- current epoch
- iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch)
- losses (OrderedDict) -- training losses stored in the format of (name, float) pairs
- t_comp (float) -- computational time per data point (normalized by batch_size)
- t_data (float) -- data loading time per data point (normalized by batch_size)
- """
- message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, iters, t_comp, t_data)
- for k, v in losses.items():
- message += '%s: %.3f ' % (k, v)
-
- print(message) # print the message
- with open(self.log_name, "a") as log_file:
- log_file.write('%s\n' % message) # save the message
-
-
-class MyVisualizer:
- def __init__(self, opt):
- """Initialize the Visualizer class
-
- Parameters:
- opt -- stores all the experiment flags; needs to be a subclass of BaseOptions
- Step 1: Cache the training/test options
- Step 2: create a tensorboard writer
- Step 3: create an HTML object for saveing HTML filters
- Step 4: create a logging file to store training losses
- """
- self.opt = opt # cache the optio
- self.name = opt.name
- self.img_dir = os.path.join(opt.checkpoints_dir, opt.name, 'results')
-
- if opt.phase != 'test':
- self.writer = SummaryWriter(os.path.join(opt.checkpoints_dir, opt.name, 'logs'))
- # create a logging file to store training losses
- self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
- with open(self.log_name, "a") as log_file:
- now = time.strftime("%c")
- log_file.write('================ Training Loss (%s) ================\n' % now)
-
-
- def display_current_results(self, visuals, total_iters, epoch, dataset='train', save_results=False, count=0, name=None,
- add_image=True):
- """Display current results on tensorboad; save current results to an HTML file.
-
- Parameters:
- visuals (OrderedDict) - - dictionary of images to display or save
- total_iters (int) -- total iterations
- epoch (int) - - the current epoch
- dataset (str) - - 'train' or 'val' or 'test'
- """
- # if (not add_image) and (not save_results): return
-
- for label, image in visuals.items():
- for i in range(image.shape[0]):
- image_numpy = util.tensor2im(image[i])
- if add_image:
- self.writer.add_image(label + '%s_%02d'%(dataset, i + count),
- image_numpy, total_iters, dataformats='HWC')
-
- if save_results:
- save_path = os.path.join(self.img_dir, dataset, 'epoch_%s_%06d'%(epoch, total_iters))
- if not os.path.isdir(save_path):
- os.makedirs(save_path)
-
- if name is not None:
- img_path = os.path.join(save_path, '%s.png' % name)
- else:
- img_path = os.path.join(save_path, '%s_%03d.png' % (label, i + count))
- util.save_image(image_numpy, img_path)
-
-
- def plot_current_losses(self, total_iters, losses, dataset='train'):
- for name, value in losses.items():
- self.writer.add_scalar(name + '/%s'%dataset, value, total_iters)
-
- # losses: same format as |losses| of plot_current_losses
- def print_current_losses(self, epoch, iters, losses, t_comp, t_data, dataset='train'):
- """print current losses on console; also save the losses to the disk
-
- Parameters:
- epoch (int) -- current epoch
- iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch)
- losses (OrderedDict) -- training losses stored in the format of (name, float) pairs
- t_comp (float) -- computational time per data point (normalized by batch_size)
- t_data (float) -- data loading time per data point (normalized by batch_size)
- """
- message = '(dataset: %s, epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (
- dataset, epoch, iters, t_comp, t_data)
- for k, v in losses.items():
- message += '%s: %.3f ' % (k, v)
-
- print(message) # print the message
- with open(self.log_name, "a") as log_file:
- log_file.write('%s\n' % message) # save the message
diff --git a/spaces/kevinwang676/VITS2-Mandarin/mel_processing.py b/spaces/kevinwang676/VITS2-Mandarin/mel_processing.py
deleted file mode 100644
index 359abdf705cf601338356e10b60a7ae05c8f965d..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VITS2-Mandarin/mel_processing.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import math
-import os
-from packaging import version
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- """
- PARAMS
- ------
- C: compression factor
- """
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
- """
- PARAMS
- ------
- C: compression factor used to compress
- """
- return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
- output = dynamic_range_compression_torch(magnitudes)
- return output
-
-
-def spectral_de_normalize_torch(magnitudes):
- output = dynamic_range_decompression_torch(magnitudes)
- return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- if version.parse(torch.__version__) >= version.parse("2"):
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
- else:
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
- return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
- global mel_basis
- dtype_device = str(spec.dtype) + '_' + str(spec.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
- return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global mel_basis, hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- if version.parse(torch.__version__) >= version.parse("2"):
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
- else:
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
-
- return spec
diff --git a/spaces/kevkev05/Chat-To-Sequence/utility.py b/spaces/kevkev05/Chat-To-Sequence/utility.py
deleted file mode 100644
index fdb093ac7432a63714930b306864dd8fb7644200..0000000000000000000000000000000000000000
--- a/spaces/kevkev05/Chat-To-Sequence/utility.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from datasets import load_dataset
-
-def load_from_hub_csv(path, data_file, token, csv_output_file):
-
- dataset = load_dataset(path=path,
- data_files=data_file,
- token=token,)
-
- for split, data in dataset.items():
- data.to_csv(csv_output_file, index=None)
diff --git a/spaces/kidcoconut/spcdkr_omdenasaudi_liverhccxai/lib/fctTile.py b/spaces/kidcoconut/spcdkr_omdenasaudi_liverhccxai/lib/fctTile.py
deleted file mode 100644
index aa2415d9b5f6b221e14f3bceb9553deaf61418fc..0000000000000000000000000000000000000000
--- a/spaces/kidcoconut/spcdkr_omdenasaudi_liverhccxai/lib/fctTile.py
+++ /dev/null
@@ -1 +0,0 @@
-#--- factory class for tile operations
\ No newline at end of file
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg2mel_train.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg2mel_train.py
deleted file mode 100644
index 5a6a06c805109159ff40cad69668f1fc38cf1e9b..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg2mel_train.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import sys
-import torch
-import argparse
-import numpy as np
-from utils.load_yaml import HpsYaml
-from ppg2mel.train.train_linglf02mel_seq2seq_oneshotvc import Solver
-
-# For reproducibility, comment these may speed up training
-torch.backends.cudnn.deterministic = True
-torch.backends.cudnn.benchmark = False
-
-def main():
- # Arguments
- parser = argparse.ArgumentParser(description=
- 'Training PPG2Mel VC model.')
- parser.add_argument('--config', type=str,
- help='Path to experiment config, e.g., config/vc.yaml')
- parser.add_argument('--name', default=None, type=str, help='Name for logging.')
- parser.add_argument('--logdir', default='log/', type=str,
- help='Logging path.', required=False)
- parser.add_argument('--ckpdir', default='ppg2mel/saved_models/', type=str,
- help='Checkpoint path.', required=False)
- parser.add_argument('--outdir', default='result/', type=str,
- help='Decode output path.', required=False)
- parser.add_argument('--load', default=None, type=str,
- help='Load pre-trained model (for training only)', required=False)
- parser.add_argument('--warm_start', action='store_true',
- help='Load model weights only, ignore specified layers.')
- parser.add_argument('--seed', default=0, type=int,
- help='Random seed for reproducable results.', required=False)
- parser.add_argument('--njobs', default=8, type=int,
- help='Number of threads for dataloader/decoding.', required=False)
- parser.add_argument('--cpu', action='store_true', help='Disable GPU training.')
- parser.add_argument('--no-pin', action='store_true',
- help='Disable pin-memory for dataloader')
- parser.add_argument('--test', action='store_true', help='Test the model.')
- parser.add_argument('--no-msg', action='store_true', help='Hide all messages.')
- parser.add_argument('--finetune', action='store_true', help='Finetune model')
- parser.add_argument('--oneshotvc', action='store_true', help='Oneshot VC model')
- parser.add_argument('--bilstm', action='store_true', help='BiLSTM VC model')
- parser.add_argument('--lsa', action='store_true', help='Use location-sensitive attention (LSA)')
-
- ###
-
- paras = parser.parse_args()
- setattr(paras, 'gpu', not paras.cpu)
- setattr(paras, 'pin_memory', not paras.no_pin)
- setattr(paras, 'verbose', not paras.no_msg)
- # Make the config dict dot visitable
- config = HpsYaml(paras.config)
-
- np.random.seed(paras.seed)
- torch.manual_seed(paras.seed)
- if torch.cuda.is_available():
- torch.cuda.manual_seed_all(paras.seed)
-
- print(">>> OneShot VC training ...")
- mode = "train"
- solver = Solver(config, paras, mode)
- solver.load_data()
- solver.set_model()
- solver.exec()
- print(">>> Oneshot VC train finished!")
- sys.exit(0)
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg_extractor/encoder/embedding.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg_extractor/encoder/embedding.py
deleted file mode 100644
index fa3199cf7e3da2ed834d4781b694cf4ccb2a433c..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg_extractor/encoder/embedding.py
+++ /dev/null
@@ -1,166 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Shigeki Karita
-# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Positonal Encoding Module."""
-
-import math
-
-import torch
-
-
-def _pre_hook(
- state_dict,
- prefix,
- local_metadata,
- strict,
- missing_keys,
- unexpected_keys,
- error_msgs,
-):
- """Perform pre-hook in load_state_dict for backward compatibility.
-
- Note:
- We saved self.pe until v.0.5.2 but we have omitted it later.
- Therefore, we remove the item "pe" from `state_dict` for backward compatibility.
-
- """
- k = prefix + "pe"
- if k in state_dict:
- state_dict.pop(k)
-
-
-class PositionalEncoding(torch.nn.Module):
- """Positional encoding.
-
- :param int d_model: embedding dim
- :param float dropout_rate: dropout rate
- :param int max_len: maximum input length
- :param reverse: whether to reverse the input position
-
- """
-
- def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False):
- """Construct an PositionalEncoding object."""
- super(PositionalEncoding, self).__init__()
- self.d_model = d_model
- self.reverse = reverse
- self.xscale = math.sqrt(self.d_model)
- self.dropout = torch.nn.Dropout(p=dropout_rate)
- self.pe = None
- self.extend_pe(torch.tensor(0.0).expand(1, max_len))
- self._register_load_state_dict_pre_hook(_pre_hook)
-
- def extend_pe(self, x):
- """Reset the positional encodings."""
- if self.pe is not None:
- if self.pe.size(1) >= x.size(1):
- if self.pe.dtype != x.dtype or self.pe.device != x.device:
- self.pe = self.pe.to(dtype=x.dtype, device=x.device)
- return
- pe = torch.zeros(x.size(1), self.d_model)
- if self.reverse:
- position = torch.arange(
- x.size(1) - 1, -1, -1.0, dtype=torch.float32
- ).unsqueeze(1)
- else:
- position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
- div_term = torch.exp(
- torch.arange(0, self.d_model, 2, dtype=torch.float32)
- * -(math.log(10000.0) / self.d_model)
- )
- pe[:, 0::2] = torch.sin(position * div_term)
- pe[:, 1::2] = torch.cos(position * div_term)
- pe = pe.unsqueeze(0)
- self.pe = pe.to(device=x.device, dtype=x.dtype)
-
- def forward(self, x: torch.Tensor):
- """Add positional encoding.
-
- Args:
- x (torch.Tensor): Input. Its shape is (batch, time, ...)
-
- Returns:
- torch.Tensor: Encoded tensor. Its shape is (batch, time, ...)
-
- """
- self.extend_pe(x)
- x = x * self.xscale + self.pe[:, : x.size(1)]
- return self.dropout(x)
-
-
-class ScaledPositionalEncoding(PositionalEncoding):
- """Scaled positional encoding module.
-
- See also: Sec. 3.2 https://arxiv.org/pdf/1809.08895.pdf
-
- """
-
- def __init__(self, d_model, dropout_rate, max_len=5000):
- """Initialize class.
-
- :param int d_model: embedding dim
- :param float dropout_rate: dropout rate
- :param int max_len: maximum input length
-
- """
- super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len)
- self.alpha = torch.nn.Parameter(torch.tensor(1.0))
-
- def reset_parameters(self):
- """Reset parameters."""
- self.alpha.data = torch.tensor(1.0)
-
- def forward(self, x):
- """Add positional encoding.
-
- Args:
- x (torch.Tensor): Input. Its shape is (batch, time, ...)
-
- Returns:
- torch.Tensor: Encoded tensor. Its shape is (batch, time, ...)
-
- """
- self.extend_pe(x)
- x = x + self.alpha * self.pe[:, : x.size(1)]
- return self.dropout(x)
-
-
-class RelPositionalEncoding(PositionalEncoding):
- """Relitive positional encoding module.
-
- See : Appendix B in https://arxiv.org/abs/1901.02860
-
- :param int d_model: embedding dim
- :param float dropout_rate: dropout rate
- :param int max_len: maximum input length
-
- """
-
- def __init__(self, d_model, dropout_rate, max_len=5000):
- """Initialize class.
-
- :param int d_model: embedding dim
- :param float dropout_rate: dropout rate
- :param int max_len: maximum input length
-
- """
- super().__init__(d_model, dropout_rate, max_len, reverse=True)
-
- def forward(self, x):
- """Compute positional encoding.
-
- Args:
- x (torch.Tensor): Input. Its shape is (batch, time, ...)
-
- Returns:
- torch.Tensor: x. Its shape is (batch, time, ...)
- torch.Tensor: pos_emb. Its shape is (1, time, ...)
-
- """
- self.extend_pe(x)
- x = x * self.xscale
- pos_emb = self.pe[:, : x.size(1)]
- return self.dropout(x), self.dropout(pos_emb)
diff --git a/spaces/kllmagn/sberbank-ai-rugpt3large_based_on_gpt2/README.md b/spaces/kllmagn/sberbank-ai-rugpt3large_based_on_gpt2/README.md
deleted file mode 100644
index 9075a708f9f91b6413151d7f1c489538a4bd24aa..0000000000000000000000000000000000000000
--- a/spaces/kllmagn/sberbank-ai-rugpt3large_based_on_gpt2/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Sberbank-ai-rugpt3large Based On Gpt2
-emoji: 💻
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/kmirijan/NBA-Stats/README.md b/spaces/kmirijan/NBA-Stats/README.md
deleted file mode 100644
index fc9ac935ea5ecf1c2557a9174ac826b3c1db94f6..0000000000000000000000000000000000000000
--- a/spaces/kmirijan/NBA-Stats/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: NBA Stats
-emoji: 📉
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.32.0
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/kukuhtw/VToonify/vtoonify/model/raft/alt_cuda_corr/setup.py b/spaces/kukuhtw/VToonify/vtoonify/model/raft/alt_cuda_corr/setup.py
deleted file mode 100644
index c0207ff285ffac4c8146c79d154f12416dbef48c..0000000000000000000000000000000000000000
--- a/spaces/kukuhtw/VToonify/vtoonify/model/raft/alt_cuda_corr/setup.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from setuptools import setup
-from torch.utils.cpp_extension import BuildExtension, CUDAExtension
-
-
-setup(
- name='correlation',
- ext_modules=[
- CUDAExtension('alt_cuda_corr',
- sources=['correlation.cpp', 'correlation_kernel.cu'],
- extra_compile_args={'cxx': [], 'nvcc': ['-O3']}),
- ],
- cmdclass={
- 'build_ext': BuildExtension
- })
-
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-0028e07e.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-0028e07e.js
deleted file mode 100644
index 8da18a5500bbf6fc1b563e449c429d1503b95ecd..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-0028e07e.js
+++ /dev/null
@@ -1,67 +0,0 @@
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-# coding=utf-8
-# Copyright 2019-present, the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from __future__ import annotations
-
-import inspect
-import json
-import pprint
-import re
-import textwrap
-import warnings
-from concurrent.futures import Future, ThreadPoolExecutor
-from dataclasses import dataclass, field
-from datetime import datetime
-from functools import wraps
-from itertools import islice
-from pathlib import Path
-from typing import Any, BinaryIO, Callable, Dict, Iterable, Iterator, List, Optional, Tuple, TypeVar, Union, overload
-from urllib.parse import quote
-
-import requests
-from requests.exceptions import HTTPError
-
-from huggingface_hub.utils import (
- IGNORE_GIT_FOLDER_PATTERNS,
- EntryNotFoundError,
- LocalTokenNotFoundError,
- RepositoryNotFoundError,
- experimental,
- get_session,
-)
-
-from ._commit_api import (
- CommitOperation,
- CommitOperationAdd,
- CommitOperationDelete,
- fetch_upload_modes,
- prepare_commit_payload,
- upload_lfs_files,
- warn_on_overwriting_operations,
-)
-from ._multi_commits import (
- MULTI_COMMIT_PR_CLOSE_COMMENT_FAILURE_BAD_REQUEST_TEMPLATE,
- MULTI_COMMIT_PR_CLOSE_COMMENT_FAILURE_NO_CHANGES_TEMPLATE,
- MULTI_COMMIT_PR_CLOSING_COMMENT_TEMPLATE,
- MULTI_COMMIT_PR_COMPLETION_COMMENT_TEMPLATE,
- MultiCommitException,
- MultiCommitStep,
- MultiCommitStrategy,
- multi_commit_create_pull_request,
- multi_commit_generate_comment,
- multi_commit_parse_pr_description,
- plan_multi_commits,
-)
-from ._space_api import SpaceHardware, SpaceRuntime
-from .community import (
- Discussion,
- DiscussionComment,
- DiscussionStatusChange,
- DiscussionTitleChange,
- DiscussionWithDetails,
- deserialize_event,
-)
-from .constants import (
- DEFAULT_REVISION,
- ENDPOINT,
- REGEX_COMMIT_OID,
- REPO_TYPE_MODEL,
- REPO_TYPES,
- REPO_TYPES_MAPPING,
- REPO_TYPES_URL_PREFIXES,
- SPACES_SDK_TYPES,
-)
-from .utils import ( # noqa: F401 # imported for backward compatibility
- BadRequestError,
- HfFolder,
- HfHubHTTPError,
- build_hf_headers,
- filter_repo_objects,
- hf_raise_for_status,
- logging,
- paginate,
- parse_datetime,
- validate_hf_hub_args,
-)
-from .utils._deprecation import (
- _deprecate_arguments,
-)
-from .utils._typing import CallableT, Literal, TypedDict
-from .utils.endpoint_helpers import (
- AttributeDictionary,
- DatasetFilter,
- DatasetTags,
- ModelFilter,
- ModelTags,
- _filter_emissions,
-)
-
-
-R = TypeVar("R") # Return type
-
-USERNAME_PLACEHOLDER = "hf_user"
-_REGEX_DISCUSSION_URL = re.compile(r".*/discussions/(\d+)$")
-
-
-logger = logging.get_logger(__name__)
-
-
-class ReprMixin:
- """Mixin to create the __repr__ for a class"""
-
- def __repr__(self):
- formatted_value = pprint.pformat(self.__dict__, width=119, compact=True)
- if "\n" in formatted_value:
- return f"{self.__class__.__name__}: {{ \n{textwrap.indent(formatted_value, ' ')}\n}}"
- else:
- return f"{self.__class__.__name__}: {formatted_value}"
-
-
-def repo_type_and_id_from_hf_id(hf_id: str, hub_url: Optional[str] = None) -> Tuple[Optional[str], Optional[str], str]:
- """
- Returns the repo type and ID from a huggingface.co URL linking to a
- repository
-
- Args:
- hf_id (`str`):
- An URL or ID of a repository on the HF hub. Accepted values are:
-
- - https://huggingface.co///
- - https://huggingface.co//
- - hf:////
- - hf:///
- - //
- - /
- -
- hub_url (`str`, *optional*):
- The URL of the HuggingFace Hub, defaults to https://huggingface.co
-
- Returns:
- A tuple with three items: repo_type (`str` or `None`), namespace (`str` or
- `None`) and repo_id (`str`).
-
- Raises:
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- If URL cannot be parsed.
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- If `repo_type` is unknown.
- """
- input_hf_id = hf_id
- hub_url = re.sub(r"https?://", "", hub_url if hub_url is not None else ENDPOINT)
- is_hf_url = hub_url in hf_id and "@" not in hf_id
-
- HFFS_PREFIX = "hf://"
- if hf_id.startswith(HFFS_PREFIX): # Remove "hf://" prefix if exists
- hf_id = hf_id[len(HFFS_PREFIX) :]
-
- url_segments = hf_id.split("/")
- is_hf_id = len(url_segments) <= 3
-
- namespace: Optional[str]
- if is_hf_url:
- namespace, repo_id = url_segments[-2:]
- if namespace == hub_url:
- namespace = None
- if len(url_segments) > 2 and hub_url not in url_segments[-3]:
- repo_type = url_segments[-3]
- elif namespace in REPO_TYPES_MAPPING:
- # Mean canonical dataset or model
- repo_type = REPO_TYPES_MAPPING[namespace]
- namespace = None
- else:
- repo_type = None
- elif is_hf_id:
- if len(url_segments) == 3:
- # Passed // or //
- repo_type, namespace, repo_id = url_segments[-3:]
- elif len(url_segments) == 2:
- if url_segments[0] in REPO_TYPES_MAPPING:
- # Passed '' or 'datasets/' for a canonical model or dataset
- repo_type = REPO_TYPES_MAPPING[url_segments[0]]
- namespace = None
- repo_id = hf_id.split("/")[-1]
- else:
- # Passed / or /
- namespace, repo_id = hf_id.split("/")[-2:]
- repo_type = None
- else:
- # Passed
- repo_id = url_segments[0]
- namespace, repo_type = None, None
- else:
- raise ValueError(f"Unable to retrieve user and repo ID from the passed HF ID: {hf_id}")
-
- # Check if repo type is known (mapping "spaces" => "space" + empty value => `None`)
- if repo_type in REPO_TYPES_MAPPING:
- repo_type = REPO_TYPES_MAPPING[repo_type]
- if repo_type == "":
- repo_type = None
- if repo_type not in REPO_TYPES:
- raise ValueError(f"Unknown `repo_type`: '{repo_type}' ('{input_hf_id}')")
-
- return repo_type, namespace, repo_id
-
-
-class BlobLfsInfo(TypedDict, total=False):
- size: int
- sha256: str
- pointer_size: int
-
-
-@dataclass
-class CommitInfo:
- """Data structure containing information about a newly created commit.
-
- Returned by [`create_commit`].
-
- Args:
- commit_url (`str`):
- Url where to find the commit.
-
- commit_message (`str`):
- The summary (first line) of the commit that has been created.
-
- commit_description (`str`):
- Description of the commit that has been created. Can be empty.
-
- oid (`str`):
- Commit hash id. Example: `"91c54ad1727ee830252e457677f467be0bfd8a57"`.
-
- pr_url (`str`, *optional*):
- Url to the PR that has been created, if any. Populated when `create_pr=True`
- is passed.
-
- pr_revision (`str`, *optional*):
- Revision of the PR that has been created, if any. Populated when
- `create_pr=True` is passed. Example: `"refs/pr/1"`.
-
- pr_num (`int`, *optional*):
- Number of the PR discussion that has been created, if any. Populated when
- `create_pr=True` is passed. Can be passed as `discussion_num` in
- [`get_discussion_details`]. Example: `1`.
- """
-
- commit_url: str
- commit_message: str
- commit_description: str
- oid: str
- pr_url: Optional[str] = None
-
- # Computed from `pr_url` in `__post_init__`
- pr_revision: Optional[str] = field(init=False)
- pr_num: Optional[str] = field(init=False)
-
- def __post_init__(self):
- """Populate pr-related fields after initialization.
-
- See https://docs.python.org/3.10/library/dataclasses.html#post-init-processing.
- """
- if self.pr_url is not None:
- self.pr_revision = _parse_revision_from_pr_url(self.pr_url)
- self.pr_num = int(self.pr_revision.split("/")[-1])
- else:
- self.pr_revision = None
- self.pr_num = None
-
-
-class RepoUrl(str):
- """Subclass of `str` describing a repo URL on the Hub.
-
- `RepoUrl` is returned by `HfApi.create_repo`. It inherits from `str` for backward
- compatibility. At initialization, the URL is parsed to populate properties:
- - endpoint (`str`)
- - namespace (`Optional[str]`)
- - repo_name (`str`)
- - repo_id (`str`)
- - repo_type (`Literal["model", "dataset", "space"]`)
- - url (`str`)
-
- Args:
- url (`Any`):
- String value of the repo url.
- endpoint (`str`, *optional*):
- Endpoint of the Hub. Defaults to .
-
- Example:
- ```py
- >>> RepoUrl('https://huggingface.co/gpt2')
- RepoUrl('https://huggingface.co/gpt2', endpoint='https://huggingface.co', repo_type='model', repo_id='gpt2')
-
- >>> RepoUrl('https://hub-ci.huggingface.co/datasets/dummy_user/dummy_dataset', endpoint='https://hub-ci.huggingface.co')
- RepoUrl('https://hub-ci.huggingface.co/datasets/dummy_user/dummy_dataset', endpoint='https://hub-ci.huggingface.co', repo_type='dataset', repo_id='dummy_user/dummy_dataset')
-
- >>> RepoUrl('hf://datasets/my-user/my-dataset')
- RepoUrl('hf://datasets/my-user/my-dataset', endpoint='https://huggingface.co', repo_type='dataset', repo_id='user/dataset')
-
- >>> HfApi.create_repo("dummy_model")
- RepoUrl('https://huggingface.co/Wauplin/dummy_model', endpoint='https://huggingface.co', repo_type='model', repo_id='Wauplin/dummy_model')
- ```
-
- Raises:
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- If URL cannot be parsed.
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- If `repo_type` is unknown.
- """
-
- def __new__(cls, url: Any, endpoint: Optional[str] = None):
- return super(RepoUrl, cls).__new__(cls, url)
-
- def __init__(self, url: Any, endpoint: Optional[str] = None) -> None:
- super().__init__()
- # Parse URL
- self.endpoint = endpoint or ENDPOINT
- repo_type, namespace, repo_name = repo_type_and_id_from_hf_id(self, hub_url=self.endpoint)
-
- # Populate fields
- self.namespace = namespace
- self.repo_name = repo_name
- self.repo_id = repo_name if namespace is None else f"{namespace}/{repo_name}"
- self.repo_type = repo_type or REPO_TYPE_MODEL
- self.url = str(self) # just in case it's needed
-
- def __repr__(self) -> str:
- return f"RepoUrl('{self}', endpoint='{self.endpoint}', repo_type='{self.repo_type}', repo_id='{self.repo_id}')"
-
-
-class RepoFile(ReprMixin):
- """
- Data structure that represents a public file inside a repo, accessible from huggingface.co
-
- Args:
- rfilename (str):
- file name, relative to the repo root. This is the only attribute that's guaranteed to be here, but under
- certain conditions there can certain other stuff.
- size (`int`, *optional*):
- The file's size, in bytes. This attribute is present when `files_metadata` argument of [`repo_info`] is set
- to `True`. It's `None` otherwise.
- blob_id (`str`, *optional*):
- The file's git OID. This attribute is present when `files_metadata` argument of [`repo_info`] is set to
- `True`. It's `None` otherwise.
- lfs (`BlobLfsInfo`, *optional*):
- The file's LFS metadata. This attribute is present when`files_metadata` argument of [`repo_info`] is set to
- `True` and the file is stored with Git LFS. It's `None` otherwise.
- """
-
- def __init__(
- self,
- rfilename: str,
- size: Optional[int] = None,
- blobId: Optional[str] = None,
- lfs: Optional[BlobLfsInfo] = None,
- **kwargs,
- ):
- self.rfilename = rfilename # filename relative to the repo root
-
- # Optional file metadata
- self.size = size
- self.blob_id = blobId
- self.lfs = lfs
-
- # Hack to ensure backward compatibility with future versions of the API.
- # See discussion in https://github.com/huggingface/huggingface_hub/pull/951#discussion_r926460408
- for k, v in kwargs.items():
- setattr(self, k, v)
-
-
-class ModelInfo(ReprMixin):
- """
- Info about a model accessible from huggingface.co
-
- Attributes:
- modelId (`str`, *optional*):
- ID of model repository.
- sha (`str`, *optional*):
- repo sha at this particular revision
- lastModified (`str`, *optional*):
- date of last commit to repo
- tags (`List[str]`, *optional*):
- List of tags.
- pipeline_tag (`str`, *optional*):
- Pipeline tag to identify the correct widget.
- siblings (`List[RepoFile]`, *optional*):
- list of ([`huggingface_hub.hf_api.RepoFile`]) objects that constitute the model.
- private (`bool`, *optional*, defaults to `False`):
- is the repo private
- author (`str`, *optional*):
- repo author
- config (`Dict`, *optional*):
- Model configuration information
- securityStatus (`Dict`, *optional*):
- Security status of the model.
- Example: `{"containsInfected": False}`
- kwargs (`Dict`, *optional*):
- Kwargs that will be become attributes of the class.
- """
-
- def __init__(
- self,
- *,
- modelId: Optional[str] = None,
- sha: Optional[str] = None,
- lastModified: Optional[str] = None,
- tags: Optional[List[str]] = None,
- pipeline_tag: Optional[str] = None,
- siblings: Optional[List[Dict]] = None,
- private: bool = False,
- author: Optional[str] = None,
- config: Optional[Dict] = None,
- securityStatus: Optional[Dict] = None,
- **kwargs,
- ):
- self.modelId = modelId
- self.sha = sha
- self.lastModified = lastModified
- self.tags = tags
- self.pipeline_tag = pipeline_tag
- self.siblings = [RepoFile(**x) for x in siblings] if siblings is not None else []
- self.private = private
- self.author = author
- self.config = config
- self.securityStatus = securityStatus
- for k, v in kwargs.items():
- setattr(self, k, v)
-
- def __str__(self):
- r = f"Model Name: {self.modelId}, Tags: {self.tags}"
- if self.pipeline_tag:
- r += f", Task: {self.pipeline_tag}"
- return r
-
-
-class DatasetInfo(ReprMixin):
- """
- Info about a dataset accessible from huggingface.co
-
- Attributes:
- id (`str`, *optional*):
- ID of dataset repository.
- sha (`str`, *optional*):
- repo sha at this particular revision
- lastModified (`str`, *optional*):
- date of last commit to repo
- tags (`List[str]`, *optional*):
- List of tags.
- siblings (`List[RepoFile]`, *optional*):
- list of [`huggingface_hub.hf_api.RepoFile`] objects that constitute the dataset.
- private (`bool`, *optional*, defaults to `False`):
- is the repo private
- author (`str`, *optional*):
- repo author
- description (`str`, *optional*):
- Description of the dataset
- citation (`str`, *optional*):
- Dataset citation
- cardData (`Dict`, *optional*):
- Metadata of the model card as a dictionary.
- kwargs (`Dict`, *optional*):
- Kwargs that will be become attributes of the class.
- """
-
- def __init__(
- self,
- *,
- id: Optional[str] = None,
- sha: Optional[str] = None,
- lastModified: Optional[str] = None,
- tags: Optional[List[str]] = None,
- siblings: Optional[List[Dict]] = None,
- private: bool = False,
- author: Optional[str] = None,
- description: Optional[str] = None,
- citation: Optional[str] = None,
- cardData: Optional[dict] = None,
- **kwargs,
- ):
- self.id = id
- self.sha = sha
- self.lastModified = lastModified
- self.tags = tags
- self.private = private
- self.author = author
- self.description = description
- self.citation = citation
- self.cardData = cardData
- self.siblings = [RepoFile(**x) for x in siblings] if siblings is not None else []
- # Legacy stuff, "key" is always returned with an empty string
- # because of old versions of the datasets lib that need this field
- kwargs.pop("key", None)
- # Store all the other fields returned by the API
- for k, v in kwargs.items():
- setattr(self, k, v)
-
- def __str__(self):
- r = f"Dataset Name: {self.id}, Tags: {self.tags}"
- return r
-
-
-class SpaceInfo(ReprMixin):
- """
- Info about a Space accessible from huggingface.co
-
- This is a "dataclass" like container that just sets on itself any attribute
- passed by the server.
-
- Attributes:
- id (`str`, *optional*):
- id of space
- sha (`str`, *optional*):
- repo sha at this particular revision
- lastModified (`str`, *optional*):
- date of last commit to repo
- siblings (`List[RepoFile]`, *optional*):
- list of [`huggingface_hub.hf_api.RepoFIle`] objects that constitute the Space
- private (`bool`, *optional*, defaults to `False`):
- is the repo private
- author (`str`, *optional*):
- repo author
- kwargs (`Dict`, *optional*):
- Kwargs that will be become attributes of the class.
- """
-
- def __init__(
- self,
- *,
- id: Optional[str] = None,
- sha: Optional[str] = None,
- lastModified: Optional[str] = None,
- siblings: Optional[List[Dict]] = None,
- private: bool = False,
- author: Optional[str] = None,
- **kwargs,
- ):
- self.id = id
- self.sha = sha
- self.lastModified = lastModified
- self.siblings = [RepoFile(**x) for x in siblings] if siblings is not None else []
- self.private = private
- self.author = author
- for k, v in kwargs.items():
- setattr(self, k, v)
-
-
-class MetricInfo(ReprMixin):
- """
- Info about a public metric accessible from huggingface.co
- """
-
- def __init__(
- self,
- *,
- id: Optional[str] = None, # id of metric
- description: Optional[str] = None,
- citation: Optional[str] = None,
- **kwargs,
- ):
- self.id = id
- self.description = description
- self.citation = citation
- # Legacy stuff, "key" is always returned with an empty string
- # because of old versions of the datasets lib that need this field
- kwargs.pop("key", None)
- # Store all the other fields returned by the API
- for k, v in kwargs.items():
- setattr(self, k, v)
-
- def __str__(self):
- r = f"Metric Name: {self.id}"
- return r
-
-
-class ModelSearchArguments(AttributeDictionary):
- """
- A nested namespace object holding all possible values for properties of
- models currently hosted in the Hub with tab-completion. If a value starts
- with a number, it will only exist in the dictionary
-
- Example:
-
- ```python
- >>> args = ModelSearchArguments()
-
- >>> args.author.huggingface
- 'huggingface'
-
- >>> args.language.en
- 'en'
- ```
-
-
-
- `ModelSearchArguments` is a legacy class meant for exploratory purposes only. Its
- initialization requires listing all models on the Hub which makes it increasingly
- slower as the number of repos on the Hub increases.
-
-
- """
-
- def __init__(self, api: Optional["HfApi"] = None):
- self._api = api if api is not None else HfApi()
- tags = self._api.get_model_tags()
- super().__init__(tags)
- self._process_models()
-
- def _process_models(self):
- def clean(s: str) -> str:
- return s.replace(" ", "").replace("-", "_").replace(".", "_")
-
- models = self._api.list_models()
- author_dict, model_name_dict = AttributeDictionary(), AttributeDictionary()
- for model in models:
- if "/" in model.modelId:
- author, name = model.modelId.split("/")
- author_dict[author] = clean(author)
- else:
- name = model.modelId
- model_name_dict[name] = clean(name)
- self["model_name"] = model_name_dict
- self["author"] = author_dict
-
-
-class DatasetSearchArguments(AttributeDictionary):
- """
- A nested namespace object holding all possible values for properties of
- datasets currently hosted in the Hub with tab-completion. If a value starts
- with a number, it will only exist in the dictionary
-
- Example:
-
- ```python
- >>> args = DatasetSearchArguments()
-
- >>> args.author.huggingface
- 'huggingface'
-
- >>> args.language.en
- 'language:en'
- ```
-
-
-
- `DatasetSearchArguments` is a legacy class meant for exploratory purposes only. Its
- initialization requires listing all datasets on the Hub which makes it increasingly
- slower as the number of repos on the Hub increases.
-
-
- """
-
- def __init__(self, api: Optional["HfApi"] = None):
- self._api = api if api is not None else HfApi()
- tags = self._api.get_dataset_tags()
- super().__init__(tags)
- self._process_models()
-
- def _process_models(self):
- def clean(s: str):
- return s.replace(" ", "").replace("-", "_").replace(".", "_")
-
- datasets = self._api.list_datasets()
- author_dict, dataset_name_dict = AttributeDictionary(), AttributeDictionary()
- for dataset in datasets:
- if "/" in dataset.id:
- author, name = dataset.id.split("/")
- author_dict[author] = clean(author)
- else:
- name = dataset.id
- dataset_name_dict[name] = clean(name)
- self["dataset_name"] = dataset_name_dict
- self["author"] = author_dict
-
-
-@dataclass
-class GitRefInfo:
- """
- Contains information about a git reference for a repo on the Hub.
-
- Args:
- name (`str`):
- Name of the reference (e.g. tag name or branch name).
- ref (`str`):
- Full git ref on the Hub (e.g. `"refs/heads/main"` or `"refs/tags/v1.0"`).
- target_commit (`str`):
- OID of the target commit for the ref (e.g. `"e7da7f221d5bf496a48136c0cd264e630fe9fcc8"`)
- """
-
- name: str
- ref: str
- target_commit: str
-
- def __init__(self, data: Dict) -> None:
- self.name = data["name"]
- self.ref = data["ref"]
- self.target_commit = data["targetCommit"]
-
-
-@dataclass
-class GitRefs:
- """
- Contains information about all git references for a repo on the Hub.
-
- Object is returned by [`list_repo_refs`].
-
- Args:
- branches (`List[GitRefInfo]`):
- A list of [`GitRefInfo`] containing information about branches on the repo.
- converts (`List[GitRefInfo]`):
- A list of [`GitRefInfo`] containing information about "convert" refs on the repo.
- Converts are refs used (internally) to push preprocessed data in Dataset repos.
- tags (`List[GitRefInfo]`):
- A list of [`GitRefInfo`] containing information about tags on the repo.
- """
-
- branches: List[GitRefInfo]
- converts: List[GitRefInfo]
- tags: List[GitRefInfo]
-
-
-@dataclass
-class GitCommitInfo:
- """
- Contains information about a git commit for a repo on the Hub. Check out [`list_repo_commits`] for more details.
-
- Args:
- commit_id (`str`):
- OID of the commit (e.g. `"e7da7f221d5bf496a48136c0cd264e630fe9fcc8"`)
- authors (`List[str]`):
- List of authors of the commit.
- created_at (`datetime`):
- Datetime when the commit was created.
- title (`str`):
- Title of the commit. This is a free-text value entered by the authors.
- message (`str`):
- Description of the commit. This is a free-text value entered by the authors.
- formatted_title (`str`):
- Title of the commit formatted as HTML. Only returned if `formatted=True` is set.
- formatted_message (`str`):
- Description of the commit formatted as HTML. Only returned if `formatted=True` is set.
- """
-
- commit_id: str
-
- authors: List[str]
- created_at: datetime
- title: str
- message: str
-
- formatted_title: Optional[str]
- formatted_message: Optional[str]
-
- def __init__(self, data: Dict) -> None:
- self.commit_id = data["id"]
- self.authors = [author["user"] for author in data["authors"]]
- self.created_at = parse_datetime(data["date"])
- self.title = data["title"]
- self.message = data["message"]
-
- self.formatted_title = data.get("formatted", {}).get("title")
- self.formatted_message = data.get("formatted", {}).get("message")
-
-
-@dataclass
-class UserLikes:
- """
- Contains information about a user likes on the Hub.
-
- Args:
- user (`str`):
- Name of the user for which we fetched the likes.
- total (`int`):
- Total number of likes.
- datasets (`List[str]`):
- List of datasets liked by the user (as repo_ids).
- models (`List[str]`):
- List of models liked by the user (as repo_ids).
- spaces (`List[str]`):
- List of spaces liked by the user (as repo_ids).
- """
-
- # Metadata
- user: str
- total: int
-
- # User likes
- datasets: List[str]
- models: List[str]
- spaces: List[str]
-
-
-def future_compatible(fn: CallableT) -> CallableT:
- """Wrap a method of `HfApi` to handle `run_as_future=True`.
-
- A method flagged as "future_compatible" will be called in a thread if `run_as_future=True` and return a
- `concurrent.futures.Future` instance. Otherwise, it will be called normally and return the result.
- """
- sig = inspect.signature(fn)
- args_params = list(sig.parameters)[1:] # remove "self" from list
-
- @wraps(fn)
- def _inner(self, *args, **kwargs):
- # Get `run_as_future` value if provided (default to False)
- if "run_as_future" in kwargs:
- run_as_future = kwargs["run_as_future"]
- kwargs["run_as_future"] = False # avoid recursion error
- else:
- run_as_future = False
- for param, value in zip(args_params, args):
- if param == "run_as_future":
- run_as_future = value
- break
-
- # Call the function in a thread if `run_as_future=True`
- if run_as_future:
- return self.run_as_future(fn, self, *args, **kwargs)
-
- # Otherwise, call the function normally
- return fn(self, *args, **kwargs)
-
- _inner.is_future_compatible = True # type: ignore
- return _inner # type: ignore
-
-
-class HfApi:
- def __init__(
- self,
- endpoint: Optional[str] = None,
- token: Optional[str] = None,
- library_name: Optional[str] = None,
- library_version: Optional[str] = None,
- user_agent: Union[Dict, str, None] = None,
- ) -> None:
- """Create a HF client to interact with the Hub via HTTP.
-
- The client is initialized with some high-level settings used in all requests
- made to the Hub (HF endpoint, authentication, user agents...). Using the `HfApi`
- client is preferred but not mandatory as all of its public methods are exposed
- directly at the root of `huggingface_hub`.
-
- Args:
- endpoint (`str`, *optional*):
- Hugging Face Hub base url. Will default to https://huggingface.co/. To
- be set if you are using a private hub. Otherwise, one can set the
- `HF_ENDPOINT` environment variable.
- token (`str`, *optional*):
- Hugging Face token. Will default to the locally saved token if
- not provided.
- library_name (`str`, *optional*):
- The name of the library that is making the HTTP request. Will be added to
- the user-agent header. Example: `"transformers"`.
- library_version (`str`, *optional*):
- The version of the library that is making the HTTP request. Will be added
- to the user-agent header. Example: `"4.24.0"`.
- user_agent (`str`, `dict`, *optional*):
- The user agent info in the form of a dictionary or a single string. It will
- be completed with information about the installed packages.
- """
- self.endpoint = endpoint if endpoint is not None else ENDPOINT
- self.token = token
- self.library_name = library_name
- self.library_version = library_version
- self.user_agent = user_agent
- self._thread_pool: Optional[ThreadPoolExecutor] = None
-
- def run_as_future(self, fn: Callable[..., R], *args, **kwargs) -> Future[R]:
- """
- Run a method in the background and return a Future instance.
-
- The main goal is to run methods without blocking the main thread (e.g. to push data during a training).
- Background jobs are queued to preserve order but are not ran in parallel. If you need to speed-up your scripts
- by parallelizing lots of call to the API, you must setup and use your own [ThreadPoolExecutor](https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor).
-
- Note: Most-used methods like [`upload_file`], [`upload_folder`] and [`create_commit`] have a `run_as_future: bool`
- argument to directly call them in the background. This is equivalent to calling `api.run_as_future(...)` on them
- but less verbose.
-
- Args:
- fn (`Callable`):
- The method to run in the background.
- *args, **kwargs:
- Arguments with which the method will be called.
-
- Return:
- `Future`: a [Future](https://docs.python.org/3/library/concurrent.futures.html#future-objects) instance to
- get the result of the task.
-
- Example:
- ```py
- >>> from huggingface_hub import HfApi
- >>> api = HfApi()
- >>> future = api.run_as_future(api.whoami) # instant
- >>> future.done()
- False
- >>> future.result() # wait until complete and return result
- (...)
- >>> future.done()
- True
- ```
- """
- if self._thread_pool is None:
- self._thread_pool = ThreadPoolExecutor(max_workers=1)
- self._thread_pool
- return self._thread_pool.submit(fn, *args, **kwargs)
-
- @validate_hf_hub_args
- def whoami(self, token: Optional[str] = None) -> Dict:
- """
- Call HF API to know "whoami".
-
- Args:
- token (`str`, *optional*):
- Hugging Face token. Will default to the locally saved token if
- not provided.
- """
- r = get_session().get(
- f"{self.endpoint}/api/whoami-v2",
- headers=self._build_hf_headers(
- # If `token` is provided and not `None`, it will be used by default.
- # Otherwise, the token must be retrieved from cache or env variable.
- token=(token or self.token or True),
- ),
- )
- try:
- hf_raise_for_status(r)
- except HTTPError as e:
- raise HTTPError(
- "Invalid user token. If you didn't pass a user token, make sure you "
- "are properly logged in by executing `huggingface-cli login`, and "
- "if you did pass a user token, double-check it's correct."
- ) from e
- return r.json()
-
- def get_token_permission(self, token: Optional[str] = None) -> Literal["read", "write", None]:
- """
- Check if a given `token` is valid and return its permissions.
-
- For more details about tokens, please refer to https://huggingface.co/docs/hub/security-tokens#what-are-user-access-tokens.
-
- Args:
- token (`str`, *optional*):
- The token to check for validity. Defaults to the one saved locally.
-
- Returns:
- `Literal["read", "write", None]`: Permission granted by the token ("read" or "write"). Returns `None` if no
- token passed or token is invalid.
- """
- try:
- return self.whoami(token=token)["auth"]["accessToken"]["role"]
- except (LocalTokenNotFoundError, HTTPError):
- return None
-
- def get_model_tags(self) -> ModelTags:
- """
- List all valid model tags as a nested namespace object
- """
- path = f"{self.endpoint}/api/models-tags-by-type"
- r = get_session().get(path)
- hf_raise_for_status(r)
- d = r.json()
- return ModelTags(d)
-
- def get_dataset_tags(self) -> DatasetTags:
- """
- List all valid dataset tags as a nested namespace object.
- """
- path = f"{self.endpoint}/api/datasets-tags-by-type"
- r = get_session().get(path)
- hf_raise_for_status(r)
- d = r.json()
- return DatasetTags(d)
-
- @validate_hf_hub_args
- def list_models(
- self,
- *,
- filter: Union[ModelFilter, str, Iterable[str], None] = None,
- author: Optional[str] = None,
- search: Optional[str] = None,
- emissions_thresholds: Optional[Tuple[float, float]] = None,
- sort: Union[Literal["lastModified"], str, None] = None,
- direction: Optional[Literal[-1]] = None,
- limit: Optional[int] = None,
- full: Optional[bool] = None,
- cardData: bool = False,
- fetch_config: bool = False,
- token: Optional[Union[bool, str]] = None,
- ) -> Iterable[ModelInfo]:
- """
- List models hosted on the Huggingface Hub, given some filters.
-
- Args:
- filter ([`ModelFilter`] or `str` or `Iterable`, *optional*):
- A string or [`ModelFilter`] which can be used to identify models
- on the Hub.
- author (`str`, *optional*):
- A string which identify the author (user or organization) of the
- returned models
- search (`str`, *optional*):
- A string that will be contained in the returned model ids.
- emissions_thresholds (`Tuple`, *optional*):
- A tuple of two ints or floats representing a minimum and maximum
- carbon footprint to filter the resulting models with in grams.
- sort (`Literal["lastModified"]` or `str`, *optional*):
- The key with which to sort the resulting models. Possible values
- are the properties of the [`huggingface_hub.hf_api.ModelInfo`] class.
- direction (`Literal[-1]` or `int`, *optional*):
- Direction in which to sort. The value `-1` sorts by descending
- order while all other values sort by ascending order.
- limit (`int`, *optional*):
- The limit on the number of models fetched. Leaving this option
- to `None` fetches all models.
- full (`bool`, *optional*):
- Whether to fetch all model data, including the `lastModified`,
- the `sha`, the files and the `tags`. This is set to `True` by
- default when using a filter.
- cardData (`bool`, *optional*):
- Whether to grab the metadata for the model as well. Can contain
- useful information such as carbon emissions, metrics, and
- datasets trained on.
- fetch_config (`bool`, *optional*):
- Whether to fetch the model configs as well. This is not included
- in `full` due to its size.
- token (`bool` or `str`, *optional*):
- A valid authentication token (see https://huggingface.co/settings/token).
- If `None` or `True` and machine is logged in (through `huggingface-cli login`
- or [`~huggingface_hub.login`]), token will be retrieved from the cache.
- If `False`, token is not sent in the request header.
-
- Returns:
- `Iterable[ModelInfo]`: an iterable of [`huggingface_hub.hf_api.ModelInfo`] objects.
-
- Example usage with the `filter` argument:
-
- ```python
- >>> from huggingface_hub import HfApi
-
- >>> api = HfApi()
-
- >>> # List all models
- >>> api.list_models()
-
- >>> # Get all valid search arguments
- >>> args = ModelSearchArguments()
-
- >>> # List only the text classification models
- >>> api.list_models(filter="text-classification")
- >>> # Using the `ModelFilter`
- >>> filt = ModelFilter(task="text-classification")
- >>> # With `ModelSearchArguments`
- >>> filt = ModelFilter(task=args.pipeline_tags.TextClassification)
- >>> api.list_models(filter=filt)
-
- >>> # Using `ModelFilter` and `ModelSearchArguments` to find text classification in both PyTorch and TensorFlow
- >>> filt = ModelFilter(
- ... task=args.pipeline_tags.TextClassification,
- ... library=[args.library.PyTorch, args.library.TensorFlow],
- ... )
- >>> api.list_models(filter=filt)
-
- >>> # List only models from the AllenNLP library
- >>> api.list_models(filter="allennlp")
- >>> # Using `ModelFilter` and `ModelSearchArguments`
- >>> filt = ModelFilter(library=args.library.allennlp)
- ```
-
- Example usage with the `search` argument:
-
- ```python
- >>> from huggingface_hub import HfApi
-
- >>> api = HfApi()
-
- >>> # List all models with "bert" in their name
- >>> api.list_models(search="bert")
-
- >>> # List all models with "bert" in their name made by google
- >>> api.list_models(search="bert", author="google")
- ```
- """
- if emissions_thresholds is not None and cardData is None:
- raise ValueError("`emissions_thresholds` were passed without setting `cardData=True`.")
-
- path = f"{self.endpoint}/api/models"
- headers = self._build_hf_headers(token=token)
- params = {}
- if filter is not None:
- if isinstance(filter, ModelFilter):
- params = self._unpack_model_filter(filter)
- else:
- params.update({"filter": filter})
- params.update({"full": True})
- if author is not None:
- params.update({"author": author})
- if search is not None:
- params.update({"search": search})
- if sort is not None:
- params.update({"sort": sort})
- if direction is not None:
- params.update({"direction": direction})
- if limit is not None:
- params.update({"limit": limit})
- if full is not None:
- if full:
- params.update({"full": True})
- elif "full" in params:
- del params["full"]
- if fetch_config:
- params.update({"config": True})
- if cardData:
- params.update({"cardData": True})
-
- # `items` is a generator
- items = paginate(path, params=params, headers=headers)
- if limit is not None:
- items = islice(items, limit) # Do not iterate over all pages
- if emissions_thresholds is not None:
- items = _filter_emissions(items, *emissions_thresholds)
- for item in items:
- yield ModelInfo(**item)
-
- def _unpack_model_filter(self, model_filter: ModelFilter):
- """
- Unpacks a [`ModelFilter`] into something readable for `list_models`
- """
- model_str = ""
- tags = []
-
- # Handling author
- if model_filter.author is not None:
- model_str = f"{model_filter.author}/"
-
- # Handling model_name
- if model_filter.model_name is not None:
- model_str += model_filter.model_name
-
- filter_list: List[str] = []
-
- # Handling tasks
- if model_filter.task is not None:
- filter_list.extend([model_filter.task] if isinstance(model_filter.task, str) else model_filter.task)
-
- # Handling dataset
- if model_filter.trained_dataset is not None:
- if not isinstance(model_filter.trained_dataset, (list, tuple)):
- model_filter.trained_dataset = [model_filter.trained_dataset]
- for dataset in model_filter.trained_dataset:
- if "dataset:" not in dataset:
- dataset = f"dataset:{dataset}"
- filter_list.append(dataset)
-
- # Handling library
- if model_filter.library:
- filter_list.extend(
- [model_filter.library] if isinstance(model_filter.library, str) else model_filter.library
- )
-
- # Handling tags
- if model_filter.tags:
- tags.extend([model_filter.tags] if isinstance(model_filter.tags, str) else model_filter.tags)
-
- query_dict: Dict[str, Any] = {}
- if model_str is not None:
- query_dict["search"] = model_str
- if len(tags) > 0:
- query_dict["tags"] = tags
- if isinstance(model_filter.language, list):
- filter_list.extend(model_filter.language)
- elif isinstance(model_filter.language, str):
- filter_list.append(model_filter.language)
- query_dict["filter"] = tuple(filter_list)
- return query_dict
-
- @validate_hf_hub_args
- def list_datasets(
- self,
- *,
- filter: Union[DatasetFilter, str, Iterable[str], None] = None,
- author: Optional[str] = None,
- search: Optional[str] = None,
- sort: Union[Literal["lastModified"], str, None] = None,
- direction: Optional[Literal[-1]] = None,
- limit: Optional[int] = None,
- full: Optional[bool] = None,
- token: Optional[str] = None,
- ) -> Iterable[DatasetInfo]:
- """
- List datasets hosted on the Huggingface Hub, given some filters.
-
- Args:
- filter ([`DatasetFilter`] or `str` or `Iterable`, *optional*):
- A string or [`DatasetFilter`] which can be used to identify
- datasets on the hub.
- author (`str`, *optional*):
- A string which identify the author of the returned datasets.
- search (`str`, *optional*):
- A string that will be contained in the returned datasets.
- sort (`Literal["lastModified"]` or `str`, *optional*):
- The key with which to sort the resulting datasets. Possible
- values are the properties of the [`huggingface_hub.hf_api.DatasetInfo`] class.
- direction (`Literal[-1]` or `int`, *optional*):
- Direction in which to sort. The value `-1` sorts by descending
- order while all other values sort by ascending order.
- limit (`int`, *optional*):
- The limit on the number of datasets fetched. Leaving this option
- to `None` fetches all datasets.
- full (`bool`, *optional*):
- Whether to fetch all dataset data, including the `lastModified`
- and the `cardData`. Can contain useful information such as the
- PapersWithCode ID.
- token (`bool` or `str`, *optional*):
- A valid authentication token (see https://huggingface.co/settings/token).
- If `None` or `True` and machine is logged in (through `huggingface-cli login`
- or [`~huggingface_hub.login`]), token will be retrieved from the cache.
- If `False`, token is not sent in the request header.
-
- Returns:
- `Iterable[DatasetInfo]`: an iterable of [`huggingface_hub.hf_api.DatasetInfo`] objects.
-
- Example usage with the `filter` argument:
-
- ```python
- >>> from huggingface_hub import HfApi
-
- >>> api = HfApi()
-
- >>> # List all datasets
- >>> api.list_datasets()
-
- >>> # Get all valid search arguments
- >>> args = DatasetSearchArguments()
-
- >>> # List only the text classification datasets
- >>> api.list_datasets(filter="task_categories:text-classification")
- >>> # Using the `DatasetFilter`
- >>> filt = DatasetFilter(task_categories="text-classification")
- >>> # With `DatasetSearchArguments`
- >>> filt = DatasetFilter(task=args.task_categories.text_classification)
- >>> api.list_models(filter=filt)
-
- >>> # List only the datasets in russian for language modeling
- >>> api.list_datasets(
- ... filter=("language:ru", "task_ids:language-modeling")
- ... )
- >>> # Using the `DatasetFilter`
- >>> filt = DatasetFilter(language="ru", task_ids="language-modeling")
- >>> # With `DatasetSearchArguments`
- >>> filt = DatasetFilter(
- ... language=args.language.ru,
- ... task_ids=args.task_ids.language_modeling,
- ... )
- >>> api.list_datasets(filter=filt)
- ```
-
- Example usage with the `search` argument:
-
- ```python
- >>> from huggingface_hub import HfApi
-
- >>> api = HfApi()
-
- >>> # List all datasets with "text" in their name
- >>> api.list_datasets(search="text")
-
- >>> # List all datasets with "text" in their name made by google
- >>> api.list_datasets(search="text", author="google")
- ```
- """
- path = f"{self.endpoint}/api/datasets"
- headers = self._build_hf_headers(token=token)
- params = {}
- if filter is not None:
- if isinstance(filter, DatasetFilter):
- params = self._unpack_dataset_filter(filter)
- else:
- params.update({"filter": filter})
- if author is not None:
- params.update({"author": author})
- if search is not None:
- params.update({"search": search})
- if sort is not None:
- params.update({"sort": sort})
- if direction is not None:
- params.update({"direction": direction})
- if limit is not None:
- params.update({"limit": limit})
- if full:
- params.update({"full": True})
-
- items = paginate(path, params=params, headers=headers)
- if limit is not None:
- items = islice(items, limit) # Do not iterate over all pages
- for item in items:
- yield DatasetInfo(**item)
-
- def _unpack_dataset_filter(self, dataset_filter: DatasetFilter):
- """
- Unpacks a [`DatasetFilter`] into something readable for `list_datasets`
- """
- dataset_str = ""
-
- # Handling author
- if dataset_filter.author is not None:
- dataset_str = f"{dataset_filter.author}/"
-
- # Handling dataset_name
- if dataset_filter.dataset_name is not None:
- dataset_str += dataset_filter.dataset_name
-
- filter_list = []
- data_attributes = [
- "benchmark",
- "language_creators",
- "language",
- "multilinguality",
- "size_categories",
- "task_categories",
- "task_ids",
- ]
-
- for attr in data_attributes:
- curr_attr = getattr(dataset_filter, attr)
- if curr_attr is not None:
- if not isinstance(curr_attr, (list, tuple)):
- curr_attr = [curr_attr]
- for data in curr_attr:
- if f"{attr}:" not in data:
- data = f"{attr}:{data}"
- filter_list.append(data)
-
- query_dict: Dict[str, Any] = {}
- if dataset_str is not None:
- query_dict["search"] = dataset_str
- query_dict["filter"] = tuple(filter_list)
- return query_dict
-
- def list_metrics(self) -> List[MetricInfo]:
- """
- Get the public list of all the metrics on huggingface.co
-
- Returns:
- `List[MetricInfo]`: a list of [`MetricInfo`] objects which.
- """
- path = f"{self.endpoint}/api/metrics"
- r = get_session().get(path)
- hf_raise_for_status(r)
- d = r.json()
- return [MetricInfo(**x) for x in d]
-
- @validate_hf_hub_args
- def list_spaces(
- self,
- *,
- filter: Union[str, Iterable[str], None] = None,
- author: Optional[str] = None,
- search: Optional[str] = None,
- sort: Union[Literal["lastModified"], str, None] = None,
- direction: Optional[Literal[-1]] = None,
- limit: Optional[int] = None,
- datasets: Union[str, Iterable[str], None] = None,
- models: Union[str, Iterable[str], None] = None,
- linked: bool = False,
- full: Optional[bool] = None,
- token: Optional[str] = None,
- ) -> Iterable[SpaceInfo]:
- """
- List spaces hosted on the Huggingface Hub, given some filters.
-
- Args:
- filter (`str` or `Iterable`, *optional*):
- A string tag or list of tags that can be used to identify Spaces on the Hub.
- author (`str`, *optional*):
- A string which identify the author of the returned Spaces.
- search (`str`, *optional*):
- A string that will be contained in the returned Spaces.
- sort (`Literal["lastModified"]` or `str`, *optional*):
- The key with which to sort the resulting Spaces. Possible
- values are the properties of the [`huggingface_hub.hf_api.SpaceInfo`]` class.
- direction (`Literal[-1]` or `int`, *optional*):
- Direction in which to sort. The value `-1` sorts by descending
- order while all other values sort by ascending order.
- limit (`int`, *optional*):
- The limit on the number of Spaces fetched. Leaving this option
- to `None` fetches all Spaces.
- datasets (`str` or `Iterable`, *optional*):
- Whether to return Spaces that make use of a dataset.
- The name of a specific dataset can be passed as a string.
- models (`str` or `Iterable`, *optional*):
- Whether to return Spaces that make use of a model.
- The name of a specific model can be passed as a string.
- linked (`bool`, *optional*):
- Whether to return Spaces that make use of either a model or a dataset.
- full (`bool`, *optional*):
- Whether to fetch all Spaces data, including the `lastModified`
- and the `cardData`.
- token (`bool` or `str`, *optional*):
- A valid authentication token (see https://huggingface.co/settings/token).
- If `None` or `True` and machine is logged in (through `huggingface-cli login`
- or [`~huggingface_hub.login`]), token will be retrieved from the cache.
- If `False`, token is not sent in the request header.
-
- Returns:
- `Iterable[SpaceInfo]`: an iterable of [`huggingface_hub.hf_api.SpaceInfo`] objects.
- """
- path = f"{self.endpoint}/api/spaces"
- headers = self._build_hf_headers(token=token)
- params: Dict[str, Any] = {}
- if filter is not None:
- params.update({"filter": filter})
- if author is not None:
- params.update({"author": author})
- if search is not None:
- params.update({"search": search})
- if sort is not None:
- params.update({"sort": sort})
- if direction is not None:
- params.update({"direction": direction})
- if limit is not None:
- params.update({"limit": limit})
- if full:
- params.update({"full": True})
- if linked:
- params.update({"linked": True})
- if datasets is not None:
- params.update({"datasets": datasets})
- if models is not None:
- params.update({"models": models})
-
- items = paginate(path, params=params, headers=headers)
- if limit is not None:
- items = islice(items, limit) # Do not iterate over all pages
- for item in items:
- yield SpaceInfo(**item)
-
- @validate_hf_hub_args
- def like(
- self,
- repo_id: str,
- *,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- ) -> None:
- """
- Like a given repo on the Hub (e.g. set as favorite).
-
- See also [`unlike`] and [`list_liked_repos`].
-
- Args:
- repo_id (`str`):
- The repository to like. Example: `"user/my-cool-model"`.
-
- token (`str`, *optional*):
- Authentication token. Will default to the stored token.
-
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if liking a dataset or space, `None` or
- `"model"` if liking a model. Default is `None`.
-
- Raises:
- [`~utils.RepositoryNotFoundError`]:
- If repository is not found (error 404): wrong repo_id/repo_type, private
- but not authenticated or repo does not exist.
-
- Example:
- ```python
- >>> from huggingface_hub import like, list_liked_repos, unlike
- >>> like("gpt2")
- >>> "gpt2" in list_liked_repos().models
- True
- >>> unlike("gpt2")
- >>> "gpt2" in list_liked_repos().models
- False
- ```
- """
- if repo_type is None:
- repo_type = REPO_TYPE_MODEL
- response = get_session().post(
- url=f"{self.endpoint}/api/{repo_type}s/{repo_id}/like",
- headers=self._build_hf_headers(token=token),
- )
- hf_raise_for_status(response)
-
- @validate_hf_hub_args
- def unlike(
- self,
- repo_id: str,
- *,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- ) -> None:
- """
- Unlike a given repo on the Hub (e.g. remove from favorite list).
-
- See also [`like`] and [`list_liked_repos`].
-
- Args:
- repo_id (`str`):
- The repository to unlike. Example: `"user/my-cool-model"`.
-
- token (`str`, *optional*):
- Authentication token. Will default to the stored token.
-
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if unliking a dataset or space, `None` or
- `"model"` if unliking a model. Default is `None`.
-
- Raises:
- [`~utils.RepositoryNotFoundError`]:
- If repository is not found (error 404): wrong repo_id/repo_type, private
- but not authenticated or repo does not exist.
-
- Example:
- ```python
- >>> from huggingface_hub import like, list_liked_repos, unlike
- >>> like("gpt2")
- >>> "gpt2" in list_liked_repos().models
- True
- >>> unlike("gpt2")
- >>> "gpt2" in list_liked_repos().models
- False
- ```
- """
- if repo_type is None:
- repo_type = REPO_TYPE_MODEL
- response = get_session().delete(
- url=f"{self.endpoint}/api/{repo_type}s/{repo_id}/like", headers=self._build_hf_headers(token=token)
- )
- hf_raise_for_status(response)
-
- @validate_hf_hub_args
- def list_liked_repos(
- self,
- user: Optional[str] = None,
- *,
- token: Optional[str] = None,
- ) -> UserLikes:
- """
- List all public repos liked by a user on huggingface.co.
-
- This list is public so token is optional. If `user` is not passed, it defaults to
- the logged in user.
-
- See also [`like`] and [`unlike`].
-
- Args:
- user (`str`, *optional*):
- Name of the user for which you want to fetch the likes.
- token (`str`, *optional*):
- A valid authentication token (see https://huggingface.co/settings/token).
- Used only if `user` is not passed to implicitly determine the current
- user name.
-
- Returns:
- [`UserLikes`]: object containing the user name and 3 lists of repo ids (1 for
- models, 1 for datasets and 1 for Spaces).
-
- Raises:
- [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- If `user` is not passed and no token found (either from argument or from machine).
-
- Example:
- ```python
- >>> from huggingface_hub import list_liked_repos
-
- >>> likes = list_liked_repos("julien-c")
-
- >>> likes.user
- "julien-c"
-
- >>> likes.models
- ["osanseviero/streamlit_1.15", "Xhaheen/ChatGPT_HF", ...]
- ```
- """
- # User is either provided explicitly or retrieved from current token.
- if user is None:
- me = self.whoami(token=token)
- if me["type"] == "user":
- user = me["name"]
- else:
- raise ValueError(
- "Cannot list liked repos. You must provide a 'user' as input or be logged in as a user."
- )
-
- path = f"{self.endpoint}/api/users/{user}/likes"
- headers = self._build_hf_headers(token=token)
-
- likes = list(paginate(path, params={}, headers=headers))
- # Looping over a list of items similar to:
- # {
- # 'createdAt': '2021-09-09T21:53:27.000Z',
- # 'repo': {
- # 'name': 'PaddlePaddle/PaddleOCR',
- # 'type': 'space'
- # }
- # }
- # Let's loop 3 times over the received list. Less efficient but more straightforward to read.
- return UserLikes(
- user=user,
- total=len(likes),
- models=[like["repo"]["name"] for like in likes if like["repo"]["type"] == "model"],
- datasets=[like["repo"]["name"] for like in likes if like["repo"]["type"] == "dataset"],
- spaces=[like["repo"]["name"] for like in likes if like["repo"]["type"] == "space"],
- )
-
- @validate_hf_hub_args
- def model_info(
- self,
- repo_id: str,
- *,
- revision: Optional[str] = None,
- timeout: Optional[float] = None,
- securityStatus: Optional[bool] = None,
- files_metadata: bool = False,
- token: Optional[Union[bool, str]] = None,
- ) -> ModelInfo:
- """
- Get info on one specific model on huggingface.co
-
- Model can be private if you pass an acceptable token or are logged in.
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- revision (`str`, *optional*):
- The revision of the model repository from which to get the
- information.
- timeout (`float`, *optional*):
- Whether to set a timeout for the request to the Hub.
- securityStatus (`bool`, *optional*):
- Whether to retrieve the security status from the model
- repository as well.
- files_metadata (`bool`, *optional*):
- Whether or not to retrieve metadata for files in the repository
- (size, LFS metadata, etc). Defaults to `False`.
- token (`bool` or `str`, *optional*):
- A valid authentication token (see https://huggingface.co/settings/token).
- If `None` or `True` and machine is logged in (through `huggingface-cli login`
- or [`~huggingface_hub.login`]), token will be retrieved from the cache.
- If `False`, token is not sent in the request header.
-
- Returns:
- [`huggingface_hub.hf_api.ModelInfo`]: The model repository information.
-
-
-
- Raises the following errors:
-
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
- - [`~utils.RevisionNotFoundError`]
- If the revision to download from cannot be found.
-
-
- """
- headers = self._build_hf_headers(token=token)
- path = (
- f"{self.endpoint}/api/models/{repo_id}"
- if revision is None
- else (f"{self.endpoint}/api/models/{repo_id}/revision/{quote(revision, safe='')}")
- )
- params = {}
- if securityStatus:
- params["securityStatus"] = True
- if files_metadata:
- params["blobs"] = True
- r = get_session().get(path, headers=headers, timeout=timeout, params=params)
- hf_raise_for_status(r)
- d = r.json()
- return ModelInfo(**d)
-
- @validate_hf_hub_args
- def dataset_info(
- self,
- repo_id: str,
- *,
- revision: Optional[str] = None,
- timeout: Optional[float] = None,
- files_metadata: bool = False,
- token: Optional[Union[bool, str]] = None,
- ) -> DatasetInfo:
- """
- Get info on one specific dataset on huggingface.co.
-
- Dataset can be private if you pass an acceptable token.
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- revision (`str`, *optional*):
- The revision of the dataset repository from which to get the
- information.
- timeout (`float`, *optional*):
- Whether to set a timeout for the request to the Hub.
- files_metadata (`bool`, *optional*):
- Whether or not to retrieve metadata for files in the repository
- (size, LFS metadata, etc). Defaults to `False`.
- token (`bool` or `str`, *optional*):
- A valid authentication token (see https://huggingface.co/settings/token).
- If `None` or `True` and machine is logged in (through `huggingface-cli login`
- or [`~huggingface_hub.login`]), token will be retrieved from the cache.
- If `False`, token is not sent in the request header.
-
- Returns:
- [`hf_api.DatasetInfo`]: The dataset repository information.
-
-
-
- Raises the following errors:
-
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
- - [`~utils.RevisionNotFoundError`]
- If the revision to download from cannot be found.
-
-
- """
- headers = self._build_hf_headers(token=token)
- path = (
- f"{self.endpoint}/api/datasets/{repo_id}"
- if revision is None
- else (f"{self.endpoint}/api/datasets/{repo_id}/revision/{quote(revision, safe='')}")
- )
- params = {}
- if files_metadata:
- params["blobs"] = True
-
- r = get_session().get(path, headers=headers, timeout=timeout, params=params)
- hf_raise_for_status(r)
- d = r.json()
- return DatasetInfo(**d)
-
- @validate_hf_hub_args
- def space_info(
- self,
- repo_id: str,
- *,
- revision: Optional[str] = None,
- timeout: Optional[float] = None,
- files_metadata: bool = False,
- token: Optional[Union[bool, str]] = None,
- ) -> SpaceInfo:
- """
- Get info on one specific Space on huggingface.co.
-
- Space can be private if you pass an acceptable token.
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- revision (`str`, *optional*):
- The revision of the space repository from which to get the
- information.
- timeout (`float`, *optional*):
- Whether to set a timeout for the request to the Hub.
- files_metadata (`bool`, *optional*):
- Whether or not to retrieve metadata for files in the repository
- (size, LFS metadata, etc). Defaults to `False`.
- token (`bool` or `str`, *optional*):
- A valid authentication token (see https://huggingface.co/settings/token).
- If `None` or `True` and machine is logged in (through `huggingface-cli login`
- or [`~huggingface_hub.login`]), token will be retrieved from the cache.
- If `False`, token is not sent in the request header.
-
- Returns:
- [`~hf_api.SpaceInfo`]: The space repository information.
-
-
-
- Raises the following errors:
-
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
- - [`~utils.RevisionNotFoundError`]
- If the revision to download from cannot be found.
-
-
- """
- headers = self._build_hf_headers(token=token)
- path = (
- f"{self.endpoint}/api/spaces/{repo_id}"
- if revision is None
- else (f"{self.endpoint}/api/spaces/{repo_id}/revision/{quote(revision, safe='')}")
- )
- params = {}
- if files_metadata:
- params["blobs"] = True
-
- r = get_session().get(path, headers=headers, timeout=timeout, params=params)
- hf_raise_for_status(r)
- d = r.json()
- return SpaceInfo(**d)
-
- @validate_hf_hub_args
- def repo_info(
- self,
- repo_id: str,
- *,
- revision: Optional[str] = None,
- repo_type: Optional[str] = None,
- timeout: Optional[float] = None,
- files_metadata: bool = False,
- token: Optional[Union[bool, str]] = None,
- ) -> Union[ModelInfo, DatasetInfo, SpaceInfo]:
- """
- Get the info object for a given repo of a given type.
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- revision (`str`, *optional*):
- The revision of the repository from which to get the
- information.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if getting repository info from a dataset or a space,
- `None` or `"model"` if getting repository info from a model. Default is `None`.
- timeout (`float`, *optional*):
- Whether to set a timeout for the request to the Hub.
- files_metadata (`bool`, *optional*):
- Whether or not to retrieve metadata for files in the repository
- (size, LFS metadata, etc). Defaults to `False`.
- token (`bool` or `str`, *optional*):
- A valid authentication token (see https://huggingface.co/settings/token).
- If `None` or `True` and machine is logged in (through `huggingface-cli login`
- or [`~huggingface_hub.login`]), token will be retrieved from the cache.
- If `False`, token is not sent in the request header.
-
- Returns:
- `Union[SpaceInfo, DatasetInfo, ModelInfo]`: The repository information, as a
- [`huggingface_hub.hf_api.DatasetInfo`], [`huggingface_hub.hf_api.ModelInfo`]
- or [`huggingface_hub.hf_api.SpaceInfo`] object.
-
-
-
- Raises the following errors:
-
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
- - [`~utils.RevisionNotFoundError`]
- If the revision to download from cannot be found.
-
-
- """
- if repo_type is None or repo_type == "model":
- method = self.model_info
- elif repo_type == "dataset":
- method = self.dataset_info # type: ignore
- elif repo_type == "space":
- method = self.space_info # type: ignore
- else:
- raise ValueError("Unsupported repo type.")
- return method(
- repo_id,
- revision=revision,
- token=token,
- timeout=timeout,
- files_metadata=files_metadata,
- )
-
- @validate_hf_hub_args
- def list_files_info(
- self,
- repo_id: str,
- paths: Union[List[str], str, None] = None,
- *,
- expand: bool = False,
- revision: Optional[str] = None,
- repo_type: Optional[str] = None,
- token: Optional[Union[bool, str]] = None,
- ) -> Iterable[RepoFile]:
- """
- List files on a repo and get information about them.
-
- Takes as input a list of paths. Those paths can be either files or folders. Two server endpoints are called:
- 1. POST "/paths-info" to get information about the provided paths. Called once.
- 2. GET "/tree?recursive=True" to paginate over the input folders. Called only if a folder path is provided as
- input. Will be called multiple times to follow pagination.
- If no path is provided as input, step 1. is ignored and all files from the repo are listed.
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated by a `/`.
- paths (`Union[List[str], str, None]`, *optional*):
- The paths to get information about. Paths to files are directly resolved. Paths to folders are resolved
- recursively which means that information is returned about all files in the folder and its subfolders.
- If `None`, all files are returned (the default). If a path do not exist, it is ignored without raising
- an exception.
- expand (`bool`, *optional*, defaults to `False`):
- Whether to fetch more information about the files (e.g. last commit and security scan results). This
- operation is more expensive for the server so only 50 results are returned per page (instead of 1000).
- As pagination is implemented in `huggingface_hub`, this is transparent for you except for the time it
- takes to get the results.
- revision (`str`, *optional*):
- The revision of the repository from which to get the information. Defaults to `"main"` branch.
- repo_type (`str`, *optional*):
- The type of the repository from which to get the information (`"model"`, `"dataset"` or `"space"`.
- Defaults to `"model"`.
- token (`bool` or `str`, *optional*):
- A valid authentication token (see https://huggingface.co/settings/token). If `None` or `True` and
- machine is logged in (through `huggingface-cli login` or [`~huggingface_hub.login`]), token will be
- retrieved from the cache. If `False`, token is not sent in the request header.
-
- Returns:
- `Iterable[RepoFile]`:
- The information about the files, as an iterable of [`RepoFile`] objects. The order of the files is
- not guaranteed.
-
- Raises:
- [`~utils.RepositoryNotFoundError`]:
- If repository is not found (error 404): wrong repo_id/repo_type, private but not authenticated or repo
- does not exist.
- [`~utils.RevisionNotFoundError`]:
- If revision is not found (error 404) on the repo.
-
- Examples:
-
- Get information about files on a repo.
- ```py
- >>> from huggingface_hub import list_files_info
- >>> files_info = list_files_info("lysandre/arxiv-nlp", ["README.md", "config.json"])
- >>> files_info
-
- >>> list(files_info)
- [
- RepoFile: {"blob_id": "43bd404b159de6fba7c2f4d3264347668d43af25", "lfs": None, "rfilename": "README.md", "size": 391},
- RepoFile: {"blob_id": "2f9618c3a19b9a61add74f70bfb121335aeef666", "lfs": None, "rfilename": "config.json", "size": 554},
- ]
- ```
-
- Get even more information about files on a repo (last commit and security scan results)
- ```py
- >>> from huggingface_hub import list_files_info
- >>> files_info = list_files_info("prompthero/openjourney-v4", expand=True)
- >>> list(files_info)
- [
- RepoFile: {
- {'blob_id': '815004af1a321eaed1d93f850b2e94b0c0678e42',
- 'lastCommit': {'date': '2023-03-21T09:05:27.000Z',
- 'id': '47b62b20b20e06b9de610e840282b7e6c3d51190',
- 'title': 'Upload diffusers weights (#48)'},
- 'lfs': None,
- 'rfilename': 'model_index.json',
- 'security': {'avScan': {'virusFound': False, 'virusNames': None},
- 'blobId': '815004af1a321eaed1d93f850b2e94b0c0678e42',
- 'name': 'model_index.json',
- 'pickleImportScan': None,
- 'repositoryId': 'models/prompthero/openjourney-v4',
- 'safe': True},
- 'size': 584}
- },
- RepoFile: {
- {'blob_id': 'd2343d78b33ac03dade1d525538b02b130d0a3a0',
- 'lastCommit': {'date': '2023-03-21T09:05:27.000Z',
- 'id': '47b62b20b20e06b9de610e840282b7e6c3d51190',
- 'title': 'Upload diffusers weights (#48)'},
- 'lfs': {'pointer_size': 134,
- 'sha256': 'dcf4507d99b88db73f3916e2a20169fe74ada6b5582e9af56cfa80f5f3141765',
- 'size': 334711857},
- 'rfilename': 'vae/diffusion_pytorch_model.bin',
- 'security': {'avScan': {'virusFound': False, 'virusNames': None},
- 'blobId': 'd2343d78b33ac03dade1d525538b02b130d0a3a0',
- 'name': 'vae/diffusion_pytorch_model.bin',
- 'pickleImportScan': {'highestSafetyLevel': 'innocuous',
- 'imports': [{'module': 'torch._utils',
- 'name': '_rebuild_tensor_v2',
- 'safety': 'innocuous'},
- {'module': 'collections', 'name': 'OrderedDict', 'safety': 'innocuous'},
- {'module': 'torch', 'name': 'FloatStorage', 'safety': 'innocuous'}]},
- 'repositoryId': 'models/prompthero/openjourney-v4',
- 'safe': True},
- 'size': 334711857}
- },
- (...)
- ]
- ```
-
- List LFS files from the "vae/" folder in "stabilityai/stable-diffusion-2" repository.
-
- ```py
- >>> from huggingface_hub import list_files_info
- >>> [info.rfilename for info in list_files_info("stabilityai/stable-diffusion-2", "vae") if info.lfs is not None]
- ['vae/diffusion_pytorch_model.bin', 'vae/diffusion_pytorch_model.safetensors']
- ```
-
- List all files on a repo.
- ```py
- >>> from huggingface_hub import list_files_info
- >>> [info.rfilename for info in list_files_info("glue", repo_type="dataset")]
- ['.gitattributes', 'README.md', 'dataset_infos.json', 'glue.py']
- ```
- """
- repo_type = repo_type or REPO_TYPE_MODEL
- revision = quote(revision, safe="") if revision is not None else DEFAULT_REVISION
- headers = self._build_hf_headers(token=token)
-
- def _format_as_repo_file(info: Dict) -> RepoFile:
- # Quick alias very specific to the server return type of /paths-info and /tree endpoints. Let's keep this
- # logic here.
- rfilename = info.pop("path")
- size = info.pop("size")
- blobId = info.pop("oid")
- lfs = info.pop("lfs", None)
- info.pop("type", None) # "file" or "folder" -> not needed in practice since we know it's a file
- if lfs is not None:
- lfs = BlobLfsInfo(size=lfs["size"], sha256=lfs["oid"], pointer_size=lfs["pointerSize"])
- return RepoFile(rfilename=rfilename, size=size, blobId=blobId, lfs=lfs, **info)
-
- folder_paths = []
- if paths is None:
- # `paths` is not provided => list all files from the repo
- folder_paths.append("")
- elif paths == []:
- # corner case: server would return a 400 error if `paths` is an empty list. Let's return early.
- return
- else:
- # `paths` is provided => get info about those
- response = get_session().post(
- f"{self.endpoint}/api/{repo_type}s/{repo_id}/paths-info/{revision}",
- data={
- "paths": paths if isinstance(paths, list) else [paths],
- "expand": True,
- },
- headers=headers,
- )
- hf_raise_for_status(response)
- paths_info = response.json()
-
- # List top-level files first
- for path_info in paths_info:
- if path_info["type"] == "file":
- yield _format_as_repo_file(path_info)
- else:
- folder_paths.append(path_info["path"])
-
- # List files in subdirectories
- for path in folder_paths:
- encoded_path = "/" + quote(path, safe="") if path else ""
- tree_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/tree/{revision}{encoded_path}"
- for subpath_info in paginate(path=tree_url, headers=headers, params={"recursive": True, "expand": expand}):
- if subpath_info["type"] == "file":
- yield _format_as_repo_file(subpath_info)
-
- @_deprecate_arguments(version="0.17", deprecated_args=["timeout"], custom_message="timeout is not used anymore.")
- @validate_hf_hub_args
- def list_repo_files(
- self,
- repo_id: str,
- *,
- revision: Optional[str] = None,
- repo_type: Optional[str] = None,
- timeout: Optional[float] = None,
- token: Optional[Union[bool, str]] = None,
- ) -> List[str]:
- """
- Get the list of files in a given repo.
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated by a `/`.
- revision (`str`, *optional*):
- The revision of the model repository from which to get the information.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or space, `None` or `"model"` if uploading to
- a model. Default is `None`.
- token (`bool` or `str`, *optional*):
- A valid authentication token (see https://huggingface.co/settings/token). If `None` or `True` and
- machine is logged in (through `huggingface-cli login` or [`~huggingface_hub.login`]), token will be
- retrieved from the cache. If `False`, token is not sent in the request header.
-
- Returns:
- `List[str]`: the list of files in a given repository.
- """
- return [
- f.rfilename
- for f in self.list_files_info(
- repo_id=repo_id, paths=None, revision=revision, repo_type=repo_type, token=token
- )
- ]
-
- @validate_hf_hub_args
- def list_repo_refs(
- self,
- repo_id: str,
- *,
- repo_type: Optional[str] = None,
- token: Optional[Union[bool, str]] = None,
- ) -> GitRefs:
- """
- Get the list of refs of a given repo (both tags and branches).
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if listing refs from a dataset or a Space,
- `None` or `"model"` if listing from a model. Default is `None`.
- token (`bool` or `str`, *optional*):
- A valid authentication token (see https://huggingface.co/settings/token).
- If `None` or `True` and machine is logged in (through `huggingface-cli login`
- or [`~huggingface_hub.login`]), token will be retrieved from the cache.
- If `False`, token is not sent in the request header.
-
- Example:
- ```py
- >>> from huggingface_hub import HfApi
- >>> api = HfApi()
- >>> api.list_repo_refs("gpt2")
- GitRefs(branches=[GitRefInfo(name='main', ref='refs/heads/main', target_commit='e7da7f221d5bf496a48136c0cd264e630fe9fcc8')], converts=[], tags=[])
-
- >>> api.list_repo_refs("bigcode/the-stack", repo_type='dataset')
- GitRefs(
- branches=[
- GitRefInfo(name='main', ref='refs/heads/main', target_commit='18edc1591d9ce72aa82f56c4431b3c969b210ae3'),
- GitRefInfo(name='v1.1.a1', ref='refs/heads/v1.1.a1', target_commit='f9826b862d1567f3822d3d25649b0d6d22ace714')
- ],
- converts=[],
- tags=[
- GitRefInfo(name='v1.0', ref='refs/tags/v1.0', target_commit='c37a8cd1e382064d8aced5e05543c5f7753834da')
- ]
- )
- ```
-
- Returns:
- [`GitRefs`]: object containing all information about branches and tags for a
- repo on the Hub.
- """
- repo_type = repo_type or REPO_TYPE_MODEL
- response = get_session().get(
- f"{self.endpoint}/api/{repo_type}s/{repo_id}/refs", headers=self._build_hf_headers(token=token)
- )
- hf_raise_for_status(response)
- data = response.json()
- return GitRefs(
- branches=[GitRefInfo(item) for item in data["branches"]],
- converts=[GitRefInfo(item) for item in data["converts"]],
- tags=[GitRefInfo(item) for item in data["tags"]],
- )
-
- @validate_hf_hub_args
- def list_repo_commits(
- self,
- repo_id: str,
- *,
- repo_type: Optional[str] = None,
- token: Optional[Union[bool, str]] = None,
- revision: Optional[str] = None,
- formatted: bool = False,
- ) -> List[GitCommitInfo]:
- """
- Get the list of commits of a given revision for a repo on the Hub.
-
- Commits are sorted by date (last commit first).
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated by a `/`.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if listing commits from a dataset or a Space, `None` or `"model"` if
- listing from a model. Default is `None`.
- token (`bool` or `str`, *optional*):
- A valid authentication token (see https://huggingface.co/settings/token).
- If `None` or `True` and machine is logged in (through `huggingface-cli login`
- or [`~huggingface_hub.login`]), token will be retrieved from the cache.
- If `False`, token is not sent in the request header.
- revision (`str`, *optional*):
- The git revision to commit from. Defaults to the head of the `"main"` branch.
- formatted (`bool`):
- Whether to return the HTML-formatted title and description of the commits. Defaults to False.
-
- Example:
- ```py
- >>> from huggingface_hub import HfApi
- >>> api = HfApi()
-
- # Commits are sorted by date (last commit first)
- >>> initial_commit = api.list_repo_commits("gpt2")[-1]
-
- # Initial commit is always a system commit containing the `.gitattributes` file.
- >>> initial_commit
- GitCommitInfo(
- commit_id='9b865efde13a30c13e0a33e536cf3e4a5a9d71d8',
- authors=['system'],
- created_at=datetime.datetime(2019, 2, 18, 10, 36, 15, tzinfo=datetime.timezone.utc),
- title='initial commit',
- message='',
- formatted_title=None,
- formatted_message=None
- )
-
- # Create an empty branch by deriving from initial commit
- >>> api.create_branch("gpt2", "new_empty_branch", revision=initial_commit.commit_id)
- ```
-
- Returns:
- List[[`GitCommitInfo`]]: list of objects containing information about the commits for a repo on the Hub.
-
- Raises:
- [`~utils.RepositoryNotFoundError`]:
- If repository is not found (error 404): wrong repo_id/repo_type, private but not authenticated or repo
- does not exist.
- [`~utils.RevisionNotFoundError`]:
- If revision is not found (error 404) on the repo.
- """
- repo_type = repo_type or REPO_TYPE_MODEL
- revision = quote(revision, safe="") if revision is not None else DEFAULT_REVISION
-
- # Paginate over results and return the list of commits.
- return [
- GitCommitInfo(item)
- for item in paginate(
- f"{self.endpoint}/api/{repo_type}s/{repo_id}/commits/{revision}",
- headers=self._build_hf_headers(token=token),
- params={"expand[]": "formatted"} if formatted else {},
- )
- ]
-
- @validate_hf_hub_args
- def create_repo(
- self,
- repo_id: str,
- *,
- token: Optional[str] = None,
- private: bool = False,
- repo_type: Optional[str] = None,
- exist_ok: bool = False,
- space_sdk: Optional[str] = None,
- space_hardware: Optional[str] = None,
- ) -> RepoUrl:
- """Create an empty repo on the HuggingFace Hub.
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- token (`str`, *optional*):
- An authentication token (See https://huggingface.co/settings/token)
- private (`bool`, *optional*, defaults to `False`):
- Whether the model repo should be private.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or
- space, `None` or `"model"` if uploading to a model. Default is
- `None`.
- exist_ok (`bool`, *optional*, defaults to `False`):
- If `True`, do not raise an error if repo already exists.
- space_sdk (`str`, *optional*):
- Choice of SDK to use if repo_type is "space". Can be "streamlit", "gradio", "docker", or "static".
- space_hardware (`SpaceHardware` or `str`, *optional*):
- Choice of Hardware if repo_type is "space". See [`SpaceHardware`] for a complete list.
-
- Returns:
- [`RepoUrl`]: URL to the newly created repo. Value is a subclass of `str` containing
- attributes like `endpoint`, `repo_type` and `repo_id`.
- """
- organization, name = repo_id.split("/") if "/" in repo_id else (None, repo_id)
-
- path = f"{self.endpoint}/api/repos/create"
-
- if repo_type not in REPO_TYPES:
- raise ValueError("Invalid repo type")
-
- json = {"name": name, "organization": organization, "private": private}
- if repo_type is not None:
- json["type"] = repo_type
- if repo_type == "space":
- if space_sdk is None:
- raise ValueError(
- "No space_sdk provided. `create_repo` expects space_sdk to be one"
- f" of {SPACES_SDK_TYPES} when repo_type is 'space'`"
- )
- if space_sdk not in SPACES_SDK_TYPES:
- raise ValueError(f"Invalid space_sdk. Please choose one of {SPACES_SDK_TYPES}.")
- json["sdk"] = space_sdk
-
- if space_sdk is not None and repo_type != "space":
- warnings.warn("Ignoring provided space_sdk because repo_type is not 'space'.")
-
- if space_hardware is not None:
- if repo_type == "space":
- json["hardware"] = space_hardware
- else:
- warnings.warn("Ignoring provided space_hardware because repo_type is not 'space'.")
-
- if getattr(self, "_lfsmultipartthresh", None):
- # Testing purposes only.
- # See https://github.com/huggingface/huggingface_hub/pull/733/files#r820604472
- json["lfsmultipartthresh"] = self._lfsmultipartthresh # type: ignore
- headers = self._build_hf_headers(token=token, is_write_action=True)
- r = get_session().post(path, headers=headers, json=json)
-
- try:
- hf_raise_for_status(r)
- except HTTPError as err:
- if exist_ok and err.response.status_code == 409:
- # Repo already exists and `exist_ok=True`
- pass
- elif exist_ok and err.response.status_code == 403:
- # No write permission on the namespace but repo might already exist
- try:
- self.repo_info(repo_id=repo_id, repo_type=repo_type, token=token)
- if repo_type is None or repo_type == REPO_TYPE_MODEL:
- return RepoUrl(f"{self.endpoint}/{repo_id}")
- return RepoUrl(f"{self.endpoint}/{repo_type}/{repo_id}")
- except HfHubHTTPError:
- raise
- else:
- raise
-
- d = r.json()
- return RepoUrl(d["url"], endpoint=self.endpoint)
-
- @validate_hf_hub_args
- def delete_repo(
- self,
- repo_id: str,
- *,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- ):
- """
- Delete a repo from the HuggingFace Hub. CAUTION: this is irreversible.
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- token (`str`, *optional*):
- An authentication token (See https://huggingface.co/settings/token)
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or
- space, `None` or `"model"` if uploading to a model.
-
-
-
- Raises the following errors:
-
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
-
-
- """
- organization, name = repo_id.split("/") if "/" in repo_id else (None, repo_id)
-
- path = f"{self.endpoint}/api/repos/delete"
-
- if repo_type not in REPO_TYPES:
- raise ValueError("Invalid repo type")
-
- json = {"name": name, "organization": organization}
- if repo_type is not None:
- json["type"] = repo_type
-
- headers = self._build_hf_headers(token=token, is_write_action=True)
- r = get_session().delete(path, headers=headers, json=json)
- hf_raise_for_status(r)
-
- @validate_hf_hub_args
- def update_repo_visibility(
- self,
- repo_id: str,
- private: bool = False,
- *,
- token: Optional[str] = None,
- organization: Optional[str] = None,
- repo_type: Optional[str] = None,
- name: Optional[str] = None,
- ) -> Dict[str, bool]:
- """Update the visibility setting of a repository.
-
- Args:
- repo_id (`str`, *optional*):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- private (`bool`, *optional*, defaults to `False`):
- Whether the model repo should be private.
- token (`str`, *optional*):
- An authentication token (See https://huggingface.co/settings/token)
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or
- space, `None` or `"model"` if uploading to a model. Default is
- `None`.
-
- Returns:
- The HTTP response in json.
-
-
-
- Raises the following errors:
-
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
-
-
- """
- if repo_type not in REPO_TYPES:
- raise ValueError("Invalid repo type")
-
- organization, name = repo_id.split("/") if "/" in repo_id else (None, repo_id)
-
- if organization is None:
- namespace = self.whoami(token)["name"]
- else:
- namespace = organization
-
- if repo_type is None:
- repo_type = REPO_TYPE_MODEL # default repo type
-
- r = get_session().put(
- url=f"{self.endpoint}/api/{repo_type}s/{namespace}/{name}/settings",
- headers=self._build_hf_headers(token=token, is_write_action=True),
- json={"private": private},
- )
- hf_raise_for_status(r)
- return r.json()
-
- def move_repo(
- self,
- from_id: str,
- to_id: str,
- *,
- repo_type: Optional[str] = None,
- token: Optional[str] = None,
- ):
- """
- Moving a repository from namespace1/repo_name1 to namespace2/repo_name2
-
- Note there are certain limitations. For more information about moving
- repositories, please see
- https://hf.co/docs/hub/repositories-settings#renaming-or-transferring-a-repo.
-
- Args:
- from_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`. Original repository identifier.
- to_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`. Final repository identifier.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or
- space, `None` or `"model"` if uploading to a model. Default is
- `None`.
- token (`str`, *optional*):
- An authentication token (See https://huggingface.co/settings/token)
-
-
-
- Raises the following errors:
-
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
-
-
- """
- if len(from_id.split("/")) != 2:
- raise ValueError(f"Invalid repo_id: {from_id}. It should have a namespace (:namespace:/:repo_name:)")
-
- if len(to_id.split("/")) != 2:
- raise ValueError(f"Invalid repo_id: {to_id}. It should have a namespace (:namespace:/:repo_name:)")
-
- if repo_type is None:
- repo_type = REPO_TYPE_MODEL # Hub won't accept `None`.
-
- json = {"fromRepo": from_id, "toRepo": to_id, "type": repo_type}
-
- path = f"{self.endpoint}/api/repos/move"
- headers = self._build_hf_headers(token=token, is_write_action=True)
- r = get_session().post(path, headers=headers, json=json)
- try:
- hf_raise_for_status(r)
- except HfHubHTTPError as e:
- e.append_to_message(
- "\nFor additional documentation please see"
- " https://hf.co/docs/hub/repositories-settings#renaming-or-transferring-a-repo."
- )
- raise
-
- @overload
- def create_commit( # type: ignore
- self,
- repo_id: str,
- operations: Iterable[CommitOperation],
- *,
- commit_message: str,
- commit_description: Optional[str] = None,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- revision: Optional[str] = None,
- create_pr: Optional[bool] = None,
- num_threads: int = 5,
- parent_commit: Optional[str] = None,
- run_as_future: Literal[False] = ...,
- ) -> CommitInfo:
- ...
-
- @overload
- def create_commit(
- self,
- repo_id: str,
- operations: Iterable[CommitOperation],
- *,
- commit_message: str,
- commit_description: Optional[str] = None,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- revision: Optional[str] = None,
- create_pr: Optional[bool] = None,
- num_threads: int = 5,
- parent_commit: Optional[str] = None,
- run_as_future: Literal[True] = ...,
- ) -> Future[CommitInfo]:
- ...
-
- @validate_hf_hub_args
- @future_compatible
- def create_commit(
- self,
- repo_id: str,
- operations: Iterable[CommitOperation],
- *,
- commit_message: str,
- commit_description: Optional[str] = None,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- revision: Optional[str] = None,
- create_pr: Optional[bool] = None,
- num_threads: int = 5,
- parent_commit: Optional[str] = None,
- run_as_future: bool = False,
- ) -> Union[CommitInfo, Future[CommitInfo]]:
- """
- Creates a commit in the given repo, deleting & uploading files as needed.
-
- Args:
- repo_id (`str`):
- The repository in which the commit will be created, for example:
- `"username/custom_transformers"`
-
- operations (`Iterable` of [`~hf_api.CommitOperation`]):
- An iterable of operations to include in the commit, either:
-
- - [`~hf_api.CommitOperationAdd`] to upload a file
- - [`~hf_api.CommitOperationDelete`] to delete a file
-
- commit_message (`str`):
- The summary (first line) of the commit that will be created.
-
- commit_description (`str`, *optional*):
- The description of the commit that will be created
-
- token (`str`, *optional*):
- Authentication token, obtained with `HfApi.login` method. Will
- default to the stored token.
-
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or
- space, `None` or `"model"` if uploading to a model. Default is
- `None`.
-
- revision (`str`, *optional*):
- The git revision to commit from. Defaults to the head of the `"main"` branch.
-
- create_pr (`boolean`, *optional*):
- Whether or not to create a Pull Request with that commit. Defaults to `False`.
- If `revision` is not set, PR is opened against the `"main"` branch. If
- `revision` is set and is a branch, PR is opened against this branch. If
- `revision` is set and is not a branch name (example: a commit oid), an
- `RevisionNotFoundError` is returned by the server.
-
- num_threads (`int`, *optional*):
- Number of concurrent threads for uploading files. Defaults to 5.
- Setting it to 2 means at most 2 files will be uploaded concurrently.
-
- parent_commit (`str`, *optional*):
- The OID / SHA of the parent commit, as a hexadecimal string.
- Shorthands (7 first characters) are also supported. If specified and `create_pr` is `False`,
- the commit will fail if `revision` does not point to `parent_commit`. If specified and `create_pr`
- is `True`, the pull request will be created from `parent_commit`. Specifying `parent_commit`
- ensures the repo has not changed before committing the changes, and can be especially useful
- if the repo is updated / committed to concurrently.
- run_as_future (`bool`, *optional*):
- Whether or not to run this method in the background. Background jobs are run sequentially without
- blocking the main thread. Passing `run_as_future=True` will return a [Future](https://docs.python.org/3/library/concurrent.futures.html#future-objects)
- object. Defaults to `False`.
-
- Returns:
- [`CommitInfo`] or `Future`:
- Instance of [`CommitInfo`] containing information about the newly created commit (commit hash, commit
- url, pr url, commit message,...). If `run_as_future=True` is passed, returns a Future object which will
- contain the result when executed.
-
- Raises:
- [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- If commit message is empty.
- [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- If parent commit is not a valid commit OID.
- [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- If the Hub API returns an HTTP 400 error (bad request)
- [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- If `create_pr` is `True` and revision is neither `None` nor `"main"`.
- [`~utils.RepositoryNotFoundError`]:
- If repository is not found (error 404): wrong repo_id/repo_type, private
- but not authenticated or repo does not exist.
-
-
-
- `create_commit` assumes that the repo already exists on the Hub. If you get a
- Client error 404, please make sure you are authenticated and that `repo_id` and
- `repo_type` are set correctly. If repo does not exist, create it first using
- [`~hf_api.create_repo`].
-
-
-
-
-
- `create_commit` is limited to 25k LFS files and a 1GB payload for regular files.
-
-
- """
- _CREATE_COMMIT_NO_REPO_ERROR_MESSAGE = (
- "\nNote: Creating a commit assumes that the repo already exists on the"
- " Huggingface Hub. Please use `create_repo` if it's not the case."
- )
-
- if parent_commit is not None and not REGEX_COMMIT_OID.fullmatch(parent_commit):
- raise ValueError(
- f"`parent_commit` is not a valid commit OID. It must match the following regex: {REGEX_COMMIT_OID}"
- )
-
- if commit_message is None or len(commit_message) == 0:
- raise ValueError("`commit_message` can't be empty, please pass a value.")
-
- commit_description = commit_description if commit_description is not None else ""
- repo_type = repo_type if repo_type is not None else REPO_TYPE_MODEL
- if repo_type not in REPO_TYPES:
- raise ValueError(f"Invalid repo type, must be one of {REPO_TYPES}")
- revision = quote(revision, safe="") if revision is not None else DEFAULT_REVISION
- create_pr = create_pr if create_pr is not None else False
-
- operations = list(operations)
- additions = [op for op in operations if isinstance(op, CommitOperationAdd)]
- nb_additions = len(additions)
- nb_deletions = len(operations) - nb_additions
-
- logger.debug(f"About to commit to the hub: {len(additions)} addition(s) and {nb_deletions} deletion(s).")
-
- # If updating twice the same file or update then delete a file in a single commit
- warn_on_overwriting_operations(operations)
-
- try:
- upload_modes = fetch_upload_modes(
- additions=additions,
- repo_type=repo_type,
- repo_id=repo_id,
- token=token or self.token,
- revision=revision,
- endpoint=self.endpoint,
- create_pr=create_pr,
- )
- except RepositoryNotFoundError as e:
- e.append_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE)
- raise
-
- upload_lfs_files(
- additions=[addition for addition in additions if upload_modes[addition.path_in_repo] == "lfs"],
- repo_type=repo_type,
- repo_id=repo_id,
- token=token or self.token,
- endpoint=self.endpoint,
- num_threads=num_threads,
- )
- commit_payload = prepare_commit_payload(
- operations=operations,
- upload_modes=upload_modes,
- commit_message=commit_message,
- commit_description=commit_description,
- parent_commit=parent_commit,
- )
- commit_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/commit/{revision}"
-
- def _payload_as_ndjson() -> Iterable[bytes]:
- for item in commit_payload:
- yield json.dumps(item).encode()
- yield b"\n"
-
- headers = {
- # See https://github.com/huggingface/huggingface_hub/issues/1085#issuecomment-1265208073
- "Content-Type": "application/x-ndjson",
- **self._build_hf_headers(token=token, is_write_action=True),
- }
- data = b"".join(_payload_as_ndjson())
- params = {"create_pr": "1"} if create_pr else None
-
- try:
- commit_resp = get_session().post(url=commit_url, headers=headers, data=data, params=params)
- hf_raise_for_status(commit_resp, endpoint_name="commit")
- except RepositoryNotFoundError as e:
- e.append_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE)
- raise
- except EntryNotFoundError as e:
- if nb_deletions > 0 and "A file with this name doesn't exist" in str(e):
- e.append_to_message(
- "\nMake sure to differentiate file and folder paths in delete"
- " operations with a trailing '/' or using `is_folder=True/False`."
- )
- raise
-
- commit_data = commit_resp.json()
- return CommitInfo(
- commit_url=commit_data["commitUrl"],
- commit_message=commit_message,
- commit_description=commit_description,
- oid=commit_data["commitOid"],
- pr_url=commit_data["pullRequestUrl"] if create_pr else None,
- )
-
- @experimental
- @validate_hf_hub_args
- def create_commits_on_pr(
- self,
- *,
- repo_id: str,
- addition_commits: List[List[CommitOperationAdd]],
- deletion_commits: List[List[CommitOperationDelete]],
- commit_message: str,
- commit_description: Optional[str] = None,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- merge_pr: bool = True,
- num_threads: int = 5, # TODO: use to multithread uploads
- verbose: bool = False,
- ) -> str:
- """Push changes to the Hub in multiple commits.
-
- Commits are pushed to a draft PR branch. If the upload fails or gets interrupted, it can be resumed. Progress
- is tracked in the PR description. At the end of the process, the PR is set as open and the title is updated to
- match the initial commit message. If `merge_pr=True` is passed, the PR is merged automatically.
-
- All deletion commits are pushed first, followed by the addition commits. The order of the commits is not
- guaranteed as we might implement parallel commits in the future. Be sure that your are not updating several
- times the same file.
-
-
-
- `create_commits_on_pr` is experimental. Its API and behavior is subject to change in the future without prior notice.
-
-
-
- Args:
- repo_id (`str`):
- The repository in which the commits will be pushed. Example: `"username/my-cool-model"`.
-
- addition_commits (`List` of `List` of [`~hf_api.CommitOperationAdd`]):
- A list containing lists of [`~hf_api.CommitOperationAdd`]. Each sublist will result in a commit on the
- PR.
-
- deletion_commits
- A list containing lists of [`~hf_api.CommitOperationDelete`]. Each sublist will result in a commit on
- the PR. Deletion commits are pushed before addition commits.
-
- commit_message (`str`):
- The summary (first line) of the commit that will be created. Will also be the title of the PR.
-
- commit_description (`str`, *optional*):
- The description of the commit that will be created. The description will be added to the PR.
-
- token (`str`, *optional*):
- Authentication token, obtained with `HfApi.login` method. Will default to the stored token.
-
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or space, `None` or `"model"` if uploading to
- a model. Default is `None`.
-
- merge_pr (`bool`):
- If set to `True`, the Pull Request is merged at the end of the process. Defaults to `True`.
-
- num_threads (`int`, *optional*):
- Number of concurrent threads for uploading files. Defaults to 5.
-
- verbose (`bool`):
- If set to `True`, process will run on verbose mode i.e. print information about the ongoing tasks.
- Defaults to `False`.
-
- Returns:
- `str`: URL to the created PR.
-
- Example:
- ```python
- >>> from huggingface_hub import HfApi, plan_multi_commits
- >>> addition_commits, deletion_commits = plan_multi_commits(
- ... operations=[
- ... CommitOperationAdd(...),
- ... CommitOperationAdd(...),
- ... CommitOperationDelete(...),
- ... CommitOperationDelete(...),
- ... CommitOperationAdd(...),
- ... ],
- ... )
- >>> HfApi().create_commits_on_pr(
- ... repo_id="my-cool-model",
- ... addition_commits=addition_commits,
- ... deletion_commits=deletion_commits,
- ... (...)
- ... verbose=True,
- ... )
- ```
-
- Raises:
- [`MultiCommitException`]:
- If an unexpected issue occur in the process: empty commits, unexpected commits in a PR, unexpected PR
- description, etc.
-
-
-
- `create_commits_on_pr` assumes that the repo already exists on the Hub. If you get a Client error 404, please
- make sure you are authenticated and that `repo_id` and `repo_type` are set correctly. If repo does not exist,
- create it first using [`~hf_api.create_repo`].
-
-
- """
- logger = logging.get_logger(__name__ + ".create_commits_on_pr")
- if verbose:
- logger.setLevel("INFO")
-
- # 1. Get strategy ID
- logger.info(
- f"Will create {len(deletion_commits)} deletion commit(s) and {len(addition_commits)} addition commit(s),"
- f" totalling {sum(len(ops) for ops in addition_commits+deletion_commits)} atomic operations."
- )
- strategy = MultiCommitStrategy(
- addition_commits=[MultiCommitStep(operations=operations) for operations in addition_commits], # type: ignore
- deletion_commits=[MultiCommitStep(operations=operations) for operations in deletion_commits], # type: ignore
- )
- logger.info(f"Multi-commits strategy with ID {strategy.id}.")
-
- # 2. Get or create a PR with this strategy ID
- for discussion in self.get_repo_discussions(repo_id=repo_id, repo_type=repo_type, token=token):
- # search for a draft PR with strategy ID
- if discussion.is_pull_request and discussion.status == "draft" and strategy.id in discussion.title:
- pr = self.get_discussion_details(
- repo_id=repo_id, discussion_num=discussion.num, repo_type=repo_type, token=token
- )
- logger.info(f"PR already exists: {pr.url}. Will resume process where it stopped.")
- break
- else:
- # did not find a PR matching the strategy ID
- pr = multi_commit_create_pull_request(
- self,
- repo_id=repo_id,
- commit_message=commit_message,
- commit_description=commit_description,
- strategy=strategy,
- token=token,
- repo_type=repo_type,
- )
- logger.info(f"New PR created: {pr.url}")
-
- # 3. Parse PR description to check consistency with strategy (e.g. same commits are scheduled)
- for event in pr.events:
- if isinstance(event, DiscussionComment):
- pr_comment = event
- break
- else:
- raise MultiCommitException(f"PR #{pr.num} must have at least 1 comment")
-
- description_commits = multi_commit_parse_pr_description(pr_comment.content)
- if len(description_commits) != len(strategy.all_steps):
- raise MultiCommitException(
- f"Corrupted multi-commit PR #{pr.num}: got {len(description_commits)} steps in"
- f" description but {len(strategy.all_steps)} in strategy."
- )
- for step_id in strategy.all_steps:
- if step_id not in description_commits:
- raise MultiCommitException(
- f"Corrupted multi-commit PR #{pr.num}: expected step {step_id} but didn't find"
- f" it (have {', '.join(description_commits)})."
- )
-
- # 4. Retrieve commit history (and check consistency)
- commits_on_main_branch = {
- commit.commit_id
- for commit in self.list_repo_commits(
- repo_id=repo_id, repo_type=repo_type, token=token, revision=DEFAULT_REVISION
- )
- }
- pr_commits = [
- commit
- for commit in self.list_repo_commits(
- repo_id=repo_id, repo_type=repo_type, token=token, revision=pr.git_reference
- )
- if commit.commit_id not in commits_on_main_branch
- ]
- if len(pr_commits) > 0:
- logger.info(f"Found {len(pr_commits)} existing commits on the PR.")
-
- # At this point `pr_commits` is a list of commits pushed to the PR. We expect all of these commits (if any) to have
- # a step_id as title. We raise exception if an unexpected commit has been pushed.
- if len(pr_commits) > len(strategy.all_steps):
- raise MultiCommitException(
- f"Corrupted multi-commit PR #{pr.num}: scheduled {len(strategy.all_steps)} steps but"
- f" {len(pr_commits)} commits have already been pushed to the PR."
- )
-
- # Check which steps are already completed
- remaining_additions = {step.id: step for step in strategy.addition_commits}
- remaining_deletions = {step.id: step for step in strategy.deletion_commits}
- for commit in pr_commits:
- if commit.title in remaining_additions:
- step = remaining_additions.pop(commit.title)
- step.completed = True
- elif commit.title in remaining_deletions:
- step = remaining_deletions.pop(commit.title)
- step.completed = True
-
- if len(remaining_deletions) > 0 and len(remaining_additions) < len(strategy.addition_commits):
- raise MultiCommitException(
- f"Corrupted multi-commit PR #{pr.num}: some addition commits have already been pushed to the PR but"
- " deletion commits are not all completed yet."
- )
- nb_remaining = len(remaining_deletions) + len(remaining_additions)
- if len(pr_commits) > 0:
- logger.info(
- f"{nb_remaining} commits remaining ({len(remaining_deletions)} deletion commits and"
- f" {len(remaining_additions)} addition commits)"
- )
-
- # 5. Push remaining commits to the PR + update description
- # TODO: multi-thread this
- for step in list(remaining_deletions.values()) + list(remaining_additions.values()):
- # Push new commit
- self.create_commit(
- repo_id=repo_id,
- repo_type=repo_type,
- token=token,
- commit_message=step.id,
- revision=pr.git_reference,
- num_threads=num_threads,
- operations=step.operations,
- create_pr=False,
- )
- step.completed = True
- nb_remaining -= 1
- logger.info(f" step {step.id} completed (still {nb_remaining} to go).")
-
- # Update PR description
- self.edit_discussion_comment(
- repo_id=repo_id,
- repo_type=repo_type,
- token=token,
- discussion_num=pr.num,
- comment_id=pr_comment.id,
- new_content=multi_commit_generate_comment(
- commit_message=commit_message, commit_description=commit_description, strategy=strategy
- ),
- )
- logger.info("All commits have been pushed.")
-
- # 6. Update PR (and merge)
- self.rename_discussion(
- repo_id=repo_id,
- repo_type=repo_type,
- token=token,
- discussion_num=pr.num,
- new_title=commit_message,
- )
- self.change_discussion_status(
- repo_id=repo_id,
- repo_type=repo_type,
- token=token,
- discussion_num=pr.num,
- new_status="open",
- comment=MULTI_COMMIT_PR_COMPLETION_COMMENT_TEMPLATE,
- )
- logger.info("PR is now open for reviews.")
-
- if merge_pr: # User don't want a PR => merge it
- try:
- self.merge_pull_request(
- repo_id=repo_id,
- repo_type=repo_type,
- token=token,
- discussion_num=pr.num,
- comment=MULTI_COMMIT_PR_CLOSING_COMMENT_TEMPLATE,
- )
- logger.info("PR has been automatically merged (`merge_pr=True` was passed).")
- except BadRequestError as error:
- if error.server_message is not None and "no associated changes" in error.server_message:
- # PR cannot be merged as no changes are associated. We close the PR without merging with a comment to
- # explain.
- self.change_discussion_status(
- repo_id=repo_id,
- repo_type=repo_type,
- token=token,
- discussion_num=pr.num,
- comment=MULTI_COMMIT_PR_CLOSE_COMMENT_FAILURE_NO_CHANGES_TEMPLATE,
- new_status="closed",
- )
- logger.warning("Couldn't merge the PR: no associated changes.")
- else:
- # PR cannot be merged for another reason (conflicting files for example). We comment the PR to explain
- # and re-raise the exception.
- self.comment_discussion(
- repo_id=repo_id,
- repo_type=repo_type,
- token=token,
- discussion_num=pr.num,
- comment=MULTI_COMMIT_PR_CLOSE_COMMENT_FAILURE_BAD_REQUEST_TEMPLATE.format(
- error_message=error.server_message
- ),
- )
- raise MultiCommitException(
- f"Couldn't merge Pull Request in multi-commit: {error.server_message}"
- ) from error
-
- return pr.url
-
- @overload
- def upload_file( # type: ignore
- self,
- *,
- path_or_fileobj: Union[str, Path, bytes, BinaryIO],
- path_in_repo: str,
- repo_id: str,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- revision: Optional[str] = None,
- commit_message: Optional[str] = None,
- commit_description: Optional[str] = None,
- create_pr: Optional[bool] = None,
- parent_commit: Optional[str] = None,
- run_as_future: Literal[False] = ...,
- ) -> str:
- ...
-
- @overload
- def upload_file(
- self,
- *,
- path_or_fileobj: Union[str, Path, bytes, BinaryIO],
- path_in_repo: str,
- repo_id: str,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- revision: Optional[str] = None,
- commit_message: Optional[str] = None,
- commit_description: Optional[str] = None,
- create_pr: Optional[bool] = None,
- parent_commit: Optional[str] = None,
- run_as_future: Literal[True] = ...,
- ) -> Future[str]:
- ...
-
- @validate_hf_hub_args
- @future_compatible
- def upload_file(
- self,
- *,
- path_or_fileobj: Union[str, Path, bytes, BinaryIO],
- path_in_repo: str,
- repo_id: str,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- revision: Optional[str] = None,
- commit_message: Optional[str] = None,
- commit_description: Optional[str] = None,
- create_pr: Optional[bool] = None,
- parent_commit: Optional[str] = None,
- run_as_future: bool = False,
- ) -> Union[str, Future[str]]:
- """
- Upload a local file (up to 50 GB) to the given repo. The upload is done
- through a HTTP post request, and doesn't require git or git-lfs to be
- installed.
-
- Args:
- path_or_fileobj (`str`, `Path`, `bytes`, or `IO`):
- Path to a file on the local machine or binary data stream /
- fileobj / buffer.
- path_in_repo (`str`):
- Relative filepath in the repo, for example:
- `"checkpoints/1fec34a/weights.bin"`
- repo_id (`str`):
- The repository to which the file will be uploaded, for example:
- `"username/custom_transformers"`
- token (`str`, *optional*):
- Authentication token, obtained with `HfApi.login` method. Will
- default to the stored token.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or
- space, `None` or `"model"` if uploading to a model. Default is
- `None`.
- revision (`str`, *optional*):
- The git revision to commit from. Defaults to the head of the `"main"` branch.
- commit_message (`str`, *optional*):
- The summary / title / first line of the generated commit
- commit_description (`str` *optional*)
- The description of the generated commit
- create_pr (`boolean`, *optional*):
- Whether or not to create a Pull Request with that commit. Defaults to `False`.
- If `revision` is not set, PR is opened against the `"main"` branch. If
- `revision` is set and is a branch, PR is opened against this branch. If
- `revision` is set and is not a branch name (example: a commit oid), an
- `RevisionNotFoundError` is returned by the server.
- parent_commit (`str`, *optional*):
- The OID / SHA of the parent commit, as a hexadecimal string. Shorthands (7 first characters) are also supported.
- If specified and `create_pr` is `False`, the commit will fail if `revision` does not point to `parent_commit`.
- If specified and `create_pr` is `True`, the pull request will be created from `parent_commit`.
- Specifying `parent_commit` ensures the repo has not changed before committing the changes, and can be
- especially useful if the repo is updated / committed to concurrently.
- run_as_future (`bool`, *optional*):
- Whether or not to run this method in the background. Background jobs are run sequentially without
- blocking the main thread. Passing `run_as_future=True` will return a [Future](https://docs.python.org/3/library/concurrent.futures.html#future-objects)
- object. Defaults to `False`.
-
-
- Returns:
- `str` or `Future`: The URL to visualize the uploaded file on the hub. If `run_as_future=True` is passed,
- returns a Future object which will contain the result when executed.
-
-
-
- Raises the following errors:
-
- - [`HTTPError`](https://2.python-requests.org/en/master/api/#requests.HTTPError)
- if the HuggingFace API returned an error
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- if some parameter value is invalid
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
- - [`~utils.RevisionNotFoundError`]
- If the revision to download from cannot be found.
-
-
-
-
-
- `upload_file` assumes that the repo already exists on the Hub. If you get a
- Client error 404, please make sure you are authenticated and that `repo_id` and
- `repo_type` are set correctly. If repo does not exist, create it first using
- [`~hf_api.create_repo`].
-
-
-
- Example:
-
- ```python
- >>> from huggingface_hub import upload_file
-
- >>> with open("./local/filepath", "rb") as fobj:
- ... upload_file(
- ... path_or_fileobj=fileobj,
- ... path_in_repo="remote/file/path.h5",
- ... repo_id="username/my-dataset",
- ... repo_type="dataset",
- ... token="my_token",
- ... )
- "https://huggingface.co/datasets/username/my-dataset/blob/main/remote/file/path.h5"
-
- >>> upload_file(
- ... path_or_fileobj=".\\\\local\\\\file\\\\path",
- ... path_in_repo="remote/file/path.h5",
- ... repo_id="username/my-model",
- ... token="my_token",
- ... )
- "https://huggingface.co/username/my-model/blob/main/remote/file/path.h5"
-
- >>> upload_file(
- ... path_or_fileobj=".\\\\local\\\\file\\\\path",
- ... path_in_repo="remote/file/path.h5",
- ... repo_id="username/my-model",
- ... token="my_token",
- ... create_pr=True,
- ... )
- "https://huggingface.co/username/my-model/blob/refs%2Fpr%2F1/remote/file/path.h5"
- ```
- """
- if repo_type not in REPO_TYPES:
- raise ValueError(f"Invalid repo type, must be one of {REPO_TYPES}")
-
- commit_message = (
- commit_message if commit_message is not None else f"Upload {path_in_repo} with huggingface_hub"
- )
- operation = CommitOperationAdd(
- path_or_fileobj=path_or_fileobj,
- path_in_repo=path_in_repo,
- )
-
- commit_info = self.create_commit(
- repo_id=repo_id,
- repo_type=repo_type,
- operations=[operation],
- commit_message=commit_message,
- commit_description=commit_description,
- token=token,
- revision=revision,
- create_pr=create_pr,
- parent_commit=parent_commit,
- )
-
- if commit_info.pr_url is not None:
- revision = quote(_parse_revision_from_pr_url(commit_info.pr_url), safe="")
- if repo_type in REPO_TYPES_URL_PREFIXES:
- repo_id = REPO_TYPES_URL_PREFIXES[repo_type] + repo_id
- revision = revision if revision is not None else DEFAULT_REVISION
- # Similar to `hf_hub_url` but it's "blob" instead of "resolve"
- return f"{self.endpoint}/{repo_id}/blob/{revision}/{path_in_repo}"
-
- @overload
- def upload_folder( # type: ignore
- self,
- *,
- repo_id: str,
- folder_path: Union[str, Path],
- path_in_repo: Optional[str] = None,
- commit_message: Optional[str] = None,
- commit_description: Optional[str] = None,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- revision: Optional[str] = None,
- create_pr: Optional[bool] = None,
- parent_commit: Optional[str] = None,
- allow_patterns: Optional[Union[List[str], str]] = None,
- ignore_patterns: Optional[Union[List[str], str]] = None,
- delete_patterns: Optional[Union[List[str], str]] = None,
- multi_commits: bool = False,
- multi_commits_verbose: bool = False,
- run_as_future: Literal[False] = ...,
- ) -> str:
- ...
-
- @overload
- def upload_folder(
- self,
- *,
- repo_id: str,
- folder_path: Union[str, Path],
- path_in_repo: Optional[str] = None,
- commit_message: Optional[str] = None,
- commit_description: Optional[str] = None,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- revision: Optional[str] = None,
- create_pr: Optional[bool] = None,
- parent_commit: Optional[str] = None,
- allow_patterns: Optional[Union[List[str], str]] = None,
- ignore_patterns: Optional[Union[List[str], str]] = None,
- delete_patterns: Optional[Union[List[str], str]] = None,
- multi_commits: bool = False,
- multi_commits_verbose: bool = False,
- run_as_future: Literal[True] = ...,
- ) -> Future[str]:
- ...
-
- @validate_hf_hub_args
- @future_compatible
- def upload_folder(
- self,
- *,
- repo_id: str,
- folder_path: Union[str, Path],
- path_in_repo: Optional[str] = None,
- commit_message: Optional[str] = None,
- commit_description: Optional[str] = None,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- revision: Optional[str] = None,
- create_pr: Optional[bool] = None,
- parent_commit: Optional[str] = None,
- allow_patterns: Optional[Union[List[str], str]] = None,
- ignore_patterns: Optional[Union[List[str], str]] = None,
- delete_patterns: Optional[Union[List[str], str]] = None,
- multi_commits: bool = False,
- multi_commits_verbose: bool = False,
- run_as_future: bool = False,
- ) -> Union[str, Future[str]]:
- """
- Upload a local folder to the given repo. The upload is done through a HTTP requests, and doesn't require git or
- git-lfs to be installed.
-
- The structure of the folder will be preserved. Files with the same name already present in the repository will
- be overwritten. Others will be left untouched.
-
- Use the `allow_patterns` and `ignore_patterns` arguments to specify which files to upload. These parameters
- accept either a single pattern or a list of patterns. Patterns are Standard Wildcards (globbing patterns) as
- documented [here](https://tldp.org/LDP/GNU-Linux-Tools-Summary/html/x11655.htm). If both `allow_patterns` and
- `ignore_patterns` are provided, both constraints apply. By default, all files from the folder are uploaded.
-
- Use the `delete_patterns` argument to specify remote files you want to delete. Input type is the same as for
- `allow_patterns` (see above). If `path_in_repo` is also provided, the patterns are matched against paths
- relative to this folder. For example, `upload_folder(..., path_in_repo="experiment", delete_patterns="logs/*")`
- will delete any remote file under `./experiment/logs/`. Note that the `.gitattributes` file will not be deleted
- even if it matches the patterns.
-
- Any `.git/` folder present in any subdirectory will be ignored. However, please be aware that the `.gitignore`
- file is not taken into account.
-
- Uses `HfApi.create_commit` under the hood.
-
- Args:
- repo_id (`str`):
- The repository to which the file will be uploaded, for example:
- `"username/custom_transformers"`
- folder_path (`str` or `Path`):
- Path to the folder to upload on the local file system
- path_in_repo (`str`, *optional*):
- Relative path of the directory in the repo, for example:
- `"checkpoints/1fec34a/results"`. Will default to the root folder of the repository.
- token (`str`, *optional*):
- Authentication token, obtained with `HfApi.login` method. Will
- default to the stored token.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or
- space, `None` or `"model"` if uploading to a model. Default is
- `None`.
- revision (`str`, *optional*):
- The git revision to commit from. Defaults to the head of the `"main"` branch.
- commit_message (`str`, *optional*):
- The summary / title / first line of the generated commit. Defaults to:
- `f"Upload {path_in_repo} with huggingface_hub"`
- commit_description (`str` *optional*):
- The description of the generated commit
- create_pr (`boolean`, *optional*):
- Whether or not to create a Pull Request with that commit. Defaults to `False`. If `revision` is not
- set, PR is opened against the `"main"` branch. If `revision` is set and is a branch, PR is opened
- against this branch. If `revision` is set and is not a branch name (example: a commit oid), an
- `RevisionNotFoundError` is returned by the server. If both `multi_commits` and `create_pr` are True,
- the PR created in the multi-commit process is kept opened.
- parent_commit (`str`, *optional*):
- The OID / SHA of the parent commit, as a hexadecimal string. Shorthands (7 first characters) are also supported.
- If specified and `create_pr` is `False`, the commit will fail if `revision` does not point to `parent_commit`.
- If specified and `create_pr` is `True`, the pull request will be created from `parent_commit`.
- Specifying `parent_commit` ensures the repo has not changed before committing the changes, and can be
- especially useful if the repo is updated / committed to concurrently.
- allow_patterns (`List[str]` or `str`, *optional*):
- If provided, only files matching at least one pattern are uploaded.
- ignore_patterns (`List[str]` or `str`, *optional*):
- If provided, files matching any of the patterns are not uploaded.
- delete_patterns (`List[str]` or `str`, *optional*):
- If provided, remote files matching any of the patterns will be deleted from the repo while committing
- new files. This is useful if you don't know which files have already been uploaded.
- Note: to avoid discrepancies the `.gitattributes` file is not deleted even if it matches the pattern.
- multi_commits (`bool`):
- If True, changes are pushed to a PR using a multi-commit process. Defaults to `False`.
- multi_commits_verbose (`bool`):
- If True and `multi_commits` is used, more information will be displayed to the user.
- run_as_future (`bool`, *optional*):
- Whether or not to run this method in the background. Background jobs are run sequentially without
- blocking the main thread. Passing `run_as_future=True` will return a [Future](https://docs.python.org/3/library/concurrent.futures.html#future-objects)
- object. Defaults to `False`.
-
- Returns:
- `str` or `Future[str]`: A URL to visualize the uploaded folder on the hub. If `run_as_future=True` is passed,
- returns a Future object which will contain the result when executed.
-
-
-
- Raises the following errors:
-
- - [`HTTPError`](https://2.python-requests.org/en/master/api/#requests.HTTPError)
- if the HuggingFace API returned an error
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- if some parameter value is invalid
-
-
-
-
-
- `upload_folder` assumes that the repo already exists on the Hub. If you get a Client error 404, please make
- sure you are authenticated and that `repo_id` and `repo_type` are set correctly. If repo does not exist, create
- it first using [`~hf_api.create_repo`].
-
-
-
-
-
- `multi_commits` is experimental. Its API and behavior is subject to change in the future without prior notice.
-
-
-
- Example:
-
- ```python
- # Upload checkpoints folder except the log files
- >>> upload_folder(
- ... folder_path="local/checkpoints",
- ... path_in_repo="remote/experiment/checkpoints",
- ... repo_id="username/my-dataset",
- ... repo_type="datasets",
- ... token="my_token",
- ... ignore_patterns="**/logs/*.txt",
- ... )
- # "https://huggingface.co/datasets/username/my-dataset/tree/main/remote/experiment/checkpoints"
-
- # Upload checkpoints folder including logs while deleting existing logs from the repo
- # Useful if you don't know exactly which log files have already being pushed
- >>> upload_folder(
- ... folder_path="local/checkpoints",
- ... path_in_repo="remote/experiment/checkpoints",
- ... repo_id="username/my-dataset",
- ... repo_type="datasets",
- ... token="my_token",
- ... delete_patterns="**/logs/*.txt",
- ... )
- "https://huggingface.co/datasets/username/my-dataset/tree/main/remote/experiment/checkpoints"
-
- # Upload checkpoints folder while creating a PR
- >>> upload_folder(
- ... folder_path="local/checkpoints",
- ... path_in_repo="remote/experiment/checkpoints",
- ... repo_id="username/my-dataset",
- ... repo_type="datasets",
- ... token="my_token",
- ... create_pr=True,
- ... )
- "https://huggingface.co/datasets/username/my-dataset/tree/refs%2Fpr%2F1/remote/experiment/checkpoints"
-
- ```
- """
- if repo_type not in REPO_TYPES:
- raise ValueError(f"Invalid repo type, must be one of {REPO_TYPES}")
-
- if multi_commits:
- if revision is not None and revision != DEFAULT_REVISION:
- raise ValueError("Cannot use `multi_commit` to commit changes other than the main branch.")
-
- # By default, upload folder to the root directory in repo.
- if path_in_repo is None:
- path_in_repo = ""
-
- # Do not upload .git folder
- if ignore_patterns is None:
- ignore_patterns = []
- elif isinstance(ignore_patterns, str):
- ignore_patterns = [ignore_patterns]
- ignore_patterns += IGNORE_GIT_FOLDER_PATTERNS
-
- delete_operations = self._prepare_upload_folder_deletions(
- repo_id=repo_id,
- repo_type=repo_type,
- revision=DEFAULT_REVISION if create_pr else revision,
- token=token,
- path_in_repo=path_in_repo,
- delete_patterns=delete_patterns,
- )
- add_operations = _prepare_upload_folder_additions(
- folder_path,
- path_in_repo,
- allow_patterns=allow_patterns,
- ignore_patterns=ignore_patterns,
- )
-
- # Optimize operations: if some files will be overwritten, we don't need to delete them first
- if len(add_operations) > 0:
- added_paths = set(op.path_in_repo for op in add_operations)
- delete_operations = [
- delete_op for delete_op in delete_operations if delete_op.path_in_repo not in added_paths
- ]
- commit_operations = delete_operations + add_operations
-
- pr_url: Optional[str]
- commit_message = commit_message or "Upload folder using huggingface_hub"
- if multi_commits:
- addition_commits, deletion_commits = plan_multi_commits(operations=commit_operations)
- pr_url = self.create_commits_on_pr(
- repo_id=repo_id,
- repo_type=repo_type,
- addition_commits=addition_commits,
- deletion_commits=deletion_commits,
- commit_message=commit_message,
- commit_description=commit_description,
- token=token,
- merge_pr=not create_pr,
- verbose=multi_commits_verbose,
- )
- else:
- commit_info = self.create_commit(
- repo_type=repo_type,
- repo_id=repo_id,
- operations=commit_operations,
- commit_message=commit_message,
- commit_description=commit_description,
- token=token,
- revision=revision,
- create_pr=create_pr,
- parent_commit=parent_commit,
- )
- pr_url = commit_info.pr_url
-
- if create_pr and pr_url is not None:
- revision = quote(_parse_revision_from_pr_url(pr_url), safe="")
- if repo_type in REPO_TYPES_URL_PREFIXES:
- repo_id = REPO_TYPES_URL_PREFIXES[repo_type] + repo_id
- revision = revision if revision is not None else DEFAULT_REVISION
- # Similar to `hf_hub_url` but it's "tree" instead of "resolve"
- return f"{self.endpoint}/{repo_id}/tree/{revision}/{path_in_repo}"
-
- @validate_hf_hub_args
- def delete_file(
- self,
- path_in_repo: str,
- repo_id: str,
- *,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- revision: Optional[str] = None,
- commit_message: Optional[str] = None,
- commit_description: Optional[str] = None,
- create_pr: Optional[bool] = None,
- parent_commit: Optional[str] = None,
- ) -> CommitInfo:
- """
- Deletes a file in the given repo.
-
- Args:
- path_in_repo (`str`):
- Relative filepath in the repo, for example:
- `"checkpoints/1fec34a/weights.bin"`
- repo_id (`str`):
- The repository from which the file will be deleted, for example:
- `"username/custom_transformers"`
- token (`str`, *optional*):
- Authentication token, obtained with `HfApi.login` method. Will
- default to the stored token.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if the file is in a dataset or
- space, `None` or `"model"` if in a model. Default is `None`.
- revision (`str`, *optional*):
- The git revision to commit from. Defaults to the head of the `"main"` branch.
- commit_message (`str`, *optional*):
- The summary / title / first line of the generated commit. Defaults to
- `f"Delete {path_in_repo} with huggingface_hub"`.
- commit_description (`str` *optional*)
- The description of the generated commit
- create_pr (`boolean`, *optional*):
- Whether or not to create a Pull Request with that commit. Defaults to `False`.
- If `revision` is not set, PR is opened against the `"main"` branch. If
- `revision` is set and is a branch, PR is opened against this branch. If
- `revision` is set and is not a branch name (example: a commit oid), an
- `RevisionNotFoundError` is returned by the server.
- parent_commit (`str`, *optional*):
- The OID / SHA of the parent commit, as a hexadecimal string. Shorthands (7 first characters) are also supported.
- If specified and `create_pr` is `False`, the commit will fail if `revision` does not point to `parent_commit`.
- If specified and `create_pr` is `True`, the pull request will be created from `parent_commit`.
- Specifying `parent_commit` ensures the repo has not changed before committing the changes, and can be
- especially useful if the repo is updated / committed to concurrently.
-
-
-
-
- Raises the following errors:
-
- - [`HTTPError`](https://2.python-requests.org/en/master/api/#requests.HTTPError)
- if the HuggingFace API returned an error
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- if some parameter value is invalid
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
- - [`~utils.RevisionNotFoundError`]
- If the revision to download from cannot be found.
- - [`~utils.EntryNotFoundError`]
- If the file to download cannot be found.
-
-
-
- """
- commit_message = (
- commit_message if commit_message is not None else f"Delete {path_in_repo} with huggingface_hub"
- )
-
- operations = [CommitOperationDelete(path_in_repo=path_in_repo)]
-
- return self.create_commit(
- repo_id=repo_id,
- repo_type=repo_type,
- token=token,
- operations=operations,
- revision=revision,
- commit_message=commit_message,
- commit_description=commit_description,
- create_pr=create_pr,
- parent_commit=parent_commit,
- )
-
- @validate_hf_hub_args
- def delete_folder(
- self,
- path_in_repo: str,
- repo_id: str,
- *,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- revision: Optional[str] = None,
- commit_message: Optional[str] = None,
- commit_description: Optional[str] = None,
- create_pr: Optional[bool] = None,
- parent_commit: Optional[str] = None,
- ) -> CommitInfo:
- """
- Deletes a folder in the given repo.
-
- Simple wrapper around [`create_commit`] method.
-
- Args:
- path_in_repo (`str`):
- Relative folder path in the repo, for example: `"checkpoints/1fec34a"`.
- repo_id (`str`):
- The repository from which the folder will be deleted, for example:
- `"username/custom_transformers"`
- token (`str`, *optional*):
- Authentication token, obtained with `HfApi.login` method. Will default
- to the stored token.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if the folder is in a dataset or
- space, `None` or `"model"` if in a model. Default is `None`.
- revision (`str`, *optional*):
- The git revision to commit from. Defaults to the head of the `"main"` branch.
- commit_message (`str`, *optional*):
- The summary / title / first line of the generated commit. Defaults to
- `f"Delete folder {path_in_repo} with huggingface_hub"`.
- commit_description (`str` *optional*)
- The description of the generated commit.
- create_pr (`boolean`, *optional*):
- Whether or not to create a Pull Request with that commit. Defaults to `False`.
- If `revision` is not set, PR is opened against the `"main"` branch. If
- `revision` is set and is a branch, PR is opened against this branch. If
- `revision` is set and is not a branch name (example: a commit oid), an
- `RevisionNotFoundError` is returned by the server.
- parent_commit (`str`, *optional*):
- The OID / SHA of the parent commit, as a hexadecimal string. Shorthands (7 first characters) are also supported.
- If specified and `create_pr` is `False`, the commit will fail if `revision` does not point to `parent_commit`.
- If specified and `create_pr` is `True`, the pull request will be created from `parent_commit`.
- Specifying `parent_commit` ensures the repo has not changed before committing the changes, and can be
- especially useful if the repo is updated / committed to concurrently.
- """
- return self.create_commit(
- repo_id=repo_id,
- repo_type=repo_type,
- token=token,
- operations=[CommitOperationDelete(path_in_repo=path_in_repo, is_folder=True)],
- revision=revision,
- commit_message=(
- commit_message if commit_message is not None else f"Delete folder {path_in_repo} with huggingface_hub"
- ),
- commit_description=commit_description,
- create_pr=create_pr,
- parent_commit=parent_commit,
- )
-
- @validate_hf_hub_args
- def create_branch(
- self,
- repo_id: str,
- *,
- branch: str,
- revision: Optional[str] = None,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- exist_ok: bool = False,
- ) -> None:
- """
- Create a new branch for a repo on the Hub, starting from the specified revision (defaults to `main`).
- To find a revision suiting your needs, you can use [`list_repo_refs`] or [`list_repo_commits`].
-
- Args:
- repo_id (`str`):
- The repository in which the branch will be created.
- Example: `"user/my-cool-model"`.
-
- branch (`str`):
- The name of the branch to create.
-
- revision (`str`, *optional*):
- The git revision to create the branch from. It can be a branch name or
- the OID/SHA of a commit, as a hexadecimal string. Defaults to the head
- of the `"main"` branch.
-
- token (`str`, *optional*):
- Authentication token. Will default to the stored token.
-
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if creating a branch on a dataset or
- space, `None` or `"model"` if tagging a model. Default is `None`.
-
- exist_ok (`bool`, *optional*, defaults to `False`):
- If `True`, do not raise an error if branch already exists.
-
- Raises:
- [`~utils.RepositoryNotFoundError`]:
- If repository is not found (error 404): wrong repo_id/repo_type, private
- but not authenticated or repo does not exist.
- [`~utils.BadRequestError`]:
- If invalid reference for a branch. Ex: `refs/pr/5` or 'refs/foo/bar'.
- [`~utils.HfHubHTTPError`]:
- If the branch already exists on the repo (error 409) and `exist_ok` is
- set to `False`.
- """
- if repo_type is None:
- repo_type = REPO_TYPE_MODEL
- branch = quote(branch, safe="")
-
- # Prepare request
- branch_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/branch/{branch}"
- headers = self._build_hf_headers(token=token, is_write_action=True)
- payload = {}
- if revision is not None:
- payload["startingPoint"] = revision
-
- # Create branch
- response = get_session().post(url=branch_url, headers=headers, json=payload)
- try:
- hf_raise_for_status(response)
- except HfHubHTTPError as e:
- if not (e.response.status_code == 409 and exist_ok):
- raise
-
- @validate_hf_hub_args
- def delete_branch(
- self,
- repo_id: str,
- *,
- branch: str,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- ) -> None:
- """
- Delete a branch from a repo on the Hub.
-
- Args:
- repo_id (`str`):
- The repository in which a branch will be deleted.
- Example: `"user/my-cool-model"`.
-
- branch (`str`):
- The name of the branch to delete.
-
- token (`str`, *optional*):
- Authentication token. Will default to the stored token.
-
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if creating a branch on a dataset or
- space, `None` or `"model"` if tagging a model. Default is `None`.
-
- Raises:
- [`~utils.RepositoryNotFoundError`]:
- If repository is not found (error 404): wrong repo_id/repo_type, private
- but not authenticated or repo does not exist.
- [`~utils.HfHubHTTPError`]:
- If trying to delete a protected branch. Ex: `main` cannot be deleted.
- [`~utils.HfHubHTTPError`]:
- If trying to delete a branch that does not exist.
-
- """
- if repo_type is None:
- repo_type = REPO_TYPE_MODEL
- branch = quote(branch, safe="")
-
- # Prepare request
- branch_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/branch/{branch}"
- headers = self._build_hf_headers(token=token, is_write_action=True)
-
- # Delete branch
- response = get_session().delete(url=branch_url, headers=headers)
- hf_raise_for_status(response)
-
- @validate_hf_hub_args
- def create_tag(
- self,
- repo_id: str,
- *,
- tag: str,
- tag_message: Optional[str] = None,
- revision: Optional[str] = None,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- exist_ok: bool = False,
- ) -> None:
- """
- Tag a given commit of a repo on the Hub.
-
- Args:
- repo_id (`str`):
- The repository in which a commit will be tagged.
- Example: `"user/my-cool-model"`.
-
- tag (`str`):
- The name of the tag to create.
-
- tag_message (`str`, *optional*):
- The description of the tag to create.
-
- revision (`str`, *optional*):
- The git revision to tag. It can be a branch name or the OID/SHA of a
- commit, as a hexadecimal string. Shorthands (7 first characters) are
- also supported. Defaults to the head of the `"main"` branch.
-
- token (`str`, *optional*):
- Authentication token. Will default to the stored token.
-
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if tagging a dataset or
- space, `None` or `"model"` if tagging a model. Default is
- `None`.
-
- exist_ok (`bool`, *optional*, defaults to `False`):
- If `True`, do not raise an error if tag already exists.
-
- Raises:
- [`~utils.RepositoryNotFoundError`]:
- If repository is not found (error 404): wrong repo_id/repo_type, private
- but not authenticated or repo does not exist.
- [`~utils.RevisionNotFoundError`]:
- If revision is not found (error 404) on the repo.
- [`~utils.HfHubHTTPError`]:
- If the branch already exists on the repo (error 409) and `exist_ok` is
- set to `False`.
- """
- if repo_type is None:
- repo_type = REPO_TYPE_MODEL
- revision = quote(revision, safe="") if revision is not None else DEFAULT_REVISION
-
- # Prepare request
- tag_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/tag/{revision}"
- headers = self._build_hf_headers(token=token, is_write_action=True)
- payload = {"tag": tag}
- if tag_message is not None:
- payload["message"] = tag_message
-
- # Tag
- response = get_session().post(url=tag_url, headers=headers, json=payload)
- try:
- hf_raise_for_status(response)
- except HfHubHTTPError as e:
- if not (e.response.status_code == 409 and exist_ok):
- raise
-
- @validate_hf_hub_args
- def delete_tag(
- self,
- repo_id: str,
- *,
- tag: str,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- ) -> None:
- """
- Delete a tag from a repo on the Hub.
-
- Args:
- repo_id (`str`):
- The repository in which a tag will be deleted.
- Example: `"user/my-cool-model"`.
-
- tag (`str`):
- The name of the tag to delete.
-
- token (`str`, *optional*):
- Authentication token. Will default to the stored token.
-
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if tagging a dataset or space, `None` or
- `"model"` if tagging a model. Default is `None`.
-
- Raises:
- [`~utils.RepositoryNotFoundError`]:
- If repository is not found (error 404): wrong repo_id/repo_type, private
- but not authenticated or repo does not exist.
- [`~utils.RevisionNotFoundError`]:
- If tag is not found.
- """
- if repo_type is None:
- repo_type = REPO_TYPE_MODEL
- tag = quote(tag, safe="")
-
- # Prepare request
- tag_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/tag/{tag}"
- headers = self._build_hf_headers(token=token, is_write_action=True)
-
- # Un-tag
- response = get_session().delete(url=tag_url, headers=headers)
- hf_raise_for_status(response)
-
- @validate_hf_hub_args
- def get_full_repo_name(
- self,
- model_id: str,
- *,
- organization: Optional[str] = None,
- token: Optional[Union[bool, str]] = None,
- ):
- """
- Returns the repository name for a given model ID and optional
- organization.
-
- Args:
- model_id (`str`):
- The name of the model.
- organization (`str`, *optional*):
- If passed, the repository name will be in the organization
- namespace instead of the user namespace.
- token (`bool` or `str`, *optional*):
- A valid authentication token (see https://huggingface.co/settings/token).
- If `None` or `True` and machine is logged in (through `huggingface-cli login`
- or [`~huggingface_hub.login`]), token will be retrieved from the cache.
- If `False`, token is not sent in the request header.
-
- Returns:
- `str`: The repository name in the user's namespace
- ({username}/{model_id}) if no organization is passed, and under the
- organization namespace ({organization}/{model_id}) otherwise.
- """
- if organization is None:
- if "/" in model_id:
- username = model_id.split("/")[0]
- else:
- username = self.whoami(token=token)["name"] # type: ignore
- return f"{username}/{model_id}"
- else:
- return f"{organization}/{model_id}"
-
- @validate_hf_hub_args
- def get_repo_discussions(
- self,
- repo_id: str,
- *,
- repo_type: Optional[str] = None,
- token: Optional[str] = None,
- ) -> Iterator[Discussion]:
- """
- Fetches Discussions and Pull Requests for the given repo.
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if fetching from a dataset or
- space, `None` or `"model"` if fetching from a model. Default is
- `None`.
- token (`str`, *optional*):
- An authentication token (See https://huggingface.co/settings/token).
-
- Returns:
- `Iterator[Discussion]`: An iterator of [`Discussion`] objects.
-
- Example:
- Collecting all discussions of a repo in a list:
-
- ```python
- >>> from huggingface_hub import get_repo_discussions
- >>> discussions_list = list(get_repo_discussions(repo_id="bert-base-uncased"))
- ```
-
- Iterating over discussions of a repo:
-
- ```python
- >>> from huggingface_hub import get_repo_discussions
- >>> for discussion in get_repo_discussions(repo_id="bert-base-uncased"):
- ... print(discussion.num, discussion.title)
- ```
- """
- if repo_type not in REPO_TYPES:
- raise ValueError(f"Invalid repo type, must be one of {REPO_TYPES}")
- if repo_type is None:
- repo_type = REPO_TYPE_MODEL
-
- headers = self._build_hf_headers(token=token)
-
- def _fetch_discussion_page(page_index: int):
- path = f"{self.endpoint}/api/{repo_type}s/{repo_id}/discussions?p={page_index}"
- resp = get_session().get(path, headers=headers)
- hf_raise_for_status(resp)
- paginated_discussions = resp.json()
- total = paginated_discussions["count"]
- start = paginated_discussions["start"]
- discussions = paginated_discussions["discussions"]
- has_next = (start + len(discussions)) < total
- return discussions, has_next
-
- has_next, page_index = True, 0
-
- while has_next:
- discussions, has_next = _fetch_discussion_page(page_index=page_index)
- for discussion in discussions:
- yield Discussion(
- title=discussion["title"],
- num=discussion["num"],
- author=discussion.get("author", {}).get("name", "deleted"),
- created_at=parse_datetime(discussion["createdAt"]),
- status=discussion["status"],
- repo_id=discussion["repo"]["name"],
- repo_type=discussion["repo"]["type"],
- is_pull_request=discussion["isPullRequest"],
- endpoint=self.endpoint,
- )
- page_index = page_index + 1
-
- @validate_hf_hub_args
- def get_discussion_details(
- self,
- repo_id: str,
- discussion_num: int,
- *,
- repo_type: Optional[str] = None,
- token: Optional[str] = None,
- ) -> DiscussionWithDetails:
- """Fetches a Discussion's / Pull Request 's details from the Hub.
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- discussion_num (`int`):
- The number of the Discussion or Pull Request . Must be a strictly positive integer.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or
- space, `None` or `"model"` if uploading to a model. Default is
- `None`.
- token (`str`, *optional*):
- An authentication token (See https://huggingface.co/settings/token)
-
- Returns: [`DiscussionWithDetails`]
-
-
-
- Raises the following errors:
-
- - [`HTTPError`](https://2.python-requests.org/en/master/api/#requests.HTTPError)
- if the HuggingFace API returned an error
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- if some parameter value is invalid
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
-
-
- """
- if not isinstance(discussion_num, int) or discussion_num <= 0:
- raise ValueError("Invalid discussion_num, must be a positive integer")
- if repo_type not in REPO_TYPES:
- raise ValueError(f"Invalid repo type, must be one of {REPO_TYPES}")
- if repo_type is None:
- repo_type = REPO_TYPE_MODEL
-
- path = f"{self.endpoint}/api/{repo_type}s/{repo_id}/discussions/{discussion_num}"
- headers = self._build_hf_headers(token=token)
- resp = get_session().get(path, params={"diff": "1"}, headers=headers)
- hf_raise_for_status(resp)
-
- discussion_details = resp.json()
- is_pull_request = discussion_details["isPullRequest"]
-
- target_branch = discussion_details["changes"]["base"] if is_pull_request else None
- conflicting_files = discussion_details["filesWithConflicts"] if is_pull_request else None
- merge_commit_oid = discussion_details["changes"].get("mergeCommitId", None) if is_pull_request else None
-
- return DiscussionWithDetails(
- title=discussion_details["title"],
- num=discussion_details["num"],
- author=discussion_details.get("author", {}).get("name", "deleted"),
- created_at=parse_datetime(discussion_details["createdAt"]),
- status=discussion_details["status"],
- repo_id=discussion_details["repo"]["name"],
- repo_type=discussion_details["repo"]["type"],
- is_pull_request=discussion_details["isPullRequest"],
- events=[deserialize_event(evt) for evt in discussion_details["events"]],
- conflicting_files=conflicting_files,
- target_branch=target_branch,
- merge_commit_oid=merge_commit_oid,
- diff=discussion_details.get("diff"),
- endpoint=self.endpoint,
- )
-
- @validate_hf_hub_args
- def create_discussion(
- self,
- repo_id: str,
- title: str,
- *,
- token: Optional[str] = None,
- description: Optional[str] = None,
- repo_type: Optional[str] = None,
- pull_request: bool = False,
- ) -> DiscussionWithDetails:
- """Creates a Discussion or Pull Request.
-
- Pull Requests created programmatically will be in `"draft"` status.
-
- Creating a Pull Request with changes can also be done at once with [`HfApi.create_commit`].
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- title (`str`):
- The title of the discussion. It can be up to 200 characters long,
- and must be at least 3 characters long. Leading and trailing whitespaces
- will be stripped.
- token (`str`, *optional*):
- An authentication token (See https://huggingface.co/settings/token)
- description (`str`, *optional*):
- An optional description for the Pull Request.
- Defaults to `"Discussion opened with the huggingface_hub Python library"`
- pull_request (`bool`, *optional*):
- Whether to create a Pull Request or discussion. If `True`, creates a Pull Request.
- If `False`, creates a discussion. Defaults to `False`.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or
- space, `None` or `"model"` if uploading to a model. Default is
- `None`.
-
- Returns: [`DiscussionWithDetails`]
-
-
-
- Raises the following errors:
-
- - [`HTTPError`](https://2.python-requests.org/en/master/api/#requests.HTTPError)
- if the HuggingFace API returned an error
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- if some parameter value is invalid
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
-
- """
- if repo_type not in REPO_TYPES:
- raise ValueError(f"Invalid repo type, must be one of {REPO_TYPES}")
- if repo_type is None:
- repo_type = REPO_TYPE_MODEL
-
- if description is not None:
- description = description.strip()
- description = (
- description
- if description
- else (
- f"{'Pull Request' if pull_request else 'Discussion'} opened with the"
- " [huggingface_hub Python"
- " library](https://huggingface.co/docs/huggingface_hub)"
- )
- )
-
- headers = self._build_hf_headers(token=token, is_write_action=True)
- resp = get_session().post(
- f"{self.endpoint}/api/{repo_type}s/{repo_id}/discussions",
- json={
- "title": title.strip(),
- "description": description,
- "pullRequest": pull_request,
- },
- headers=headers,
- )
- hf_raise_for_status(resp)
- num = resp.json()["num"]
- return self.get_discussion_details(
- repo_id=repo_id,
- repo_type=repo_type,
- discussion_num=num,
- token=token,
- )
-
- @validate_hf_hub_args
- def create_pull_request(
- self,
- repo_id: str,
- title: str,
- *,
- token: Optional[str] = None,
- description: Optional[str] = None,
- repo_type: Optional[str] = None,
- ) -> DiscussionWithDetails:
- """Creates a Pull Request . Pull Requests created programmatically will be in `"draft"` status.
-
- Creating a Pull Request with changes can also be done at once with [`HfApi.create_commit`];
-
- This is a wrapper around [`HfApi.create_discussion`].
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- title (`str`):
- The title of the discussion. It can be up to 200 characters long,
- and must be at least 3 characters long. Leading and trailing whitespaces
- will be stripped.
- token (`str`, *optional*):
- An authentication token (See https://huggingface.co/settings/token)
- description (`str`, *optional*):
- An optional description for the Pull Request.
- Defaults to `"Discussion opened with the huggingface_hub Python library"`
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or
- space, `None` or `"model"` if uploading to a model. Default is
- `None`.
-
- Returns: [`DiscussionWithDetails`]
-
-
-
- Raises the following errors:
-
- - [`HTTPError`](https://2.python-requests.org/en/master/api/#requests.HTTPError)
- if the HuggingFace API returned an error
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- if some parameter value is invalid
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
-
- """
- return self.create_discussion(
- repo_id=repo_id,
- title=title,
- token=token,
- description=description,
- repo_type=repo_type,
- pull_request=True,
- )
-
- def _post_discussion_changes(
- self,
- *,
- repo_id: str,
- discussion_num: int,
- resource: str,
- body: Optional[dict] = None,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- ) -> requests.Response:
- """Internal utility to POST changes to a Discussion or Pull Request"""
- if not isinstance(discussion_num, int) or discussion_num <= 0:
- raise ValueError("Invalid discussion_num, must be a positive integer")
- if repo_type not in REPO_TYPES:
- raise ValueError(f"Invalid repo type, must be one of {REPO_TYPES}")
- if repo_type is None:
- repo_type = REPO_TYPE_MODEL
- repo_id = f"{repo_type}s/{repo_id}"
-
- path = f"{self.endpoint}/api/{repo_id}/discussions/{discussion_num}/{resource}"
-
- headers = self._build_hf_headers(token=token, is_write_action=True)
- resp = requests.post(path, headers=headers, json=body)
- hf_raise_for_status(resp)
- return resp
-
- @validate_hf_hub_args
- def comment_discussion(
- self,
- repo_id: str,
- discussion_num: int,
- comment: str,
- *,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- ) -> DiscussionComment:
- """Creates a new comment on the given Discussion.
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- discussion_num (`int`):
- The number of the Discussion or Pull Request . Must be a strictly positive integer.
- comment (`str`):
- The content of the comment to create. Comments support markdown formatting.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or
- space, `None` or `"model"` if uploading to a model. Default is
- `None`.
- token (`str`, *optional*):
- An authentication token (See https://huggingface.co/settings/token)
-
- Returns:
- [`DiscussionComment`]: the newly created comment
-
-
- Examples:
- ```python
-
- >>> comment = \"\"\"
- ... Hello @otheruser!
- ...
- ... # This is a title
- ...
- ... **This is bold**, *this is italic* and ~this is strikethrough~
- ... And [this](http://url) is a link
- ... \"\"\"
-
- >>> HfApi().comment_discussion(
- ... repo_id="username/repo_name",
- ... discussion_num=34
- ... comment=comment
- ... )
- # DiscussionComment(id='deadbeef0000000', type='comment', ...)
-
- ```
-
-
-
- Raises the following errors:
-
- - [`HTTPError`](https://2.python-requests.org/en/master/api/#requests.HTTPError)
- if the HuggingFace API returned an error
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- if some parameter value is invalid
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
-
-
- """
- resp = self._post_discussion_changes(
- repo_id=repo_id,
- repo_type=repo_type,
- discussion_num=discussion_num,
- token=token,
- resource="comment",
- body={"comment": comment},
- )
- return deserialize_event(resp.json()["newMessage"]) # type: ignore
-
- @validate_hf_hub_args
- def rename_discussion(
- self,
- repo_id: str,
- discussion_num: int,
- new_title: str,
- *,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- ) -> DiscussionTitleChange:
- """Renames a Discussion.
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- discussion_num (`int`):
- The number of the Discussion or Pull Request . Must be a strictly positive integer.
- new_title (`str`):
- The new title for the discussion
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or
- space, `None` or `"model"` if uploading to a model. Default is
- `None`.
- token (`str`, *optional*):
- An authentication token (See https://huggingface.co/settings/token)
-
- Returns:
- [`DiscussionTitleChange`]: the title change event
-
-
- Examples:
- ```python
- >>> new_title = "New title, fixing a typo"
- >>> HfApi().rename_discussion(
- ... repo_id="username/repo_name",
- ... discussion_num=34
- ... new_title=new_title
- ... )
- # DiscussionTitleChange(id='deadbeef0000000', type='title-change', ...)
-
- ```
-
-
-
- Raises the following errors:
-
- - [`HTTPError`](https://2.python-requests.org/en/master/api/#requests.HTTPError)
- if the HuggingFace API returned an error
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- if some parameter value is invalid
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
-
-
- """
- resp = self._post_discussion_changes(
- repo_id=repo_id,
- repo_type=repo_type,
- discussion_num=discussion_num,
- token=token,
- resource="title",
- body={"title": new_title},
- )
- return deserialize_event(resp.json()["newTitle"]) # type: ignore
-
- @validate_hf_hub_args
- def change_discussion_status(
- self,
- repo_id: str,
- discussion_num: int,
- new_status: Literal["open", "closed"],
- *,
- token: Optional[str] = None,
- comment: Optional[str] = None,
- repo_type: Optional[str] = None,
- ) -> DiscussionStatusChange:
- """Closes or re-opens a Discussion or Pull Request.
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- discussion_num (`int`):
- The number of the Discussion or Pull Request . Must be a strictly positive integer.
- new_status (`str`):
- The new status for the discussion, either `"open"` or `"closed"`.
- comment (`str`, *optional*):
- An optional comment to post with the status change.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or
- space, `None` or `"model"` if uploading to a model. Default is
- `None`.
- token (`str`, *optional*):
- An authentication token (See https://huggingface.co/settings/token)
-
- Returns:
- [`DiscussionStatusChange`]: the status change event
-
-
- Examples:
- ```python
- >>> new_title = "New title, fixing a typo"
- >>> HfApi().rename_discussion(
- ... repo_id="username/repo_name",
- ... discussion_num=34
- ... new_title=new_title
- ... )
- # DiscussionStatusChange(id='deadbeef0000000', type='status-change', ...)
-
- ```
-
-
-
- Raises the following errors:
-
- - [`HTTPError`](https://2.python-requests.org/en/master/api/#requests.HTTPError)
- if the HuggingFace API returned an error
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- if some parameter value is invalid
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
-
-
- """
- if new_status not in ["open", "closed"]:
- raise ValueError("Invalid status, valid statuses are: 'open' and 'closed'")
- body: Dict[str, str] = {"status": new_status}
- if comment and comment.strip():
- body["comment"] = comment.strip()
- resp = self._post_discussion_changes(
- repo_id=repo_id,
- repo_type=repo_type,
- discussion_num=discussion_num,
- token=token,
- resource="status",
- body=body,
- )
- return deserialize_event(resp.json()["newStatus"]) # type: ignore
-
- @validate_hf_hub_args
- def merge_pull_request(
- self,
- repo_id: str,
- discussion_num: int,
- *,
- token: Optional[str] = None,
- comment: Optional[str] = None,
- repo_type: Optional[str] = None,
- ):
- """Merges a Pull Request.
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- discussion_num (`int`):
- The number of the Discussion or Pull Request . Must be a strictly positive integer.
- comment (`str`, *optional*):
- An optional comment to post with the status change.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or
- space, `None` or `"model"` if uploading to a model. Default is
- `None`.
- token (`str`, *optional*):
- An authentication token (See https://huggingface.co/settings/token)
-
- Returns:
- [`DiscussionStatusChange`]: the status change event
-
-
-
- Raises the following errors:
-
- - [`HTTPError`](https://2.python-requests.org/en/master/api/#requests.HTTPError)
- if the HuggingFace API returned an error
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- if some parameter value is invalid
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
-
-
- """
- self._post_discussion_changes(
- repo_id=repo_id,
- repo_type=repo_type,
- discussion_num=discussion_num,
- token=token,
- resource="merge",
- body={"comment": comment.strip()} if comment and comment.strip() else None,
- )
-
- @validate_hf_hub_args
- def edit_discussion_comment(
- self,
- repo_id: str,
- discussion_num: int,
- comment_id: str,
- new_content: str,
- *,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- ) -> DiscussionComment:
- """Edits a comment on a Discussion / Pull Request.
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- discussion_num (`int`):
- The number of the Discussion or Pull Request . Must be a strictly positive integer.
- comment_id (`str`):
- The ID of the comment to edit.
- new_content (`str`):
- The new content of the comment. Comments support markdown formatting.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or
- space, `None` or `"model"` if uploading to a model. Default is
- `None`.
- token (`str`, *optional*):
- An authentication token (See https://huggingface.co/settings/token)
-
- Returns:
- [`DiscussionComment`]: the edited comment
-
-
-
- Raises the following errors:
-
- - [`HTTPError`](https://2.python-requests.org/en/master/api/#requests.HTTPError)
- if the HuggingFace API returned an error
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- if some parameter value is invalid
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
-
-
- """
- resp = self._post_discussion_changes(
- repo_id=repo_id,
- repo_type=repo_type,
- discussion_num=discussion_num,
- token=token,
- resource=f"comment/{comment_id.lower()}/edit",
- body={"content": new_content},
- )
- return deserialize_event(resp.json()["updatedComment"]) # type: ignore
-
- @validate_hf_hub_args
- def hide_discussion_comment(
- self,
- repo_id: str,
- discussion_num: int,
- comment_id: str,
- *,
- token: Optional[str] = None,
- repo_type: Optional[str] = None,
- ) -> DiscussionComment:
- """Hides a comment on a Discussion / Pull Request.
-
-
- Hidden comments' content cannot be retrieved anymore. Hiding a comment is irreversible.
-
-
- Args:
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- discussion_num (`int`):
- The number of the Discussion or Pull Request . Must be a strictly positive integer.
- comment_id (`str`):
- The ID of the comment to edit.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if uploading to a dataset or
- space, `None` or `"model"` if uploading to a model. Default is
- `None`.
- token (`str`, *optional*):
- An authentication token (See https://huggingface.co/settings/token)
-
- Returns:
- [`DiscussionComment`]: the hidden comment
-
-
-
- Raises the following errors:
-
- - [`HTTPError`](https://2.python-requests.org/en/master/api/#requests.HTTPError)
- if the HuggingFace API returned an error
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- if some parameter value is invalid
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
-
-
- """
- warnings.warn(
- "Hidden comments' content cannot be retrieved anymore. Hiding a comment is irreversible.",
- UserWarning,
- )
- resp = self._post_discussion_changes(
- repo_id=repo_id,
- repo_type=repo_type,
- discussion_num=discussion_num,
- token=token,
- resource=f"comment/{comment_id.lower()}/hide",
- )
- return deserialize_event(resp.json()["updatedComment"]) # type: ignore
-
- @validate_hf_hub_args
- def add_space_secret(self, repo_id: str, key: str, value: str, *, token: Optional[str] = None) -> None:
- """Adds or updates a secret in a Space.
-
- Secrets allow to set secret keys or tokens to a Space without hardcoding them.
- For more details, see https://huggingface.co/docs/hub/spaces-overview#managing-secrets.
-
- Args:
- repo_id (`str`):
- ID of the repo to update. Example: `"bigcode/in-the-stack"`.
- key (`str`):
- Secret key. Example: `"GITHUB_API_KEY"`
- value (`str`):
- Secret value. Example: `"your_github_api_key"`.
- token (`str`, *optional*):
- Hugging Face token. Will default to the locally saved token if not provided.
- """
- r = get_session().post(
- f"{self.endpoint}/api/spaces/{repo_id}/secrets",
- headers=self._build_hf_headers(token=token),
- json={"key": key, "value": value},
- )
- hf_raise_for_status(r)
-
- @validate_hf_hub_args
- def delete_space_secret(self, repo_id: str, key: str, *, token: Optional[str] = None) -> None:
- """Deletes a secret from a Space.
-
- Secrets allow to set secret keys or tokens to a Space without hardcoding them.
- For more details, see https://huggingface.co/docs/hub/spaces-overview#managing-secrets.
-
- Args:
- repo_id (`str`):
- ID of the repo to update. Example: `"bigcode/in-the-stack"`.
- key (`str`):
- Secret key. Example: `"GITHUB_API_KEY"`.
- token (`str`, *optional*):
- Hugging Face token. Will default to the locally saved token if not provided.
- """
- r = get_session().delete(
- f"{self.endpoint}/api/spaces/{repo_id}/secrets",
- headers=self._build_hf_headers(token=token),
- json={"key": key},
- )
- hf_raise_for_status(r)
-
- @validate_hf_hub_args
- def get_space_runtime(self, repo_id: str, *, token: Optional[str] = None) -> SpaceRuntime:
- """Gets runtime information about a Space.
-
- Args:
- repo_id (`str`):
- ID of the repo to update. Example: `"bigcode/in-the-stack"`.
- token (`str`, *optional*):
- Hugging Face token. Will default to the locally saved token if
- not provided.
- Returns:
- [`SpaceRuntime`]: Runtime information about a Space including Space stage and hardware.
- """
- r = get_session().get(
- f"{self.endpoint}/api/spaces/{repo_id}/runtime", headers=self._build_hf_headers(token=token)
- )
- hf_raise_for_status(r)
- return SpaceRuntime(r.json())
-
- @validate_hf_hub_args
- def request_space_hardware(
- self,
- repo_id: str,
- hardware: SpaceHardware,
- *,
- token: Optional[str] = None,
- sleep_time: Optional[int] = None,
- ) -> SpaceRuntime:
- """Request new hardware for a Space.
-
- Args:
- repo_id (`str`):
- ID of the repo to update. Example: `"bigcode/in-the-stack"`.
- hardware (`str` or [`SpaceHardware`]):
- Hardware on which to run the Space. Example: `"t4-medium"`.
- token (`str`, *optional*):
- Hugging Face token. Will default to the locally saved token if not provided.
- sleep_time (`int`, *optional*):
- Number of seconds of inactivity to wait before a Space is put to sleep. Set to `-1` if you don't want
- your Space to sleep (default behavior for upgraded hardware). For free hardware, you can't configure
- the sleep time (value is fixed to 48 hours of inactivity).
- See https://huggingface.co/docs/hub/spaces-gpus#sleep-time for more details.
- Returns:
- [`SpaceRuntime`]: Runtime information about a Space including Space stage and hardware.
-
-
-
- It is also possible to request hardware directly when creating the Space repo! See [`create_repo`] for details.
-
-
- """
- if sleep_time is not None and hardware == SpaceHardware.CPU_BASIC:
- warnings.warn(
- (
- "If your Space runs on the default 'cpu-basic' hardware, it will go to sleep if inactive for more"
- " than 48 hours. This value is not configurable. If you don't want your Space to deactivate or if"
- " you want to set a custom sleep time, you need to upgrade to a paid Hardware."
- ),
- UserWarning,
- )
- payload: Dict[str, Any] = {"flavor": hardware}
- if sleep_time is not None:
- payload["sleepTimeSeconds"] = sleep_time
- r = get_session().post(
- f"{self.endpoint}/api/spaces/{repo_id}/hardware",
- headers=self._build_hf_headers(token=token),
- json=payload,
- )
- hf_raise_for_status(r)
- return SpaceRuntime(r.json())
-
- @validate_hf_hub_args
- def set_space_sleep_time(self, repo_id: str, sleep_time: int, *, token: Optional[str] = None) -> SpaceRuntime:
- """Set a custom sleep time for a Space running on upgraded hardware..
-
- Your Space will go to sleep after X seconds of inactivity. You are not billed when your Space is in "sleep"
- mode. If a new visitor lands on your Space, it will "wake it up". Only upgraded hardware can have a
- configurable sleep time. To know more about the sleep stage, please refer to
- https://huggingface.co/docs/hub/spaces-gpus#sleep-time.
-
- Args:
- repo_id (`str`):
- ID of the repo to update. Example: `"bigcode/in-the-stack"`.
- sleep_time (`int`, *optional*):
- Number of seconds of inactivity to wait before a Space is put to sleep. Set to `-1` if you don't want
- your Space to pause (default behavior for upgraded hardware). For free hardware, you can't configure
- the sleep time (value is fixed to 48 hours of inactivity).
- See https://huggingface.co/docs/hub/spaces-gpus#sleep-time for more details.
- token (`str`, *optional*):
- Hugging Face token. Will default to the locally saved token if not provided.
- Returns:
- [`SpaceRuntime`]: Runtime information about a Space including Space stage and hardware.
-
-
-
- It is also possible to set a custom sleep time when requesting hardware with [`request_space_hardware`].
-
-
- """
- r = get_session().post(
- f"{self.endpoint}/api/spaces/{repo_id}/sleeptime",
- headers=self._build_hf_headers(token=token),
- json={"seconds": sleep_time},
- )
- hf_raise_for_status(r)
- runtime = SpaceRuntime(r.json())
-
- hardware = runtime.requested_hardware or runtime.hardware
- if hardware == SpaceHardware.CPU_BASIC:
- warnings.warn(
- (
- "If your Space runs on the default 'cpu-basic' hardware, it will go to sleep if inactive for more"
- " than 48 hours. This value is not configurable. If you don't want your Space to deactivate or if"
- " you want to set a custom sleep time, you need to upgrade to a paid Hardware."
- ),
- UserWarning,
- )
- return runtime
-
- @validate_hf_hub_args
- def pause_space(self, repo_id: str, *, token: Optional[str] = None) -> SpaceRuntime:
- """Pause your Space.
-
- A paused Space stops executing until manually restarted by its owner. This is different from the sleeping
- state in which free Spaces go after 48h of inactivity. Paused time is not billed to your account, no matter the
- hardware you've selected. To restart your Space, use [`restart_space`] and go to your Space settings page.
-
- For more details, please visit [the docs](https://huggingface.co/docs/hub/spaces-gpus#pause).
-
- Args:
- repo_id (`str`):
- ID of the Space to pause. Example: `"Salesforce/BLIP2"`.
- token (`str`, *optional*):
- Hugging Face token. Will default to the locally saved token if not provided.
-
- Returns:
- [`SpaceRuntime`]: Runtime information about your Space including `stage=PAUSED` and requested hardware.
-
- Raises:
- [`~utils.RepositoryNotFoundError`]:
- If your Space is not found (error 404). Most probably wrong repo_id or your space is private but you
- are not authenticated.
- [`~utils.HfHubHTTPError`]:
- 403 Forbidden: only the owner of a Space can pause it. If you want to manage a Space that you don't
- own, either ask the owner by opening a Discussion or duplicate the Space.
- [`~utils.BadRequestError`]:
- If your Space is a static Space. Static Spaces are always running and never billed. If you want to hide
- a static Space, you can set it to private.
- """
- r = get_session().post(
- f"{self.endpoint}/api/spaces/{repo_id}/pause", headers=self._build_hf_headers(token=token)
- )
- hf_raise_for_status(r)
- return SpaceRuntime(r.json())
-
- @validate_hf_hub_args
- def restart_space(self, repo_id: str, *, token: Optional[str] = None) -> SpaceRuntime:
- """Restart your Space.
-
- This is the only way to programmatically restart a Space if you've put it on Pause (see [`pause_space`]). You
- must be the owner of the Space to restart it. If you are using an upgraded hardware, your account will be
- billed as soon as the Space is restarted. You can trigger a restart no matter the current state of a Space.
-
- For more details, please visit [the docs](https://huggingface.co/docs/hub/spaces-gpus#pause).
-
- Args:
- repo_id (`str`):
- ID of the Space to restart. Example: `"Salesforce/BLIP2"`.
- token (`str`, *optional*):
- Hugging Face token. Will default to the locally saved token if not provided.
-
- Returns:
- [`SpaceRuntime`]: Runtime information about your Space.
-
- Raises:
- [`~utils.RepositoryNotFoundError`]:
- If your Space is not found (error 404). Most probably wrong repo_id or your space is private but you
- are not authenticated.
- [`~utils.HfHubHTTPError`]:
- 403 Forbidden: only the owner of a Space can restart it. If you want to restart a Space that you don't
- own, either ask the owner by opening a Discussion or duplicate the Space.
- [`~utils.BadRequestError`]:
- If your Space is a static Space. Static Spaces are always running and never billed. If you want to hide
- a static Space, you can set it to private.
- """
- r = get_session().post(
- f"{self.endpoint}/api/spaces/{repo_id}/restart", headers=self._build_hf_headers(token=token)
- )
- hf_raise_for_status(r)
- return SpaceRuntime(r.json())
-
- @validate_hf_hub_args
- def duplicate_space(
- self,
- from_id: str,
- to_id: Optional[str] = None,
- *,
- private: Optional[bool] = None,
- token: Optional[str] = None,
- exist_ok: bool = False,
- ) -> RepoUrl:
- """Duplicate a Space.
-
- Programmatically duplicate a Space. The new Space will be created in your account and will be in the same state
- as the original Space (running or paused). You can duplicate a Space no matter the current state of a Space.
-
- Args:
- from_id (`str`):
- ID of the Space to duplicate. Example: `"pharma/CLIP-Interrogator"`.
- to_id (`str`, *optional*):
- ID of the new Space. Example: `"dog/CLIP-Interrogator"`. If not provided, the new Space will have the same
- name as the original Space, but in your account.
- private (`bool`, *optional*):
- Whether the new Space should be private or not. Defaults to the same privacy as the original Space.
- token (`str`, *optional*):
- Hugging Face token. Will default to the locally saved token if not provided.
- exist_ok (`bool`, *optional*, defaults to `False`):
- If `True`, do not raise an error if repo already exists.
-
- Returns:
- [`RepoUrl`]: URL to the newly created repo. Value is a subclass of `str` containing
- attributes like `endpoint`, `repo_type` and `repo_id`.
-
- Raises:
- - [`HTTPError`](https://2.python-requests.org/en/master/api/#requests.HTTPError)
- if the HuggingFace API returned an error
- - [`~utils.RepositoryNotFoundError`]
- If one of `from_id` or `to_id` cannot be found. This may be because it doesn't exist,
- or because it is set to `private` and you do not have access.
-
- Example:
- ```python
- >>> from huggingface_hub import duplicate_space
-
- # Duplicate a Space to your account
- >>> duplicate_space("multimodalart/dreambooth-training")
- RepoUrl('https://huggingface.co/spaces/nateraw/dreambooth-training',...)
-
- # Can set custom destination id and visibility flag.
- >>> duplicate_space("multimodalart/dreambooth-training", to_id="my-dreambooth", private=True)
- RepoUrl('https://huggingface.co/spaces/nateraw/my-dreambooth',...)
- ```
- """
- # Parse to_id if provided
- parsed_to_id = RepoUrl(to_id) if to_id is not None else None
-
- # Infer target repo_id
- to_namespace = ( # set namespace manually or default to username
- parsed_to_id.namespace
- if parsed_to_id is not None and parsed_to_id.namespace is not None
- else self.whoami(token)["name"]
- )
- to_repo_name = parsed_to_id.repo_name if to_id is not None else RepoUrl(from_id).repo_name # type: ignore
-
- # repository must be a valid repo_id (namespace/repo_name).
- payload: Dict[str, Any] = {"repository": f"{to_namespace}/{to_repo_name}"}
-
- # private is optional with this endpoint, with None defaulting to the original space's privacy.
- if private is not None:
- payload["private"] = private
-
- r = get_session().post(
- f"{self.endpoint}/api/spaces/{from_id}/duplicate",
- headers=self._build_hf_headers(token=token, is_write_action=True),
- json=payload,
- )
-
- try:
- hf_raise_for_status(r)
- except HTTPError as err:
- if exist_ok and err.response.status_code == 409:
- # Repo already exists and `exist_ok=True`
- pass
- else:
- raise
-
- return RepoUrl(r.json()["url"], endpoint=self.endpoint)
-
- def _build_hf_headers(
- self,
- token: Optional[Union[bool, str]] = None,
- is_write_action: bool = False,
- library_name: Optional[str] = None,
- library_version: Optional[str] = None,
- user_agent: Union[Dict, str, None] = None,
- ) -> Dict[str, str]:
- """
- Alias for [`build_hf_headers`] that uses the token from [`HfApi`] client
- when `token` is not provided.
- """
- if token is None:
- # Cannot do `token = token or self.token` as token can be `False`.
- token = self.token
- return build_hf_headers(
- token=token,
- is_write_action=is_write_action,
- library_name=library_name or self.library_name,
- library_version=library_version or self.library_version,
- user_agent=user_agent or self.user_agent,
- )
-
- def _prepare_upload_folder_deletions(
- self,
- repo_id: str,
- repo_type: Optional[str],
- revision: Optional[str],
- token: Optional[str],
- path_in_repo: str,
- delete_patterns: Optional[Union[List[str], str]],
- ) -> List[CommitOperationDelete]:
- """Generate the list of Delete operations for a commit to delete files from a repo.
-
- List remote files and match them against the `delete_patterns` constraints. Returns a list of [`CommitOperationDelete`]
- with the matching items.
-
- Note: `.gitattributes` file is essential to make a repo work properly on the Hub. This file will always be
- kept even if it matches the `delete_patterns` constraints.
- """
- if delete_patterns is None:
- # If no delete patterns, no need to list and filter remote files
- return []
-
- # List remote files
- filenames = self.list_repo_files(repo_id=repo_id, revision=revision, repo_type=repo_type, token=token)
-
- # Compute relative path in repo
- if path_in_repo:
- path_in_repo = path_in_repo.strip("/") + "/" # harmonize
- relpath_to_abspath = {
- file[len(path_in_repo) :]: file for file in filenames if file.startswith(path_in_repo)
- }
- else:
- relpath_to_abspath = {file: file for file in filenames}
-
- # Apply filter on relative paths and return
- return [
- CommitOperationDelete(path_in_repo=relpath_to_abspath[relpath], is_folder=False)
- for relpath in filter_repo_objects(relpath_to_abspath.keys(), allow_patterns=delete_patterns)
- if relpath_to_abspath[relpath] != ".gitattributes"
- ]
-
-
-def _prepare_upload_folder_additions(
- folder_path: Union[str, Path],
- path_in_repo: str,
- allow_patterns: Optional[Union[List[str], str]] = None,
- ignore_patterns: Optional[Union[List[str], str]] = None,
-) -> List[CommitOperationAdd]:
- """Generate the list of Add operations for a commit to upload a folder.
-
- Files not matching the `allow_patterns` (allowlist) and `ignore_patterns` (denylist)
- constraints are discarded.
- """
- folder_path = Path(folder_path).expanduser().resolve()
- if not folder_path.is_dir():
- raise ValueError(f"Provided path: '{folder_path}' is not a directory")
-
- # List files from folder
- relpath_to_abspath = {
- path.relative_to(folder_path).as_posix(): path
- for path in sorted(folder_path.glob("**/*")) # sorted to be deterministic
- if path.is_file()
- }
-
- # Filter files and return
- # Patterns are applied on the path relative to `folder_path`. `path_in_repo` is prefixed after the filtering.
- prefix = f"{path_in_repo.strip('/')}/" if path_in_repo else ""
- return [
- CommitOperationAdd(
- path_or_fileobj=relpath_to_abspath[relpath], # absolute path on disk
- path_in_repo=prefix + relpath, # "absolute" path in repo
- )
- for relpath in filter_repo_objects(
- relpath_to_abspath.keys(), allow_patterns=allow_patterns, ignore_patterns=ignore_patterns
- )
- ]
-
-
-def _parse_revision_from_pr_url(pr_url: str) -> str:
- """Safely parse revision number from a PR url.
-
- Example:
- ```py
- >>> _parse_revision_from_pr_url("https://huggingface.co/bigscience/bloom/discussions/2")
- "refs/pr/2"
- ```
- """
- re_match = re.match(_REGEX_DISCUSSION_URL, pr_url)
- if re_match is None:
- raise RuntimeError(f"Unexpected response from the hub, expected a Pull Request URL but got: '{pr_url}'")
- return f"refs/pr/{re_match[1]}"
-
-
-api = HfApi()
-
-whoami = api.whoami
-get_token_permission = api.get_token_permission
-
-list_models = api.list_models
-model_info = api.model_info
-
-list_datasets = api.list_datasets
-dataset_info = api.dataset_info
-
-list_spaces = api.list_spaces
-space_info = api.space_info
-
-repo_info = api.repo_info
-list_repo_files = api.list_repo_files
-list_repo_refs = api.list_repo_refs
-list_repo_commits = api.list_repo_commits
-list_files_info = api.list_files_info
-
-list_metrics = api.list_metrics
-
-get_model_tags = api.get_model_tags
-get_dataset_tags = api.get_dataset_tags
-
-create_commit = api.create_commit
-create_repo = api.create_repo
-delete_repo = api.delete_repo
-update_repo_visibility = api.update_repo_visibility
-move_repo = api.move_repo
-upload_file = api.upload_file
-upload_folder = api.upload_folder
-delete_file = api.delete_file
-delete_folder = api.delete_folder
-create_commits_on_pr = api.create_commits_on_pr
-create_branch = api.create_branch
-delete_branch = api.delete_branch
-create_tag = api.create_tag
-delete_tag = api.delete_tag
-get_full_repo_name = api.get_full_repo_name
-
-# Background jobs
-run_as_future = api.run_as_future
-
-# Activity API
-list_liked_repos = api.list_liked_repos
-like = api.like
-unlike = api.unlike
-
-# Community API
-get_discussion_details = api.get_discussion_details
-get_repo_discussions = api.get_repo_discussions
-create_discussion = api.create_discussion
-create_pull_request = api.create_pull_request
-change_discussion_status = api.change_discussion_status
-comment_discussion = api.comment_discussion
-edit_discussion_comment = api.edit_discussion_comment
-rename_discussion = api.rename_discussion
-merge_pull_request = api.merge_pull_request
-
-# Space API
-add_space_secret = api.add_space_secret
-delete_space_secret = api.delete_space_secret
-get_space_runtime = api.get_space_runtime
-request_space_hardware = api.request_space_hardware
-set_space_sleep_time = api.set_space_sleep_time
-pause_space = api.pause_space
-restart_space = api.restart_space
-duplicate_space = api.duplicate_space
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/importlib_resources/tests/data01/subdirectory/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/importlib_resources/tests/data01/subdirectory/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/collections.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/collections.py
deleted file mode 100644
index bf88dd2b68a36bd3b6594da2ba9b084513134457..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/collections.py
+++ /dev/null
@@ -1,2113 +0,0 @@
-"""
-Classes for the efficient drawing of large collections of objects that
-share most properties, e.g., a large number of line segments or
-polygons.
-
-The classes are not meant to be as flexible as their single element
-counterparts (e.g., you may not be able to select all line styles) but
-they are meant to be fast for common use cases (e.g., a large set of solid
-line segments).
-"""
-
-import math
-from numbers import Number
-import warnings
-
-import numpy as np
-
-import matplotlib as mpl
-from . import (_api, _path, artist, cbook, cm, colors as mcolors, _docstring,
- hatch as mhatch, lines as mlines, path as mpath, transforms)
-from ._enums import JoinStyle, CapStyle
-
-
-# "color" is excluded; it is a compound setter, and its docstring differs
-# in LineCollection.
-@_api.define_aliases({
- "antialiased": ["antialiaseds", "aa"],
- "edgecolor": ["edgecolors", "ec"],
- "facecolor": ["facecolors", "fc"],
- "linestyle": ["linestyles", "dashes", "ls"],
- "linewidth": ["linewidths", "lw"],
- "offset_transform": ["transOffset"],
-})
-class Collection(artist.Artist, cm.ScalarMappable):
- r"""
- Base class for Collections. Must be subclassed to be usable.
-
- A Collection represents a sequence of `.Patch`\es that can be drawn
- more efficiently together than individually. For example, when a single
- path is being drawn repeatedly at different offsets, the renderer can
- typically execute a ``draw_marker()`` call much more efficiently than a
- series of repeated calls to ``draw_path()`` with the offsets put in
- one-by-one.
-
- Most properties of a collection can be configured per-element. Therefore,
- Collections have "plural" versions of many of the properties of a `.Patch`
- (e.g. `.Collection.get_paths` instead of `.Patch.get_path`). Exceptions are
- the *zorder*, *hatch*, *pickradius*, *capstyle* and *joinstyle* properties,
- which can only be set globally for the whole collection.
-
- Besides these exceptions, all properties can be specified as single values
- (applying to all elements) or sequences of values. The property of the
- ``i``\th element of the collection is::
-
- prop[i % len(prop)]
-
- Each Collection can optionally be used as its own `.ScalarMappable` by
- passing the *norm* and *cmap* parameters to its constructor. If the
- Collection's `.ScalarMappable` matrix ``_A`` has been set (via a call
- to `.Collection.set_array`), then at draw time this internal scalar
- mappable will be used to set the ``facecolors`` and ``edgecolors``,
- ignoring those that were manually passed in.
- """
- #: Either a list of 3x3 arrays or an Nx3x3 array (representing N
- #: transforms), suitable for the `all_transforms` argument to
- #: `~matplotlib.backend_bases.RendererBase.draw_path_collection`;
- #: each 3x3 array is used to initialize an
- #: `~matplotlib.transforms.Affine2D` object.
- #: Each kind of collection defines this based on its arguments.
- _transforms = np.empty((0, 3, 3))
-
- # Whether to draw an edge by default. Set on a
- # subclass-by-subclass basis.
- _edge_default = False
-
- @_docstring.interpd
- @_api.make_keyword_only("3.6", name="edgecolors")
- def __init__(self,
- edgecolors=None,
- facecolors=None,
- linewidths=None,
- linestyles='solid',
- capstyle=None,
- joinstyle=None,
- antialiaseds=None,
- offsets=None,
- offset_transform=None,
- norm=None, # optional for ScalarMappable
- cmap=None, # ditto
- pickradius=5.0,
- hatch=None,
- urls=None,
- *,
- zorder=1,
- **kwargs
- ):
- """
- Parameters
- ----------
- edgecolors : color or list of colors, default: :rc:`patch.edgecolor`
- Edge color for each patch making up the collection. The special
- value 'face' can be passed to make the edgecolor match the
- facecolor.
- facecolors : color or list of colors, default: :rc:`patch.facecolor`
- Face color for each patch making up the collection.
- linewidths : float or list of floats, default: :rc:`patch.linewidth`
- Line width for each patch making up the collection.
- linestyles : str or tuple or list thereof, default: 'solid'
- Valid strings are ['solid', 'dashed', 'dashdot', 'dotted', '-',
- '--', '-.', ':']. Dash tuples should be of the form::
-
- (offset, onoffseq),
-
- where *onoffseq* is an even length tuple of on and off ink lengths
- in points. For examples, see
- :doc:`/gallery/lines_bars_and_markers/linestyles`.
- capstyle : `.CapStyle`-like, default: :rc:`patch.capstyle`
- Style to use for capping lines for all paths in the collection.
- Allowed values are %(CapStyle)s.
- joinstyle : `.JoinStyle`-like, default: :rc:`patch.joinstyle`
- Style to use for joining lines for all paths in the collection.
- Allowed values are %(JoinStyle)s.
- antialiaseds : bool or list of bool, default: :rc:`patch.antialiased`
- Whether each patch in the collection should be drawn with
- antialiasing.
- offsets : (float, float) or list thereof, default: (0, 0)
- A vector by which to translate each patch after rendering (default
- is no translation). The translation is performed in screen (pixel)
- coordinates (i.e. after the Artist's transform is applied).
- offset_transform : `~.Transform`, default: `.IdentityTransform`
- A single transform which will be applied to each *offsets* vector
- before it is used.
- cmap, norm
- Data normalization and colormapping parameters. See
- `.ScalarMappable` for a detailed description.
- hatch : str, optional
- Hatching pattern to use in filled paths, if any. Valid strings are
- ['/', '\\', '|', '-', '+', 'x', 'o', 'O', '.', '*']. See
- :doc:`/gallery/shapes_and_collections/hatch_style_reference` for
- the meaning of each hatch type.
- pickradius : float, default: 5.0
- If ``pickradius <= 0``, then `.Collection.contains` will return
- ``True`` whenever the test point is inside of one of the polygons
- formed by the control points of a Path in the Collection. On the
- other hand, if it is greater than 0, then we instead check if the
- test point is contained in a stroke of width ``2*pickradius``
- following any of the Paths in the Collection.
- urls : list of str, default: None
- A URL for each patch to link to once drawn. Currently only works
- for the SVG backend. See :doc:`/gallery/misc/hyperlinks_sgskip` for
- examples.
- zorder : float, default: 1
- The drawing order, shared by all Patches in the Collection. See
- :doc:`/gallery/misc/zorder_demo` for all defaults and examples.
- """
- artist.Artist.__init__(self)
- cm.ScalarMappable.__init__(self, norm, cmap)
- # list of un-scaled dash patterns
- # this is needed scaling the dash pattern by linewidth
- self._us_linestyles = [(0, None)]
- # list of dash patterns
- self._linestyles = [(0, None)]
- # list of unbroadcast/scaled linewidths
- self._us_lw = [0]
- self._linewidths = [0]
- # Flags set by _set_mappable_flags: are colors from mapping an array?
- self._face_is_mapped = None
- self._edge_is_mapped = None
- self._mapped_colors = None # calculated in update_scalarmappable
- self._hatch_color = mcolors.to_rgba(mpl.rcParams['hatch.color'])
- self.set_facecolor(facecolors)
- self.set_edgecolor(edgecolors)
- self.set_linewidth(linewidths)
- self.set_linestyle(linestyles)
- self.set_antialiased(antialiaseds)
- self.set_pickradius(pickradius)
- self.set_urls(urls)
- self.set_hatch(hatch)
- self.set_zorder(zorder)
-
- if capstyle:
- self.set_capstyle(capstyle)
- else:
- self._capstyle = None
-
- if joinstyle:
- self.set_joinstyle(joinstyle)
- else:
- self._joinstyle = None
-
- if offsets is not None:
- offsets = np.asanyarray(offsets, float)
- # Broadcast (2,) -> (1, 2) but nothing else.
- if offsets.shape == (2,):
- offsets = offsets[None, :]
-
- self._offsets = offsets
- self._offset_transform = offset_transform
-
- self._path_effects = None
- self._internal_update(kwargs)
- self._paths = None
-
- def get_paths(self):
- return self._paths
-
- def set_paths(self, paths):
- raise NotImplementedError
-
- def get_transforms(self):
- return self._transforms
-
- def get_offset_transform(self):
- """Return the `.Transform` instance used by this artist offset."""
- if self._offset_transform is None:
- self._offset_transform = transforms.IdentityTransform()
- elif (not isinstance(self._offset_transform, transforms.Transform)
- and hasattr(self._offset_transform, '_as_mpl_transform')):
- self._offset_transform = \
- self._offset_transform._as_mpl_transform(self.axes)
- return self._offset_transform
-
- @_api.rename_parameter("3.6", "transOffset", "offset_transform")
- def set_offset_transform(self, offset_transform):
- """
- Set the artist offset transform.
-
- Parameters
- ----------
- offset_transform : `.Transform`
- """
- self._offset_transform = offset_transform
-
- def get_datalim(self, transData):
- # Calculate the data limits and return them as a `.Bbox`.
- #
- # This operation depends on the transforms for the data in the
- # collection and whether the collection has offsets:
- #
- # 1. offsets = None, transform child of transData: use the paths for
- # the automatic limits (i.e. for LineCollection in streamline).
- # 2. offsets != None: offset_transform is child of transData:
- #
- # a. transform is child of transData: use the path + offset for
- # limits (i.e for bar).
- # b. transform is not a child of transData: just use the offsets
- # for the limits (i.e. for scatter)
- #
- # 3. otherwise return a null Bbox.
-
- transform = self.get_transform()
- offset_trf = self.get_offset_transform()
- if not (isinstance(offset_trf, transforms.IdentityTransform)
- or offset_trf.contains_branch(transData)):
- # if the offsets are in some coords other than data,
- # then don't use them for autoscaling.
- return transforms.Bbox.null()
- offsets = self.get_offsets()
-
- paths = self.get_paths()
- if not len(paths):
- # No paths to transform
- return transforms.Bbox.null()
-
- if not transform.is_affine:
- paths = [transform.transform_path_non_affine(p) for p in paths]
- # Don't convert transform to transform.get_affine() here because
- # we may have transform.contains_branch(transData) but not
- # transforms.get_affine().contains_branch(transData). But later,
- # be careful to only apply the affine part that remains.
-
- if any(transform.contains_branch_seperately(transData)):
- # collections that are just in data units (like quiver)
- # can properly have the axes limits set by their shape +
- # offset. LineCollections that have no offsets can
- # also use this algorithm (like streamplot).
- if isinstance(offsets, np.ma.MaskedArray):
- offsets = offsets.filled(np.nan)
- # get_path_collection_extents handles nan but not masked arrays
- return mpath.get_path_collection_extents(
- transform.get_affine() - transData, paths,
- self.get_transforms(),
- offset_trf.transform_non_affine(offsets),
- offset_trf.get_affine().frozen())
-
- # NOTE: None is the default case where no offsets were passed in
- if self._offsets is not None:
- # this is for collections that have their paths (shapes)
- # in physical, axes-relative, or figure-relative units
- # (i.e. like scatter). We can't uniquely set limits based on
- # those shapes, so we just set the limits based on their
- # location.
- offsets = (offset_trf - transData).transform(offsets)
- # note A-B means A B^{-1}
- offsets = np.ma.masked_invalid(offsets)
- if not offsets.mask.all():
- bbox = transforms.Bbox.null()
- bbox.update_from_data_xy(offsets)
- return bbox
- return transforms.Bbox.null()
-
- def get_window_extent(self, renderer=None):
- # TODO: check to ensure that this does not fail for
- # cases other than scatter plot legend
- return self.get_datalim(transforms.IdentityTransform())
-
- def _prepare_points(self):
- # Helper for drawing and hit testing.
-
- transform = self.get_transform()
- offset_trf = self.get_offset_transform()
- offsets = self.get_offsets()
- paths = self.get_paths()
-
- if self.have_units():
- paths = []
- for path in self.get_paths():
- vertices = path.vertices
- xs, ys = vertices[:, 0], vertices[:, 1]
- xs = self.convert_xunits(xs)
- ys = self.convert_yunits(ys)
- paths.append(mpath.Path(np.column_stack([xs, ys]), path.codes))
- xs = self.convert_xunits(offsets[:, 0])
- ys = self.convert_yunits(offsets[:, 1])
- offsets = np.ma.column_stack([xs, ys])
-
- if not transform.is_affine:
- paths = [transform.transform_path_non_affine(path)
- for path in paths]
- transform = transform.get_affine()
- if not offset_trf.is_affine:
- offsets = offset_trf.transform_non_affine(offsets)
- # This might have changed an ndarray into a masked array.
- offset_trf = offset_trf.get_affine()
-
- if isinstance(offsets, np.ma.MaskedArray):
- offsets = offsets.filled(np.nan)
- # Changing from a masked array to nan-filled ndarray
- # is probably most efficient at this point.
-
- return transform, offset_trf, offsets, paths
-
- @artist.allow_rasterization
- def draw(self, renderer):
- if not self.get_visible():
- return
- renderer.open_group(self.__class__.__name__, self.get_gid())
-
- self.update_scalarmappable()
-
- transform, offset_trf, offsets, paths = self._prepare_points()
-
- gc = renderer.new_gc()
- self._set_gc_clip(gc)
- gc.set_snap(self.get_snap())
-
- if self._hatch:
- gc.set_hatch(self._hatch)
- gc.set_hatch_color(self._hatch_color)
-
- if self.get_sketch_params() is not None:
- gc.set_sketch_params(*self.get_sketch_params())
-
- if self.get_path_effects():
- from matplotlib.patheffects import PathEffectRenderer
- renderer = PathEffectRenderer(self.get_path_effects(), renderer)
-
- # If the collection is made up of a single shape/color/stroke,
- # it can be rendered once and blitted multiple times, using
- # `draw_markers` rather than `draw_path_collection`. This is
- # *much* faster for Agg, and results in smaller file sizes in
- # PDF/SVG/PS.
-
- trans = self.get_transforms()
- facecolors = self.get_facecolor()
- edgecolors = self.get_edgecolor()
- do_single_path_optimization = False
- if (len(paths) == 1 and len(trans) <= 1 and
- len(facecolors) == 1 and len(edgecolors) == 1 and
- len(self._linewidths) == 1 and
- all(ls[1] is None for ls in self._linestyles) and
- len(self._antialiaseds) == 1 and len(self._urls) == 1 and
- self.get_hatch() is None):
- if len(trans):
- combined_transform = transforms.Affine2D(trans[0]) + transform
- else:
- combined_transform = transform
- extents = paths[0].get_extents(combined_transform)
- if (extents.width < self.figure.bbox.width
- and extents.height < self.figure.bbox.height):
- do_single_path_optimization = True
-
- if self._joinstyle:
- gc.set_joinstyle(self._joinstyle)
-
- if self._capstyle:
- gc.set_capstyle(self._capstyle)
-
- if do_single_path_optimization:
- gc.set_foreground(tuple(edgecolors[0]))
- gc.set_linewidth(self._linewidths[0])
- gc.set_dashes(*self._linestyles[0])
- gc.set_antialiased(self._antialiaseds[0])
- gc.set_url(self._urls[0])
- renderer.draw_markers(
- gc, paths[0], combined_transform.frozen(),
- mpath.Path(offsets), offset_trf, tuple(facecolors[0]))
- else:
- renderer.draw_path_collection(
- gc, transform.frozen(), paths,
- self.get_transforms(), offsets, offset_trf,
- self.get_facecolor(), self.get_edgecolor(),
- self._linewidths, self._linestyles,
- self._antialiaseds, self._urls,
- "screen") # offset_position, kept for backcompat.
-
- gc.restore()
- renderer.close_group(self.__class__.__name__)
- self.stale = False
-
- @_api.rename_parameter("3.6", "pr", "pickradius")
- def set_pickradius(self, pickradius):
- """
- Set the pick radius used for containment tests.
-
- Parameters
- ----------
- pickradius : float
- Pick radius, in points.
- """
- self._pickradius = pickradius
-
- def get_pickradius(self):
- return self._pickradius
-
- def contains(self, mouseevent):
- """
- Test whether the mouse event occurred in the collection.
-
- Returns ``bool, dict(ind=itemlist)``, where every item in itemlist
- contains the event.
- """
- inside, info = self._default_contains(mouseevent)
- if inside is not None:
- return inside, info
-
- if not self.get_visible():
- return False, {}
-
- pickradius = (
- float(self._picker)
- if isinstance(self._picker, Number) and
- self._picker is not True # the bool, not just nonzero or 1
- else self._pickradius)
-
- if self.axes:
- self.axes._unstale_viewLim()
-
- transform, offset_trf, offsets, paths = self._prepare_points()
-
- # Tests if the point is contained on one of the polygons formed
- # by the control points of each of the paths. A point is considered
- # "on" a path if it would lie within a stroke of width 2*pickradius
- # following the path. If pickradius <= 0, then we instead simply check
- # if the point is *inside* of the path instead.
- ind = _path.point_in_path_collection(
- mouseevent.x, mouseevent.y, pickradius,
- transform.frozen(), paths, self.get_transforms(),
- offsets, offset_trf, pickradius <= 0)
-
- return len(ind) > 0, dict(ind=ind)
-
- def set_urls(self, urls):
- """
- Parameters
- ----------
- urls : list of str or None
-
- Notes
- -----
- URLs are currently only implemented by the SVG backend. They are
- ignored by all other backends.
- """
- self._urls = urls if urls is not None else [None]
- self.stale = True
-
- def get_urls(self):
- """
- Return a list of URLs, one for each element of the collection.
-
- The list contains *None* for elements without a URL. See
- :doc:`/gallery/misc/hyperlinks_sgskip` for an example.
- """
- return self._urls
-
- def set_hatch(self, hatch):
- r"""
- Set the hatching pattern
-
- *hatch* can be one of::
-
- / - diagonal hatching
- \ - back diagonal
- | - vertical
- - - horizontal
- + - crossed
- x - crossed diagonal
- o - small circle
- O - large circle
- . - dots
- * - stars
-
- Letters can be combined, in which case all the specified
- hatchings are done. If same letter repeats, it increases the
- density of hatching of that pattern.
-
- Hatching is supported in the PostScript, PDF, SVG and Agg
- backends only.
-
- Unlike other properties such as linewidth and colors, hatching
- can only be specified for the collection as a whole, not separately
- for each member.
-
- Parameters
- ----------
- hatch : {'/', '\\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}
- """
- # Use validate_hatch(list) after deprecation.
- mhatch._validate_hatch_pattern(hatch)
- self._hatch = hatch
- self.stale = True
-
- def get_hatch(self):
- """Return the current hatching pattern."""
- return self._hatch
-
- def set_offsets(self, offsets):
- """
- Set the offsets for the collection.
-
- Parameters
- ----------
- offsets : (N, 2) or (2,) array-like
- """
- offsets = np.asanyarray(offsets)
- if offsets.shape == (2,): # Broadcast (2,) -> (1, 2) but nothing else.
- offsets = offsets[None, :]
- cstack = (np.ma.column_stack if isinstance(offsets, np.ma.MaskedArray)
- else np.column_stack)
- self._offsets = cstack(
- (np.asanyarray(self.convert_xunits(offsets[:, 0]), float),
- np.asanyarray(self.convert_yunits(offsets[:, 1]), float)))
- self.stale = True
-
- def get_offsets(self):
- """Return the offsets for the collection."""
- # Default to zeros in the no-offset (None) case
- return np.zeros((1, 2)) if self._offsets is None else self._offsets
-
- def _get_default_linewidth(self):
- # This may be overridden in a subclass.
- return mpl.rcParams['patch.linewidth'] # validated as float
-
- def set_linewidth(self, lw):
- """
- Set the linewidth(s) for the collection. *lw* can be a scalar
- or a sequence; if it is a sequence the patches will cycle
- through the sequence
-
- Parameters
- ----------
- lw : float or list of floats
- """
- if lw is None:
- lw = self._get_default_linewidth()
- # get the un-scaled/broadcast lw
- self._us_lw = np.atleast_1d(lw)
-
- # scale all of the dash patterns.
- self._linewidths, self._linestyles = self._bcast_lwls(
- self._us_lw, self._us_linestyles)
- self.stale = True
-
- def set_linestyle(self, ls):
- """
- Set the linestyle(s) for the collection.
-
- =========================== =================
- linestyle description
- =========================== =================
- ``'-'`` or ``'solid'`` solid line
- ``'--'`` or ``'dashed'`` dashed line
- ``'-.'`` or ``'dashdot'`` dash-dotted line
- ``':'`` or ``'dotted'`` dotted line
- =========================== =================
-
- Alternatively a dash tuple of the following form can be provided::
-
- (offset, onoffseq),
-
- where ``onoffseq`` is an even length tuple of on and off ink in points.
-
- Parameters
- ----------
- ls : str or tuple or list thereof
- Valid values for individual linestyles include {'-', '--', '-.',
- ':', '', (offset, on-off-seq)}. See `.Line2D.set_linestyle` for a
- complete description.
- """
- try:
- dashes = [mlines._get_dash_pattern(ls)]
- except ValueError:
- try:
- dashes = [mlines._get_dash_pattern(x) for x in ls]
- except ValueError as err:
- emsg = f'Do not know how to convert {ls!r} to dashes'
- raise ValueError(emsg) from err
-
- # get the list of raw 'unscaled' dash patterns
- self._us_linestyles = dashes
-
- # broadcast and scale the lw and dash patterns
- self._linewidths, self._linestyles = self._bcast_lwls(
- self._us_lw, self._us_linestyles)
-
- @_docstring.interpd
- def set_capstyle(self, cs):
- """
- Set the `.CapStyle` for the collection (for all its elements).
-
- Parameters
- ----------
- cs : `.CapStyle` or %(CapStyle)s
- """
- self._capstyle = CapStyle(cs)
-
- def get_capstyle(self):
- return self._capstyle.name
-
- @_docstring.interpd
- def set_joinstyle(self, js):
- """
- Set the `.JoinStyle` for the collection (for all its elements).
-
- Parameters
- ----------
- js : `.JoinStyle` or %(JoinStyle)s
- """
- self._joinstyle = JoinStyle(js)
-
- def get_joinstyle(self):
- return self._joinstyle.name
-
- @staticmethod
- def _bcast_lwls(linewidths, dashes):
- """
- Internal helper function to broadcast + scale ls/lw
-
- In the collection drawing code, the linewidth and linestyle are cycled
- through as circular buffers (via ``v[i % len(v)]``). Thus, if we are
- going to scale the dash pattern at set time (not draw time) we need to
- do the broadcasting now and expand both lists to be the same length.
-
- Parameters
- ----------
- linewidths : list
- line widths of collection
- dashes : list
- dash specification (offset, (dash pattern tuple))
-
- Returns
- -------
- linewidths, dashes : list
- Will be the same length, dashes are scaled by paired linewidth
- """
- if mpl.rcParams['_internal.classic_mode']:
- return linewidths, dashes
- # make sure they are the same length so we can zip them
- if len(dashes) != len(linewidths):
- l_dashes = len(dashes)
- l_lw = len(linewidths)
- gcd = math.gcd(l_dashes, l_lw)
- dashes = list(dashes) * (l_lw // gcd)
- linewidths = list(linewidths) * (l_dashes // gcd)
-
- # scale the dash patterns
- dashes = [mlines._scale_dashes(o, d, lw)
- for (o, d), lw in zip(dashes, linewidths)]
-
- return linewidths, dashes
-
- def set_antialiased(self, aa):
- """
- Set the antialiasing state for rendering.
-
- Parameters
- ----------
- aa : bool or list of bools
- """
- if aa is None:
- aa = self._get_default_antialiased()
- self._antialiaseds = np.atleast_1d(np.asarray(aa, bool))
- self.stale = True
-
- def _get_default_antialiased(self):
- # This may be overridden in a subclass.
- return mpl.rcParams['patch.antialiased']
-
- def set_color(self, c):
- """
- Set both the edgecolor and the facecolor.
-
- Parameters
- ----------
- c : color or list of RGBA tuples
-
- See Also
- --------
- Collection.set_facecolor, Collection.set_edgecolor
- For setting the edge or face color individually.
- """
- self.set_facecolor(c)
- self.set_edgecolor(c)
-
- def _get_default_facecolor(self):
- # This may be overridden in a subclass.
- return mpl.rcParams['patch.facecolor']
-
- def _set_facecolor(self, c):
- if c is None:
- c = self._get_default_facecolor()
-
- self._facecolors = mcolors.to_rgba_array(c, self._alpha)
- self.stale = True
-
- def set_facecolor(self, c):
- """
- Set the facecolor(s) of the collection. *c* can be a color (all patches
- have same color), or a sequence of colors; if it is a sequence the
- patches will cycle through the sequence.
-
- If *c* is 'none', the patch will not be filled.
-
- Parameters
- ----------
- c : color or list of colors
- """
- if isinstance(c, str) and c.lower() in ("none", "face"):
- c = c.lower()
- self._original_facecolor = c
- self._set_facecolor(c)
-
- def get_facecolor(self):
- return self._facecolors
-
- def get_edgecolor(self):
- if cbook._str_equal(self._edgecolors, 'face'):
- return self.get_facecolor()
- else:
- return self._edgecolors
-
- def _get_default_edgecolor(self):
- # This may be overridden in a subclass.
- return mpl.rcParams['patch.edgecolor']
-
- def _set_edgecolor(self, c):
- set_hatch_color = True
- if c is None:
- if (mpl.rcParams['patch.force_edgecolor']
- or self._edge_default
- or cbook._str_equal(self._original_facecolor, 'none')):
- c = self._get_default_edgecolor()
- else:
- c = 'none'
- set_hatch_color = False
- if cbook._str_lower_equal(c, 'face'):
- self._edgecolors = 'face'
- self.stale = True
- return
- self._edgecolors = mcolors.to_rgba_array(c, self._alpha)
- if set_hatch_color and len(self._edgecolors):
- self._hatch_color = tuple(self._edgecolors[0])
- self.stale = True
-
- def set_edgecolor(self, c):
- """
- Set the edgecolor(s) of the collection.
-
- Parameters
- ----------
- c : color or list of colors or 'face'
- The collection edgecolor(s). If a sequence, the patches cycle
- through it. If 'face', match the facecolor.
- """
- # We pass through a default value for use in LineCollection.
- # This allows us to maintain None as the default indicator in
- # _original_edgecolor.
- if isinstance(c, str) and c.lower() in ("none", "face"):
- c = c.lower()
- self._original_edgecolor = c
- self._set_edgecolor(c)
-
- def set_alpha(self, alpha):
- """
- Set the transparency of the collection.
-
- Parameters
- ----------
- alpha : float or array of float or None
- If not None, *alpha* values must be between 0 and 1, inclusive.
- If an array is provided, its length must match the number of
- elements in the collection. Masked values and nans are not
- supported.
- """
- artist.Artist._set_alpha_for_array(self, alpha)
- self._set_facecolor(self._original_facecolor)
- self._set_edgecolor(self._original_edgecolor)
-
- set_alpha.__doc__ = artist.Artist._set_alpha_for_array.__doc__
-
- def get_linewidth(self):
- return self._linewidths
-
- def get_linestyle(self):
- return self._linestyles
-
- def _set_mappable_flags(self):
- """
- Determine whether edges and/or faces are color-mapped.
-
- This is a helper for update_scalarmappable.
- It sets Boolean flags '_edge_is_mapped' and '_face_is_mapped'.
-
- Returns
- -------
- mapping_change : bool
- True if either flag is True, or if a flag has changed.
- """
- # The flags are initialized to None to ensure this returns True
- # the first time it is called.
- edge0 = self._edge_is_mapped
- face0 = self._face_is_mapped
- # After returning, the flags must be Booleans, not None.
- self._edge_is_mapped = False
- self._face_is_mapped = False
- if self._A is not None:
- if not cbook._str_equal(self._original_facecolor, 'none'):
- self._face_is_mapped = True
- if cbook._str_equal(self._original_edgecolor, 'face'):
- self._edge_is_mapped = True
- else:
- if self._original_edgecolor is None:
- self._edge_is_mapped = True
-
- mapped = self._face_is_mapped or self._edge_is_mapped
- changed = (edge0 is None or face0 is None
- or self._edge_is_mapped != edge0
- or self._face_is_mapped != face0)
- return mapped or changed
-
- def update_scalarmappable(self):
- """
- Update colors from the scalar mappable array, if any.
-
- Assign colors to edges and faces based on the array and/or
- colors that were directly set, as appropriate.
- """
- if not self._set_mappable_flags():
- return
- # Allow possibility to call 'self.set_array(None)'.
- if self._A is not None:
- # QuadMesh can map 2d arrays (but pcolormesh supplies 1d array)
- if self._A.ndim > 1 and not isinstance(self, QuadMesh):
- raise ValueError('Collections can only map rank 1 arrays')
- if np.iterable(self._alpha):
- if self._alpha.size != self._A.size:
- raise ValueError(
- f'Data array shape, {self._A.shape} '
- 'is incompatible with alpha array shape, '
- f'{self._alpha.shape}. '
- 'This can occur with the deprecated '
- 'behavior of the "flat" shading option, '
- 'in which a row and/or column of the data '
- 'array is dropped.')
- # pcolormesh, scatter, maybe others flatten their _A
- self._alpha = self._alpha.reshape(self._A.shape)
- self._mapped_colors = self.to_rgba(self._A, self._alpha)
-
- if self._face_is_mapped:
- self._facecolors = self._mapped_colors
- else:
- self._set_facecolor(self._original_facecolor)
- if self._edge_is_mapped:
- self._edgecolors = self._mapped_colors
- else:
- self._set_edgecolor(self._original_edgecolor)
- self.stale = True
-
- def get_fill(self):
- """Return whether face is colored."""
- return not cbook._str_lower_equal(self._original_facecolor, "none")
-
- def update_from(self, other):
- """Copy properties from other to self."""
-
- artist.Artist.update_from(self, other)
- self._antialiaseds = other._antialiaseds
- self._mapped_colors = other._mapped_colors
- self._edge_is_mapped = other._edge_is_mapped
- self._original_edgecolor = other._original_edgecolor
- self._edgecolors = other._edgecolors
- self._face_is_mapped = other._face_is_mapped
- self._original_facecolor = other._original_facecolor
- self._facecolors = other._facecolors
- self._linewidths = other._linewidths
- self._linestyles = other._linestyles
- self._us_linestyles = other._us_linestyles
- self._pickradius = other._pickradius
- self._hatch = other._hatch
-
- # update_from for scalarmappable
- self._A = other._A
- self.norm = other.norm
- self.cmap = other.cmap
- self.stale = True
-
-
-class _CollectionWithSizes(Collection):
- """
- Base class for collections that have an array of sizes.
- """
- _factor = 1.0
-
- def get_sizes(self):
- """
- Return the sizes ('areas') of the elements in the collection.
-
- Returns
- -------
- array
- The 'area' of each element.
- """
- return self._sizes
-
- def set_sizes(self, sizes, dpi=72.0):
- """
- Set the sizes of each member of the collection.
-
- Parameters
- ----------
- sizes : `numpy.ndarray` or None
- The size to set for each element of the collection. The
- value is the 'area' of the element.
- dpi : float, default: 72
- The dpi of the canvas.
- """
- if sizes is None:
- self._sizes = np.array([])
- self._transforms = np.empty((0, 3, 3))
- else:
- self._sizes = np.asarray(sizes)
- self._transforms = np.zeros((len(self._sizes), 3, 3))
- scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor
- self._transforms[:, 0, 0] = scale
- self._transforms[:, 1, 1] = scale
- self._transforms[:, 2, 2] = 1.0
- self.stale = True
-
- @artist.allow_rasterization
- def draw(self, renderer):
- self.set_sizes(self._sizes, self.figure.dpi)
- super().draw(renderer)
-
-
-class PathCollection(_CollectionWithSizes):
- r"""
- A collection of `~.path.Path`\s, as created by e.g. `~.Axes.scatter`.
- """
-
- def __init__(self, paths, sizes=None, **kwargs):
- """
- Parameters
- ----------
- paths : list of `.path.Path`
- The paths that will make up the `.Collection`.
- sizes : array-like
- The factor by which to scale each drawn `~.path.Path`. One unit
- squared in the Path's data space is scaled to be ``sizes**2``
- points when rendered.
- **kwargs
- Forwarded to `.Collection`.
- """
-
- super().__init__(**kwargs)
- self.set_paths(paths)
- self.set_sizes(sizes)
- self.stale = True
-
- def set_paths(self, paths):
- self._paths = paths
- self.stale = True
-
- def get_paths(self):
- return self._paths
-
- def legend_elements(self, prop="colors", num="auto",
- fmt=None, func=lambda x: x, **kwargs):
- """
- Create legend handles and labels for a PathCollection.
-
- Each legend handle is a `.Line2D` representing the Path that was drawn,
- and each label is a string what each Path represents.
-
- This is useful for obtaining a legend for a `~.Axes.scatter` plot;
- e.g.::
-
- scatter = plt.scatter([1, 2, 3], [4, 5, 6], c=[7, 2, 3])
- plt.legend(*scatter.legend_elements())
-
- creates three legend elements, one for each color with the numerical
- values passed to *c* as the labels.
-
- Also see the :ref:`automatedlegendcreation` example.
-
- Parameters
- ----------
- prop : {"colors", "sizes"}, default: "colors"
- If "colors", the legend handles will show the different colors of
- the collection. If "sizes", the legend will show the different
- sizes. To set both, use *kwargs* to directly edit the `.Line2D`
- properties.
- num : int, None, "auto" (default), array-like, or `~.ticker.Locator`
- Target number of elements to create.
- If None, use all unique elements of the mappable array. If an
- integer, target to use *num* elements in the normed range.
- If *"auto"*, try to determine which option better suits the nature
- of the data.
- The number of created elements may slightly deviate from *num* due
- to a `~.ticker.Locator` being used to find useful locations.
- If a list or array, use exactly those elements for the legend.
- Finally, a `~.ticker.Locator` can be provided.
- fmt : str, `~matplotlib.ticker.Formatter`, or None (default)
- The format or formatter to use for the labels. If a string must be
- a valid input for a `.StrMethodFormatter`. If None (the default),
- use a `.ScalarFormatter`.
- func : function, default: ``lambda x: x``
- Function to calculate the labels. Often the size (or color)
- argument to `~.Axes.scatter` will have been pre-processed by the
- user using a function ``s = f(x)`` to make the markers visible;
- e.g. ``size = np.log10(x)``. Providing the inverse of this
- function here allows that pre-processing to be inverted, so that
- the legend labels have the correct values; e.g. ``func = lambda
- x: 10**x``.
- **kwargs
- Allowed keyword arguments are *color* and *size*. E.g. it may be
- useful to set the color of the markers if *prop="sizes"* is used;
- similarly to set the size of the markers if *prop="colors"* is
- used. Any further parameters are passed onto the `.Line2D`
- instance. This may be useful to e.g. specify a different
- *markeredgecolor* or *alpha* for the legend handles.
-
- Returns
- -------
- handles : list of `.Line2D`
- Visual representation of each element of the legend.
- labels : list of str
- The string labels for elements of the legend.
- """
- handles = []
- labels = []
- hasarray = self.get_array() is not None
- if fmt is None:
- fmt = mpl.ticker.ScalarFormatter(useOffset=False, useMathText=True)
- elif isinstance(fmt, str):
- fmt = mpl.ticker.StrMethodFormatter(fmt)
- fmt.create_dummy_axis()
-
- if prop == "colors":
- if not hasarray:
- warnings.warn("Collection without array used. Make sure to "
- "specify the values to be colormapped via the "
- "`c` argument.")
- return handles, labels
- u = np.unique(self.get_array())
- size = kwargs.pop("size", mpl.rcParams["lines.markersize"])
- elif prop == "sizes":
- u = np.unique(self.get_sizes())
- color = kwargs.pop("color", "k")
- else:
- raise ValueError("Valid values for `prop` are 'colors' or "
- f"'sizes'. You supplied '{prop}' instead.")
-
- fu = func(u)
- fmt.axis.set_view_interval(fu.min(), fu.max())
- fmt.axis.set_data_interval(fu.min(), fu.max())
- if num == "auto":
- num = 9
- if len(u) <= num:
- num = None
- if num is None:
- values = u
- label_values = func(values)
- else:
- if prop == "colors":
- arr = self.get_array()
- elif prop == "sizes":
- arr = self.get_sizes()
- if isinstance(num, mpl.ticker.Locator):
- loc = num
- elif np.iterable(num):
- loc = mpl.ticker.FixedLocator(num)
- else:
- num = int(num)
- loc = mpl.ticker.MaxNLocator(nbins=num, min_n_ticks=num-1,
- steps=[1, 2, 2.5, 3, 5, 6, 8, 10])
- label_values = loc.tick_values(func(arr).min(), func(arr).max())
- cond = ((label_values >= func(arr).min()) &
- (label_values <= func(arr).max()))
- label_values = label_values[cond]
- yarr = np.linspace(arr.min(), arr.max(), 256)
- xarr = func(yarr)
- ix = np.argsort(xarr)
- values = np.interp(label_values, xarr[ix], yarr[ix])
-
- kw = {"markeredgewidth": self.get_linewidths()[0],
- "alpha": self.get_alpha(),
- **kwargs}
-
- for val, lab in zip(values, label_values):
- if prop == "colors":
- color = self.cmap(self.norm(val))
- elif prop == "sizes":
- size = np.sqrt(val)
- if np.isclose(size, 0.0):
- continue
- h = mlines.Line2D([0], [0], ls="", color=color, ms=size,
- marker=self.get_paths()[0], **kw)
- handles.append(h)
- if hasattr(fmt, "set_locs"):
- fmt.set_locs(label_values)
- l = fmt(lab)
- labels.append(l)
-
- return handles, labels
-
-
-class PolyCollection(_CollectionWithSizes):
-
- @_api.make_keyword_only("3.6", name="closed")
- def __init__(self, verts, sizes=None, closed=True, **kwargs):
- """
- Parameters
- ----------
- verts : list of array-like
- The sequence of polygons [*verts0*, *verts1*, ...] where each
- element *verts_i* defines the vertices of polygon *i* as a 2D
- array-like of shape (M, 2).
- sizes : array-like, default: None
- Squared scaling factors for the polygons. The coordinates of each
- polygon *verts_i* are multiplied by the square-root of the
- corresponding entry in *sizes* (i.e., *sizes* specify the scaling
- of areas). The scaling is applied before the Artist master
- transform.
- closed : bool, default: True
- Whether the polygon should be closed by adding a CLOSEPOLY
- connection at the end.
- **kwargs
- Forwarded to `.Collection`.
- """
- super().__init__(**kwargs)
- self.set_sizes(sizes)
- self.set_verts(verts, closed)
- self.stale = True
-
- def set_verts(self, verts, closed=True):
- """
- Set the vertices of the polygons.
-
- Parameters
- ----------
- verts : list of array-like
- The sequence of polygons [*verts0*, *verts1*, ...] where each
- element *verts_i* defines the vertices of polygon *i* as a 2D
- array-like of shape (M, 2).
- closed : bool, default: True
- Whether the polygon should be closed by adding a CLOSEPOLY
- connection at the end.
- """
- self.stale = True
- if isinstance(verts, np.ma.MaskedArray):
- verts = verts.astype(float).filled(np.nan)
-
- # No need to do anything fancy if the path isn't closed.
- if not closed:
- self._paths = [mpath.Path(xy) for xy in verts]
- return
-
- # Fast path for arrays
- if isinstance(verts, np.ndarray) and len(verts.shape) == 3:
- verts_pad = np.concatenate((verts, verts[:, :1]), axis=1)
- # Creating the codes once is much faster than having Path do it
- # separately each time by passing closed=True.
- codes = np.empty(verts_pad.shape[1], dtype=mpath.Path.code_type)
- codes[:] = mpath.Path.LINETO
- codes[0] = mpath.Path.MOVETO
- codes[-1] = mpath.Path.CLOSEPOLY
- self._paths = [mpath.Path(xy, codes) for xy in verts_pad]
- return
-
- self._paths = []
- for xy in verts:
- if len(xy):
- self._paths.append(mpath.Path._create_closed(xy))
- else:
- self._paths.append(mpath.Path(xy))
-
- set_paths = set_verts
-
- def set_verts_and_codes(self, verts, codes):
- """Initialize vertices with path codes."""
- if len(verts) != len(codes):
- raise ValueError("'codes' must be a 1D list or array "
- "with the same length of 'verts'")
- self._paths = [mpath.Path(xy, cds) if len(xy) else mpath.Path(xy)
- for xy, cds in zip(verts, codes)]
- self.stale = True
-
- @classmethod
- @_api.deprecated("3.7", alternative="fill_between")
- def span_where(cls, x, ymin, ymax, where, **kwargs):
- """
- Return a `.BrokenBarHCollection` that plots horizontal bars from
- over the regions in *x* where *where* is True. The bars range
- on the y-axis from *ymin* to *ymax*
-
- *kwargs* are passed on to the collection.
- """
- xranges = []
- for ind0, ind1 in cbook.contiguous_regions(where):
- xslice = x[ind0:ind1]
- if not len(xslice):
- continue
- xranges.append((xslice[0], xslice[-1] - xslice[0]))
- return BrokenBarHCollection(xranges, [ymin, ymax - ymin], **kwargs)
-
-
-@_api.deprecated("3.7")
-class BrokenBarHCollection(PolyCollection):
- """
- A collection of horizontal bars spanning *yrange* with a sequence of
- *xranges*.
- """
- def __init__(self, xranges, yrange, **kwargs):
- """
- Parameters
- ----------
- xranges : list of (float, float)
- The sequence of (left-edge-position, width) pairs for each bar.
- yrange : (float, float)
- The (lower-edge, height) common to all bars.
- **kwargs
- Forwarded to `.Collection`.
- """
- ymin, ywidth = yrange
- ymax = ymin + ywidth
- verts = [[(xmin, ymin),
- (xmin, ymax),
- (xmin + xwidth, ymax),
- (xmin + xwidth, ymin),
- (xmin, ymin)] for xmin, xwidth in xranges]
- super().__init__(verts, **kwargs)
-
-
-class RegularPolyCollection(_CollectionWithSizes):
- """A collection of n-sided regular polygons."""
-
- _path_generator = mpath.Path.unit_regular_polygon
- _factor = np.pi ** (-1/2)
-
- @_api.make_keyword_only("3.6", name="rotation")
- def __init__(self,
- numsides,
- rotation=0,
- sizes=(1,),
- **kwargs):
- """
- Parameters
- ----------
- numsides : int
- The number of sides of the polygon.
- rotation : float
- The rotation of the polygon in radians.
- sizes : tuple of float
- The area of the circle circumscribing the polygon in points^2.
- **kwargs
- Forwarded to `.Collection`.
-
- Examples
- --------
- See :doc:`/gallery/event_handling/lasso_demo` for a complete example::
-
- offsets = np.random.rand(20, 2)
- facecolors = [cm.jet(x) for x in np.random.rand(20)]
-
- collection = RegularPolyCollection(
- numsides=5, # a pentagon
- rotation=0, sizes=(50,),
- facecolors=facecolors,
- edgecolors=("black",),
- linewidths=(1,),
- offsets=offsets,
- offset_transform=ax.transData,
- )
- """
- super().__init__(**kwargs)
- self.set_sizes(sizes)
- self._numsides = numsides
- self._paths = [self._path_generator(numsides)]
- self._rotation = rotation
- self.set_transform(transforms.IdentityTransform())
-
- def get_numsides(self):
- return self._numsides
-
- def get_rotation(self):
- return self._rotation
-
- @artist.allow_rasterization
- def draw(self, renderer):
- self.set_sizes(self._sizes, self.figure.dpi)
- self._transforms = [
- transforms.Affine2D(x).rotate(-self._rotation).get_matrix()
- for x in self._transforms
- ]
- # Explicitly not super().draw, because set_sizes must be called before
- # updating self._transforms.
- Collection.draw(self, renderer)
-
-
-class StarPolygonCollection(RegularPolyCollection):
- """Draw a collection of regular stars with *numsides* points."""
- _path_generator = mpath.Path.unit_regular_star
-
-
-class AsteriskPolygonCollection(RegularPolyCollection):
- """Draw a collection of regular asterisks with *numsides* points."""
- _path_generator = mpath.Path.unit_regular_asterisk
-
-
-class LineCollection(Collection):
- r"""
- Represents a sequence of `.Line2D`\s that should be drawn together.
-
- This class extends `.Collection` to represent a sequence of
- `.Line2D`\s instead of just a sequence of `.Patch`\s.
- Just as in `.Collection`, each property of a *LineCollection* may be either
- a single value or a list of values. This list is then used cyclically for
- each element of the LineCollection, so the property of the ``i``\th element
- of the collection is::
-
- prop[i % len(prop)]
-
- The properties of each member of a *LineCollection* default to their values
- in :rc:`lines.*` instead of :rc:`patch.*`, and the property *colors* is
- added in place of *edgecolors*.
- """
-
- _edge_default = True
-
- def __init__(self, segments, # Can be None.
- *,
- zorder=2, # Collection.zorder is 1
- **kwargs
- ):
- """
- Parameters
- ----------
- segments : list of array-like
- A sequence of (*line0*, *line1*, *line2*), where::
-
- linen = (x0, y0), (x1, y1), ... (xm, ym)
-
- or the equivalent numpy array with two columns. Each line
- can have a different number of segments.
- linewidths : float or list of float, default: :rc:`lines.linewidth`
- The width of each line in points.
- colors : color or list of color, default: :rc:`lines.color`
- A sequence of RGBA tuples (e.g., arbitrary color strings, etc, not
- allowed).
- antialiaseds : bool or list of bool, default: :rc:`lines.antialiased`
- Whether to use antialiasing for each line.
- zorder : float, default: 2
- zorder of the lines once drawn.
-
- facecolors : color or list of color, default: 'none'
- When setting *facecolors*, each line is interpreted as a boundary
- for an area, implicitly closing the path from the last point to the
- first point. The enclosed area is filled with *facecolor*.
- In order to manually specify what should count as the "interior" of
- each line, please use `.PathCollection` instead, where the
- "interior" can be specified by appropriate usage of
- `~.path.Path.CLOSEPOLY`.
-
- **kwargs
- Forwarded to `.Collection`.
- """
- # Unfortunately, mplot3d needs this explicit setting of 'facecolors'.
- kwargs.setdefault('facecolors', 'none')
- super().__init__(
- zorder=zorder,
- **kwargs)
- self.set_segments(segments)
-
- def set_segments(self, segments):
- if segments is None:
- return
-
- self._paths = [mpath.Path(seg) if isinstance(seg, np.ma.MaskedArray)
- else mpath.Path(np.asarray(seg, float))
- for seg in segments]
- self.stale = True
-
- set_verts = set_segments # for compatibility with PolyCollection
- set_paths = set_segments
-
- def get_segments(self):
- """
- Returns
- -------
- list
- List of segments in the LineCollection. Each list item contains an
- array of vertices.
- """
- segments = []
-
- for path in self._paths:
- vertices = [
- vertex
- for vertex, _
- # Never simplify here, we want to get the data-space values
- # back and there in no way to know the "right" simplification
- # threshold so never try.
- in path.iter_segments(simplify=False)
- ]
- vertices = np.asarray(vertices)
- segments.append(vertices)
-
- return segments
-
- def _get_default_linewidth(self):
- return mpl.rcParams['lines.linewidth']
-
- def _get_default_antialiased(self):
- return mpl.rcParams['lines.antialiased']
-
- def _get_default_edgecolor(self):
- return mpl.rcParams['lines.color']
-
- def _get_default_facecolor(self):
- return 'none'
-
- def set_color(self, c):
- """
- Set the edgecolor(s) of the LineCollection.
-
- Parameters
- ----------
- c : color or list of colors
- Single color (all lines have same color), or a
- sequence of RGBA tuples; if it is a sequence the lines will
- cycle through the sequence.
- """
- self.set_edgecolor(c)
-
- set_colors = set_color
-
- def get_color(self):
- return self._edgecolors
-
- get_colors = get_color # for compatibility with old versions
-
-
-class EventCollection(LineCollection):
- """
- A collection of locations along a single axis at which an "event" occurred.
-
- The events are given by a 1-dimensional array. They do not have an
- amplitude and are displayed as parallel lines.
- """
-
- _edge_default = True
-
- @_api.make_keyword_only("3.6", name="lineoffset")
- def __init__(self,
- positions, # Cannot be None.
- orientation='horizontal',
- lineoffset=0,
- linelength=1,
- linewidth=None,
- color=None,
- linestyle='solid',
- antialiased=None,
- **kwargs
- ):
- """
- Parameters
- ----------
- positions : 1D array-like
- Each value is an event.
- orientation : {'horizontal', 'vertical'}, default: 'horizontal'
- The sequence of events is plotted along this direction.
- The marker lines of the single events are along the orthogonal
- direction.
- lineoffset : float, default: 0
- The offset of the center of the markers from the origin, in the
- direction orthogonal to *orientation*.
- linelength : float, default: 1
- The total height of the marker (i.e. the marker stretches from
- ``lineoffset - linelength/2`` to ``lineoffset + linelength/2``).
- linewidth : float or list thereof, default: :rc:`lines.linewidth`
- The line width of the event lines, in points.
- color : color or list of colors, default: :rc:`lines.color`
- The color of the event lines.
- linestyle : str or tuple or list thereof, default: 'solid'
- Valid strings are ['solid', 'dashed', 'dashdot', 'dotted',
- '-', '--', '-.', ':']. Dash tuples should be of the form::
-
- (offset, onoffseq),
-
- where *onoffseq* is an even length tuple of on and off ink
- in points.
- antialiased : bool or list thereof, default: :rc:`lines.antialiased`
- Whether to use antialiasing for drawing the lines.
- **kwargs
- Forwarded to `.LineCollection`.
-
- Examples
- --------
- .. plot:: gallery/lines_bars_and_markers/eventcollection_demo.py
- """
- super().__init__([],
- linewidths=linewidth, linestyles=linestyle,
- colors=color, antialiaseds=antialiased,
- **kwargs)
- self._is_horizontal = True # Initial value, may be switched below.
- self._linelength = linelength
- self._lineoffset = lineoffset
- self.set_orientation(orientation)
- self.set_positions(positions)
-
- def get_positions(self):
- """
- Return an array containing the floating-point values of the positions.
- """
- pos = 0 if self.is_horizontal() else 1
- return [segment[0, pos] for segment in self.get_segments()]
-
- def set_positions(self, positions):
- """Set the positions of the events."""
- if positions is None:
- positions = []
- if np.ndim(positions) != 1:
- raise ValueError('positions must be one-dimensional')
- lineoffset = self.get_lineoffset()
- linelength = self.get_linelength()
- pos_idx = 0 if self.is_horizontal() else 1
- segments = np.empty((len(positions), 2, 2))
- segments[:, :, pos_idx] = np.sort(positions)[:, None]
- segments[:, 0, 1 - pos_idx] = lineoffset + linelength / 2
- segments[:, 1, 1 - pos_idx] = lineoffset - linelength / 2
- self.set_segments(segments)
-
- def add_positions(self, position):
- """Add one or more events at the specified positions."""
- if position is None or (hasattr(position, 'len') and
- len(position) == 0):
- return
- positions = self.get_positions()
- positions = np.hstack([positions, np.asanyarray(position)])
- self.set_positions(positions)
- extend_positions = append_positions = add_positions
-
- def is_horizontal(self):
- """True if the eventcollection is horizontal, False if vertical."""
- return self._is_horizontal
-
- def get_orientation(self):
- """
- Return the orientation of the event line ('horizontal' or 'vertical').
- """
- return 'horizontal' if self.is_horizontal() else 'vertical'
-
- def switch_orientation(self):
- """
- Switch the orientation of the event line, either from vertical to
- horizontal or vice versus.
- """
- segments = self.get_segments()
- for i, segment in enumerate(segments):
- segments[i] = np.fliplr(segment)
- self.set_segments(segments)
- self._is_horizontal = not self.is_horizontal()
- self.stale = True
-
- def set_orientation(self, orientation):
- """
- Set the orientation of the event line.
-
- Parameters
- ----------
- orientation : {'horizontal', 'vertical'}
- """
- is_horizontal = _api.check_getitem(
- {"horizontal": True, "vertical": False},
- orientation=orientation)
- if is_horizontal == self.is_horizontal():
- return
- self.switch_orientation()
-
- def get_linelength(self):
- """Return the length of the lines used to mark each event."""
- return self._linelength
-
- def set_linelength(self, linelength):
- """Set the length of the lines used to mark each event."""
- if linelength == self.get_linelength():
- return
- lineoffset = self.get_lineoffset()
- segments = self.get_segments()
- pos = 1 if self.is_horizontal() else 0
- for segment in segments:
- segment[0, pos] = lineoffset + linelength / 2.
- segment[1, pos] = lineoffset - linelength / 2.
- self.set_segments(segments)
- self._linelength = linelength
-
- def get_lineoffset(self):
- """Return the offset of the lines used to mark each event."""
- return self._lineoffset
-
- def set_lineoffset(self, lineoffset):
- """Set the offset of the lines used to mark each event."""
- if lineoffset == self.get_lineoffset():
- return
- linelength = self.get_linelength()
- segments = self.get_segments()
- pos = 1 if self.is_horizontal() else 0
- for segment in segments:
- segment[0, pos] = lineoffset + linelength / 2.
- segment[1, pos] = lineoffset - linelength / 2.
- self.set_segments(segments)
- self._lineoffset = lineoffset
-
- def get_linewidth(self):
- """Get the width of the lines used to mark each event."""
- return super().get_linewidth()[0]
-
- def get_linewidths(self):
- return super().get_linewidth()
-
- def get_color(self):
- """Return the color of the lines used to mark each event."""
- return self.get_colors()[0]
-
-
-class CircleCollection(_CollectionWithSizes):
- """A collection of circles, drawn using splines."""
-
- _factor = np.pi ** (-1/2)
-
- def __init__(self, sizes, **kwargs):
- """
- Parameters
- ----------
- sizes : float or array-like
- The area of each circle in points^2.
- **kwargs
- Forwarded to `.Collection`.
- """
- super().__init__(**kwargs)
- self.set_sizes(sizes)
- self.set_transform(transforms.IdentityTransform())
- self._paths = [mpath.Path.unit_circle()]
-
-
-class EllipseCollection(Collection):
- """A collection of ellipses, drawn using splines."""
-
- @_api.make_keyword_only("3.6", name="units")
- def __init__(self, widths, heights, angles, units='points', **kwargs):
- """
- Parameters
- ----------
- widths : array-like
- The lengths of the first axes (e.g., major axis lengths).
- heights : array-like
- The lengths of second axes.
- angles : array-like
- The angles of the first axes, degrees CCW from the x-axis.
- units : {'points', 'inches', 'dots', 'width', 'height', 'x', 'y', 'xy'}
- The units in which majors and minors are given; 'width' and
- 'height' refer to the dimensions of the axes, while 'x' and 'y'
- refer to the *offsets* data units. 'xy' differs from all others in
- that the angle as plotted varies with the aspect ratio, and equals
- the specified angle only when the aspect ratio is unity. Hence
- it behaves the same as the `~.patches.Ellipse` with
- ``axes.transData`` as its transform.
- **kwargs
- Forwarded to `Collection`.
- """
- super().__init__(**kwargs)
- self._widths = 0.5 * np.asarray(widths).ravel()
- self._heights = 0.5 * np.asarray(heights).ravel()
- self._angles = np.deg2rad(angles).ravel()
- self._units = units
- self.set_transform(transforms.IdentityTransform())
- self._transforms = np.empty((0, 3, 3))
- self._paths = [mpath.Path.unit_circle()]
-
- def _set_transforms(self):
- """Calculate transforms immediately before drawing."""
-
- ax = self.axes
- fig = self.figure
-
- if self._units == 'xy':
- sc = 1
- elif self._units == 'x':
- sc = ax.bbox.width / ax.viewLim.width
- elif self._units == 'y':
- sc = ax.bbox.height / ax.viewLim.height
- elif self._units == 'inches':
- sc = fig.dpi
- elif self._units == 'points':
- sc = fig.dpi / 72.0
- elif self._units == 'width':
- sc = ax.bbox.width
- elif self._units == 'height':
- sc = ax.bbox.height
- elif self._units == 'dots':
- sc = 1.0
- else:
- raise ValueError(f'Unrecognized units: {self._units!r}')
-
- self._transforms = np.zeros((len(self._widths), 3, 3))
- widths = self._widths * sc
- heights = self._heights * sc
- sin_angle = np.sin(self._angles)
- cos_angle = np.cos(self._angles)
- self._transforms[:, 0, 0] = widths * cos_angle
- self._transforms[:, 0, 1] = heights * -sin_angle
- self._transforms[:, 1, 0] = widths * sin_angle
- self._transforms[:, 1, 1] = heights * cos_angle
- self._transforms[:, 2, 2] = 1.0
-
- _affine = transforms.Affine2D
- if self._units == 'xy':
- m = ax.transData.get_affine().get_matrix().copy()
- m[:2, 2:] = 0
- self.set_transform(_affine(m))
-
- @artist.allow_rasterization
- def draw(self, renderer):
- self._set_transforms()
- super().draw(renderer)
-
-
-class PatchCollection(Collection):
- """
- A generic collection of patches.
-
- PatchCollection draws faster than a large number of equivalent individual
- Patches. It also makes it easier to assign a colormap to a heterogeneous
- collection of patches.
- """
-
- @_api.make_keyword_only("3.6", name="match_original")
- def __init__(self, patches, match_original=False, **kwargs):
- """
- Parameters
- ----------
- patches : list of `.Patch`
- A sequence of Patch objects. This list may include
- a heterogeneous assortment of different patch types.
-
- match_original : bool, default: False
- If True, use the colors and linewidths of the original
- patches. If False, new colors may be assigned by
- providing the standard collection arguments, facecolor,
- edgecolor, linewidths, norm or cmap.
-
- **kwargs
- All other parameters are forwarded to `.Collection`.
-
- If any of *edgecolors*, *facecolors*, *linewidths*, *antialiaseds*
- are None, they default to their `.rcParams` patch setting, in
- sequence form.
-
- Notes
- -----
- The use of `~matplotlib.cm.ScalarMappable` functionality is optional.
- If the `~matplotlib.cm.ScalarMappable` matrix ``_A`` has been set (via
- a call to `~.ScalarMappable.set_array`), at draw time a call to scalar
- mappable will be made to set the face colors.
- """
-
- if match_original:
- def determine_facecolor(patch):
- if patch.get_fill():
- return patch.get_facecolor()
- return [0, 0, 0, 0]
-
- kwargs['facecolors'] = [determine_facecolor(p) for p in patches]
- kwargs['edgecolors'] = [p.get_edgecolor() for p in patches]
- kwargs['linewidths'] = [p.get_linewidth() for p in patches]
- kwargs['linestyles'] = [p.get_linestyle() for p in patches]
- kwargs['antialiaseds'] = [p.get_antialiased() for p in patches]
-
- super().__init__(**kwargs)
-
- self.set_paths(patches)
-
- def set_paths(self, patches):
- paths = [p.get_transform().transform_path(p.get_path())
- for p in patches]
- self._paths = paths
-
-
-class TriMesh(Collection):
- """
- Class for the efficient drawing of a triangular mesh using Gouraud shading.
-
- A triangular mesh is a `~matplotlib.tri.Triangulation` object.
- """
- def __init__(self, triangulation, **kwargs):
- super().__init__(**kwargs)
- self._triangulation = triangulation
- self._shading = 'gouraud'
-
- self._bbox = transforms.Bbox.unit()
-
- # Unfortunately this requires a copy, unless Triangulation
- # was rewritten.
- xy = np.hstack((triangulation.x.reshape(-1, 1),
- triangulation.y.reshape(-1, 1)))
- self._bbox.update_from_data_xy(xy)
-
- def get_paths(self):
- if self._paths is None:
- self.set_paths()
- return self._paths
-
- def set_paths(self):
- self._paths = self.convert_mesh_to_paths(self._triangulation)
-
- @staticmethod
- def convert_mesh_to_paths(tri):
- """
- Convert a given mesh into a sequence of `.Path` objects.
-
- This function is primarily of use to implementers of backends that do
- not directly support meshes.
- """
- triangles = tri.get_masked_triangles()
- verts = np.stack((tri.x[triangles], tri.y[triangles]), axis=-1)
- return [mpath.Path(x) for x in verts]
-
- @artist.allow_rasterization
- def draw(self, renderer):
- if not self.get_visible():
- return
- renderer.open_group(self.__class__.__name__, gid=self.get_gid())
- transform = self.get_transform()
-
- # Get a list of triangles and the color at each vertex.
- tri = self._triangulation
- triangles = tri.get_masked_triangles()
-
- verts = np.stack((tri.x[triangles], tri.y[triangles]), axis=-1)
-
- self.update_scalarmappable()
- colors = self._facecolors[triangles]
-
- gc = renderer.new_gc()
- self._set_gc_clip(gc)
- gc.set_linewidth(self.get_linewidth()[0])
- renderer.draw_gouraud_triangles(gc, verts, colors, transform.frozen())
- gc.restore()
- renderer.close_group(self.__class__.__name__)
-
-
-class QuadMesh(Collection):
- r"""
- Class for the efficient drawing of a quadrilateral mesh.
-
- A quadrilateral mesh is a grid of M by N adjacent quadrilaterals that are
- defined via a (M+1, N+1) grid of vertices. The quadrilateral (m, n) is
- defined by the vertices ::
-
- (m+1, n) ----------- (m+1, n+1)
- / /
- / /
- / /
- (m, n) -------- (m, n+1)
-
- The mesh need not be regular and the polygons need not be convex.
-
- Parameters
- ----------
- coordinates : (M+1, N+1, 2) array-like
- The vertices. ``coordinates[m, n]`` specifies the (x, y) coordinates
- of vertex (m, n).
-
- antialiased : bool, default: True
-
- shading : {'flat', 'gouraud'}, default: 'flat'
-
- Notes
- -----
- Unlike other `.Collection`\s, the default *pickradius* of `.QuadMesh` is 0,
- i.e. `~.Artist.contains` checks whether the test point is within any of the
- mesh quadrilaterals.
-
- """
-
- def __init__(self, coordinates, *, antialiased=True, shading='flat',
- **kwargs):
- kwargs.setdefault("pickradius", 0)
- # end of signature deprecation code
-
- _api.check_shape((None, None, 2), coordinates=coordinates)
- self._coordinates = coordinates
- self._antialiased = antialiased
- self._shading = shading
- self._bbox = transforms.Bbox.unit()
- self._bbox.update_from_data_xy(self._coordinates.reshape(-1, 2))
- # super init delayed after own init because array kwarg requires
- # self._coordinates and self._shading
- super().__init__(**kwargs)
- self.set_mouseover(False)
-
- def get_paths(self):
- if self._paths is None:
- self.set_paths()
- return self._paths
-
- def set_paths(self):
- self._paths = self._convert_mesh_to_paths(self._coordinates)
- self.stale = True
-
- def set_array(self, A):
- """
- Set the data values.
-
- Parameters
- ----------
- A : array-like
- The mesh data. Supported array shapes are:
-
- - (M, N) or (M*N,): a mesh with scalar data. The values are mapped
- to colors using normalization and a colormap. See parameters
- *norm*, *cmap*, *vmin*, *vmax*.
- - (M, N, 3): an image with RGB values (0-1 float or 0-255 int).
- - (M, N, 4): an image with RGBA values (0-1 float or 0-255 int),
- i.e. including transparency.
-
- If the values are provided as a 2D grid, the shape must match the
- coordinates grid. If the values are 1D, they are reshaped to 2D.
- M, N follow from the coordinates grid, where the coordinates grid
- shape is (M, N) for 'gouraud' *shading* and (M+1, N+1) for 'flat'
- shading.
- """
- height, width = self._coordinates.shape[0:-1]
- if self._shading == 'flat':
- h, w = height - 1, width - 1
- else:
- h, w = height, width
- ok_shapes = [(h, w, 3), (h, w, 4), (h, w), (h * w,)]
- if A is not None:
- shape = np.shape(A)
- if shape not in ok_shapes:
- raise ValueError(
- f"For X ({width}) and Y ({height}) with {self._shading} "
- f"shading, A should have shape "
- f"{' or '.join(map(str, ok_shapes))}, not {A.shape}")
- return super().set_array(A)
-
- def get_datalim(self, transData):
- return (self.get_transform() - transData).transform_bbox(self._bbox)
-
- def get_coordinates(self):
- """
- Return the vertices of the mesh as an (M+1, N+1, 2) array.
-
- M, N are the number of quadrilaterals in the rows / columns of the
- mesh, corresponding to (M+1, N+1) vertices.
- The last dimension specifies the components (x, y).
- """
- return self._coordinates
-
- @staticmethod
- def _convert_mesh_to_paths(coordinates):
- """
- Convert a given mesh into a sequence of `.Path` objects.
-
- This function is primarily of use to implementers of backends that do
- not directly support quadmeshes.
- """
- if isinstance(coordinates, np.ma.MaskedArray):
- c = coordinates.data
- else:
- c = coordinates
- points = np.concatenate([
- c[:-1, :-1],
- c[:-1, 1:],
- c[1:, 1:],
- c[1:, :-1],
- c[:-1, :-1]
- ], axis=2).reshape((-1, 5, 2))
- return [mpath.Path(x) for x in points]
-
- def _convert_mesh_to_triangles(self, coordinates):
- """
- Convert a given mesh into a sequence of triangles, each point
- with its own color. The result can be used to construct a call to
- `~.RendererBase.draw_gouraud_triangles`.
- """
- if isinstance(coordinates, np.ma.MaskedArray):
- p = coordinates.data
- else:
- p = coordinates
-
- p_a = p[:-1, :-1]
- p_b = p[:-1, 1:]
- p_c = p[1:, 1:]
- p_d = p[1:, :-1]
- p_center = (p_a + p_b + p_c + p_d) / 4.0
- triangles = np.concatenate([
- p_a, p_b, p_center,
- p_b, p_c, p_center,
- p_c, p_d, p_center,
- p_d, p_a, p_center,
- ], axis=2).reshape((-1, 3, 2))
-
- c = self.get_facecolor().reshape((*coordinates.shape[:2], 4))
- c_a = c[:-1, :-1]
- c_b = c[:-1, 1:]
- c_c = c[1:, 1:]
- c_d = c[1:, :-1]
- c_center = (c_a + c_b + c_c + c_d) / 4.0
- colors = np.concatenate([
- c_a, c_b, c_center,
- c_b, c_c, c_center,
- c_c, c_d, c_center,
- c_d, c_a, c_center,
- ], axis=2).reshape((-1, 3, 4))
-
- return triangles, colors
-
- @artist.allow_rasterization
- def draw(self, renderer):
- if not self.get_visible():
- return
- renderer.open_group(self.__class__.__name__, self.get_gid())
- transform = self.get_transform()
- offset_trf = self.get_offset_transform()
- offsets = self.get_offsets()
-
- if self.have_units():
- xs = self.convert_xunits(offsets[:, 0])
- ys = self.convert_yunits(offsets[:, 1])
- offsets = np.column_stack([xs, ys])
-
- self.update_scalarmappable()
-
- if not transform.is_affine:
- coordinates = self._coordinates.reshape((-1, 2))
- coordinates = transform.transform(coordinates)
- coordinates = coordinates.reshape(self._coordinates.shape)
- transform = transforms.IdentityTransform()
- else:
- coordinates = self._coordinates
-
- if not offset_trf.is_affine:
- offsets = offset_trf.transform_non_affine(offsets)
- offset_trf = offset_trf.get_affine()
-
- gc = renderer.new_gc()
- gc.set_snap(self.get_snap())
- self._set_gc_clip(gc)
- gc.set_linewidth(self.get_linewidth()[0])
-
- if self._shading == 'gouraud':
- triangles, colors = self._convert_mesh_to_triangles(coordinates)
- renderer.draw_gouraud_triangles(
- gc, triangles, colors, transform.frozen())
- else:
- renderer.draw_quad_mesh(
- gc, transform.frozen(),
- coordinates.shape[1] - 1, coordinates.shape[0] - 1,
- coordinates, offsets, offset_trf,
- # Backends expect flattened rgba arrays (n*m, 4) for fc and ec
- self.get_facecolor().reshape((-1, 4)),
- self._antialiased, self.get_edgecolors().reshape((-1, 4)))
- gc.restore()
- renderer.close_group(self.__class__.__name__)
- self.stale = False
-
- def get_cursor_data(self, event):
- contained, info = self.contains(event)
- if contained and self.get_array() is not None:
- return self.get_array().ravel()[info["ind"]]
- return None
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/testing/jpl_units/EpochConverter.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/testing/jpl_units/EpochConverter.py
deleted file mode 100644
index f42d7b71d0419770815efec1deace31fa4ec4fe4..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/testing/jpl_units/EpochConverter.py
+++ /dev/null
@@ -1,96 +0,0 @@
-"""EpochConverter module containing class EpochConverter."""
-
-from matplotlib import cbook, units
-import matplotlib.dates as date_ticker
-
-__all__ = ['EpochConverter']
-
-
-class EpochConverter(units.ConversionInterface):
- """
- Provides Matplotlib conversion functionality for Monte Epoch and Duration
- classes.
- """
-
- # julian date reference for "Jan 1, 0001" minus 1 day because
- # Matplotlib really wants "Jan 0, 0001"
- jdRef = 1721425.5 - 1
-
- @staticmethod
- def axisinfo(unit, axis):
- # docstring inherited
- majloc = date_ticker.AutoDateLocator()
- majfmt = date_ticker.AutoDateFormatter(majloc)
- return units.AxisInfo(majloc=majloc, majfmt=majfmt, label=unit)
-
- @staticmethod
- def float2epoch(value, unit):
- """
- Convert a Matplotlib floating-point date into an Epoch of the specified
- units.
-
- = INPUT VARIABLES
- - value The Matplotlib floating-point date.
- - unit The unit system to use for the Epoch.
-
- = RETURN VALUE
- - Returns the value converted to an Epoch in the specified time system.
- """
- # Delay-load due to circular dependencies.
- import matplotlib.testing.jpl_units as U
-
- secPastRef = value * 86400.0 * U.UnitDbl(1.0, 'sec')
- return U.Epoch(unit, secPastRef, EpochConverter.jdRef)
-
- @staticmethod
- def epoch2float(value, unit):
- """
- Convert an Epoch value to a float suitable for plotting as a python
- datetime object.
-
- = INPUT VARIABLES
- - value An Epoch or list of Epochs that need to be converted.
- - unit The units to use for an axis with Epoch data.
-
- = RETURN VALUE
- - Returns the value parameter converted to floats.
- """
- return value.julianDate(unit) - EpochConverter.jdRef
-
- @staticmethod
- def duration2float(value):
- """
- Convert a Duration value to a float suitable for plotting as a python
- datetime object.
-
- = INPUT VARIABLES
- - value A Duration or list of Durations that need to be converted.
-
- = RETURN VALUE
- - Returns the value parameter converted to floats.
- """
- return value.seconds() / 86400.0
-
- @staticmethod
- def convert(value, unit, axis):
- # docstring inherited
-
- # Delay-load due to circular dependencies.
- import matplotlib.testing.jpl_units as U
-
- if not cbook.is_scalar_or_string(value):
- return [EpochConverter.convert(x, unit, axis) for x in value]
- if unit is None:
- unit = EpochConverter.default_units(value, axis)
- if isinstance(value, U.Duration):
- return EpochConverter.duration2float(value)
- else:
- return EpochConverter.epoch2float(value, unit)
-
- @staticmethod
- def default_units(value, axis):
- # docstring inherited
- if cbook.is_scalar_or_string(value):
- return value.frame()
- else:
- return EpochConverter.default_units(value[0], axis)
diff --git a/spaces/lafi23333/aikomori/inference/__init__.py b/spaces/lafi23333/aikomori/inference/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/librarian-bots/hub-analysis/README.md b/spaces/librarian-bots/hub-analysis/README.md
deleted file mode 100644
index ec65f53ab365c3e9fd70513c0138daec58274925..0000000000000000000000000000000000000000
--- a/spaces/librarian-bots/hub-analysis/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Hub Metadata Analysis
-emoji: ⚡
-colorFrom: indigo
-colorTo: gray
-sdk: static
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Delphi XE 2 Update 3 Cracked Download.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Delphi XE 2 Update 3 Cracked Download.md
deleted file mode 100644
index 658cb7bd8c5bf481ae66c18651e38609775970e2..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Delphi XE 2 Update 3 Cracked Download.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Delphi XE 2 Update 3 Cracked Download DOWNLOAD ⚙ https://bytlly.com/2uGwf7
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/HD Online Player (Everest (2015) 720p BluRay X264 [Dua).md b/spaces/lincquiQcaudo/Top-20-Diffusion/HD Online Player (Everest (2015) 720p BluRay X264 [Dua).md
deleted file mode 100644
index 4d110714fe5d0d5cc34a936fd9ac62429e20d387..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/HD Online Player (Everest (2015) 720p BluRay X264 [Dua).md
+++ /dev/null
@@ -1,9 +0,0 @@
-Generally, the higher the connection bandwidth, the more fun the game will be. Most live streaming games, especially those that are internet-based, can require a high-speed internet connection. There are a few games that are only available for consoles with their own online gaming options and require a dialup connection. Most online games that have multiplayer modes require a broadband connection, though games with LAN play can run on dialup, DSL, or even cable.
-The Xbox One X and PS4 Pro are the most powerful consoles released to date. Both consoles are capable of 4K video, with the X focusing on gaming, while the Pro targets content creators. For those who don't game, however, neither console will play 4K BluRays; they will only play BluRays at the PlayStation 4s 1080p resolution, which leaves out a fair number of movies.
-HD Online Player (Everest (2015) 720p BluRay x264 [Dua) Download Zip https://bytlly.com/2uGyhm
At the launch of the new Xbox, Microsoft was one of the early proponents of online gameplay, purchasing and debuting a multiplayer game called Jigsaw and offering it on retail shelves a month before its 2015 fall launch. With Forza Horizon 3 , which arrives in less than two weeks, Microsoft is promoting a game that encourages players to compete online, but a wide variety of titles on a wide variety of platforms come together to give the Xbox a strong online multiplayer presence. If you don't have the PlayStation VR, that still presents an advantage for Xbox One, as its game library is a bit shorter but also includes fewer of the types of smaller single-player, offline titles that don't yet support VR.
-When you go online, youll also be able to play ranked matches, where you and other players around the world battle to be ranked highest. The rankings are updated regularly and the more points you accumulate, the more rewards you earn. Youll also see other players’ scores, but you wont be able to directly challenge them or give them advice if youre not ranked near them.
-
899543212b
-
-namespace thrust
-{
-
-namespace detail
-{
-
-template
- struct minimum_category
- : minimum_type
-{
-}; // end minimum_category
-
-} // end detail
-
-} // end thrust
-
-
diff --git a/spaces/marker22/Bark-Voice-Cloning/util/settings.py b/spaces/marker22/Bark-Voice-Cloning/util/settings.py
deleted file mode 100644
index 2ab66b0c7605d2b877defdd8592097a8a4c6f21a..0000000000000000000000000000000000000000
--- a/spaces/marker22/Bark-Voice-Cloning/util/settings.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import yaml
-
-class Settings:
- def __init__(self, config_file):
- self.config_file = config_file
- self.load()
-
- def load(self):
- try:
- with open(self.config_file, 'r') as f:
- data = yaml.load(f, Loader=yaml.FullLoader)
- self.selected_theme = data.get('selected_theme', "gstaff/xkcd")
- self.server_name = data.get('server_name', "")
- self.server_port = data.get('server_port', 0)
- self.server_share = data.get('server_share', False)
- self.input_text_desired_length = data.get('input_text_desired_length', 110)
- self.input_text_max_length = data.get('input_text_max_length', 170)
- self.silence_sentence = data.get('silence_between_sentences', 250)
- self.silence_speakers = data.get('silence_between_speakers', 500)
- self.output_folder_path = data.get('output_folder_path', 'outputs')
-
- except:
- self.selected_theme = "gstaff/xkcd"
-
- def save(self):
- data = {
- 'selected_theme': self.selected_theme,
- 'server_name': self.server_name,
- 'server_port': self.server_port,
- 'server_share': self.server_share,
- 'input_text_desired_length' : self.input_text_desired_length,
- 'input_text_max_length' : self.input_text_max_length,
- 'silence_between_sentences': self.silence_sentence,
- 'silence_between_speakers': self.silence_speakers,
- 'output_folder_path': self.output_folder_path
- }
- with open(self.config_file, 'w') as f:
- yaml.dump(data, f)
-
-
-
diff --git a/spaces/matthoffner/open-codetree/utils/createIframeContent.ts b/spaces/matthoffner/open-codetree/utils/createIframeContent.ts
deleted file mode 100644
index 1bbb2f006f332cbefd7b2e67610d47dec1d8173d..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/open-codetree/utils/createIframeContent.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-export const createIframeContent = (css: string, html: string) => {
- return `
-
-
- Codetree
-
-
-
-
- ${html}
-
-
-
-
- `;
-};
diff --git a/spaces/mayuri120/anime-remove-background/app.py b/spaces/mayuri120/anime-remove-background/app.py
deleted file mode 100644
index 230a0d5f8a3da6ab18ecb8db1cd90016a489b96a..0000000000000000000000000000000000000000
--- a/spaces/mayuri120/anime-remove-background/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-
-
-def get_mask(img, s=1024):
- img = (img / 255).astype(np.float32)
- h, w = h0, w0 = img.shape[:-1]
- h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
- ph, pw = s - h, s - w
- img_input = np.zeros([s, s, 3], dtype=np.float32)
- img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
- img_input = np.transpose(img_input, (2, 0, 1))
- img_input = img_input[np.newaxis, :]
- mask = rmbg_model.run(None, {'img': img_input})[0][0]
- mask = np.transpose(mask, (1, 2, 0))
- mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
- mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
- return mask
-
-
-def rmbg_fn(img):
- mask = get_mask(img)
- img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
- mask = (mask * 255).astype(np.uint8)
- img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
- mask = mask.repeat(3, axis=2)
- return mask, img
-
-
-if __name__ == "__main__":
- providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
- model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
- rmbg_model = rt.InferenceSession(model_path, providers=providers)
- app = gr.Blocks()
- with app:
- gr.Markdown("# Anime Remove Background\n\n"
- "\n\n"
- "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)")
- with gr.Row():
- with gr.Column():
- input_img = gr.Image(label="input image")
- examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
- examples = gr.Dataset(components=[input_img], samples=examples_data)
- run_btn = gr.Button(variant="primary")
- output_mask = gr.Image(label="mask")
- output_img = gr.Image(label="result", image_mode="RGBA")
- examples.click(lambda x: x[0], [examples], [input_img])
- run_btn.click(rmbg_fn, [input_img], [output_mask, output_img])
- app.launch()
diff --git a/spaces/mdj1412/stock_news_summaries_AI/static/js/chartjs-chart-financial.js b/spaces/mdj1412/stock_news_summaries_AI/static/js/chartjs-chart-financial.js
deleted file mode 100644
index c3719fbfc9fed28fe33ddfe420da8a7c51b5a2df..0000000000000000000000000000000000000000
--- a/spaces/mdj1412/stock_news_summaries_AI/static/js/chartjs-chart-financial.js
+++ /dev/null
@@ -1,525 +0,0 @@
-/*!
- * @license
- * chartjs-chart-financial
- * http://chartjs.org/
- * Version: 0.1.0
- *
- * Copyright 2021 Chart.js Contributors
- * Released under the MIT license
- * https://github.com/chartjs/chartjs-chart-financial/blob/master/LICENSE.md
- */
-(function (global, factory) {
-typeof exports === 'object' && typeof module !== 'undefined' ? factory(require('chart.js'), require('chart.js/helpers')) :
-typeof define === 'function' && define.amd ? define(['chart.js', 'chart.js/helpers'], factory) :
-(global = typeof globalThis !== 'undefined' ? globalThis : global || self, factory(global.Chart, global.Chart.helpers));
-}(this, (function (chart_js, helpers) { 'use strict';
-
-/**
- * Computes the "optimal" sample size to maintain bars equally sized while preventing overlap.
- * @private
- */
-function computeMinSampleSize(scale, pixels) {
- let min = scale._length;
- let prev, curr, i, ilen;
-
- for (i = 1, ilen = pixels.length; i < ilen; ++i) {
- min = Math.min(min, Math.abs(pixels[i] - pixels[i - 1]));
- }
-
- for (i = 0, ilen = scale.ticks.length; i < ilen; ++i) {
- curr = scale.getPixelForTick(i);
- min = i > 0 ? Math.min(min, Math.abs(curr - prev)) : min;
- prev = curr;
- }
-
- return min;
-}
-
-/**
- * This class is based off controller.bar.js from the upstream Chart.js library
- */
-class FinancialController extends chart_js.BarController {
-
- getLabelAndValue(index) {
- const me = this;
- const parsed = me.getParsed(index);
- const axis = me._cachedMeta.iScale.axis;
-
- const {o, h, l, c} = parsed;
- const value = `O: ${o} H: ${h} L: ${l} C: ${c}`;
-
- return {
- label: `${me._cachedMeta.iScale.getLabelForValue(parsed[axis])}`,
- value
- };
- }
-
- getAllParsedValues() {
- const meta = this._cachedMeta;
- const axis = meta.iScale.axis;
- const parsed = meta._parsed;
- const values = [];
- for (let i = 0; i < parsed.length; ++i) {
- values.push(parsed[i][axis]);
- }
- return values;
- }
-
- /**
- * Implement this ourselves since it doesn't handle high and low values
- * https://github.com/chartjs/Chart.js/issues/7328
- * @protected
- */
- getMinMax(scale) {
- const meta = this._cachedMeta;
- const _parsed = meta._parsed;
- const axis = meta.iScale.axis;
-
- if (_parsed.length < 2) {
- return {min: 0, max: 1};
- }
-
- if (scale === meta.iScale) {
- return {min: _parsed[0][axis], max: _parsed[_parsed.length - 1][axis]};
- }
-
- let min = Number.POSITIVE_INFINITY;
- let max = Number.NEGATIVE_INFINITY;
- for (let i = 0; i < _parsed.length; i++) {
- const data = _parsed[i];
- min = Math.min(min, data.l);
- max = Math.max(max, data.h);
- }
- return {min, max};
- }
-
- _getRuler() {
- const me = this;
- const opts = me.options;
- const meta = me._cachedMeta;
- const iScale = meta.iScale;
- const axis = iScale.axis;
- const pixels = [];
- for (let i = 0; i < meta.data.length; ++i) {
- pixels.push(iScale.getPixelForValue(me.getParsed(i)[axis]));
- }
- const barThickness = opts.barThickness;
- const min = computeMinSampleSize(iScale, pixels);
- return {
- min,
- pixels,
- start: iScale._startPixel,
- end: iScale._endPixel,
- stackCount: me._getStackCount(),
- scale: iScale,
- ratio: barThickness ? 1 : opts.categoryPercentage * opts.barPercentage
- };
- }
-
- /**
- * @protected
- */
- calculateElementProperties(index, ruler, reset, options) {
- const me = this;
- const vscale = me._cachedMeta.vScale;
- const base = vscale.getBasePixel();
- const ipixels = me._calculateBarIndexPixels(index, ruler, options);
- const data = me.chart.data.datasets[me.index].data[index];
- const open = vscale.getPixelForValue(data.o);
- const high = vscale.getPixelForValue(data.h);
- const low = vscale.getPixelForValue(data.l);
- const close = vscale.getPixelForValue(data.c);
-
- return {
- base: reset ? base : low,
- x: ipixels.center,
- y: (low + high) / 2,
- width: ipixels.size,
- open,
- high,
- low,
- close
- };
- }
-
- draw() {
- const me = this;
- const chart = me.chart;
- const rects = me._cachedMeta.data;
- helpers.clipArea(chart.ctx, chart.chartArea);
- for (let i = 0; i < rects.length; ++i) {
- rects[i].draw(me._ctx);
- }
- helpers.unclipArea(chart.ctx);
- }
-
-}
-
-FinancialController.overrides = {
- label: '',
-
- parsing: false,
-
- hover: {
- mode: 'label'
- },
-
- datasets: {
- categoryPercentage: 0.8,
- barPercentage: 0.9,
- animation: {
- numbers: {
- type: 'number',
- properties: ['x', 'y', 'base', 'width', 'open', 'high', 'low', 'close']
- }
- }
- },
-
- scales: {
- x: {
- type: 'timeseries',
- offset: true,
- ticks: {
- major: {
- enabled: true,
- },
- fontStyle: context => context.tick.major ? 'bold' : undefined,
- source: 'data',
- maxRotation: 0,
- autoSkip: true,
- autoSkipPadding: 75,
- sampleSize: 100
- },
- afterBuildTicks: scale => {
- const DateTime = window && window.luxon && window.luxon.DateTime;
- if (!DateTime) {
- return;
- }
- const majorUnit = scale._majorUnit;
- const ticks = scale.ticks;
- const firstTick = ticks[0];
- if (!firstTick) {
- return;
- }
-
- let val = DateTime.fromMillis(firstTick.value);
- if ((majorUnit === 'minute' && val.second === 0)
- || (majorUnit === 'hour' && val.minute === 0)
- || (majorUnit === 'day' && val.hour === 9)
- || (majorUnit === 'month' && val.day <= 3 && val.weekday === 1)
- || (majorUnit === 'year' && val.month === 1)) {
- firstTick.major = true;
- } else {
- firstTick.major = false;
- }
- let lastMajor = val.get(majorUnit);
-
- for (let i = 1; i < ticks.length; i++) {
- const tick = ticks[i];
- val = DateTime.fromMillis(tick.value);
- const currMajor = val.get(majorUnit);
- tick.major = currMajor !== lastMajor;
- lastMajor = currMajor;
- }
- scale.ticks = ticks;
- }
- },
- y: {
- type: 'linear'
- }
- },
-
- plugins: {
- tooltip: {
- intersect: false,
- mode: 'index',
- callbacks: {
- label(ctx) {
- const point = ctx.parsed;
-
- if (!helpers.isNullOrUndef(point.y)) {
- return chart_js.defaults.plugins.tooltip.callbacks.label(ctx);
- }
-
- const {o, h, l, c} = point;
-
- return `O: ${o} H: ${h} L: ${l} C: ${c}`;
- }
- }
- }
- }
-};
-
-const globalOpts$2 = chart_js.Chart.defaults;
-
-globalOpts$2.elements.financial = {
- color: {
- up: 'rgba(255, 0, 0, 1)',
- down: 'rgba(0, 0, 255, 1)',
- unchanged: 'rgba(90, 90, 90, 1)',
- }
-};
-
-/**
- * Helper function to get the bounds of the bar regardless of the orientation
- * @param {Rectangle} bar the bar
- * @param {boolean} [useFinalPosition]
- * @return {object} bounds of the bar
- * @private
- */
-function getBarBounds(bar, useFinalPosition) {
- const {x, y, base, width, height} = bar.getProps(['x', 'low', 'high', 'width', 'height'], useFinalPosition);
-
- let left, right, top, bottom, half;
-
- if (bar.horizontal) {
- half = height / 2;
- left = Math.min(x, base);
- right = Math.max(x, base);
- top = y - half;
- bottom = y + half;
- } else {
- half = width / 2;
- left = x - half;
- right = x + half;
- top = Math.min(y, base); // use min because 0 pixel at top of screen
- bottom = Math.max(y, base);
- }
-
- return {left, top, right, bottom};
-}
-
-function inRange(bar, x, y, useFinalPosition) {
- const skipX = x === null;
- const skipY = y === null;
- const bounds = !bar || (skipX && skipY) ? false : getBarBounds(bar, useFinalPosition);
-
- return bounds
- && (skipX || x >= bounds.left && x <= bounds.right)
- && (skipY || y >= bounds.top && y <= bounds.bottom);
-}
-
-class FinancialElement extends chart_js.Element {
-
- height() {
- return this.base - this.y;
- }
-
- inRange(mouseX, mouseY, useFinalPosition) {
- return inRange(this, mouseX, mouseY, useFinalPosition);
- }
-
- inXRange(mouseX, useFinalPosition) {
- return inRange(this, mouseX, null, useFinalPosition);
- }
-
- inYRange(mouseY, useFinalPosition) {
- return inRange(this, null, mouseY, useFinalPosition);
- }
-
- getRange(axis) {
- return axis === 'x' ? this.width / 2 : this.height / 2;
- }
-
- getCenterPoint(useFinalPosition) {
- const {x, low, high} = this.getProps(['x', 'low', 'high'], useFinalPosition);
- return {
- x,
- y: (high + low) / 2
- };
- }
-
- tooltipPosition(useFinalPosition) {
- const {x, open, close} = this.getProps(['x', 'open', 'close'], useFinalPosition);
- return {
- x,
- y: (open + close) / 2
- };
- }
-}
-
-const globalOpts$1 = chart_js.Chart.defaults;
-
-class CandlestickElement extends FinancialElement {
- draw(ctx) {
- const me = this;
-
- const {x, open, high, low, close} = me;
-
- let borderColors = me.borderColor;
- if (typeof borderColors === 'string') {
- borderColors = {
- up: borderColors,
- down: borderColors,
- unchanged: borderColors
- };
- }
-
- let borderColor;
- if (close < open) {
- borderColor = helpers.valueOrDefault(borderColors ? borderColors.up : undefined, globalOpts$1.elements.candlestick.borderColor);
- ctx.fillStyle = helpers.valueOrDefault(me.color ? me.color.up : undefined, globalOpts$1.elements.candlestick.color.up);
- } else if (close > open) {
- borderColor = helpers.valueOrDefault(borderColors ? borderColors.down : undefined, globalOpts$1.elements.candlestick.borderColor);
- ctx.fillStyle = helpers.valueOrDefault(me.color ? me.color.down : undefined, globalOpts$1.elements.candlestick.color.down);
- } else {
- borderColor = helpers.valueOrDefault(borderColors ? borderColors.unchanged : undefined, globalOpts$1.elements.candlestick.borderColor);
- ctx.fillStyle = helpers.valueOrDefault(me.color ? me.color.unchanged : undefined, globalOpts$1.elements.candlestick.color.unchanged);
- }
-
- ctx.lineWidth = helpers.valueOrDefault(me.borderWidth, globalOpts$1.elements.candlestick.borderWidth);
- ctx.strokeStyle = helpers.valueOrDefault(borderColor, globalOpts$1.elements.candlestick.borderColor);
-
- ctx.beginPath();
- ctx.moveTo(x, high);
- ctx.lineTo(x, Math.min(open, close));
- ctx.moveTo(x, low);
- ctx.lineTo(x, Math.max(open, close));
- ctx.stroke();
- ctx.fillRect(x - me.width / 2, close, me.width, open - close);
- ctx.strokeRect(x - me.width / 2, close, me.width, open - close);
- ctx.closePath();
- }
-}
-
-CandlestickElement.id = 'candlestick';
-CandlestickElement.defaults = helpers.merge({}, [globalOpts$1.elements.financial, {
- borderColor: globalOpts$1.elements.financial.color.unchanged,
- borderWidth: 1,
-}]);
-
-class CandlestickController extends FinancialController {
-
- updateElements(elements, start, count, mode) {
- const me = this;
- const dataset = me.getDataset();
- const ruler = me._ruler || me._getRuler();
- const firstOpts = me.resolveDataElementOptions(start, mode);
- const sharedOptions = me.getSharedOptions(firstOpts);
- const includeOptions = me.includeOptions(mode, sharedOptions);
-
- me.updateSharedOptions(sharedOptions, mode, firstOpts);
-
- for (let i = start; i < count; i++) {
- const options = sharedOptions || me.resolveDataElementOptions(i, mode);
-
- const lineColor = (elements[i]['close'] - elements[i]['open'] < 0)? "rgb(255, 0, 0, 1)" : "rgb(0, 0, 255, 1)";
-
- const baseProperties = me.calculateElementProperties(i, ruler, mode === 'reset', options);
- const properties = {
- ...baseProperties,
- datasetLabel: dataset.label || '',
- // label: '', // to get label value please use dataset.data[index].label
-
- // Appearance
- color: dataset.color,
- borderColor: lineColor,
- // borderColor: dataset.borderColor,
- borderWidth: dataset.borderWidth,
- };
-
- if (includeOptions) {
- properties.options = options;
- }
- me.updateElement(elements[i], i, properties, mode);
- }
- }
-
-}
-
-CandlestickController.id = 'candlestick';
-CandlestickController.defaults = helpers.merge({
- dataElementType: CandlestickElement.id
-}, chart_js.Chart.defaults.financial);
-
-const globalOpts = chart_js.Chart.defaults;
-
-class OhlcElement extends FinancialElement {
- draw(ctx) {
- const me = this;
-
- const {x, open, high, low, close} = me;
-
- const armLengthRatio = helpers.valueOrDefault(me.armLengthRatio, globalOpts.elements.ohlc.armLengthRatio);
- let armLength = helpers.valueOrDefault(me.armLength, globalOpts.elements.ohlc.armLength);
- if (armLength === null) {
- // The width of an ohlc is affected by barPercentage and categoryPercentage
- // This behavior is caused by extending controller.financial, which extends controller.bar
- // barPercentage and categoryPercentage are now set to 1.0 (see controller.ohlc)
- // and armLengthRatio is multipled by 0.5,
- // so that when armLengthRatio=1.0, the arms from neighbour ohcl touch,
- // and when armLengthRatio=0.0, ohcl are just vertical lines.
- armLength = me.width * armLengthRatio * 0.5;
- }
-
- if (close < open) {
- ctx.strokeStyle = helpers.valueOrDefault(me.color ? me.color.up : undefined, globalOpts.elements.ohlc.color.up);
- } else if (close > open) {
- ctx.strokeStyle = helpers.valueOrDefault(me.color ? me.color.down : undefined, globalOpts.elements.ohlc.color.down);
- } else {
- ctx.strokeStyle = helpers.valueOrDefault(me.color ? me.color.unchanged : undefined, globalOpts.elements.ohlc.color.unchanged);
- }
- ctx.lineWidth = helpers.valueOrDefault(me.lineWidth, globalOpts.elements.ohlc.lineWidth);
-
- ctx.beginPath();
- ctx.moveTo(x, high);
- ctx.lineTo(x, low);
- ctx.moveTo(x - armLength, open);
- ctx.lineTo(x, open);
- ctx.moveTo(x + armLength, close);
- ctx.lineTo(x, close);
- ctx.stroke();
- }
-}
-
-OhlcElement.id = 'ohlc';
-OhlcElement.defaults = helpers.merge({}, [globalOpts.elements.financial, {
- lineWidth: 2,
- armLength: null,
- armLengthRatio: 0.8,
-}]);
-
-class OhlcController extends FinancialController {
-
- updateElements(elements, start, count, mode) {
- const me = this;
- const dataset = me.getDataset();
- const ruler = me._ruler || me._getRuler();
- const firstOpts = me.resolveDataElementOptions(start, mode);
- const sharedOptions = me.getSharedOptions(firstOpts);
- const includeOptions = me.includeOptions(mode, sharedOptions);
-
- for (let i = 0; i < count; i++) {
- const options = sharedOptions || me.resolveDataElementOptions(i, mode);
-
- const baseProperties = me.calculateElementProperties(i, ruler, mode === 'reset', options);
- const properties = {
- ...baseProperties,
- datasetLabel: dataset.label || '',
- lineWidth: dataset.lineWidth,
- armLength: dataset.armLength,
- armLengthRatio: dataset.armLengthRatio,
- color: dataset.color,
- };
-
- if (includeOptions) {
- properties.options = options;
- }
- me.updateElement(elements[i], i, properties, mode);
- }
- }
-
-}
-
-OhlcController.id = 'ohlc';
-OhlcController.defaults = helpers.merge({
- dataElementType: OhlcElement.id,
- datasets: {
- barPercentage: 1.0,
- categoryPercentage: 1.0
- }
-}, chart_js.Chart.defaults.financial);
-
-chart_js.Chart.register(CandlestickController, OhlcController, CandlestickElement, OhlcElement);
-
-})));
diff --git a/spaces/mehdidc/text_to_image_ddgan/score_sde/models/projected_discriminator.py b/spaces/mehdidc/text_to_image_ddgan/score_sde/models/projected_discriminator.py
deleted file mode 100644
index 2e1548f2da252a7a950550ecbd2a81a27b7db639..0000000000000000000000000000000000000000
--- a/spaces/mehdidc/text_to_image_ddgan/score_sde/models/projected_discriminator.py
+++ /dev/null
@@ -1,783 +0,0 @@
-from functools import partial
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-#from pg_modules.blocks import DownBlock, DownBlockPatch, conv2d
-import functools
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn.utils import spectral_norm
-from . import layers
-from .layers import CondAttnBlock
-from .discriminator import *
-
-
-def conv2d(*args, **kwargs):
- return spectral_norm(nn.Conv2d(*args, **kwargs))
-
-
-def convTranspose2d(*args, **kwargs):
- return spectral_norm(nn.ConvTranspose2d(*args, **kwargs))
-
-
-def embedding(*args, **kwargs):
- return spectral_norm(nn.Embedding(*args, **kwargs))
-
-
-def linear(*args, **kwargs):
- return spectral_norm(nn.Linear(*args, **kwargs))
-
-
-def NormLayer(c, mode='batch'):
- if mode == 'group':
- return nn.GroupNorm(c//2, c)
- elif mode == 'batch':
- return nn.BatchNorm2d(c)
-
-
-### Activations
-
-
-class GLU(nn.Module):
- def forward(self, x):
- nc = x.size(1)
- assert nc % 2 == 0, 'channels dont divide 2!'
- nc = int(nc/2)
- return x[:, :nc] * torch.sigmoid(x[:, nc:])
-
-
-class Swish(nn.Module):
- def forward(self, feat):
- return feat * torch.sigmoid(feat)
-
-
-### Upblocks
-
-
-class InitLayer(nn.Module):
- def __init__(self, nz, channel, sz=4):
- super().__init__()
-
- self.init = nn.Sequential(
- convTranspose2d(nz, channel*2, sz, 1, 0, bias=False),
- NormLayer(channel*2),
- GLU(),
- )
-
- def forward(self, noise):
- noise = noise.view(noise.shape[0], -1, 1, 1)
- return self.init(noise)
-
-
-def UpBlockSmall(in_planes, out_planes):
- block = nn.Sequential(
- nn.Upsample(scale_factor=2, mode='nearest'),
- conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False),
- NormLayer(out_planes*2), GLU())
- return block
-
-
-class UpBlockSmallCond(nn.Module):
- def __init__(self, in_planes, out_planes, z_dim):
- super().__init__()
- self.in_planes = in_planes
- self.out_planes = out_planes
- self.up = nn.Upsample(scale_factor=2, mode='nearest')
- self.conv = conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False)
-
- which_bn = functools.partial(CCBN, which_linear=linear, input_size=z_dim)
- self.bn = which_bn(2*out_planes)
- self.act = GLU()
-
- def forward(self, x, c):
- x = self.up(x)
- x = self.conv(x)
- x = self.bn(x, c)
- x = self.act(x)
- return x
-
-
-def UpBlockBig(in_planes, out_planes):
- block = nn.Sequential(
- nn.Upsample(scale_factor=2, mode='nearest'),
- conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False),
- NoiseInjection(),
- NormLayer(out_planes*2), GLU(),
- conv2d(out_planes, out_planes*2, 3, 1, 1, bias=False),
- NoiseInjection(),
- NormLayer(out_planes*2), GLU()
- )
- return block
-
-
-class UpBlockBigCond(nn.Module):
- def __init__(self, in_planes, out_planes, z_dim):
- super().__init__()
- self.in_planes = in_planes
- self.out_planes = out_planes
- self.up = nn.Upsample(scale_factor=2, mode='nearest')
- self.conv1 = conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False)
- self.conv2 = conv2d(out_planes, out_planes*2, 3, 1, 1, bias=False)
-
- which_bn = functools.partial(CCBN, which_linear=linear, input_size=z_dim)
- self.bn1 = which_bn(2*out_planes)
- self.bn2 = which_bn(2*out_planes)
- self.act = GLU()
- self.noise = NoiseInjection()
-
- def forward(self, x, c):
- # block 1
- x = self.up(x)
- x = self.conv1(x)
- x = self.noise(x)
- x = self.bn1(x, c)
- x = self.act(x)
-
- # block 2
- x = self.conv2(x)
- x = self.noise(x)
- x = self.bn2(x, c)
- x = self.act(x)
-
- return x
-
-
-class SEBlock(nn.Module):
- def __init__(self, ch_in, ch_out):
- super().__init__()
- self.main = nn.Sequential(
- nn.AdaptiveAvgPool2d(4),
- conv2d(ch_in, ch_out, 4, 1, 0, bias=False),
- Swish(),
- conv2d(ch_out, ch_out, 1, 1, 0, bias=False),
- nn.Sigmoid(),
- )
-
- def forward(self, feat_small, feat_big):
- return feat_big * self.main(feat_small)
-
-
-### Downblocks
-
-
-class SeparableConv2d(nn.Module):
- def __init__(self, in_channels, out_channels, kernel_size, bias=False):
- super(SeparableConv2d, self).__init__()
- self.depthwise = conv2d(in_channels, in_channels, kernel_size=kernel_size,
- groups=in_channels, bias=bias, padding=1)
- self.pointwise = conv2d(in_channels, out_channels,
- kernel_size=1, bias=bias)
-
- def forward(self, x):
- out = self.depthwise(x)
- out = self.pointwise(out)
- return out
-
-
-class DownBlock(nn.Module):
- def __init__(self, in_planes, out_planes, separable=False):
- super().__init__()
- if not separable:
- self.main = nn.Sequential(
- conv2d(in_planes, out_planes, 4, 2, 1),
- NormLayer(out_planes),
- nn.LeakyReLU(0.2, inplace=True),
- )
- else:
- self.main = nn.Sequential(
- SeparableConv2d(in_planes, out_planes, 3),
- NormLayer(out_planes),
- nn.LeakyReLU(0.2, inplace=True),
- nn.AvgPool2d(2, 2),
- )
-
- def forward(self, feat):
- return self.main(feat)
-
-
-class DownBlockPatch(nn.Module):
- def __init__(self, in_planes, out_planes, separable=False):
- super().__init__()
- self.main = nn.Sequential(
- DownBlock(in_planes, out_planes, separable),
- conv2d(out_planes, out_planes, 1, 1, 0, bias=False),
- NormLayer(out_planes),
- nn.LeakyReLU(0.2, inplace=True),
- )
-
- def forward(self, feat):
- return self.main(feat)
-
-
-### CSM
-
-
-class ResidualConvUnit(nn.Module):
- def __init__(self, cin, activation, bn):
- super().__init__()
- self.conv = nn.Conv2d(cin, cin, kernel_size=3, stride=1, padding=1, bias=True)
- self.skip_add = nn.quantized.FloatFunctional()
-
- def forward(self, x):
- return self.skip_add.add(self.conv(x), x)
-
-
-class FeatureFusionBlock(nn.Module):
- def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True, lowest=False):
- super().__init__()
-
- self.deconv = deconv
- self.align_corners = align_corners
-
- self.expand = expand
- out_features = features
- if self.expand==True:
- out_features = features//2
-
- self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
- self.skip_add = nn.quantized.FloatFunctional()
-
- def forward(self, *xs):
- output = xs[0]
-
- if len(xs) == 2:
- output = self.skip_add.add(output, xs[1])
-
- output = nn.functional.interpolate(
- output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
- )
-
- output = self.out_conv(output)
-
- return output
-
-
-### Misc
-
-
-class NoiseInjection(nn.Module):
- def __init__(self):
- super().__init__()
- self.weight = nn.Parameter(torch.zeros(1), requires_grad=True)
-
- def forward(self, feat, noise=None):
- if noise is None:
- batch, _, height, width = feat.shape
- noise = torch.randn(batch, 1, height, width).to(feat.device)
-
- return feat + self.weight * noise
-
-
-class CCBN(nn.Module):
- ''' conditional batchnorm '''
- def __init__(self, output_size, input_size, which_linear, eps=1e-5, momentum=0.1):
- super().__init__()
- self.output_size, self.input_size = output_size, input_size
-
- # Prepare gain and bias layers
- self.gain = which_linear(input_size, output_size)
- self.bias = which_linear(input_size, output_size)
-
- # epsilon to avoid dividing by 0
- self.eps = eps
- # Momentum
- self.momentum = momentum
-
- self.register_buffer('stored_mean', torch.zeros(output_size))
- self.register_buffer('stored_var', torch.ones(output_size))
-
- def forward(self, x, y):
- # Calculate class-conditional gains and biases
- gain = (1 + self.gain(y)).view(y.size(0), -1, 1, 1)
- bias = self.bias(y).view(y.size(0), -1, 1, 1)
- out = F.batch_norm(x, self.stored_mean, self.stored_var, None, None,
- self.training, 0.1, self.eps)
- return out * gain + bias
-
-
-class Interpolate(nn.Module):
- """Interpolation module."""
-
- def __init__(self, size, mode='bilinear', align_corners=False):
- """Init.
- Args:
- scale_factor (float): scaling
- mode (str): interpolation mode
- """
- super(Interpolate, self).__init__()
-
- self.interp = nn.functional.interpolate
- self.size = size
- self.mode = mode
- self.align_corners = align_corners
-
- def forward(self, x):
- """Forward pass.
- Args:
- x (tensor): input
- Returns:
- tensor: interpolated data
- """
-
- x = self.interp(
- x,
- size=self.size,
- mode=self.mode,
- align_corners=self.align_corners,
- )
-
- return x
-
-
-
-#from pg_modules.projector import F_RandomProj
-
-import torch
-import torch.nn as nn
-import timm
-#from pg_modules.blocks import FeatureFusionBlock
-
-
-def _make_scratch_ccm(scratch, in_channels, cout, expand=False):
- # shapes
- out_channels = [cout, cout*2, cout*4, cout*8] if expand else [cout]*4
-
- scratch.layer0_ccm = nn.Conv2d(in_channels[0], out_channels[0], kernel_size=1, stride=1, padding=0, bias=True)
- scratch.layer1_ccm = nn.Conv2d(in_channels[1], out_channels[1], kernel_size=1, stride=1, padding=0, bias=True)
- scratch.layer2_ccm = nn.Conv2d(in_channels[2], out_channels[2], kernel_size=1, stride=1, padding=0, bias=True)
- scratch.layer3_ccm = nn.Conv2d(in_channels[3], out_channels[3], kernel_size=1, stride=1, padding=0, bias=True)
-
- scratch.CHANNELS = out_channels
-
- return scratch
-
-
-def _make_scratch_csm(scratch, in_channels, cout, expand):
- scratch.layer3_csm = FeatureFusionBlock(in_channels[3], nn.ReLU(False), expand=expand, lowest=True)
- scratch.layer2_csm = FeatureFusionBlock(in_channels[2], nn.ReLU(False), expand=expand)
- scratch.layer1_csm = FeatureFusionBlock(in_channels[1], nn.ReLU(False), expand=expand)
- scratch.layer0_csm = FeatureFusionBlock(in_channels[0], nn.ReLU(False))
-
- # last refinenet does not expand to save channels in higher dimensions
- scratch.CHANNELS = [cout, cout, cout*2, cout*4] if expand else [cout]*4
-
- return scratch
-
-
-def _make_efficientnet(model):
- pretrained = nn.Module()
- pretrained.layer0 = nn.Sequential(model.conv_stem, model.bn1, model.act1, *model.blocks[0:2])
- pretrained.layer1 = nn.Sequential(*model.blocks[2:3])
- pretrained.layer2 = nn.Sequential(*model.blocks[3:5])
- pretrained.layer3 = nn.Sequential(*model.blocks[5:9])
- return pretrained
-
-
-def calc_channels(pretrained, inp_res=224):
- channels = []
- tmp = torch.zeros(1, 3, inp_res, inp_res)
-
- # forward pass
- tmp = pretrained.layer0(tmp)
- channels.append(tmp.shape[1])
- tmp = pretrained.layer1(tmp)
- channels.append(tmp.shape[1])
- tmp = pretrained.layer2(tmp)
- channels.append(tmp.shape[1])
- tmp = pretrained.layer3(tmp)
- channels.append(tmp.shape[1])
-
- return channels
-
-
-def _make_projector(im_res, cout, proj_type, expand=False):
- assert proj_type in [0, 1, 2], "Invalid projection type"
-
- ### Build pretrained feature network
- model = timm.create_model('tf_efficientnet_lite0', pretrained=True)
- pretrained = _make_efficientnet(model)
-
- # determine resolution of feature maps, this is later used to calculate the number
- # of down blocks in the discriminators. Interestingly, the best results are achieved
- # by fixing this to 256, ie., we use the same number of down blocks per discriminator
- # independent of the dataset resolution
- im_res = 256
- pretrained.RESOLUTIONS = [im_res//4, im_res//8, im_res//16, im_res//32]
- pretrained.CHANNELS = calc_channels(pretrained)
-
- if proj_type == 0: return pretrained, None
-
- ### Build CCM
- scratch = nn.Module()
- scratch = _make_scratch_ccm(scratch, in_channels=pretrained.CHANNELS, cout=cout, expand=expand)
- pretrained.CHANNELS = scratch.CHANNELS
-
- if proj_type == 1: return pretrained, scratch
-
- ### build CSM
- scratch = _make_scratch_csm(scratch, in_channels=scratch.CHANNELS, cout=cout, expand=expand)
-
- # CSM upsamples x2 so the feature map resolution doubles
- pretrained.RESOLUTIONS = [res*2 for res in pretrained.RESOLUTIONS]
- pretrained.CHANNELS = scratch.CHANNELS
-
- return pretrained, scratch
-
-
-class F_RandomProj(nn.Module):
- def __init__(
- self,
- im_res=256,
- cout=64,
- expand=True,
- proj_type=2, # 0 = no projection, 1 = cross channel mixing, 2 = cross scale mixing
- **kwargs,
- ):
- super().__init__()
- self.proj_type = proj_type
- self.cout = cout
- self.expand = expand
-
- # build pretrained feature network and random decoder (scratch)
- self.pretrained, self.scratch = _make_projector(im_res=im_res, cout=self.cout, proj_type=self.proj_type, expand=self.expand)
- self.CHANNELS = self.pretrained.CHANNELS
- self.RESOLUTIONS = self.pretrained.RESOLUTIONS
-
- def forward(self, x):
- # predict feature maps
- out0 = self.pretrained.layer0(x)
- out1 = self.pretrained.layer1(out0)
- out2 = self.pretrained.layer2(out1)
- out3 = self.pretrained.layer3(out2)
-
- # start enumerating at the lowest layer (this is where we put the first discriminator)
- out = {
- '0': out0,
- '1': out1,
- '2': out2,
- '3': out3,
- }
-
- if self.proj_type == 0: return out
-
- out0_channel_mixed = self.scratch.layer0_ccm(out['0'])
- out1_channel_mixed = self.scratch.layer1_ccm(out['1'])
- out2_channel_mixed = self.scratch.layer2_ccm(out['2'])
- out3_channel_mixed = self.scratch.layer3_ccm(out['3'])
-
- out = {
- '0': out0_channel_mixed,
- '1': out1_channel_mixed,
- '2': out2_channel_mixed,
- '3': out3_channel_mixed,
- }
-
- if self.proj_type == 1: return out
-
- # from bottom to top
- out3_scale_mixed = self.scratch.layer3_csm(out3_channel_mixed)
- out2_scale_mixed = self.scratch.layer2_csm(out3_scale_mixed, out2_channel_mixed)
- out1_scale_mixed = self.scratch.layer1_csm(out2_scale_mixed, out1_channel_mixed)
- out0_scale_mixed = self.scratch.layer0_csm(out1_scale_mixed, out0_channel_mixed)
-
- out = {
- '0': out0_scale_mixed,
- '1': out1_scale_mixed,
- '2': out2_scale_mixed,
- '3': out3_scale_mixed,
- }
-
- return out
-
-
-#from pg_modules.diffaug import DiffAugment
-# Differentiable Augmentation for Data-Efficient GAN Training
-# Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han
-# https://arxiv.org/pdf/2006.10738
-
-import torch
-import torch.nn.functional as F
-
-
-def DiffAugment(x, policy='', channels_first=True):
- if policy:
- if not channels_first:
- x = x.permute(0, 3, 1, 2)
- for p in policy.split(','):
- for f in AUGMENT_FNS[p]:
- x = f(x)
- if not channels_first:
- x = x.permute(0, 2, 3, 1)
- x = x.contiguous()
- return x
-
-
-def rand_brightness(x):
- x = x + (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) - 0.5)
- return x
-
-
-def rand_saturation(x):
- x_mean = x.mean(dim=1, keepdim=True)
- x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) * 2) + x_mean
- return x
-
-
-def rand_contrast(x):
- x_mean = x.mean(dim=[1, 2, 3], keepdim=True)
- x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) + 0.5) + x_mean
- return x
-
-
-def rand_translation(x, ratio=0.125):
- shift_x, shift_y = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
- translation_x = torch.randint(-shift_x, shift_x + 1, size=[x.size(0), 1, 1], device=x.device)
- translation_y = torch.randint(-shift_y, shift_y + 1, size=[x.size(0), 1, 1], device=x.device)
- grid_batch, grid_x, grid_y = torch.meshgrid(
- torch.arange(x.size(0), dtype=torch.long, device=x.device),
- torch.arange(x.size(2), dtype=torch.long, device=x.device),
- torch.arange(x.size(3), dtype=torch.long, device=x.device),
- )
- grid_x = torch.clamp(grid_x + translation_x + 1, 0, x.size(2) + 1)
- grid_y = torch.clamp(grid_y + translation_y + 1, 0, x.size(3) + 1)
- x_pad = F.pad(x, [1, 1, 1, 1, 0, 0, 0, 0])
- x = x_pad.permute(0, 2, 3, 1).contiguous()[grid_batch, grid_x, grid_y].permute(0, 3, 1, 2)
- return x
-
-
-def rand_cutout(x, ratio=0.2):
- cutout_size = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
- offset_x = torch.randint(0, x.size(2) + (1 - cutout_size[0] % 2), size=[x.size(0), 1, 1], device=x.device)
- offset_y = torch.randint(0, x.size(3) + (1 - cutout_size[1] % 2), size=[x.size(0), 1, 1], device=x.device)
- grid_batch, grid_x, grid_y = torch.meshgrid(
- torch.arange(x.size(0), dtype=torch.long, device=x.device),
- torch.arange(cutout_size[0], dtype=torch.long, device=x.device),
- torch.arange(cutout_size[1], dtype=torch.long, device=x.device),
- )
- grid_x = torch.clamp(grid_x + offset_x - cutout_size[0] // 2, min=0, max=x.size(2) - 1)
- grid_y = torch.clamp(grid_y + offset_y - cutout_size[1] // 2, min=0, max=x.size(3) - 1)
- mask = torch.ones(x.size(0), x.size(2), x.size(3), dtype=x.dtype, device=x.device)
- mask[grid_batch, grid_x, grid_y] = 0
- x = x * mask.unsqueeze(1)
- return x
-
-
-AUGMENT_FNS = {
- 'color': [rand_brightness, rand_saturation, rand_contrast],
- 'translation': [rand_translation],
- 'cutout': [rand_cutout],
-}
-
-
-
-class SingleDisc(nn.Module):
- def __init__(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False):
- super().__init__()
- channel_dict = {4: 512, 8: 512, 16: 256, 32: 128, 64: 64, 128: 64,
- 256: 32, 512: 16, 1024: 8}
-
- # interpolate for start sz that are not powers of two
- if start_sz not in channel_dict.keys():
- sizes = np.array(list(channel_dict.keys()))
- start_sz = sizes[np.argmin(abs(sizes - start_sz))]
- self.start_sz = start_sz
-
- # if given ndf, allocate all layers with the same ndf
- if ndf is None:
- nfc = channel_dict
- else:
- nfc = {k: ndf for k, v in channel_dict.items()}
-
- # for feature map discriminators with nfc not in channel_dict
- # this is the case for the pretrained backbone (midas.pretrained)
- if nc is not None and head is None:
- nfc[start_sz] = nc
-
- layers = []
-
- # Head if the initial input is the full modality
- if head:
- layers += [conv2d(nc, nfc[256], 3, 1, 1, bias=False),
- nn.LeakyReLU(0.2, inplace=True)]
-
- # Down Blocks
- DB = partial(DownBlockPatch, separable=separable) if patch else partial(DownBlock, separable=separable)
- while start_sz > end_sz:
- layers.append(DB(nfc[start_sz], nfc[start_sz//2]))
- start_sz = start_sz // 2
-
- layers.append(conv2d(nfc[end_sz], 1, 4, 1, 0, bias=False))
- self.main = nn.Sequential(*layers)
-
- def forward(self, x, c):
- return self.main(x)
-
-
-class SingleDiscCond(nn.Module):
- def __init__(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False, c_dim=1000, cmap_dim=64, embedding_dim=128, cond_size=128):
- super().__init__()
- self.cmap_dim = cmap_dim
- self.cond_attn = CondAttnBlock(cmap_dim, cond_size, dim_head=64, heads=8, norm_context=False, cosine_sim_attn=False)
- # midas channels
- channel_dict = {4: 512, 8: 512, 16: 256, 32: 128, 64: 64, 128: 64,
- 256: 32, 512: 16, 1024: 8}
-
- # interpolate for start sz that are not powers of two
- if start_sz not in channel_dict.keys():
- sizes = np.array(list(channel_dict.keys()))
- start_sz = sizes[np.argmin(abs(sizes - start_sz))]
- self.start_sz = start_sz
-
- # if given ndf, allocate all layers with the same ndf
- if ndf is None:
- nfc = channel_dict
- else:
- nfc = {k: ndf for k, v in channel_dict.items()}
-
- # for feature map discriminators with nfc not in channel_dict
- # this is the case for the pretrained backbone (midas.pretrained)
- if nc is not None and head is None:
- nfc[start_sz] = nc
-
- layers = []
-
- # Head if the initial input is the full modality
- if head:
- layers += [conv2d(nc, nfc[256], 3, 1, 1, bias=False),
- nn.LeakyReLU(0.2, inplace=True)]
-
- # Down Blocks
- DB = partial(DownBlockPatch, separable=separable) if patch else partial(DownBlock, separable=separable)
- while start_sz > end_sz:
- layers.append(DB(nfc[start_sz], nfc[start_sz//2]))
- start_sz = start_sz // 2
- self.main = nn.Sequential(*layers)
-
- # additions for conditioning on class information
- self.cls = conv2d(nfc[end_sz], self.cmap_dim, 4, 1, 0, bias=False)
- #self.embed = nn.Embedding(num_embeddings=c_dim, embedding_dim=embedding_dim)
- #self.embed_proj = nn.Sequential(
- # nn.Linear(self.embed.embedding_dim, self.cmap_dim),
- # nn.LeakyReLU(0.2, inplace=True),
- #)
-
- def forward(self, x, c):
- h = self.main(x)
- out = self.cls(h)
- cond_pooled, cond, cond_mask = c
- #print("COND", out.shape, cond.shape, cond_mask.shape, self.cond_sie)
- cmap = self.cond_attn(out, cond, cond_mask)
- # conditioning via projection
- #cmap = self.embed_proj(self.embed(c)).unsqueeze(-1).unsqueeze(-1)
- #cmap = 1
- out = (out * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
- return out
-
-
-class MultiScaleD(nn.Module):
- def __init__(
- self,
- channels,
- resolutions,
- num_discs=1,
- proj_type=2, # 0 = no projection, 1 = cross channel mixing, 2 = cross scale mixing
- cond=1,
- separable=False,
- patch=False,
- cond_size=128,
- **kwargs,
- ):
- super().__init__()
-
- assert num_discs in [1, 2, 3, 4]
-
- # the first disc is on the lowest level of the backbone
- self.disc_in_channels = channels[:num_discs]
- self.disc_in_res = resolutions[:num_discs]
-
- Disc = SingleDiscCond if cond else SingleDisc
- mini_discs = []
- for i, (cin, res) in enumerate(zip(self.disc_in_channels, self.disc_in_res)):
- start_sz = res if not patch else 16
- mini_discs += [str(i), Disc(nc=cin, start_sz=start_sz, end_sz=8, separable=separable, patch=patch, cond_size=cond_size)],
- self.mini_discs = nn.ModuleDict(mini_discs)
-
- def forward(self, features, c):
- all_logits = []
- for k, disc in self.mini_discs.items():
- all_logits.append(disc(features[k], c).view(features[k].size(0), -1))
-
- all_logits = torch.cat(all_logits, dim=1)
- return all_logits
-
-
-class ProjectedDiscriminator(torch.nn.Module):
- def __init__(
- self,
- diffaug=False,
- interp224=False,
- t_emb_dim = 128,
- out_dim=64,
- backbone_kwargs={},
- act=torch.nn.LeakyReLU(0.2),
- num_discs=1,
- **kwargs
- ):
- super().__init__()
- self.diffaug = diffaug
- self.act = act
- self.interp224 = interp224
- self.num_discs = num_discs
- self.feature_network = F_RandomProj(**backbone_kwargs)
- self.discriminator = MultiScaleD(
- channels=[c*2+out_dim for c in self.feature_network.CHANNELS],
- resolutions=self.feature_network.RESOLUTIONS,
- **backbone_kwargs,
- )
- self.t_embed = torch.nn.ModuleList([TimestepEmbedding(
- embedding_dim=t_emb_dim,
- hidden_dim=t_emb_dim,
- output_dim=out_dim,
- act=act,
- ) for _ in range(num_discs)])
-
-
- def train(self, mode=True):
- self.feature_network = self.feature_network.train(False)
- self.discriminator = self.discriminator.train(mode)
- return self
-
- def eval(self):
- return self.train(False)
-
- def forward(self, x, t, xprev, cond=None):
- #t_embed = self.t_embed(t)
- #t_embed = self.act(t_embed)
-
- if self.diffaug:
- x = DiffAugment(x, policy='color,translation,cutout')
-
- if self.interp224:
- x = F.interpolate(x, 256, mode='bilinear', align_corners=False)
-
- features1 = self.feature_network(x)
- features2 = self.feature_network(xprev)
- features = {}
- for k in features1.keys():
- if int(k) >= self.num_discs:
- continue
- tcat = self.t_embed[int(k)](t)
- #print(tcat.shape)
- h, w = features1[k].shape[2:]
- tcat = tcat.view(tcat.shape[0], tcat.shape[1], 1, 1).repeat(1,1, h, w)
- #print(x.shape, xprev.shape, features1[k].shape, features2[k].shape, tcat.shape)
- features[k] = torch.cat((features1[k], features2[k], tcat), dim=1)
- #print(features[k].shape)
- logits = self.discriminator(features, cond)
-
- return logits
-
diff --git a/spaces/meraih/English-Japanese-Anime-TTS/text/japanese.py b/spaces/meraih/English-Japanese-Anime-TTS/text/japanese.py
deleted file mode 100644
index 375e4d50872d5c68ee57ca17470a2ca425425eba..0000000000000000000000000000000000000000
--- a/spaces/meraih/English-Japanese-Anime-TTS/text/japanese.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import re
-from unidecode import unidecode
-import pyopenjtalk
-
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
- r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
- r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ts', 'ʦ'),
- ('u', 'ɯ'),
- ('j', 'ʥ'),
- ('y', 'j'),
- ('ni', 'n^i'),
- ('nj', 'n^'),
- ('hi', 'çi'),
- ('hj', 'ç'),
- ('f', 'ɸ'),
- ('I', 'i*'),
- ('U', 'ɯ*'),
- ('r', 'ɾ')
-]]
-
-# List of (romaji, ipa2) pairs for marks:
-_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('u', 'ɯ'),
- ('ʧ', 'tʃ'),
- ('j', 'dʑ'),
- ('y', 'j'),
- ('ni', 'n^i'),
- ('nj', 'n^'),
- ('hi', 'çi'),
- ('hj', 'ç'),
- ('f', 'ɸ'),
- ('I', 'i*'),
- ('U', 'ɯ*'),
- ('r', 'ɾ')
-]]
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'Q([↑↓]*[kg])', r'k#\1'),
- (r'Q([↑↓]*[tdjʧ])', r't#\1'),
- (r'Q([↑↓]*[sʃ])', r's\1'),
- (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'N([↑↓]*[pbm])', r'm\1'),
- (r'N([↑↓]*[ʧʥj])', r'n^\1'),
- (r'N([↑↓]*[tdn])', r'n\1'),
- (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-def symbols_to_japanese(text):
- for regex, replacement in _symbols_to_japanese:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_romaji_with_accent(text):
- '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
- text = symbols_to_japanese(text)
- sentences = re.split(_japanese_marks, text)
- marks = re.findall(_japanese_marks, text)
- text = ''
- for i, sentence in enumerate(sentences):
- if re.match(_japanese_characters, sentence):
- if text != '':
- text += ' '
- labels = pyopenjtalk.extract_fullcontext(sentence)
- for n, label in enumerate(labels):
- phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
- if phoneme not in ['sil', 'pau']:
- text += phoneme.replace('ch', 'ʧ').replace('sh',
- 'ʃ').replace('cl', 'Q')
- else:
- continue
- # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
- a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
- a2 = int(re.search(r"\+(\d+)\+", label).group(1))
- a3 = int(re.search(r"\+(\d+)/", label).group(1))
- if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
- a2_next = -1
- else:
- a2_next = int(
- re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
- # Accent phrase boundary
- if a3 == 1 and a2_next == 1:
- text += ' '
- # Falling
- elif a1 == 0 and a2_next == a2 + 1:
- text += '↓'
- # Rising
- elif a2 == 1 and a2_next == 2:
- text += '↑'
- if i < len(marks):
- text += unidecode(marks[i]).replace(' ', '')
- return text
-
-
-def get_real_sokuon(text):
- for regex, replacement in _real_sokuon:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def get_real_hatsuon(text):
- for regex, replacement in _real_hatsuon:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa(text):
- text = japanese_to_romaji_with_accent(text).replace('...', '…')
- text = re.sub(
- r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
- text = get_real_sokuon(text)
- text = get_real_hatsuon(text)
- for regex, replacement in _romaji_to_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa2(text):
- text = japanese_to_romaji_with_accent(text).replace('...', '…')
- text = get_real_sokuon(text)
- text = get_real_hatsuon(text)
- for regex, replacement in _romaji_to_ipa2:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa3(text):
- text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
- 'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
- text = re.sub(
- r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
- text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
- return text
diff --git a/spaces/merve/fill-in-the-blank/public/anonymization/style.css b/spaces/merve/fill-in-the-blank/public/anonymization/style.css
deleted file mode 100644
index c20c6ed13484b78e2cc2128cd255f4d3b4cda152..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/public/anonymization/style.css
+++ /dev/null
@@ -1,344 +0,0 @@
-
-.tooltip {
- top: -1000px;
- position: fixed;
- padding: 10px;
- background: rgba(255, 255, 255, .90);
- border: 1px solid lightgray;
- pointer-events: none;
- font-size: 14px;
- width: 267px;
-}
-.tooltip-hidden{
- opacity: 0;
- transition: all .3s;
- transition-delay: .1s;
-}
-
-@media (max-width: 590px){
- div.tooltip{
- bottom: -1px;
- width: calc(100%);
- left: -1px !important;
- right: -1px !important;
- top: auto !important;
- width: auto !important;
- }
-}
-
-
-.domain{
- display: none;
-}
-
-text{
- /*pointer-events: none;*/
- /*text-shadow: 0 1px 0 #fff, 1px 0 0 #fff, 0 -1px 0 #fff, -1px 0 0 #fff;*/
-}
-
-
-
-.note{
- font-size: 12px;
- color: #999;
- margin-top: 60px;
-}
-
-h1{
- font-weight: 100;
- font-size: 34px;
- margin-bottom: .5em;
- line-height: 1.3em;
- margin-top: 1.4em;
- text-align: center;
- font-family: "Google Sans", sans-serif;
-}
-
-.mono{
- font-family: monospace;
-}
-
-
-svg{
- overflow: visible;
-}
-
-
-
-
-.axis{
- font-size: 12px;
- pointer-events: none;
-}
-.axis{
- color: #888;
-
-}
-.axis text, .slider-label-container{
- fill: #888;
- color: #888;
- font-family: 'Roboto', Helvetica, sans-serif;
- font-size: 12px;
-}
-
-.axis text.bold, .slider-label-container{
- color: #3C4043;
- fill: #3C4043;
- font-weight: 500;
-
-}
-.axis line{
- stroke: #ccc;
-}
-
-div.axis b{
- margin-bottom: -10px;
- display: block;
-}
-
-.init-hidden{
- opacity: 0;
-}
-
-.slider-label-container{
- font-weight: 500;
-}
-
-
-
-.highlight{
- color: #fff;
- padding-left: 3px;
- padding-right: 3px;
- padding-top: 1px;
- padding-bottom: 1px;
- border-radius: 3px;
-}
-
-.highlight.blue{ background: blue; }
-.highlight.orange{ background: #ffd890; }
-.highlight.yellow{ background: #ff0; color: #000; }
-.highlight.purple{ background: #CB10CB; }
-.highlight.purple{ background: #FF7AFF; color: #000;}
-.highlight.grey{ background: #ccc; color: #000;}
-.highlight.box{
- border: 1px solid #ff6200;
- border-radius: 5px;
- color: #000;
- padding-bottom: 2px;
- white-space: nowrap;
-}
-.highlight.purple-box{
- border: 1px solid #b0b;
-}
-.highlight.grey-box{
- border: 1px solid #ccc;
-}
-.highlight.box.square{
- border-radius: 0px;
-}
-.highlight.blue-box{ border: 2px solid #007276; }
-
-
-
-.circle{
- background: #eee;
- border: 1px solid #ccc;
- font-family: monospace;
- padding-left: 4px;
- padding-right: 4px;
- padding-top: 1px;
- padding-bottom: 1px;
-
- border-radius: 100px;
-}
-
-
-.strikethrough{
- text-decoration: line-through;
- color: #000;
-}
-
-
-.annotations path{
- fill: none;
- stroke: black;
-}
-
-
-
-rect.unique{
- stroke: #ff6200;
- stroke-width: 1px;
- fill: #ffd890;
-
- animation-duration: 1s;
- animation-name: xstrokeblink;
- display: inline-block;
- animation-iteration-count: infinite;
- animation-direction: alternate;
-}
-
-
-@keyframes strokeblink {
- from {
- /*fill: black;*/
- stroke-width: 1px;
- }
-
- to {
- /*fill: green;*/
- stroke-width: 1px;
- }
-}
-
-
-
-
-
-.inline-line{
- border: 1px #f0f solid;
- width: 20px;
- display: inline-block;
- position: relative;
- top: -5px;
-}
-
-.slider-label-container{
- width: 240px;
-}
-.slider-label{
- font-size: smaller;
- margin-left: 2px;
-}
-
-.slider-text-label{
- margin-left: 5px;
- white-space: nowrap;
-}
-
-
-g.student:hover circle{
- stroke-width: 2px;
-}
-
-g{
- /*opacity: 1 !important;*/
-}
-
-.inactive{
- opacity: 0 !important;
- pointer-events: none;
-}
-
-input[type="range" i] {
- background-color:#def5ef;
- -webkit-appearance: none;
- height:20px;
- width:240px;
- overflow: hidden;
-}
-
-input[type='range']::-webkit-slider-thumb {
- -webkit-appearance: none;
- width: 16px;
- height: 20px;
- cursor: ew-resize;
- background: #007276;
- box-shadow: -200px 0 0 200px #7ed3c9;
- border: 1px solid #333;
-}
-
-input:focus {
- outline-width: 0;
-}
-
-
-
-
-.estimate{
- opacity: 0;
- pointer-events: none
-}
-
-.estimate.active{
- opacity: .70;
- pointer-events: all;
-}
-
-.est-text{
- text-shadow: 0 2px 0 rgba(255,255,255,1), 2px 0 0 rgba(255,255,255,1), 0 -2px 0 rgba(255,255,255,1), -2px 0 0 rgba(255,255,255,1);
-}
-
-
-
-
-@media (max-width: 590px){
- text{
- font-size: 120% !important;
- }
-}
-
-
-.slider{
- user-select: none;
- -webkit-tap-highlight-color: transparent;
-}
-
-.button-container{
- border: 1px solid #888;
- display: inline-block;
- padding: 10px 20px;
- cursor: pointer;
- text-align: center;
- border-radius: 10px;
- user-select: none;
- -webkit-tap-highlight-color: transparent;
- margin: 0px auto;
-/* color: #888;
- font-family: 'Roboto', Helvetica, sans-serif;
- font-size: 12px;
- font-weight: 500;*/
- position: relative;
- left: -20px;
-}
-
-.button-container:hover{
- background: #ddd;
-}
-
-.button-outer{
- text-align: center;
- margin-top: 20px;
-}
-
-.pointer{
- height: 0px;
- position: relative;
-}
-.pointer div {
- overflow: visible;
- content: "";
- background-image: url(https://pair-code.github.io/interpretability/bert-tree/pointer.svg);
- width: 27px;
- height: 27px;
- position: absolute;
- left: 165px;
- top: -35px;
-}
-
-a{
- color: rgb(60, 64, 67);
-}
-a:hover{
- color: #000;
-}
-
-
-
-
-
-
-
-
-
diff --git a/spaces/merve/uncertainty-calibration/public/measuring-diversity/image-layout.js b/spaces/merve/uncertainty-calibration/public/measuring-diversity/image-layout.js
deleted file mode 100644
index 7a06cc4399043f317e81c28da4139599a84f58da..0000000000000000000000000000000000000000
--- a/spaces/merve/uncertainty-calibration/public/measuring-diversity/image-layout.js
+++ /dev/null
@@ -1,73 +0,0 @@
-
-
-var lURLs = `
-img/green_doctor.png
-img/blue_doctor.jpg
-img/green0.png
-img/bright_blue.png
-img/blue0.png
-img/blue1.png
-`.trim().split('\n')
-
-
-var rURLs = `
-img/white0.png
-img/white1.png
-img/white2.png
-img/white3.png
-img/white4.png
-img/white5.png
-`.trim().split('\n')
-
-
-var constructionSel = d3.select('#construction')
- .html('')
-
-// constructionSel.append('div.top').each(function(){
-// var metrics = [{str: 'Male', key: 'Male', target: .5}]
-// var active ={ percents: {Male: .5}}
-// addMetrics(metrics, {topSel: d3.select(this).append('div.top'), active, isSmall: true})()
-// })
-
-constructionSel.append('img')
- .at({src: 'img/construction.jpg', width: 900})
-
-constructionSel.append('div')
- .st({fontWeight: 500, fontSize: 14})
- .text('Stock “construction worker” images')
-
-
-
-
-var width = 400
-var coatDivs = d3.select('#coat-v-gender').html('').st({marginBottom: 40})
- .appendMany('div', [lURLs, rURLs])
- .st({width: width, display: 'inline-block', marginRight: 20})
-
-
-coatDivs.each(function(d, i){
- var metrics = [
- {str: 'Blue', key: 'Blue', target: .5},
- {str: 'Male', key: 'Male', target: .5},
- ]
-
- var active = !i ? {percents: {Blue: .5, Male: 1}} : {percents: {Blue: 0, Male: .5}}
-
- addMetrics(metrics, {topSel: d3.select(this).append('div.top'), active, isSmall: true})()
-})
-
-coatDivs
- .st({fontWeight: 500, fontSize: 14})
- .appendMany('div', d => d.slice(0, 6))
- .st({backgroundImage: d => 'url(' + d + ')', width: width/3 - 10, height: 100, display: 'inline-block'})
- .st({marginRight: 8, outline: '1px solid #000'})
-
-coatDivs
- .append('div')
- .text((d, i) => d == lURLs ? 'Male-presenting doctors wearing different colored clothes' : 'Doctor of different genders wearing white clothes')
-
-
-
-
-
-// https://t3.gstatic.com/images?q=tbn:ANd9GcRziJdedqu58HeAlI9xtWhrVtCjVo6xO_uSHdQkxAI0q41XozLWT3xKd36S1NbuSoIOVvV4Huw26zAvdM_374qKuN9J88E
\ No newline at end of file
diff --git a/spaces/mfernezir/VanillaChatbot/README.md b/spaces/mfernezir/VanillaChatbot/README.md
deleted file mode 100644
index dd04782a884bb8b34906ccaa6af1b3b95b187574..0000000000000000000000000000000000000000
--- a/spaces/mfernezir/VanillaChatbot/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: VanillaChatbot
-emoji: 🔥
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.47.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/mfrashad/CharacterGAN/netdissect/workerpool.py b/spaces/mfrashad/CharacterGAN/netdissect/workerpool.py
deleted file mode 100644
index fe79124ddc86d0e7251d9e1a5d1012e7165249e3..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/CharacterGAN/netdissect/workerpool.py
+++ /dev/null
@@ -1,158 +0,0 @@
-'''
-WorkerPool and WorkerBase for handling the common problems in managing
-a multiprocess pool of workers that aren't done by multiprocessing.Pool,
-including setup with per-process state, debugging by putting the worker
-on the main thread, and correct handling of unexpected errors, and ctrl-C.
-
-To use it,
-1. Put the per-process setup and the per-task work in the
- setup() and work() methods of your own WorkerBase subclass.
-2. To prepare the process pool, instantiate a WorkerPool, passing your
- subclass type as the first (worker) argument, as well as any setup keyword
- arguments. The WorkerPool will instantiate one of your workers in each
- worker process (passing in the setup arguments in those processes).
- If debugging, the pool can have process_count=0 to force all the work
- to be done immediately on the main thread; otherwise all the work
- will be passed to other processes.
-3. Whenever there is a new piece of work to distribute, call pool.add(*args).
- The arguments will be queued and passed as worker.work(*args) to the
- next available worker.
-4. When all the work has been distributed, call pool.join() to wait for all
- the work to complete and to finish and terminate all the worker processes.
- When pool.join() returns, all the work will have been done.
-
-No arrangement is made to collect the results of the work: for example,
-the return value of work() is ignored. If you need to collect the
-results, use your own mechanism (filesystem, shared memory object, queue)
-which can be distributed using setup arguments.
-'''
-
-from multiprocessing import Process, Queue, cpu_count
-import signal
-import atexit
-import sys
-
-class WorkerBase(Process):
- '''
- Subclass this class and override its work() method (and optionally,
- setup() as well) to define the units of work to be done in a process
- worker in a woker pool.
- '''
- def __init__(self, i, process_count, queue, initargs):
- if process_count > 0:
- # Make sure we ignore ctrl-C if we are not on main process.
- signal.signal(signal.SIGINT, signal.SIG_IGN)
- self.process_id = i
- self.process_count = process_count
- self.queue = queue
- super(WorkerBase, self).__init__()
- self.setup(**initargs)
- def run(self):
- # Do the work until None is dequeued
- while True:
- try:
- work_batch = self.queue.get()
- except (KeyboardInterrupt, SystemExit):
- print('Exiting...')
- break
- if work_batch is None:
- self.queue.put(None) # for another worker
- return
- self.work(*work_batch)
- def setup(self, **initargs):
- '''
- Override this method for any per-process initialization.
- Keywoard args are passed from WorkerPool constructor.
- '''
- pass
- def work(self, *args):
- '''
- Override this method for one-time initialization.
- Args are passed from WorkerPool.add() arguments.
- '''
- raise NotImplementedError('worker subclass needed')
-
-class WorkerPool(object):
- '''
- Instantiate this object (passing a WorkerBase subclass type
- as its first argument) to create a worker pool. Then call
- pool.add(*args) to queue args to distribute to worker.work(*args),
- and call pool.join() to wait for all the workers to complete.
- '''
- def __init__(self, worker=WorkerBase, process_count=None, **initargs):
- global active_pools
- if process_count is None:
- process_count = cpu_count()
- if process_count == 0:
- # zero process_count uses only main process, for debugging.
- self.queue = None
- self.processes = None
- self.worker = worker(None, 0, None, initargs)
- return
- # Ctrl-C strategy: worker processes should ignore ctrl-C. Set
- # this up to be inherited by child processes before forking.
- original_sigint_handler = signal.signal(signal.SIGINT, signal.SIG_IGN)
- active_pools[id(self)] = self
- self.queue = Queue(maxsize=(process_count * 3))
- self.processes = None # Initialize before trying to construct workers
- self.processes = [worker(i, process_count, self.queue, initargs)
- for i in range(process_count)]
- for p in self.processes:
- p.start()
- # The main process should handle ctrl-C. Restore this now.
- signal.signal(signal.SIGINT, original_sigint_handler)
- def add(self, *work_batch):
- if self.queue is None:
- if hasattr(self, 'worker'):
- self.worker.work(*work_batch)
- else:
- print('WorkerPool shutting down.', file=sys.stderr)
- else:
- try:
- # The queue can block if the work is so slow it gets full.
- self.queue.put(work_batch)
- except (KeyboardInterrupt, SystemExit):
- # Handle ctrl-C if done while waiting for the queue.
- self.early_terminate()
- def join(self):
- # End the queue, and wait for all worker processes to complete nicely.
- if self.queue is not None:
- self.queue.put(None)
- for p in self.processes:
- p.join()
- self.queue = None
- # Remove myself from the set of pools that need cleanup on shutdown.
- try:
- del active_pools[id(self)]
- except:
- pass
- def early_terminate(self):
- # When shutting down unexpectedly, first end the queue.
- if self.queue is not None:
- try:
- self.queue.put_nowait(None) # Nonblocking put throws if full.
- self.queue = None
- except:
- pass
- # But then don't wait: just forcibly terminate workers.
- if self.processes is not None:
- for p in self.processes:
- p.terminate()
- self.processes = None
- try:
- del active_pools[id(self)]
- except:
- pass
- def __del__(self):
- if self.queue is not None:
- print('ERROR: workerpool.join() not called!', file=sys.stderr)
- self.join()
-
-# Error and ctrl-C handling: kill worker processes if the main process ends.
-active_pools = {}
-def early_terminate_pools():
- for _, pool in list(active_pools.items()):
- pool.early_terminate()
-
-atexit.register(early_terminate_pools)
-
diff --git a/spaces/mithril-security/blind_chat/src/lib/utils/sha256.ts b/spaces/mithril-security/blind_chat/src/lib/utils/sha256.ts
deleted file mode 100644
index 43059b518fc5a4da6ed08ab36aeb6c289007f6aa..0000000000000000000000000000000000000000
--- a/spaces/mithril-security/blind_chat/src/lib/utils/sha256.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-export async function sha256(input: string): Promise {
- const utf8 = new TextEncoder().encode(input);
- const hashBuffer = await crypto.subtle.digest("SHA-256", utf8);
- const hashArray = Array.from(new Uint8Array(hashBuffer));
- const hashHex = hashArray.map((bytes) => bytes.toString(16).padStart(2, "0")).join("");
- return hashHex;
-}
diff --git a/spaces/mkutarna/audiobook_gen/tests/test_output.py b/spaces/mkutarna/audiobook_gen/tests/test_output.py
deleted file mode 100644
index 2f243cec793658c041b4faaccbff2af38e6139d1..0000000000000000000000000000000000000000
--- a/spaces/mkutarna/audiobook_gen/tests/test_output.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import pytest
-
-from src import output, config
-import test_config
-
-
-def test_write_audio():
- """
- Tests write_audio function, takes in an audio tensor with a file path and writes the audio to a file.
- """
- import torch
-
- test_path = test_config.data_path / 'test_audio.wav'
- audio_path = test_config.data_path / 'test_audio.pt'
- audio_list = torch.load(audio_path)
-
- output.write_audio(audio_list, test_path)
-
- assert test_path.is_file()
- assert test_path.stat().st_size == 592858
-
- test_path.unlink()
-
-
-def test_assemble_zip():
- """
- Tests assemble_zip function, which collects all the audio files from the output directory,
- and zips them up into a zip directory.
- """
- from shutil import copy2
-
- if not config.output_path.exists():
- config.output_path.mkdir()
-
- title = "speaker_samples"
- zip_path = config.output_path / 'speaker_samples.zip'
- wav1_path = config.output_path / 'speaker_en_0.wav'
- wav2_path = config.output_path / 'speaker_en_110.wav'
-
- for file_path in config.resource_path.iterdir():
- if file_path.suffix == '.wav':
- copy2(file_path, config.output_path)
-
- _ = output.assemble_zip(title)
-
- assert zip_path.is_file()
- assert not wav1_path.is_file()
- assert not wav2_path.is_file()
-
- zip_path.unlink()
diff --git a/spaces/monra/freegpt-webui-chimera/server/bp.py b/spaces/monra/freegpt-webui-chimera/server/bp.py
deleted file mode 100644
index 61d416797039dababd9e8222b4fc910ef65c40b9..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui-chimera/server/bp.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from flask import Blueprint
-
-bp = Blueprint('bp', __name__,
- template_folder='./../client/html',
- static_folder='./../client',
- static_url_path='assets')
diff --git a/spaces/monra/freegpt-webui/client/css/message-input.css b/spaces/monra/freegpt-webui/client/css/message-input.css
deleted file mode 100644
index de5f58388133bd3b2b2333dd99cecf0110002367..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui/client/css/message-input.css
+++ /dev/null
@@ -1,27 +0,0 @@
-#message-input {
- margin-right: 30px;
- height: 64px;
-}
-
-#message-input::-webkit-scrollbar {
- width: 5px;
-}
-
-#message-input::-webkit-scrollbar-track {
- background: #f1f1f1;
-}
-
-#message-input::-webkit-scrollbar-thumb {
- background: #c7a2ff;
-}
-
-#message-input::-webkit-scrollbar-thumb:hover {
- background: #8b3dff;
-}
-
-@media screen and (max-width: 360px) {
- #message-input {
- margin: 0;
- }
-}
-
diff --git a/spaces/mthsk/sovits-models-misc/utils.py b/spaces/mthsk/sovits-models-misc/utils.py
deleted file mode 100644
index e19cac39c57f213bbf6f1435ab48fe7948a1b17b..0000000000000000000000000000000000000000
--- a/spaces/mthsk/sovits-models-misc/utils.py
+++ /dev/null
@@ -1,501 +0,0 @@
-import os
-import glob
-import re
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import random
-
-import librosa
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-from torch.nn import functional as F
-from modules.commons import sequence_mask
-from hubert import hubert_model
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-f0_bin = 256
-f0_max = 1100.0
-f0_min = 50.0
-f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
-
-# def normalize_f0(f0, random_scale=True):
-# f0_norm = f0.clone() # create a copy of the input Tensor
-# batch_size, _, frame_length = f0_norm.shape
-# for i in range(batch_size):
-# means = torch.mean(f0_norm[i, 0, :])
-# if random_scale:
-# factor = random.uniform(0.8, 1.2)
-# else:
-# factor = 1
-# f0_norm[i, 0, :] = (f0_norm[i, 0, :] - means) * factor
-# return f0_norm
-# def normalize_f0(f0, random_scale=True):
-# means = torch.mean(f0[:, 0, :], dim=1, keepdim=True)
-# if random_scale:
-# factor = torch.Tensor(f0.shape[0],1).uniform_(0.8, 1.2).to(f0.device)
-# else:
-# factor = torch.ones(f0.shape[0], 1, 1).to(f0.device)
-# f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
-# return f0_norm
-def normalize_f0(f0, x_mask, uv, random_scale=True):
- # calculate means based on x_mask
- uv_sum = torch.sum(uv, dim=1, keepdim=True)
- uv_sum[uv_sum == 0] = 9999
- means = torch.sum(f0[:, 0, :] * uv, dim=1, keepdim=True) / uv_sum
-
- if random_scale:
- factor = torch.Tensor(f0.shape[0], 1).uniform_(0.8, 1.2).to(f0.device)
- else:
- factor = torch.ones(f0.shape[0], 1).to(f0.device)
- # normalize f0 based on means and factor
- f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
- if torch.isnan(f0_norm).any():
- exit(0)
- return f0_norm * x_mask
-
-
-def plot_data_to_numpy(x, y):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10, 2))
- plt.plot(x)
- plt.plot(y)
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-
-def interpolate_f0(f0):
- '''
- 对F0进行插值处理
- '''
-
- data = np.reshape(f0, (f0.size, 1))
-
- vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
- vuv_vector[data > 0.0] = 1.0
- vuv_vector[data <= 0.0] = 0.0
-
- ip_data = data
-
- frame_number = data.size
- last_value = 0.0
- for i in range(frame_number):
- if data[i] <= 0.0:
- j = i + 1
- for j in range(i + 1, frame_number):
- if data[j] > 0.0:
- break
- if j < frame_number - 1:
- if last_value > 0.0:
- step = (data[j] - data[i - 1]) / float(j - i)
- for k in range(i, j):
- ip_data[k] = data[i - 1] + step * (k - i + 1)
- else:
- for k in range(i, j):
- ip_data[k] = data[j]
- else:
- for k in range(i, frame_number):
- ip_data[k] = last_value
- else:
- ip_data[i] = data[i]
- last_value = data[i]
-
- return ip_data[:,0], vuv_vector[:,0]
-
-
-def compute_f0_parselmouth(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
- import parselmouth
- x = wav_numpy
- if p_len is None:
- p_len = x.shape[0]//hop_length
- else:
- assert abs(p_len-x.shape[0]//hop_length) < 4, "pad length error"
- time_step = hop_length / sampling_rate * 1000
- f0_min = 50
- f0_max = 1100
- f0 = parselmouth.Sound(x, sampling_rate).to_pitch_ac(
- time_step=time_step / 1000, voicing_threshold=0.6,
- pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
-
- pad_size=(p_len - len(f0) + 1) // 2
- if(pad_size>0 or p_len - len(f0) - pad_size>0):
- f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
- return f0
-
-def resize_f0(x, target_len):
- source = np.array(x)
- source[source<0.001] = np.nan
- target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source)
- res = np.nan_to_num(target)
- return res
-
-def compute_f0_dio(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
- import pyworld
- if p_len is None:
- p_len = wav_numpy.shape[0]//hop_length
- f0, t = pyworld.dio(
- wav_numpy.astype(np.double),
- fs=sampling_rate,
- f0_ceil=800,
- frame_period=1000 * hop_length / sampling_rate,
- )
- f0 = pyworld.stonemask(wav_numpy.astype(np.double), f0, t, sampling_rate)
- for index, pitch in enumerate(f0):
- f0[index] = round(pitch, 1)
- return resize_f0(f0, p_len)
-
-def f0_to_coarse(f0):
- is_torch = isinstance(f0, torch.Tensor)
- f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
-
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
- f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int)
- assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min())
- return f0_coarse
-
-
-def get_hubert_model():
- vec_path = "hubert/checkpoint_best_legacy_500.pt"
- print("load model(s) from {}".format(vec_path))
- from fairseq import checkpoint_utils
- models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
- [vec_path],
- suffix="",
- )
- model = models[0]
- model.eval()
- return model
-
-def get_hubert_content(hmodel, wav_16k_tensor):
- feats = wav_16k_tensor
- if feats.dim() == 2: # double channels
- feats = feats.mean(-1)
- assert feats.dim() == 1, feats.dim()
- feats = feats.view(1, -1)
- padding_mask = torch.BoolTensor(feats.shape).fill_(False)
- inputs = {
- "source": feats.to(wav_16k_tensor.device),
- "padding_mask": padding_mask.to(wav_16k_tensor.device),
- "output_layer": 9, # layer 9
- }
- with torch.no_grad():
- logits = hmodel.extract_features(**inputs)
- feats = hmodel.final_proj(logits[0])
- return feats.transpose(1, 2)
-
-
-def get_content(cmodel, y):
- with torch.no_grad():
- c = cmodel.extract_features(y.squeeze(1))[0]
- c = c.transpose(1, 2)
- return c
-
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- # assert "dec" in k or "disc" in k
- # print("load", k)
- new_state_dict[k] = saved_state_dict[k]
- assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
- except:
- print("error, %s is not in the checkpoint" % k)
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})".format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info("Saving model and optimizer state at iteration {} to {}".format(
- iteration, checkpoint_path))
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- torch.save({'model': state_dict,
- 'iteration': iteration,
- 'optimizer': optimizer.state_dict(),
- 'learning_rate': learning_rate}, checkpoint_path)
-
-def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True):
- """Freeing up space by deleting saved ckpts
-
- Arguments:
- path_to_models -- Path to the model directory
- n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
- sort_by_time -- True -> chronologically delete ckpts
- False -> lexicographically delete ckpts
- """
- ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
- name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
- time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
- sort_key = time_key if sort_by_time else name_key
- x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')], key=sort_key)
- to_del = [os.path.join(path_to_models, fn) for fn in
- (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
- del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
- del_routine = lambda x: [os.remove(x), del_info(x)]
- rs = [del_routine(fn) for fn in to_del]
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
- for k, v in scalars.items():
- writer.add_scalar(k, v, global_step)
- for k, v in histograms.items():
- writer.add_histogram(k, v, global_step)
- for k, v in images.items():
- writer.add_image(k, v, global_step, dataformats='HWC')
- for k, v in audios.items():
- writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
- x = f_list[-1]
- print(x)
- return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10,2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-def repeat_expand_2d(content, target_len):
- # content : [h, t]
-
- src_len = content.shape[-1]
- target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to(content.device)
- temp = torch.arange(src_len+1) * target_len / src_len
- current_pos = 0
- for i in range(target_len):
- if i < temp[current_pos+1]:
- target[:, i] = content[:, current_pos]
- else:
- current_pos += 1
- target[:, i] = content[:, current_pos]
-
- return target
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
-
diff --git a/spaces/multimodalart/pix2pix-zero/src/make_edit_direction.py b/spaces/multimodalart/pix2pix-zero/src/make_edit_direction.py
deleted file mode 100644
index d6307694847e6f98749390ccb02c0b5ef6e2b67f..0000000000000000000000000000000000000000
--- a/spaces/multimodalart/pix2pix-zero/src/make_edit_direction.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import os, pdb
-
-import argparse
-import numpy as np
-import torch
-import requests
-from PIL import Image
-
-from diffusers import DDIMScheduler
-from utils.edit_pipeline import EditingPipeline
-
-
-## convert sentences to sentence embeddings
-def load_sentence_embeddings(l_sentences, tokenizer, text_encoder, device="cuda"):
- with torch.no_grad():
- l_embeddings = []
- for sent in l_sentences:
- text_inputs = tokenizer(
- sent,
- padding="max_length",
- max_length=tokenizer.model_max_length,
- truncation=True,
- return_tensors="pt",
- )
- text_input_ids = text_inputs.input_ids
- prompt_embeds = text_encoder(text_input_ids.to(device), attention_mask=None)[0]
- l_embeddings.append(prompt_embeds)
- return torch.concatenate(l_embeddings, dim=0).mean(dim=0).unsqueeze(0)
-
-
-if __name__=="__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument('--file_source_sentences', required=True)
- parser.add_argument('--file_target_sentences', required=True)
- parser.add_argument('--output_folder', required=True)
- parser.add_argument('--model_path', type=str, default='CompVis/stable-diffusion-v1-4')
- args = parser.parse_args()
-
- # load the model
- pipe = EditingPipeline.from_pretrained(args.model_path, torch_dtype=torch.float16).to("cuda")
- bname_src = os.path.basename(args.file_source_sentences).strip(".txt")
- outf_src = os.path.join(args.output_folder, bname_src+".pt")
- if os.path.exists(outf_src):
- print(f"Skipping source file {outf_src} as it already exists")
- else:
- with open(args.file_source_sentences, "r") as f:
- l_sents = [x.strip() for x in f.readlines()]
- mean_emb = load_sentence_embeddings(l_sents, pipe.tokenizer, pipe.text_encoder, device="cuda")
- print(mean_emb.shape)
- torch.save(mean_emb, outf_src)
-
- bname_tgt = os.path.basename(args.file_target_sentences).strip(".txt")
- outf_tgt = os.path.join(args.output_folder, bname_tgt+".pt")
- if os.path.exists(outf_tgt):
- print(f"Skipping target file {outf_tgt} as it already exists")
- else:
- with open(args.file_target_sentences, "r") as f:
- l_sents = [x.strip() for x in f.readlines()]
- mean_emb = load_sentence_embeddings(l_sents, pipe.tokenizer, pipe.text_encoder, device="cuda")
- print(mean_emb.shape)
- torch.save(mean_emb, outf_tgt)
diff --git a/spaces/mygyasir/Real-Time-Voice-Cloning/vocoder/gen_wavernn.py b/spaces/mygyasir/Real-Time-Voice-Cloning/vocoder/gen_wavernn.py
deleted file mode 100644
index 2036737f805f6055893812e48f99d524624aab07..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/Real-Time-Voice-Cloning/vocoder/gen_wavernn.py
+++ /dev/null
@@ -1,31 +0,0 @@
-from vocoder.models.fatchord_version import WaveRNN
-from vocoder.audio import *
-
-
-def gen_testset(model: WaveRNN, test_set, samples, batched, target, overlap, save_path):
- k = model.get_step() // 1000
-
- for i, (m, x) in enumerate(test_set, 1):
- if i > samples:
- break
-
- print('\n| Generating: %i/%i' % (i, samples))
-
- x = x[0].numpy()
-
- bits = 16 if hp.voc_mode == 'MOL' else hp.bits
-
- if hp.mu_law and hp.voc_mode != 'MOL' :
- x = decode_mu_law(x, 2**bits, from_labels=True)
- else :
- x = label_2_float(x, bits)
-
- save_wav(x, save_path.joinpath("%dk_steps_%d_target.wav" % (k, i)))
-
- batch_str = "gen_batched_target%d_overlap%d" % (target, overlap) if batched else \
- "gen_not_batched"
- save_str = save_path.joinpath("%dk_steps_%d_%s.wav" % (k, i, batch_str))
-
- wav = model.generate(m, batched, target, overlap, hp.mu_law)
- save_wav(wav, save_str)
-
diff --git a/spaces/nateraw/dino-clips/dino/vision_transformer.py b/spaces/nateraw/dino-clips/dino/vision_transformer.py
deleted file mode 100644
index f69a7ad0522500ca2a85305a789be5ca6ac474d0..0000000000000000000000000000000000000000
--- a/spaces/nateraw/dino-clips/dino/vision_transformer.py
+++ /dev/null
@@ -1,291 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# 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.
-"""
-Mostly copy-paste from timm library.
-https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
-"""
-import math
-from functools import partial
-
-import torch
-import torch.nn as nn
-
-from utils import trunc_normal_
-
-
-def drop_path(x, drop_prob: float = 0., training: bool = False):
- if drop_prob == 0. or not training:
- return x
- keep_prob = 1 - drop_prob
- shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
- random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
- random_tensor.floor_() # binarize
- output = x.div(keep_prob) * random_tensor
- return output
-
-
-class DropPath(nn.Module):
- """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
- """
- def __init__(self, drop_prob=None):
- super(DropPath, self).__init__()
- self.drop_prob = drop_prob
-
- def forward(self, x):
- return drop_path(x, self.drop_prob, self.training)
-
-
-class Mlp(nn.Module):
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = nn.Linear(in_features, hidden_features)
- self.act = act_layer()
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-
-class Attention(nn.Module):
- def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
- super().__init__()
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = qk_scale or head_dim ** -0.5
-
- self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
-
- def forward(self, x):
- B, N, C = x.shape
- qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
- q, k, v = qkv[0], qkv[1], qkv[2]
-
- attn = (q @ k.transpose(-2, -1)) * self.scale
- attn = attn.softmax(dim=-1)
- attn = self.attn_drop(attn)
-
- x = (attn @ v).transpose(1, 2).reshape(B, N, C)
- x = self.proj(x)
- x = self.proj_drop(x)
- return x, attn
-
-
-class Block(nn.Module):
- def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
- drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
- super().__init__()
- self.norm1 = norm_layer(dim)
- self.attn = Attention(
- dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
- self.norm2 = norm_layer(dim)
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
- def forward(self, x, return_attention=False):
- y, attn = self.attn(self.norm1(x))
- if return_attention:
- return attn
- x = x + self.drop_path(y)
- x = x + self.drop_path(self.mlp(self.norm2(x)))
- return x
-
-
-class PatchEmbed(nn.Module):
- """ Image to Patch Embedding
- """
- def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
- super().__init__()
- num_patches = (img_size // patch_size) * (img_size // patch_size)
- self.img_size = img_size
- self.patch_size = patch_size
- self.num_patches = num_patches
-
- self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-
- def forward(self, x):
- B, C, H, W = x.shape
- x = self.proj(x).flatten(2).transpose(1, 2)
- return x
-
-
-class VisionTransformer(nn.Module):
- """ Vision Transformer """
- def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_classes=0, embed_dim=768, depth=12,
- num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
- drop_path_rate=0., norm_layer=nn.LayerNorm, **kwargs):
- super().__init__()
- self.num_features = self.embed_dim = embed_dim
-
- self.patch_embed = PatchEmbed(
- img_size=img_size[0], patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
- num_patches = self.patch_embed.num_patches
-
- self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
- self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
- self.pos_drop = nn.Dropout(p=drop_rate)
-
- dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
- self.blocks = nn.ModuleList([
- Block(
- dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
- drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
- for i in range(depth)])
- self.norm = norm_layer(embed_dim)
-
- # Classifier head
- self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-
- trunc_normal_(self.pos_embed, std=.02)
- trunc_normal_(self.cls_token, std=.02)
- self.apply(self._init_weights)
-
- def _init_weights(self, m):
- if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
- if isinstance(m, nn.Linear) and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.LayerNorm):
- nn.init.constant_(m.bias, 0)
- nn.init.constant_(m.weight, 1.0)
-
- def interpolate_pos_encoding(self, x, w, h):
- npatch = x.shape[1] - 1
- N = self.pos_embed.shape[1] - 1
- if npatch == N and w == h:
- return self.pos_embed
- class_pos_embed = self.pos_embed[:, 0]
- patch_pos_embed = self.pos_embed[:, 1:]
- dim = x.shape[-1]
- w0 = w // self.patch_embed.patch_size
- h0 = h // self.patch_embed.patch_size
- # we add a small number to avoid floating point error in the interpolation
- # see discussion at https://github.com/facebookresearch/dino/issues/8
- w0, h0 = w0 + 0.1, h0 + 0.1
- patch_pos_embed = nn.functional.interpolate(
- patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),
- scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)),
- mode='bicubic',
- )
- assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1]
- patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
- return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
-
- def prepare_tokens(self, x):
- B, nc, w, h = x.shape
- x = self.patch_embed(x) # patch linear embedding
-
- # add the [CLS] token to the embed patch tokens
- cls_tokens = self.cls_token.expand(B, -1, -1)
- x = torch.cat((cls_tokens, x), dim=1)
-
- # add positional encoding to each token
- x = x + self.interpolate_pos_encoding(x, w, h)
-
- return self.pos_drop(x)
-
- def forward(self, x):
- x = self.prepare_tokens(x)
- for blk in self.blocks:
- x = blk(x)
- x = self.norm(x)
- return x[:, 0]
-
- def get_last_selfattention(self, x):
- x = self.prepare_tokens(x)
- for i, blk in enumerate(self.blocks):
- if i < len(self.blocks) - 1:
- x = blk(x)
- else:
- # return attention of the last block
- return blk(x, return_attention=True)
-
- def get_intermediate_layers(self, x, n=1):
- x = self.prepare_tokens(x)
- # we return the output tokens from the `n` last blocks
- output = []
- for i, blk in enumerate(self.blocks):
- x = blk(x)
- if len(self.blocks) - i <= n:
- output.append(self.norm(x))
- return output
-
-
-def vit_tiny(patch_size=16, **kwargs):
- model = VisionTransformer(
- patch_size=patch_size, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4,
- qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
- return model
-
-
-def vit_small(patch_size=16, **kwargs):
- model = VisionTransformer(
- patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4,
- qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
- return model
-
-
-def vit_base(patch_size=16, **kwargs):
- model = VisionTransformer(
- patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
- qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
- return model
-
-
-class DINOHead(nn.Module):
- def __init__(self, in_dim, out_dim, use_bn=False, norm_last_layer=True, nlayers=3, hidden_dim=2048, bottleneck_dim=256):
- super().__init__()
- nlayers = max(nlayers, 1)
- if nlayers == 1:
- self.mlp = nn.Linear(in_dim, bottleneck_dim)
- else:
- layers = [nn.Linear(in_dim, hidden_dim)]
- if use_bn:
- layers.append(nn.BatchNorm1d(hidden_dim))
- layers.append(nn.GELU())
- for _ in range(nlayers - 2):
- layers.append(nn.Linear(hidden_dim, hidden_dim))
- if use_bn:
- layers.append(nn.BatchNorm1d(hidden_dim))
- layers.append(nn.GELU())
- layers.append(nn.Linear(hidden_dim, bottleneck_dim))
- self.mlp = nn.Sequential(*layers)
- self.apply(self._init_weights)
- self.last_layer = nn.utils.weight_norm(nn.Linear(bottleneck_dim, out_dim, bias=False))
- self.last_layer.weight_g.data.fill_(1)
- if norm_last_layer:
- self.last_layer.weight_g.requires_grad = False
-
- def _init_weights(self, m):
- if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
- if isinstance(m, nn.Linear) and m.bias is not None:
- nn.init.constant_(m.bias, 0)
-
- def forward(self, x):
- x = self.mlp(x)
- x = nn.functional.normalize(x, dim=-1, p=2)
- x = self.last_layer(x)
- return x
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/FontExplorer X Pro 7.0.0 Build 20457 Crack MacOS.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/FontExplorer X Pro 7.0.0 Build 20457 Crack MacOS.md
deleted file mode 100644
index 1fae14c1edfe9632ba3d310a534ac0cfb6abba8b..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/FontExplorer X Pro 7.0.0 Build 20457 Crack MacOS.md
+++ /dev/null
@@ -1,65 +0,0 @@
-
-FontExplorer X Pro 7.0.0 Build 20457 Crack macOS: A Comprehensive Review
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-FontExplorer X Pro 7.0.0 Build 20457 Crack macOS DOWNLOAD ✶ https://urlcod.com/2uIclb
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- In this article, we will review FontExplorer X Pro 7.0.0 Build 20457 , the latest version of this software for macOS. We will
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-
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- FontExplorer X Pro can help you customize your experience by tailoring your font preferences and settings. You can choose from different font preview modes, such as list, grid, waterfall, or character set. You can also adjust the font size, color, and background to suit your needs. You can also create custom keyboard shortcuts, menus, and toolbars to access your favorite features quickly and easily.
- How to Install and Crack FontExplorer X Pro on macOS
- If you want to try FontExplorer X Pro for yourself, you can download, install, and crack it on your macOS device with these simple steps:
- Downloading FontExplorer X Pro
- To download FontExplorer X Pro for macOS, you can visit the official website of FontExplorer X Pro at https://www.fontexplorerx.com/mac/ . There, you can find the latest version of FontExplorer X Pro for macOS, which is 7.0.0 Build 20457 as of this writing. You can also find the system requirements, release notes, and user manual of FontExplorer X Pro on the website.
- To download FontExplorer X Pro for macOS, you need to fill out a short form with your name and email address. Then, you will receive a download link in your email inbox. You can click on the link to start downloading the installer file of FontExplorer X Pro for macOS.
- Installing FontExplorer X Pro
- To install FontExplorer X Pro for macOS, you need to run the installer file that you downloaded from the website. The installer file is named FontExplorerXPro-7.0.0-20457.dmg . You can double-click on the file to open it.
- Then, you will see a window with the FontExplorer X Pro icon and a shortcut to the Applications folder. You can drag and drop the FontExplorer X Pro icon to the Applications folder to install it on your device. Alternatively, you can double-click on the FontExplorer X Pro icon to launch it directly from the installer window.
- After installing FontExplorer X Pro for macOS, you can open it from the Applications folder or from the Launchpad. You will see a welcome screen with some information about FontExplorer X Pro and its features. You can click on the Continue button to proceed.
- Cracking FontExplorer X Pro
- To crack FontExplorer X Pro for macOS, you need to apply a crack file that will activate the full version of FontExplorer X Pro for free. The crack file is named FontExplorerXPro-7.x.x-Crack.zip . You can download it from this link: https://mega.nz/file/9Zwz2QyR#6FJLsQmY4fWnUOJgq8f9ZdQ4lGv5i8wJ6L1nY6YyqoE .
- To apply the crack file, you need to unzip it first. You can use any unzip tool such as WinZip or The Unarchiver to extract the contents of the zip file. You will see a folder named FontExplorerXPro-7.x.x-Crack . Inside the folder, you will see two files: FontAgent.framework and Readme.txt .
- The Readme.txt file contains some instructions on how to use the crack file. You can open it with any text editor such as TextEdit or Notepad to read it. The instructions are as follows:
- Quit FontExplorer X Pro if it is running.
-- Copy the FontAgent.framework file and paste it to the following location: /Applications/FontExplorer X Pro.app/Contents/Frameworks/
-- Replace the original FontAgent.framework file with the cracked one.
-- Launch FontExplorer X Pro and enjoy the full version.
- That's it! You have successfully cracked FontExplorer X Pro for macOS and unlocked all its features. You can now use FontExplorer X Pro without any limitations or restrictions.
- Conclusion
- FontExplorer X Pro is a powerful font manager that can help you organize, preview, activate, deactivate, and find fonts with ease. You can also view and edit font information, classify fonts by smart sets, keywords, ratings, and categories, access and purchase fonts from leading foundries, integrate fonts with Adobe Creative Cloud and other applications, and much more.
- In this article, we have reviewed FontExplorer X Pro 7.0.0 Build 20457, the latest version of this software for macOS. We have explored the features, benefits, and installation process of FontExplorer X Pro, and showed you how to crack it to get the full version for free.
- If you want to try FontExplorer X Pro for yourself, you can download it from the official website of FontExplorer X Pro at https://www.fontexplorerx.com/mac/ . You can also download the crack file from this link: https://mega.nz/file/9Zwz2QyR#6FJLsQmY4fWnUOJgq8f9ZdQ4lGv5i8wJ6L1nY6YyqoE .
- We hope you have found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
- FAQs
- Here are some frequently asked questions about FontExplorer X Pro:
-
-Is FontExplorer X Pro compatible with macOS Big Sur? Yes, FontExplorer X Pro is compatible with macOS Big Sur. However, you may need to grant some permissions to FontExplorer X Pro to access your fonts and applications. You can find more information on how to do that on the official website of FontExplorer X Pro.
-How can I backup my fonts and settings in FontExplorer X Pro? You can backup your fonts and settings in FontExplorer X Pro by using the Backup feature. You can find it under the File menu in FontExplorer X Pro. You can choose to backup your fonts, your settings, or both. You can also choose the destination folder for your backup files.
-How can I update FontExplorer X Pro to the latest version? You can update FontExplorer X Pro to the latest version by using the Check for Updates feature. You can find it under the FontExplorer X menu in FontExplorer X Pro. You can also enable automatic updates in the Preferences menu of FontExplorer X Pro.
-How can I uninstall FontExplorer X Pro from my device? You can uninstall FontExplorer X Pro from your device by using the Uninstall feature. You can find it under the Help menu in FontExplorer X Pro. You can choose to uninstall only the application or also remove all your fonts and settings.
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- b2dd77e56bWhat is na4hzvuxzlbenx7u thread and why you should care
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-Rihanna is one of the most successful and influential pop stars of the 21st century. She has sold over 250 million records worldwide, won nine Grammy Awards, and launched her own fashion and beauty empire. But among her nine studio albums, one stands out as a pop masterpiece: Loud.
-Loud is Rihanna's fifth album, released in 2010 by Def Jam Recordings and SRP Records. It was recorded in six months, during her Last Girl on Earth tour and the filming of her first feature film Battleship. The album marked a departure from her previous dark and edgy sound, and embraced a more upbeat and colorful style. Rihanna wanted to make an album that reflected her personality and mood at the time: fun, happy, and confident.
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Loud features 11 tracks (15 on the deluxe edition), spanning various genres such as dance-pop, R&B, reggae, rock, and hip hop. The album showcases Rihanna's versatility as a vocalist and a performer, as well as her collaborations with some of the biggest names in music, such as Drake, Nicki Minaj, Eminem, and David Guetta. The album spawned seven singles, three of which topped the Billboard Hot 100 chart: Only Girl (In the World), What's My Name?, and S&M. The album also received critical acclaim and commercial success, selling over eight million copies worldwide and earning three Grammy nominations.
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7196e7f11a
-* @version 2.1.18
-*/
-(function () {
- CKEDITOR.plugins.add('youtube', {
- lang: [ 'en', 'bg', 'pt', 'pt-br', 'ja', 'hu', 'it', 'fr', 'tr', 'ru', 'de', 'ar', 'nl', 'pl', 'vi', 'zh', 'el', 'he', 'es', 'nb', 'nn', 'fi', 'et', 'sk', 'cs', 'ko', 'eu', 'uk'],
- init: function (editor) {
- editor.addCommand('youtube', new CKEDITOR.dialogCommand('youtube', {
- allowedContent: 'div{*}(*); iframe{*}[!width,!height,!src,!frameborder,!allowfullscreen,!allow]; object param[*]; a[*]; img[*]'
- }));
-
- editor.ui.addButton('youtube', {
- label : 'Youtube',
- toolbar : 'insert',
- command : 'youtube',
- group: '44',
- icon : this.path + 'images/icon-hdpi.png'
- });
-
- CKEDITOR.dialog.add('youtube', function (instance) {
- var video,
- disabled = editor.config.youtube_disabled_fields || [];
-
- function handleLinkChange(el, api) {
- var video = ytVidId(el.getValue());
- var time = ytVidTime(el.getValue());
-
- if (el.getValue().length > 0) {
- el.getDialog().getContentElement('youtubePlugin', 'txtEmbed').disable();
- }
- else if (!disabled.length || !disabled.includes('txtEmbed')) {
- el.getDialog().getContentElement('youtubePlugin', 'txtEmbed').enable();
- }
-
- if (video && time) {
- var seconds = timeParamToSeconds(time);
- var hms = secondsToHms(seconds);
- el.getDialog().getContentElement('youtubePlugin', 'txtStartAt').setValue(hms);
- }
- }
-
- function handleEmbedChange(el, api) {
- if (el.getValue().length > 0) {
- el.getDialog().getContentElement('youtubePlugin', 'txtUrl').disable();
- }
- else {
- el.getDialog().getContentElement('youtubePlugin', 'txtUrl').enable();
- }
- }
-
- return {
- title : editor.lang.youtube.title,
- minWidth : 510,
- minHeight : 200,
- onShow: function () {
- for (var i = 0; i < disabled.length; i++) {
- this.getContentElement('youtubePlugin', disabled[i]).disable();
- }
- },
- contents :
- [{
- id : 'youtubePlugin',
- expand : true,
- elements :
- [ {
- type : 'hbox',
- widths : [ '70%', '15%', '15%' ],
- children :
- [
- {
- id : 'txtUrl',
- type : 'text',
- label : editor.lang.youtube.txtUrl,
- onChange : function (api) {
- handleLinkChange(this, api);
- },
- onKeyUp : function (api) {
- handleLinkChange(this, api);
- },
- validate : function () {
- if (this.isEnabled()) {
- if (!this.getValue()) {
- alert(editor.lang.youtube.noCode);
- return false;
- }
- else{
- video = ytVidId(this.getValue());
-
- if (this.getValue().length === 0 || video === false)
- {
- alert(editor.lang.youtube.invalidUrl);
- return false;
- }
- }
- }
- }
- },
- {
- type : 'text',
- id : 'txtWidth',
- width : '60px',
- label : editor.lang.youtube.txtWidth,
- 'default' : editor.config.youtube_width != null ? editor.config.youtube_width : '640',
- validate : function () {
- if (this.getValue()) {
- var width = Number(this.getValue());
-
- if (isNaN(width)) {
- alert(editor.lang.youtube.invalidWidth);
- return false;
- }
- }
- else {
- alert(editor.lang.youtube.noWidth);
- return false;
- }
- }
- },
- {
- type : 'text',
- id : 'txtHeight',
- width : '60px',
- label : editor.lang.youtube.txtHeight,
- 'default' : editor.config.youtube_height != null ? editor.config.youtube_height : '360',
- validate : function () {
- if (this.getValue()) {
- var height = Number(this.getValue());
-
- if (isNaN(height)) {
- alert(editor.lang.youtube.invalidHeight);
- return false;
- }
- }
- else {
- alert(editor.lang.youtube.noHeight);
- return false;
- }
- }
- }
- ]
- },
- {
- type : 'html',
- html : editor.lang.youtube.or + '';
- responsiveStyle = 'style="position:absolute;top:0;left:0;width:100%;height:100%"';
- }*/
-
- if (1 === true) { //this.getContentElement('youtubePlugin', 'chkOlderCode').getValue()
- url = url.replace('embed/', 'v/');
- url = url.replace(/&/g, '&');
-
- if (url.indexOf('?') === -1) {
- url += '?';
- }
- else {
- url += '&';
- }
- url += 'hl=' + (this.getParentEditor().config.language ? this.getParentEditor().config.language : 'en') + '&version=3';
-
- content += '
';
- content += '⚠ ")
- gr.Markdown("# ProteinMPNN")
- gr.Markdown(
- """This model takes as input a protein structure and based on its backbone predicts new sequences that will fold into that backbone.
- Optionally, we can run AlphaFold2 on the predicted sequence to check whether the predicted sequences adopt the same backbone.
-
- If you use this space please cite the ProteinMPNN paper
- > J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, D. Baker, Robust deep learning–based protein sequence design using ProteinMPNN. Science 378, 49–56 (2022).
-
- and this webapp:
-
- > Simon L. Dürr. (2023). ProteinMPNN Gradio Webapp (v0.3). Zenodo. https://doi.org/10.5281/zenodo.7630417
-
- """
- )
- gr.Markdown("")
-
- with gr.Tabs():
- with gr.TabItem("Input"):
- inp = gr.Textbox(
- placeholder="PDB Code or upload file below", label="Input structure"
- )
- file = gr.File(file_count="single")
-
- with gr.TabItem("Settings"):
- with gr.Row():
- designed_chain = gr.Textbox(value="A", label="Designed chain")
- fixed_chain = gr.Textbox(
- placeholder="Use commas to fix multiple chains", label="Fixed chain"
- )
- with gr.Row():
- num_seqs = gr.Slider(
- minimum=1, maximum=15, value=1, step=1, label="Number of sequences"
- )
- sampling_temp = gr.Radio(
- choices=["0.1", "0.15", "0.2", "0.25", "0.3"],
- value="0.1",
- label="Sampling temperature",
- )
- gr.Markdown(
- """ Sampling temperature for amino acids, `T=0.0` means taking argmax, `T>>1.0` means sample randomly. Suggested values `0.1, 0.15, 0.2, 0.25, 0.3`. Higher values will lead to more diversity.
- """
- )
- with gr.Row():
- model_name = gr.Dropdown(
- choices=[
- "vanilla—v_48_002",
- "vanilla—v_48_010",
- "vanilla—v_48_020",
- "vanilla—v_48_030",
- "soluble—v_48_010",
- "soluble—v_48_020",
- ],
- label="Model",
- value="vanilla—v_48_020",
- )
- backbone_noise = gr.Dropdown(
- choices=["0", "0.02", "0.10", "0.20", "0.30"], label="Backbone noise", value="0"
- )
- with gr.Row():
- homomer = gr.Checkbox(value=False, label="Homomer?")
- gr.Markdown(
- "for correct symmetric tying lenghts of homomer chains should be the same"
- )
- with gr.Row():
- omit_AAs = gr.Textbox(
- placeholder="Specify omitted amino acids ", label="Omitted amino acids"
- )
- gr.Markdown("## Fixed positions")
- gr.Markdown(
- """You can fix important positions in the protein. Resid should be specified with the same numbering as in the input pdb file. The fixed residues will be highlighted in the output.
- The [VMD selection](http://www.ks.uiuc.edu/Research/vmd/vmd-1.9.2/ug/node89.html) synthax is used. You can also select based on ligands or chains in the input structure to specify interfaces to be fixed.
-
- - within 5 of resid 94 All residues that have >1 atom closer than 5 Å to any atom of residue 94
- - name CA and within 5 of resid 94 All residues that have CA atom closer than 5 Å to any atom of residue 94
- - resid 94 96 119 Residues 94, 94 and 119
- - within 5 of resname ZN All residues with any atom <5 Å of zinc ion
- - chain A and within 5 of chain B All residues of chain A that are part of the interface with chain B
- - protein and within 5 of nucleic All residues that bind to DNA (if present in structure)
- - not (chain A and within 5 of chain B) only modify residues that are in the interface with the fixed chain, not further away
- - chain A or (chain B and sasa < 20) Keep chain A and all core residues fixeds
- - pLDDT >70 Redesign all residues with low pLDDT
-
- Note that sasa and pLDDT selectors modify default VMD behavior. SASA is calculated using moleculekit and written to the mass attribute. Selections based on mass do not work.
- pLDDT is an alias for beta, it only works correctly with structures that contain the appropriate values in the beta column of the PDB file. """
- )
- atomsel = gr.Textbox(
- placeholder="Specify atom selection ", label="Fixed positions"
- )
-
- btn = gr.Button("Run")
- label = gr.Textbox(label="Label", visible=False)
- examples = gr.Dataset(
- components=[
- label,
- inp,
- designed_chain,
- fixed_chain,
- homomer,
- num_seqs,
- sampling_temp,
- atomsel,
- ],
- samples=[
- ["Homomer design", "1O91", "A,B,C", "", True, "2", "0.1", ""],
- ["Monomer design", "6MRR", "A", "", False, "2", "0.1", ""],
- ["Redesign of Homomer to Heteromer", "3HTN", "A,B", "C", False, "2", "0.1", ""],
- [
- "Redesign of MID1 scaffold keeping binding site fixed",
- "3V1C",
- "A,B",
- "",
- False,
- "2",
- "0.1",
- "within 5 of resname ZN",
- ],
- [
- "Redesign of DNA binding protein",
- "3JRD",
- "A,B",
- "",
- False,
- "2",
- "0.1",
- "within 8 of nucleic",
- ],
- [
- "Surface Redesign of miniprotein",
- "7JZM",
- "A,B",
- "",
- False,
- "2",
- "0.1",
- "chain B or (chain A and sasa < 20)",
- ],
- ],
- )
-
- gr.Markdown("# Output")
-
- with gr.Tabs():
- with gr.TabItem("Designed sequences"):
- out = gr.Textbox(label="Status")
-
- with gr.TabItem("Amino acid probabilities"):
- plot = gr.Plot()
- all_log_probs = gr.File(visible=False)
- with gr.TabItem("T adjusted probabilities"):
- gr.Markdown("Sampling temperature adjusted amino acid probabilties")
- plot_tadjusted = gr.Plot()
- all_probs = gr.File(visible=False)
- with gr.TabItem("Structure validation w/ AF2"):
- gr.HTML(
- """
-
-
-
- Results might differ from DeepMind's published results.
- Predictions are made using model_5_ptm and without MSA based on the selected single sequence (designed_chain + fixed_chain).
-
-
-
City Build APK: How to Download and Play the Best City Building Game on Android
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-city build apk Download Zip ––– https://ssurll.com/2uNTqt
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- The second step is to enable unknown sources on your device. This is necessary because Android devices normally do not allow the installation of apps from unknown sources. To enable unknown sources, you have to go to your device's settings, then security, then toggle on the option that says "allow installation of apps from unknown sources". This will allow you to install City Build APK without any problems.
- Step 3: Download and install the APK file
- The third step is to download and install the City Build APK file. To do this, you have to go to the website where you found the APK file, such as [APKPure], and click on the download button. This will start the download process and save the file to your device's storage. Once the download is complete, you have to locate the file and tap on it to start the installation process. This will take a few minutes and require some permissions from your device. After the installation is done, you will see the game icon on your device's home screen or app drawer.
- Step 4: Launch the game and enjoy
- The final step is to launch the game and enjoy. To do this, you have to tap on the game icon and wait for it to load. You will see a welcome screen that will ask you to choose your language and agree to the terms of service. You will also see a tutorial that will guide you through the basics of the game. You can skip the tutorial if you want, but we recommend that you follow it to learn how to play the game. After the tutorial, you can start building your own city and have fun.
- Tips and Tricks for Playing City Build APK
- Now that you know how to download and install City Build APK, here are some tips and tricks for playing it:
- Plan your city layout carefully
- One of the most important aspects of city building games is planning your city layout carefully. You have to consider factors like space, efficiency, aesthetics, and functionality when placing your buildings and roads. You have to avoid creating dead ends, bottlenecks, or gaps that will waste space or cause traffic problems. You also have to make sure that your buildings are connected to power, water, sewage, and other services. You can use the bulldozer tool to demolish or move any buildings or roads that you want to change.
- Balance your population and happiness
- Another important aspect of city building games is balancing your population and happiness. You have to attract more citizens to your city by providing them with housing, jobs, entertainment, and other amenities. You also have to keep them happy by fulfilling their needs and demands. You can check your population and happiness levels by tapping on the population icon at the top left corner of the screen. You can also see what your citizens are saying by tapping on their speech bubbles or reading their comments on social media. You can increase your population by building more residential zones or upgrading them. You can increase your happiness by building more parks, landmarks, specializations, or services.
- Upgrade your buildings and infrastructure
- A third important aspect of city building games is upgrading your buildings and infrastructure. You have to improve your city's performance and appearance by upgrading your buildings and infrastructure. You can upgrade your residential zones by collecting materials from factories or shops, or by using golden keys or platinum keys that you can earn from completing tasks or participating in events. You can upgrade your factories, shops, services, or specializations by spending coins or simcash that you can earn from taxes, trade, or other sources. Upgrading your buildings and infrastructure will increase their capacity, efficiency, quality, or attractiveness.
- Join a club and cooperate with other mayors
- A fourth important aspect of city building games is joining a club and cooperating with other mayors. You can join a club with fellow mayors who share your interests or goals in the game. You can chat with them in real-time and share tips and strategies. You can also cooperate with them in club wars or club challenges where you can compete against other clubs for rewards and glory. You can also trade goods and resources with them in the club market where you can find better deals than in the global market.
- Conclusion
- City Build APK is a great way to download and play SimCity BuildIt, one of the best city building games ever made. You can create your own city from scratch, manage your resources and services, trade, chat, and compete with other players, and much more. You can download City Build APK from [APKPure] or other reliable sources for free. You just have to enable unknown sources on your device, download and install the APK file, launch the game and enjoy. You can also follow our tips and tricks for playing City Build APK to improve your city and have fun. We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy city building!
- FAQs
- Here are some frequently asked questions about City Build APK:
- Is City Build APK safe to download and install?
- Yes, City Build APK is safe to download and install as long as you get it from a reliable source like [APKPure]. However, you should always be careful when downloading and installing any APK file from unknown sources, as they may contain viruses or malware that can harm your device. You should also scan the APK file with an antivirus app before installing it.
- Is City Build APK legal to use?
- Yes, City Build APK is legal to use as long as you do not violate the terms of service of SimCity BuildIt or Electronic Arts. You should not use City Build APK to hack, cheat, or modify the game in any way that gives you an unfair advantage over other players or violates the game's rules. You should also not use City Build APK to distribute or sell the game or its content without the permission of the developers.
- Does City Build APK require an internet connection?
- Yes, City Build APK requires an internet connection to play. You need an internet connection to download and install the game, as well as to access its online features like trade, chat, competition, and events. You also need an internet connection to save your game progress and sync it with other devices. However, you can play the game offline for a limited time if you have already downloaded and installed it.
- How can I update City Build APK?
- You can update City Build APK by downloading and installing the latest version of the APK file from the same source where you got it. You should always update City Build APK whenever there is a new version available, as it may contain new features, improvements, bug fixes, or security patches. You should also backup your game data before updating City Build APK, in case something goes wrong during the process.
- How can I uninstall City Build APK?
- You can uninstall City Build APK by following the same steps as uninstalling any other app on your Android device. You have to go to your device's settings, then apps, then find and tap on City Build APK. You will see an option to uninstall the app. Tap on it and confirm your action. This will remove City Build APK from your device along with its data and cache.
401be4b1e0How to Create Engaging Content on TikTok and Reels
-If you're looking for a way to spice up your social media marketing strategy, you might want to consider using TikTok and Reels, the two hottest short-form video platforms right now. These platforms allow you to create fun, creative, and engaging videos that can reach millions of potential customers in minutes. But how do you get started with these platforms? And how do you create content that stands out from the crowd? In this article, we'll show you how to download Reels and TikTok videos without watermark, how to create engaging content on Reels and TikTok, and some examples of successful Reels and TikTok videos from different niches and industries. Let's dive in!
- How to Download Reels and TikTok Videos Without Watermark
-One of the first things you might want to do when using Reels and TikTok is to download some videos from these platforms. This can help you learn from other creators, get inspired by their ideas, or repurpose their content for your own use. However, downloading videos from these platforms can be tricky, especially if you want to remove the watermark that shows the platform's logo. Fortunately, there are some tools and methods that can help you download Reels and TikTok videos without watermark. Here are some of them:
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-Snaptik.app : This is a free online tool that allows you to download video tiktok without watermark. All you need to do is copy the link of the video you want to download from TikTok or Reels, paste it into the tool's website, and click on the download button. The tool will process your request and provide you with a link to download the video in MP4 format with HD quality. You can also use Snaptik's Android app for easier downloading.Ssstik.io : This is another free online tool that helps you download TikTok videos without watermark. It works similarly to Snaptik.app: you just need to copy the link of the video you want to download from TikTok or Reels, paste it into the tool's website, and click on the save button. The tool will generate a link for you to download the video in MP4 format with HD resolution. You can also use Ssstik.io's browser extension for faster downloading.Screen recording : This is a simple method that doesn't require any third-party tools or apps. You just need to use your device's built-in screen recording feature to capture the video you want to download from TikTok or Reels. However, this method has some drawbacks: you might not get the best quality, you might capture some unwanted sounds or notifications, and you might still see the watermark on the video. To avoid these issues, you should turn on the airplane mode, mute your device, and crop the video after recording.
-Downloading Reels and TikTok videos without watermark can have some advantages and disadvantages. On one hand, it can help you save time and bandwidth, access videos offline, and reuse them for your own purposes. On the other hand, it can also raise some ethical and legal issues, such as violating the platforms' terms of service, infringing the creators' intellectual property rights, and losing the original source and credit of the videos. Therefore, you should always be careful and respectful when downloading videos from these platforms, and only use them for personal or educational purposes. You should also always give proper attribution and credit to the original creators, and avoid using their content for commercial or malicious purposes.
-Once you have downloaded some videos from Reels and TikTok, you can use them for your own content creation or repurposing. For example, you can:
-
-Edit them : You can use some video editing tools or apps to trim, crop, rotate, merge, split, add filters, effects, stickers, text, music, voiceover, or subtitles to the downloaded videos. You can also adjust the speed, brightness, contrast, saturation, or volume of the videos. This can help you enhance the quality, style, and message of the videos.Share them : You can share the downloaded videos on other social media platforms or channels, such as Instagram, Facebook, YouTube, Twitter, Snapchat, WhatsApp, or your website or blog. This can help you increase your reach, exposure, and engagement with different audiences. However, you should always respect the platforms' guidelines and policies regarding video sharing and uploading.Repurpose them : You can repurpose the downloaded videos for different formats or purposes. For example, you can turn them into GIFs, memes, slideshows, podcasts, blogs, ebooks, webinars, courses, or newsletters. This can help you diversify your content, add value to your audience, and generate more leads and conversions.
-Downloading Reels and TikTok videos without watermark can be a useful way to learn from other creators, get inspired by their ideas, or repurpose their content for your own use. However, you should always be mindful of the ethical and legal implications of doing so, and always respect the platforms' terms of service and the creators' intellectual property rights.
- How to Create Engaging Content on Reels and TikTok
-Now that you know how to download Reels and TikTok videos without watermark, you might be wondering how to create your own engaging content on these platforms. Creating short-form video content can be challenging, but also rewarding, as it can help you showcase your personality, creativity, and expertise, connect with your audience, and grow your brand awareness and loyalty. However, not all short-form video content is created equal. There are some differences between Reels and TikTok that you should be aware of, as well as some tips and tricks that can help you create captivating videos that will attract and retain your viewers. Here are some of them:
- The Differences Between Reels and TikTok
-Reels and TikTok are both short-form video platforms that allow you to create and share 15-60 second videos with music, filters, effects, stickers, text, and other features. However, they are not exactly the same. Here are some of the main differences between Reels and TikTok in terms of video length, music options, editing tools, algorithm, advertising, demographics, and analytics:
-
-
-Reels
-TikTok
-
-
-Video length: 15-30 seconds
-Video length: 15-60 seconds
-
-
-Music options: Limited to Instagram's music library or original audio
-Music options: Access to a wide range of songs and sounds from TikTok's music library or original audio
-
-
-Editing tools: Basic tools such as speed, timer, align, effects, filters, stickers, text, draw, voiceover
-Editing tools: Advanced tools such as speed, timer, beauty mode, filters, effects, stickers, text, templates, transitions, voice effects, voiceover, duet, stitch, green screen
-
-
-Algorithm: Based on the user's interests, preferences, and behavior on Instagram
-Algorithm: Based on the user's interests, preferences, and behavior on TikTok
-
-
-Advertising: Limited to Instagram's ads platform or sponsored posts
-Advertising: Access to TikTok's ads platform or sponsored posts
-
-
-Demographics: Mostly millennials and Gen Z users who are already on Instagram
-Demographics: Mostly Gen Z and younger users who are new to social media
-
-
-Analytics: Basic metrics such as views, likes, comments, shares, saves, reach, impressions
-Analytics: Detailed metrics such as views, likes, comments, shares, saves, followers, watch time, traffic source, audience territories, audience interests
-
-
-As you can see, Reels and TikTok have some similarities and differences that can affect your content creation and marketing strategy. Depending on your goals, target audience, budget, and resources, you might want to choose one platform over the other, or use both platforms to maximize your exposure and engagement. However, regardless of which platform you use, there are some general principles that can help you create engaging content on Reels and TikTok.
- The Tips and Tricks for Creating Engaging Content on Reels and TikTok
-Creating engaging content on Reels and TikTok can be challenging, but also fun and rewarding. Here are some tips and tricks that can help you create captivating videos that will attract and retain your viewers:
-
-Create a captivating hook : The first few seconds of your video are crucial to capture your viewer's attention and interest. You need to create a captivating hook that will make them want to watch more. You can do this by asking a question, making a statement, telling a story, showing a teaser, or using a catchy phrase. You can also use some effects, filters, stickers, text, or music to enhance your hook and make it more appealing.Deliver value : The main purpose of your video is to deliver value to your viewer. You need to provide them with something that will benefit them, such as information, education, entertainment, inspiration, motivation, or emotion. You need to make sure that your video is relevant, useful, and valuable to your viewer's needs, wants, goals, or problems. You can do this by sharing your knowledge, skills, experience, opinions, stories, tips, tricks, hacks, or recommendations.Entertain and educate : The best way to deliver value to your viewer is to entertain and educate them at the same time. You need to balance the fun and the facts in your video. You need to make your video enjoyable and memorable for your viewer. You can do this by using humor, emotion, storytelling, personality, creativity, or surprises. You can also use some music, effects, filters, stickers, text, or transitions to make your video more dynamic and engaging.Optimize your video : The final step of creating engaging content on Reels and TikTok is to optimize your video for maximum reach and engagement. You need to make sure that your video is visible and discoverable by your target audience. You can do this by using relevant hashtags, keywords, captions, titles, descriptions, tags, or categories. You can also use some calls to action, such as asking for likes, comments, shares, follows, feedback, or suggestions. You can also use some incentives, such as giveaways, contests, challenges, or collaborations.
-Creating engaging content on Reels and TikTok can be a great way to showcase your personality, creativity, and expertise, connect with your audience, and grow your brand awareness and loyalty. However, you need to follow some tips and tricks to create captivating videos that will attract and retain your viewers. You need to create a captivating hook, deliver value, entertain and educate, and optimize your video.
-How to download reels tiktok without watermark
- The Examples of Successful Reels and TikTok Videos
-To give you some inspiration and ideas for creating engaging content on Reels and TikTok, here are some examples of successful Reels and TikTok videos from different niches and industries. You can learn from their strategies, techniques, and styles, or get inspired by their topics, themes, and messages. Here are some of them:
-
-Beauty : @jamescharles is a popular beauty influencer who creates stunning makeup tutorials, transformations, and challenges on Reels and TikTok. He uses catchy music, effects, filters, stickers, text, transitions, and voiceovers to make his videos more fun and engaging. He also delivers value by sharing his tips, tricks, hacks, and recommendations for different products, looks, and occasions.Business : @garyvee is a well-known entrepreneur, speaker, and author who creates motivational and educational videos on Reels and TikTok. He uses storytelling, emotion, humor, and personality to make his videos more inspiring and memorable. He also delivers value by sharing his insights, advice, opinions, and stories on various topics related to business, marketing, social media, and personal development.Fitness : @blogilates is a famous fitness instructor and blogger who creates fun and effective workout videos on Reels and TikTok. She uses upbeat music, effects, filters, stickers, text, and voiceovers to make her videos more lively and engaging. She also delivers value by sharing her routines, exercises, tips, tricks, and challenges for different fitness goals, levels, and body parts.Food : @buzzfeedtasty is a popular food media brand that creates delicious and easy recipes on Reels and TikTok. They use high-quality video, music, effects, filters, text, and voiceovers to make their videos more appetizing and engaging. They also deliver value by sharing their ingredients, instructions, tips, tricks, and variations for different dishes, cuisines, and occasions.Travel : @damonandjo are a dynamic duo of travel influencers who create adventurous and cultural videos on Reels and TikTok. They use stunning video, music, effects, filters, stickers, text, and voiceovers to make their videos more captivating and engaging. They also deliver value by sharing their experiences, tips, tricks, and recommendations for different destinations, activities, and cultures.
-These are just some of the examples of successful Reels and TikTok videos from different niches and industries. You can find more examples by browsing the platforms' explore pages, following popular hashtags, or searching for keywords related to your niche or industry. You can also follow some of the influencers or brands that you admire or resonate with, and see how they create engaging content on Reels and TikTok.
- Conclusion
-Reels and TikTok are two of the most popular and powerful short-form video platforms that you can use to create engaging content for your social media marketing strategy. They can help you showcase your personality, creativity, and expertise, connect with your audience, and grow your brand awareness and loyalty. However, creating engaging content on Reels and TikTok can be challenging, but also rewarding. You need to know how to download Reels and TikTok videos without watermark, how to create captivating videos that will attract and retain your viewers, and some examples of successful Reels and TikTok videos from different niches and industries. By following the tips and tricks we shared in this article, you can create amazing videos that will make your audience go wow!
-So what are you waiting for? Try out Reels and TikTok for yourself and see how they can transform your social media marketing strategy. And if you need more tips and examples on how to create engaging content on Reels and TikTok, don't forget to follow our account for more updates. We'd love to see what you create!
-Thank you for reading this article. We hope you found it helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. We'd love to hear from you!
- FAQs
-Here are some of the frequently asked questions that readers might have about Reels and TikTok:
-
-How do I switch between Reels and TikTok accounts? If you have multiple accounts on Reels or TikTok, you can easily switch between them without logging out. On Reels, you can tap on your profile icon in the bottom right corner of the screen, then tap on the arrow next to your username, then select the account you want to switch to. On TikTok, you can tap on "Me" in the bottom right corner of the screen, then tap on the three dots in the top right corner of the screen, then tap on "Manage account", then tap on "Switch account", then select the account you want to switch to.
-How do I collaborate with other creators on Reels and TikTok? If you want to collaborate with other creators on Reels or TikTok, you can use some of the features that these platforms offer, such as duet, stitch, remix, or live. These features allow you to create videos with other creators, either by adding your own video or audio to their existing video, or by creating a new video together in real time. You can also collaborate with other creators by tagging them in your videos, captions, or comments, or by joining their challenges, contests, or campaigns.
-How do I measure the performance of my Reels and TikTok videos? If you want to measure the performance of your Reels or TikTok videos, you can use some of the analytics tools that these platforms provide. On Reels, you can tap on the three lines in the top right corner of the screen, then tap on "Insights", then tap on "Content", then tap on "Reels". Here you can see some basic metrics such as views, likes, comments, shares, saves, reach, and impressions for your Reels videos. On TikTok, you can tap on "Me" in the bottom right corner of the screen, then tap on the three dots in the top right corner of the screen, then tap on "Analytics". Here you can see some detailed metrics such as views, likes, comments, shares, saves, followers, watch time, traffic source, audience territories, and audience interests for your TikTok videos.
-How do I monetize my Reels and TikTok content? If you want to monetize your Reels or TikTok content, you can use some of the monetization options that these platforms offer. On Reels, you can monetize your content by using Instagram's ads platform or sponsored posts. You can also monetize your content by promoting your products or services, selling your merchandise, offering your courses or coaching, or using affiliate links or codes. On TikTok, you can monetize your content by using TikTok's ads platform or sponsored posts. You can also monetize your content by joining TikTok's Creator Fund or Creator Marketplace programs, which pay you based on your views and engagement. You can also monetize your content by promoting your products or services, selling your merchandise, offering your courses or coaching, or using affiliate links or codes.
-How do I stay updated on the latest trends and features on Reels and TikTok? If you want to stay updated on the latest trends and features on Reels or TikTok, you can use some of the resources that these platforms provide. On Reels, you can tap on the magnifying glass icon in the bottom of the screen, then tap on "Reels". Here you can see some of the trending videos, hashtags, music, effects, and creators on Reels. You can also follow some of the official accounts of Instagram, such as @instagram, @creators, or @shop, to get the latest news and updates on Reels. On TikTok, you can tap on the discover icon in the bottom of the screen. Here you can see some of the trending videos, hashtags, music, effects, and creators on TikTok. You can also follow some of the official accounts of TikTok, such as @tiktok, @tiktoktips, or @tiktokcreators, to get the latest news and updates on TikTok.
- 197e85843dEuro Truck Simulator APK Download for PC
-If you love driving simulation games, you might have heard of Euro Truck Simulator, one of the most popular trucking games in the world. In this game, you can drive a cargo truck across Europe, delivering goods from one destination to another. You can also build your own trucking company, buy new trucks, hire drivers, and earn money.
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But did you know that you can also play this game on your PC? Yes, you can download Euro Truck Simulator APK for PC and enjoy the game on a bigger screen, with better graphics, sound, and performance. In this article, we will show you how to do that, as well as some tips and tricks to make your gameplay more fun and realistic.
- What is Euro Truck Simulator?
-Euro Truck Simulator is a simulation game developed by SCS Software, a Czech company that specializes in vehicle-related games. The first version of the game was released in 2008, and since then, several sequels and expansions have been launched. The latest version is Euro Truck Simulator 2, which was released in 2012.
-In Euro Truck Simulator 2, you can drive a variety of trucks from different European manufacturers, such as Mercedes-Benz, Volvo, Scania, MAN, Renault, DAF, Iveco, and more. You can also customize your trucks with different paint jobs, accessories, engines, transmissions, chassis, wheels, lights, horns, etc.
-The game features a realistic map of Europe, with over 70 cities in 13 countries. You can drive on different types of roads, such as highways, country roads, city streets, dirt roads, etc. You can also encounter different weather conditions, traffic situations, tolls, speed limits, police checks, etc.
-The game also has a career mode, where you can start as a low-skilled driver who works for various companies. As you complete deliveries and earn money, you can buy your own truck depot or build a fleet of trucks yourself. You can also upgrade your skills, such as fuel efficiency, long distance driving, fragile cargo handling, etc.
- Why download Euro Truck Simulator APK for PC?
-Euro Truck Simulator 2 is available for Windows PCs as a paid game. However, if you want to play it for free or if you don't have a compatible PC system, you can also download Euro Truck Simulator APK for PC. This is an Android version of the game that you can run on your PC using an emulator.
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-There are many benefits of playing Euro Truck Simulator APK for PC. Here are some of them:
-
-You can enjoy the game on a larger screen with higher resolution and better graphics quality.
-You can use your keyboard and mouse to control your truck more easily and precisely.
-You can save your battery life and storage space on your mobile device.
-You can access more features and options that are not available on the mobile version.
-You can play the game offline or online with other players from around the world.
-
-So, if you are interested in playing Euro Truck Simulator APK for PC, read on to find out how to do it.
- How to download Euro Truck Simulator APK for PC?
-To download Euro Truck Simulator APK for PC, you will need two things: an emulator and an APK file. An emulator is a software that allows you to run Android apps on your PC. An APK file is a package file that contains the app and its data. Here are the steps to follow:
- Download an emulator
-There are many emulators available for PC, but we recommend using BlueStacks, which is one of the most popular and reliable ones. You can download it from its official website: [BlueStacks]. Follow the instructions on the website to install it on your PC.
- Download the APK file
-Next, you will need to download the APK file of Euro Truck Simulator 2. You can find it on various websites, but we suggest using [APKPure], which is a trusted and safe source. Go to the website and search for Euro Truck Simulator 2. Click on the download button and save the file on your PC.
- Install and launch the game
-Now that you have both the emulator and the APK file, you can install and launch the game. Here's how:
-
-Open BlueStacks and sign in with your Google account.
-Go to the folder where you saved the APK file and right-click on it.
-Select "Open with" and choose "BlueStacks".
-The game will start installing on the emulator.
-Once the installation is complete, you will see the game icon on the BlueStacks home screen.
-Click on it and enjoy playing Euro Truck Simulator 2 on your PC.
-
- Tips and tricks for playing Euro Truck Simulator on PC
-Now that you know how to download Euro Truck Simulator APK for PC, here are some tips and tricks to make your gameplay more fun and realistic:
- Customize your controls
-One of the advantages of playing Euro Truck Simulator on PC is that you can use your keyboard and mouse to control your truck. However, you might want to adjust the settings to suit your preferences. To do that, go to "Options" > "Controls" and change the keys or buttons for steering, accelerating, braking, shifting gears, turning signals, etc. You can also enable or disable features such as automatic transmission, cruise control, speed limiter, etc.
- Use the radio feature
-Driving a truck across Europe can be boring without some music or podcasts to keep you company. Luckily, Euro Truck Simulator has a radio feature that allows you to listen to your favorite stations or files while driving. To use it, go to "Options" > "Radio" and add or remove stations or files from your list. You can also adjust the volume and switch between stations or files using the keyboard shortcuts.
- Explore different maps and trucks
-Euro Truck Simulator has a lot of variety when it comes to maps and trucks. You can unlock new maps by expanding your company or buying new garages in different countries. You can also unlock new trucks by earning money or visiting dealerships in different cities. You can choose from different models, brands, colors, accessories, etc. You can also customize your license plate, dashboard, interior, etc.
- Manage your own company
-Euro Truck Simulator also has a business simulation aspect where you can manage your own company. You can hire drivers, buy garages, assign trucks, monitor deliveries, etc. You can also view your statistics, such as income, expenses, reputation, etc. You can also access online features such as leaderboards, achievements, etc.
- Conclusion
-Euro Truck Simulator is a great game for anyone who loves driving simulation games. It offers a realistic and immersive experience of driving a truck across Europe. You can also download Euro Truck Simulator APK for PC and play it on a bigger screen with better graphics and performance. All you need is an emulator and an APK file. Follow our guide above to learn how to do it easily and quickly. Then enjoy playing Euro Truck Simulator 2 on your PC with our tips and tricks.
- FAQs
-
-Q: How much space does Euro Truck Simulator 2 take on PC? A: The game requires about 4 GB of free disk space on your PC to install and run the game.
-Q: How can I update Euro Truck Simulator 2 on PC? A: You can update the game automatically or manually. To update it automatically, make sure you have an internet connection and launch the game. The game will check for updates and download them if available. To update it manually, go to the official website of the game and download the latest patch. Then run the patch file and follow the instructions.
-Q: How can I play Euro Truck Simulator 2 online with other players? A: You can play Euro Truck Simulator 2 online with other players using a mod called TruckersMP. This mod allows you to join servers and drive with or against other players in real time. You can also chat, join convoys, participate in events, etc. To use this mod, you need to have a Steam account and a registered copy of the game. You also need to download and install the mod from its official website: [TruckersMP].
-Q: How can I get more money and experience in Euro Truck Simulator 2? A: There are several ways to get more money and experience in Euro Truck Simulator 2. Some of them are:
-
-Complete more deliveries and contracts with higher rewards and bonuses.
-Upgrade your skills and unlock more profitable types of cargo and routes.
-Buy more trucks and hire more drivers to work for you.
-Use cheats or mods to increase your money and experience. However, this might affect your gameplay balance and online features.
-
-Q: How can I fix common errors or problems in Euro Truck Simulator 2? A: If you encounter any errors or problems in Euro Truck Simulator 2, such as crashes, freezes, lags, bugs, etc., you can try some of these solutions:
-
-Make sure your PC meets the minimum system requirements for the game.
-Update your graphics card drivers and DirectX software.
-Run the game as an administrator and in compatibility mode.
-Disable any antivirus or firewall software that might interfere with the game.
-Delete any mods or files that might cause conflicts with the game.
-Verify the integrity of the game files using Steam or BlueStacks.
-Reinstall the game or the emulator if nothing else works.
-
- 401be4b1e0Review: Dennis DeYoung's One Hundred Years From Now
-One Hundred Years From Now is the sixth solo album by former Styx vocalist and keyboardist Dennis DeYoung. It was originally released in Canada in 2007, and then in the United States in 2009. The album features 12 tracks of melodic rock that showcase DeYoung's distinctive voice and songwriting skills. The title track, a duet with Canadian singer Eric Lapointe, was a hit single in Quebec and received airplay on classic rock stations in the US. Other highlights include "This Time Next Year", a tribute to the Beatles, "Rain", a ballad with a gospel choir, and "Private Jones", a rocker about a soldier in Iraq.
-One Hundred Years From Now is a rare example of an album that appeals to both old and new fans of DeYoung. It has enough elements of his Styx legacy, such as the harmonies, the keyboards, and the theatrical flair, to satisfy the nostalgic listeners. At the same time, it also has a modern sound and production, with guitars by Jim Peterik of Survivor and Tom Dziallo of Cheap Trick, that make it relevant to today's rock scene. DeYoung proves that he still has the talent and passion to create quality music that transcends time and trends.
-dennis deyoung one hundred years from now rar Download Zip ··· https://urlgoal.com/2uI7DS
One Hundred Years From Now is available as a digital download or as a CD with bonus tracks. It is also available as a rar file, which is a compressed archive format that can be extracted with software such as WinRAR or 7-Zip. The rar file contains the audio files in mp3 format, as well as the cover art and lyrics. The rar file can be downloaded from various websites that offer free music downloads, such as MediaFire or RapidShare. However, downloading music from these sources may be illegal or unsafe, so it is recommended to purchase the album from official channels instead.
-
-One Hundred Years From Now is not only a musical album, but also a personal statement by DeYoung. The album reflects his views on life, love, and legacy, as well as his gratitude to his fans and his faith in God. The album's title comes from a quote by Mahatma Gandhi: "Live as if you were to die tomorrow. Learn as if you were to live forever." DeYoung said that he wanted to make an album that would stand the test of time and inspire people to live in the present and appreciate what they have.
-The album also features some guest appearances by other artists, such as John Waite of The Babys and Bad English, who sings on "There Was a Time", and Richard Marx, who co-wrote and sings on "To the Good Old Days". DeYoung also pays homage to his former bandmates in Styx, by including a cover of "I Don't Want to Lose You", a song written by Tommy Shaw for their 1990 album Edge of the Century. DeYoung said that he wanted to show his respect and appreciation for their contributions to his career and music.
-
-One Hundred Years From Now is a testament to DeYoung's enduring talent and passion for music. It is an album that showcases his versatility as a singer, songwriter, and musician, as well as his ability to connect with his audience. It is an album that celebrates the past, embraces the present, and looks forward to the future. It is an album that will make you think, feel, and sing along.
cec2833e83longman lexicon of contemporary english pdf Download Zip 🌟 https://cinurl.com/2uEYmQ
diff --git a/spaces/tanishqvashisht/sharingan/README.md b/spaces/tanishqvashisht/sharingan/README.md
deleted file mode 100644
index 7925fd757f5b5eb6bf3f7415637e14076e60b67e..0000000000000000000000000000000000000000
--- a/spaces/tanishqvashisht/sharingan/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Sharingan
-emoji: 🐢
-colorFrom: blue
-colorTo: red
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/taquynhnga/CNNs-interpretation-visualization/backend/adversarial_attack.py b/spaces/taquynhnga/CNNs-interpretation-visualization/backend/adversarial_attack.py
deleted file mode 100644
index fcaf8bbeebc298443098dcc2dd2abda26335548f..0000000000000000000000000000000000000000
--- a/spaces/taquynhnga/CNNs-interpretation-visualization/backend/adversarial_attack.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import PIL
-from PIL import Image
-import numpy as np
-from matplotlib import pylab as P
-import cv2
-
-import torch
-from torch.utils.data import TensorDataset
-from torchvision import transforms
-import torch.nn.functional as F
-
-from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-
-from torchvex.base import ExplanationMethod
-from torchvex.utils.normalization import clamp_quantile
-
-from backend.utils import load_image, load_model
-from backend.smooth_grad import generate_smoothgrad_mask
-
-import streamlit as st
-
-IMAGENET_DEFAULT_MEAN = np.asarray(IMAGENET_DEFAULT_MEAN).reshape([1,3,1,1])
-IMAGENET_DEFAULT_STD = np.asarray(IMAGENET_DEFAULT_STD).reshape([1,3,1,1])
-
-def deprocess_image(image_inputs):
- return (image_inputs * IMAGENET_DEFAULT_STD + IMAGENET_DEFAULT_MEAN) * 255
-
-
-def feed_forward(input_image):
- model, feature_extractor = load_model('ConvNeXt')
- inputs = feature_extractor(input_image, do_resize=False, return_tensors="pt")['pixel_values']
- logits = model(inputs).logits
- prediction_prob = F.softmax(logits, dim=-1).max() # prediction probability
- # prediction class id, start from 1 to 1000 so it needs to +1 in the end
- prediction_class = logits.argmax(-1).item()
- prediction_label = model.config.id2label[prediction_class] # prediction class label
- return prediction_prob, prediction_class, prediction_label
-
-# FGSM attack code
-def fgsm_attack(image, epsilon, data_grad):
- # Collect the element-wise sign of the data gradient and normalize it
- sign_data_grad = torch.gt(data_grad, 0).type(torch.FloatTensor) * 2.0 - 1.0
- perturbed_image = image + epsilon*sign_data_grad
- return perturbed_image
-
-# perform attack on the model
-def perform_attack(input_image, target, epsilon):
- model, feature_extractor = load_model("ConvNeXt")
- # preprocess input image
- inputs = feature_extractor(input_image, do_resize=False, return_tensors="pt")['pixel_values']
- inputs.requires_grad = True
-
- # predict
- logits = model(inputs).logits
- prediction_prob = F.softmax(logits, dim=-1).max()
- prediction_class = logits.argmax(-1).item()
- prediction_label = model.config.id2label[prediction_class]
-
- # Calculate the loss
- loss = F.nll_loss(logits, torch.tensor([target]))
-
- # Zero all existing gradients
- model.zero_grad()
-
- # Calculate gradients of model in backward pass
- loss.backward()
-
- # Collect datagrad
- data_grad = inputs.grad.data
-
- # Call FGSM Attack
- perturbed_data = fgsm_attack(inputs, epsilon, data_grad)
-
- # Re-classify the perturbed image
- new_prediction = model(perturbed_data).logits
- new_pred_prob = F.softmax(new_prediction, dim=-1).max()
- new_pred_class = new_prediction.argmax(-1).item()
- new_pred_label = model.config.id2label[new_pred_class]
-
- return perturbed_data, new_pred_prob.item(), new_pred_class, new_pred_label
-
-
-def find_smallest_epsilon(input_image, target):
- epsilons = [i*0.001 for i in range(1000)]
-
- for epsilon in epsilons:
- perturbed_data, new_prob, new_id, new_label = perform_attack(input_image, target, epsilon)
- if new_id != target:
- return perturbed_data, new_prob, new_id, new_label, epsilon
- return None
-
-# @st.cache_data
-@st.cache(allow_output_mutation=True)
-def generate_images(image_id, epsilon=0):
- model, feature_extractor = load_model("ConvNeXt")
- original_image_dict = load_image(image_id)
- image = original_image_dict['image']
- return generate_smoothgrad_mask(
- image, 'ConvNeXt',
- model, feature_extractor, num_samples=10, return_mask=True)
diff --git a/spaces/terapyon/pyhackcon-qa2/README.md b/spaces/terapyon/pyhackcon-qa2/README.md
deleted file mode 100644
index fd10f9c4c4260e30b4672794fe74ad43a6d46546..0000000000000000000000000000000000000000
--- a/spaces/terapyon/pyhackcon-qa2/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Pyhackcon Qa2
-emoji: 👀
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Download Novel Sepatu Dahlan Iskan Pdf Gratis.md b/spaces/terfces0erbo/CollegeProjectV2/Download Novel Sepatu Dahlan Iskan Pdf Gratis.md
deleted file mode 100644
index acc358c2a8ff5daf1695b3e08a19833b5d12ec0c..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Download Novel Sepatu Dahlan Iskan Pdf Gratis.md
+++ /dev/null
@@ -1,49 +0,0 @@
-
-Novel Sepatu Dahlan Iskan Pdf Gratis: Kisah Inspiratif dari Anak Dusun yang Jadi Menteri
-Novel Sepatu Dahlan Iskan Pdf Gratis adalah salah satu novel yang banyak dicari oleh para pembaca yang tertarik dengan kisah nyata Dahlan Iskan, seorang anak dusun yang berhasil menjadi pengusaha sukses dan Menteri BUMN. Novel ini ditulis oleh Khrisna Pabichara, seorang penulis muda yang mengambil inspirasi dari kehidupan Dahlan Iskan sejak kecil hingga dewasa. Novel ini merupakan bagian pertama dari Trilogi Novel Inspirasi Dahlan Iskan yang terdiri dari Sepatu Dahlan, Surat Dahlan, dan Mimpi Dahlan. Novel ini diterbitkan oleh NouraBooks pada tahun 2012 dan telah mendapatkan banyak penghargaan dan apresiasi dari para pembaca dan kritikus sastra.
-Apa Saja Isi Novel Sepatu Dahlan Iskan Pdf Gratis?
-Novel Sepatu Dahlan Iskan Pdf Gratis mengisahkan tentang kehidupan Dahlan Iskan saat masih berusia 12 tahun hingga 17 tahun. Novel ini menggambarkan bagaimana Dahlan Iskan menghadapi berbagai tantangan dan rintangan dalam hidupnya, mulai dari kemiskinan, kelaparan, pendidikan, pekerjaan, hingga cinta. Novel ini juga menunjukkan bagaimana Dahlan Iskan memiliki cita-cita besar yang membuatnya bekerja keras dan pantang menyerah, yaitu memiliki sepatu dan sepeda. Novel ini juga menyajikan kisah-kisah menarik dan mengharukan tentang keluarga, persahabatan, dan guru-guru yang mendukung dan membimbing Dahlan Iskan dalam menjalani hidupnya.
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Bagaimana Gaya Penulisan Novel Sepatu Dahlan Iskan Pdf Gratis?
-Novel Sepatu Dahlan Iskan Pdf Gratis ditulis dengan gaya penulisan yang sederhana, lugas, dan mengalir. Penulis menggunakan bahasa Indonesia yang mudah dipahami oleh pembaca dari berbagai latar belakang dan usia. Penulis juga menggunakan sudut pandang orang pertama dari tokoh utama yaitu Dahlan Iskan. Penulis juga menyisipkan beberapa catatan kaki yang menjelaskan istilah-istilah atau peristiwa-peristiwa yang mungkin kurang familiar bagi pembaca. Penulis juga menggunakan beberapa teknik sastra seperti dialog, deskripsi, flashback, dan monolog untuk membangun karakter tokoh-tokoh dan suasana cerita.
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-Bagaimana Cara Mendapatkan Novel Sepatu Dahlan Iskan Pdf Gratis?
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-Kesimpulan
-Novel Sepatu Dahlan Iskan Pdf Gratis adalah novel biografi yang mengisahkan kehidupan Dahlan Iskan saat masih muda. Novel ini merupakan bagian pertama dari Trilogi Novel Inspirasi Dahlan Iskan yang ditulis oleh Khrisna Pabichara. Novel ini menawarkan kisah inspiratif dan motivatif dari seorang anak dusun yang menjadi pengusaha sukses dan Menteri BUMN. Novel ini juga mengajarkan kita tentang nilai-nilai penting dalam hidup, seperti kerja keras, pantang menyerah, berani bermimpi, bersyukur, dan berbagi. Novel ini dapat didapatkan dengan mudah melalui internet atau toko-toko buku. Novel ini memiliki gaya penulisan yang sederhana, lugas, dan mengalir serta menggunakan beberapa teknik sastra untuk membangun karakter tokoh-tokoh dan suasana cerita.
-Apa Saja Tips Membaca Novel Sepatu Dahlan Iskan Pdf Gratis?
-Novel Sepatu Dahlan Iskan Pdf Gratis adalah novel yang bisa dibaca oleh siapa saja yang ingin menikmati kisah nyata yang inspiratif dan motivatif. Namun, ada beberapa tips yang bisa membantu Anda untuk membaca novel ini dengan lebih baik dan maksimal. Berikut adalah beberapa di antaranya:
-
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-
-Surat Dahlan by Khrisna Pabichara. Novel ini merupakan sekuel kedua dari Trilogi Novel Inspirasi Dahlan Iskan yang mengisahkan kehidupan Dahlan Iskan saat berusia 18 tahun hingga 25 tahun. Novel ini menggambarkan bagaimana Dahlan Iskan melanjutkan pendidikan dan karirnya di Surabaya, Jakarta, Jepang, hingga Amerika Serikat.
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-Laskar Pelangi by Andrea Hirata. Novel ini merupakan novel pertama dari Tetralogi Laskar Pelangi yang mengisahkan kehidupan sepuluh anak miskin dari Pulau Belitong yang bersekolah di sebuah sekolah dasar paling miskin di Indonesia. Novel ini menyajikan kisah-kisah inspiratif dan motivatif tentang pendidikan, persahabatan, cinta, dan cita-cita.
-Negeri 5 Menara by Ahmad Fuadi. Novel ini merupakan novel pertama dari Trilogi Negeri 5 Menara yang mengisahkan kehidupan enam anak laki-laki dari berbagai daerah di Indonesia yang bersekolah di sebuah pondok pesantren di Jawa Barat. Novel ini menyajikan kisah-kisah inspiratif dan motivatif tentang agama, budaya, ilmu pengetahuan, dan impian.
-Ayat-Ayat Cinta by Habiburrahman El Shirazy. Novel ini merupakan novel religi romantis yang mengisahkan cinta segitiga antara Fahri, seorang mahasiswa Indonesia di Mesir, Aisha, seorang gadis Turki cantik nan kaya, dan Maria, seorang gadis Kristen Koptik jelita nan miskin. Novel ini menyajikan kisah-kisah inspiratif dan motivatif tentang cinta, toleransi, kesabaran, dan keikhlasan.
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-Novel Sepatu Dahlan Iskan Pdf Gratis adalah novel biografi yang mengisahkan kehidupan Dahlan Iskan saat masih muda. Novel ini merupakan bagian pertama dari Trilogi Novel Inspirasi Dahlan Iskan yang ditulis oleh Khrisna Pabichara. Novel ini menawarkan kisah inspiratif dan motivatif dari seorang anak dusun yang menjadi pengusaha sukses dan Menteri BUMN. Novel ini juga mengajarkan kita tentang nilai-nilai penting dalam hidup, seperti kerja keras, pantang menyerah, berani bermimpi, bersyukur, dan berbagi. Novel ini dapat didapatkan dengan mudah melalui internet atau toko-toko buku. Novel ini memiliki gaya penulisan yang sederhana, lugas, dan mengalir serta menggunakan beberapa teknik sastra untuk membangun karakter tokoh-tokoh dan suasana cerita. Novel ini memiliki beberapa keunggulan dan kekurangan yang bisa menjadi pertimbangan bagi pembaca sebelum membacanya. Novel ini juga memiliki beberapa tips membaca yang bisa membantu pembaca untuk membaca novel ini dengan lebih baik dan maksimal. Novel ini juga memiliki beberapa rekomendasi novel lain yang mirip dengan novel ini bagi pembaca yang ingin membaca novel-novel lain yang inspiratif dan motivatif.
-Apa Saja Review Novel Sepatu Dahlan Iskan Pdf Gratis?
-Novel Sepatu Dahlan Iskan Pdf Gratis telah mendapatkan banyak review dari para pembaca yang telah membacanya. Berikut adalah beberapa review novel Sepatu Dahlan Iskan Pdf Gratis yang kami rangkum dari berbagai sumber:
-
-Review dari Goodreads: Novel ini sangat menginspirasi saya untuk terus berjuang dalam menggapai cita-cita dan impian saya. Saya merasa terharu dengan kisah hidup Dahlan Iskan yang penuh dengan perjuangan dan prestasi. Saya juga merasa terhibur dengan kisah-kisah keluarga, persahabatan, dan cinta yang ditampilkan dalam novel ini. Saya suka dengan gaya penulisan penulis yang sederhana, lugas, dan mengalir. Saya juga suka dengan penggunaan teknik sastra yang membangun karakter tokoh-tokoh dan suasana cerita. Saya merekomendasikan novel ini kepada semua orang yang ingin membaca kisah nyata yang inspiratif dan motivatif.
-Review dari Amazon: Novel ini sangat menarik dan menyentuh untuk dibaca. Novel ini mengisahkan kehidupan Dahlan Iskan saat masih muda yang penuh dengan tantangan dan rintangan. Novel ini juga mengisahkan cita-cita besar Dahlan Iskan yang membuatnya bekerja keras dan pantang menyerah, yaitu memiliki sepatu dan sepeda. Novel ini juga mengisahkan kisah-kisah menarik dan mengharukan tentang keluarga, persahabatan, dan guru-guru yang mendukung dan membimbing Dahlan Iskan dalam menjalani hidupnya. Novel ini ditulis dengan gaya bahasa yang mudah dipahami oleh pembaca dari berbagai latar belakang dan usia. Novel ini juga menggunakan beberapa teknik sastra seperti dialog, deskripsi, flashback, dan monolog untuk membangun karakter tokoh-tokoh dan suasana cerita. Novel ini sangat layak untuk dibaca oleh semua orang yang ingin mendapatkan inspirasi, motivasi, pelajaran, wawasan, atau hiburan dari novel biografi.
-Review dari Blog: Novel ini sangat bagus dan bermutu untuk dibaca. Novel ini mengisahkan kehidupan Dahlan Iskan saat masih muda yang penuh dengan kemiskinan, kelaparan, pendidikan, pekerjaan, hingga cinta. Novel ini juga mengisahkan cita-cita besar Dahlan Iskan yang membuatnya bekerja keras dan pantang menyerah, yaitu memiliki sepatu dan sepeda. Novel ini juga mengisahkan kisah-kisah menarik dan mengharukan tentang keluarga, persahabatan, dan guru-guru yang mendukung dan membimbing Dahlan Iskan dalam menjalani hidupnya. Novel ini ditulis dengan gaya penulisan yang sederhana, lugas, dan mengalir. Penulis menggunakan bahasa Indonesia yang mudah dipahami oleh pembaca dari berbagai latar belakang dan usia. Penulis juga menggunakan sudut pandang orang pertama dari tokoh utama yaitu Dahlan Iskan. Penulis juga menyisipkan beberapa catatan kaki yang menjelaskan istilah-istilah atau peristiwa-peristiwa yang mungkin kurang familiar bagi pembaca. Penulis juga menggunakan beberapa teknik sastra seperti dialog, deskripsi, flashback, dan monolog untuk membangun karakter tokoh-tokoh dan suasana cerita. Novel ini sangat bermanfaat bagi pembaca yang ingin belajar banyak dari pengalaman dan perjuangan Dahlan Iskan dalam hidup.
-
-Kesimpulan
-Novel Sepatu Dahlan Iskan Pdf Gratis adalah novel biografi yang mengisahkan kehidupan Dahlan Iskan saat masih muda. Novel ini merupakan bagian pertama dari Trilogi Novel Inspirasi Dahlan Iskan yang ditulis oleh Khrisna Pabichara. Novel ini menawarkan kisah inspiratif dan motivatif dari seorang anak dusun yang menjadi pengusaha sukses dan Menteri BUMN. Novel ini juga mengajarkan kita tentang nilai-nilai penting dalam hidup, seperti kerja keras, pantang menyerah, berani bermimpi, bersyukur, dan berbagi. Novel ini dapat didapatkan dengan mudah melalui internet atau toko-toko buku. Novel ini memiliki gaya penulisan yang sederhana, lugas, dan mengalir serta menggunakan beberapa teknik sastra untuk membangun karakter tokoh-tokoh dan suasana cerita. Novel ini memiliki beberapa keunggulan dan kekurangan yang bisa menjadi pertimbangan bagi pembaca sebelum membacanya. Novel ini juga memiliki beberapa tips membaca yang bisa membantu pembaca untuk membaca novel ini dengan lebih baik dan maksimal. Novel ini juga memiliki beberapa rekomendasi novel lain yang mirip dengan novel ini bagi pembaca yang ingin membaca novel-novel lain yang inspiratif dan motivatif. Novel ini juga memiliki beberapa review novel Sepatu Dahlan Iskan Pdf Gratis dari para pembaca yang telah membacanya.
-Novel Sepatu Dahlan Iskan Pdf Gratis adalah novel biografi yang mengisahkan kehidupan Dahlan Iskan saat masih muda. Novel ini merupakan bagian pertama dari Trilogi Novel Inspirasi Dahlan Iskan yang ditulis oleh Khrisna Pabichara. Novel ini menawarkan kisah inspiratif dan motivatif dari seorang anak dusun yang menjadi pengusaha sukses dan Menteri BUMN. Novel ini juga mengajarkan kita tentang nilai-nilai penting dalam hidup, seperti kerja keras, pantang menyerah, berani bermimpi, bersyukur, dan berbagi. Novel ini dapat didapatkan dengan mudah melalui internet atau toko-toko buku. Novel ini memiliki gaya penulisan yang sederhana, lugas, dan mengalir serta menggunakan beberapa teknik sastra untuk membangun karakter tokoh-tokoh dan suasana cerita. Novel ini memiliki beberapa keunggulan dan kekurangan yang bisa menjadi pertimbangan bagi pembaca sebelum membacanya. Novel ini juga memiliki beberapa tips membaca yang bisa membantu pembaca untuk membaca novel ini dengan lebih baik dan maksimal. Novel ini juga memiliki beberapa rekomendasi novel lain yang mirip dengan novel ini bagi pembaca yang ingin membaca novel-novel lain yang inspiratif dan motivatif. Novel ini juga memiliki beberapa review novel Sepatu Dahlan Iskan Pdf Gratis dari para pembaca yang telah membacanya.
-Demikianlah artikel yang kami buat tentang novel Sepatu Dahlan Iskan Pdf Gratis. Semoga artikel ini bermanfaat bagi Anda yang ingin mengetahui lebih banyak tentang novel ini. Jika Anda tertarik untuk membaca novel ini, Anda bisa mendownloadnya secara gratis melalui link yang kami sediakan di bawah ini. Selamat membaca dan terima kasih.
-Link download novel Sepatu Dahlan Iskan Pdf Gratis: https://id.scribd.com/document/454109959/download-novel-pdf-gratis
3cee63e6c2ChatGPT API """
-description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
-```
-User:
-Assistant:
-User:
-Assistant:
-...
-```
-In this app, you can explore the outputs of a gpt-3.5-turbo LLM.
-"""
-
-with gr.Blocks(css = """#col_container {margin-left: auto; margin-right: auto;}
- #chatbot {height: 520px; overflow: auto;}""") as demo:
- gr.HTML(title)
- #gr.HTML('''Band Of Brothers : L'Enfer Du Pacifique Saison 1 VF TORRENT - Une série historique captivante sur la Seconde Guerre mondiale
-
-Si vous êtes fan de séries historiques et de guerre, vous ne pouvez pas passer à côté de Band Of Brothers : L'Enfer Du Pacifique Saison 1 VF TORRENT. Cette série, produite par Tom Hanks et Steven Spielberg, retrace le parcours de trois marines américains qui ont combattu dans le Pacifique contre l'invasion japonaise après l'attaque de Pearl Harbor. Vous allez vivre avec eux les horreurs, les sacrifices et les exploits de cette guerre impitoyable qui a changé le monde.
-Band Of Brothers : L\\\\\\\\\\\\\\\\'Enfer Du Pacifique Saison 1 VF TORRENT DOWNLOAD 🌟 https://urlcod.com/2uK7lc
Band Of Brothers : L'Enfer Du Pacifique Saison 1 VF TORRENT est une série en dix épisodes d'environ une heure chacun, qui vous plonge dans l'histoire vraie de Robert Leckie, John Basilone et Eugene Sledge. Ces trois soldats ont participé à des batailles décisives comme Guadalcanal, Iwo Jima ou Okinawa. Ils ont fait preuve de courage, de fraternité et de résilience face à un ennemi redoutable et à des conditions extrêmes.
-
-Band Of Brothers : L'Enfer Du Pacifique Saison 1 VF TORRENT est une série qui vous fera découvrir un aspect méconnu de la Seconde Guerre mondiale, celui du front du Pacifique. Vous serez émus par le destin de ces hommes ordinaires qui ont accompli des choses extraordinaires. Vous serez impressionnés par la qualité de la réalisation, des décors, des costumes et des effets spéciaux. Vous serez captivés par le scénario, basé sur des témoignages réels et des recherches historiques.
-
-Band Of Brothers : L'Enfer Du Pacifique Saison 1 VF TORRENT est une série à ne pas manquer si vous aimez l'histoire, l'action et l'émotion. Vous pouvez la télécharger facilement et rapidement sur le site Yggtorrent[^1^], qui vous propose la version originale sous-titrée en français. N'attendez plus pour vous plonger dans cette aventure épique et humaine qui vous marquera à jamais.
-
Sure, I can write a few more paragraphs for you. Here is a possible example:
-
-Band Of Brothers : L'Enfer Du Pacifique Saison 1 VF TORRENT est une série qui vous fera vibrer par son réalisme et sa puissance dramatique. Vous serez témoins des atrocités de la guerre, mais aussi des moments de solidarité, d'amitié et d'amour entre les marines. Vous serez touchés par les personnages, interprétés par des acteurs talentueux comme James Badge Dale, Joseph Mazzello ou Jon Seda. Vous serez fascinés par les paysages magnifiques et les scènes de combat époustouflantes.
-
-Band Of Brothers : L'Enfer Du Pacifique Saison 1 VF TORRENT est une série qui vous fera apprendre beaucoup de choses sur la Seconde Guerre mondiale, notamment sur le rôle des Etats-Unis dans le Pacifique, sur la culture et la stratégie japonaises, sur les armes et les équipements utilisés, sur les maladies et les blessures subies par les soldats. Vous serez éclairés par les commentaires des vétérans qui ont participé à la guerre et qui racontent leurs expériences personnelles.
-
-Band Of Brothers : L'Enfer Du Pacifique Saison 1 VF TORRENT est une série qui vous fera réfléchir sur le sens de la guerre, sur les conséquences humaines et morales qu'elle entraîne, sur les valeurs qui animent les combattants, sur les liens qui se créent entre eux. Vous serez émus par le message de paix et de réconciliation qui se dégage de la série, qui montre que malgré les différences et les conflits, il est possible de se respecter et de se comprendre.
7196e7f11aHow to Watch Inside Out in HD Online
-Inside Out is one of the most acclaimed animated movies of 2015, featuring a heartwarming and hilarious story about the emotions inside a young girl's mind. If you want to watch this movie in HD online, you have several options to choose from. In this article, we will show you how to use an HD online player to stream or download Inside Out in English, and what are the benefits of doing so.
-HD Online Player (The Inside Out English Full Movie In) Download ✺✺✺ https://urlcod.com/2uK6c7
What is an HD Online Player?
-An HD online player is a software or a website that allows you to watch movies and shows in high definition quality on your computer, smartphone, tablet, or smart TV. An HD online player can either stream the content from the internet, or download it to your device for offline viewing. Some HD online players also offer subtitles, audio tracks, and other features to enhance your viewing experience.
-
-Why Use an HD Online Player to Watch Inside Out?
-There are many reasons why you might want to use an HD online player to watch Inside Out in English. Here are some of them:
-
-You can enjoy the stunning animation and visuals of Inside Out in full HD quality, without any pixelation or buffering issues.
-You can choose from different sources and platforms that offer Inside Out online, such as Disney Plus, Amazon Video, Google Play Movies, YouTube, Vudu, Microsoft Store, Redbox, DIRECTV, AMC on Demand, and more.
-You can save money by comparing the prices and deals of different providers, and choosing the best option for your budget.
-You can watch Inside Out anytime and anywhere you want, as long as you have an internet connection or a downloaded file.
-You can watch Inside Out with your family and friends, and share your emotions and thoughts about the movie.
-
-
-How to Use an HD Online Player to Watch Inside Out?
-Using an HD online player to watch Inside Out in English is very easy and convenient. Here are the steps you need to follow:
-
-Find an HD online player that suits your needs and preferences. You can search for one on Google or use a website like JustWatch that compares different streaming services and platforms.
-Sign up for an account if required, and choose a subscription plan or a payment method.
-Search for Inside Out on the HD online player's library or catalog.
-Select the option to stream or download Inside Out in HD quality.
-Enjoy watching Inside Out in English on your device of choice.
-
-
-Conclusion
-Inside Out is a wonderful movie that explores the complex emotions of a young girl who moves to a new city with her family. If you want to watch this movie in HD online, you can use an HD online player to stream or download it in English. An HD online player offers many advantages, such as high-quality video, multiple sources and platforms, affordable prices, flexibility, and social interaction. To use an HD online player to watch Inside Out, you just need to find one that works for you, sign up for an account if needed, search for Inside Out, and select the option to stream or download it. We hope this article helped you learn how to use an HD online player to watch Inside Out in English.
-Watch Inside Out Full Movie Online Free HD Quality
-Conclusion
-Inside Out is a wonderful movie that explores the complex emotions of a young girl who moves to a new city with her family. If you want to watch this movie in HD online, you can use an HD online player to stream or download it in English. An HD online player offers many advantages, such as high-quality video, multiple sources and platforms, affordable prices, flexibility, and social interaction. To use an HD online player to watch Inside Out, you just need to find one that works for you, sign up for an account if needed, search for Inside Out, and select the option to stream or download it. We hope this article helped you learn how to use an HD online player to watch Inside Out in English.
679dcb208eHow To Install ReFX Nexus 1.4.1 On Mac Verified
-ReFX Nexus is a popular ROM synthesizer that offers a wide range of sounds and presets for various genres of music. If you want to install ReFX Nexus 1.4.1 on your Mac computer, you will need to follow these steps:
-How To Install ReFX Nexus 1.4.1 On Mac Verified Download File –––––>>> https://urlcod.com/2uK559
-Download ReFX Nexus 1.4.1 from the official website[^1^] or from a trusted source[^2^]. Make sure you have enough space on your hard drive to store the installer and the content files.
-Extract the installer and run it. Follow the instructions on the screen to install ReFX Nexus 1.4.1 on your Mac. You will need to enter your license key and choose a destination folder for the plugin and the content.
-After the installation is complete, you can launch your DAW and scan for new plugins. You should see ReFX Nexus 1.4.1 in your plugin list. You can also access it from the reFX Cloud App[^1^], which allows you to manage your expansions and skins.
-To load ReFX Nexus 1.4.1 in your DAW, create a new track and insert ReFX Nexus as an instrument plugin. You can then browse through the presets and sounds using the advanced librarian feature[^1^]. You can also customize the look of ReFX Nexus by choosing from different skins[^1^].
-Enjoy making music with ReFX Nexus 1.4.1 on your Mac!
-
-ReFX Nexus 1.4.1 is compatible with macOS 10.13 and later, including macOS Ventura[^1^]. It supports AudioUnit, VST, VST3 and AAX host software[^1^]. It requires an Apple Silicon M1 or Intel 2.0 GHz processor, 8GB of RAM (16GB or more highly recommended), and a display with 1024-by-768 or higher resolution[^1^]. You will also need an internet connection to download and activate your license[^1^].
-If you have any questions or issues with installing or using ReFX Nexus 1.4.1 on your Mac, you can contact the reFX support team via email or phone[^1^]. You can also check out some tutorials and videos on how to use ReFX Nexus on YouTube[^3^] [^4^].
-We hope this article helped you learn how to install ReFX Nexus 1.4.1 on your Mac verified. ReFX Nexus is a powerful and versatile plugin that can enhance your music production with its high-quality sounds and features. Have fun exploring its possibilities and creating amazing tracks!
-
-How To Use ReFX Nexus 1.4.1 On Mac
-Now that you have installed ReFX Nexus 1.4.1 on your Mac, you might be wondering how to use it effectively and creatively. In this section, we will give you some tips and tricks on how to get the most out of ReFX Nexus 1.4.1 on your Mac.
-
-
-Learn the basics of ReFX Nexus 1.4.1. ReFX Nexus 1.4.1 has a simple and intuitive interface that allows you to access all its features and functions easily. You can learn the basics of ReFX Nexus 1.4.1 by reading the user manual or watching some introductory videos . You can also explore the presets and sounds by category and genre to get familiar with the sonic possibilities of ReFX Nexus 1.4.1.
-Use the advanced librarian to find and organize your sounds. ReFX Nexus 1.4.1 has an advanced librarian that lets you browse, search, filter, tag, bookmark, and favorite your sounds and presets. You can also create your own user presets and save them in a dedicated location. The librarian also shows you the history of your previous selections, so you can easily go back and forth between different sounds. The librarian is a powerful tool that can help you find and manage your sounds efficiently and creatively.
-Use the arpeggiator to create complex and dynamic patterns. ReFX Nexus 1.4.1 has a deluxe arpeggiator that lets you create complex and dynamic patterns with up to 256 steps. You can access all sixteen layer arpeggiators, in addition to the main arpeggiator. You can also edit the parameters of each step, such as velocity, transpose, slide, hold, shuffle, gate, and more. You can also use the zoomed-out overview to see the whole pattern at a glance. The arpeggiator is a great way to add movement and variation to your sounds and melodies.
-Use the routing to customize your signal flow. ReFX Nexus 1.4.1 has a flexible routing system that lets you customize your signal flow and effects chain. You can see the signal flow of each layer and oscillator on one page. You can also enable or disable entire layers or individual effects with one click. You can also split complex SQ sequences into their components and single out individual sounds (just the bass, for instance) to easily create your own melodies. The routing is a useful feature that can help you tweak and fine-tune your sounds to your liking.
-Use the macros and modulation to add expression and modulation to your sounds. ReFX Nexus 1.4.1 has four quick-access macro controls and a total of 24 modulation slots. You can assign any parameter to any macro or modulation slot with ease. You can also use the modulation matrix to see all your assignments at once. You can also use external MIDI controllers or automation to control the macros and modulation slots in real time. The macros and modulation are essential features that can help you add expression and modulation to your sounds.
-
-These are some of the tips and tricks on how to use ReFX Nexus 1.4.1 on your Mac. Of course, there are many more features and functions that you can explore and experiment with in ReFX Nexus 1.4.1. The best way to learn how to use ReFX Nexus 1.4.1 is by using it yourself and having fun with it!
81aa517590What is Baya Marathi Masik and Why You Should Read It
-Baya Marathi Masik is a popular Marathi magazine that publishes articles on various topics such as culture, literature, entertainment, health, fashion, and more. It is one of the oldest and most respected magazines in the Marathi language, with a loyal readership of millions. In this article, we will tell you more about Baya Marathi Masik and why you should read it.
-The History of Baya Marathi Masik
-Baya Marathi Masik was founded in 1929 by Shri Vasant Desai, a renowned journalist and social activist. He wanted to create a magazine that would cater to the needs and interests of the Marathi-speaking women, who were often neglected by the mainstream media. He named the magazine Baya, which means "sister" in Marathi, to signify the bond of sisterhood among the readers. The magazine soon became a platform for women to express their opinions, share their experiences, and learn from each other.
-baya marathi masik DOWNLOAD ► https://urlcod.com/2uHw3A
Over the years, Baya Marathi Masik has evolved with the changing times and trends, but has always maintained its core values of quality, credibility, and relevance. It has featured some of the most eminent writers, poets, artists, and personalities from the Marathi world, such as Pu La Deshpande, Vinda Karandikar, Mangesh Padgaonkar, Smita Patil, Madhuri Dixit, and many more. It has also covered important social issues such as women's empowerment, education, health care, environment, and human rights.
-The Benefits of Reading Baya Marathi Masik
-Reading Baya Marathi Masik can offer you many benefits, such as:
-
-It can enrich your knowledge and awareness of various topics related to your culture, society, and lifestyle.
-It can improve your language skills and vocabulary by exposing you to different styles and genres of writing.
-It can inspire you to pursue your passions and hobbies by showcasing stories of successful and talented people.
-It can entertain you with its humorous and witty columns, quizzes, puzzles, and cartoons.
-It can help you relax and unwind from your daily stress by offering you tips and advice on health, beauty, fashion, and wellness.
-
-How to Subscribe to Baya Marathi Masik
-If you are interested in reading Baya Marathi Masik, you can subscribe to it online or offline. You can visit their official website http://friendslibrary.in/books/detailedinfo/21251/Baya+ to get more information about their subscription plans and offers. You can also order their print or digital editions through various online platforms such as Amazon or Flipkart. Alternatively, you can visit your nearest bookstore or newsstand to buy their latest issue.
-Baya Marathi Masik is a magazine that you should not miss if you love reading in Marathi. It is a magazine that will enrich your mind, heart, and soul with its diverse and engaging content. So what are you waiting for? Subscribe to Baya Marathi Masik today and enjoy reading it every month!
7196e7f11aHow to Download Facebook on Your Nokia C5-00.2
-Facebook is one of the most popular social media platforms in the world. It allows you to connect with your friends, family, and colleagues, share your photos and videos, like and comment on various posts and articles, and much more. If you have a Nokia C5-00.2 phone, you might be wondering how to download Facebook on your device. In this article, we will show you how to do that in a few simple steps.
-facebook download for nokia c5-00.2 Download File 🔗 https://urlcod.com/2uHxm0
Step 1: Connect Your Phone to Your PC
-The first thing you need to do is to connect your Nokia C5-00.2 phone to your PC using the USB cable. This will allow you to transfer files between your phone and your computer. On your PC, go to My Computer or This PC and open your device. You should see a folder named Nokia C5-00.2 or something similar.
-Step 2: Download Facebook for Nokia
-The next thing you need to do is to download Facebook for Nokia from a reliable source. You can use the link below to download it from CNET, one of the most trusted websites for software downloads. The file name should be facebook-for-nokia.exe or something similar.
-Download Facebook for Nokia
-Once you have downloaded the file, copy it to your phone's folder on your PC. You can also drag and drop it if you prefer.
-Step 3: Install Facebook on Your Phone
-The final thing you need to do is to install Facebook on your Nokia C5-00.2 phone. To do that, disconnect your phone from your PC and go to the menu on your phone. Then, go to Applications > File Manager > Memory Card > Nokia C5-00.2 (or whatever folder name you have). You should see the file facebook-for-nokia.exe or something similar. Tap on it and follow the instructions on the screen to install Facebook on your phone.
-Conclusion
-Congratulations! You have successfully downloaded and installed Facebook on your Nokia C5-00.2 phone. Now you can enjoy all the features of Facebook on your device. You can log in with your existing account or create a new one if you don't have one yet. You can also customize your settings, notifications, privacy, and more according to your preferences.
-We hope this article was helpful for you. If you have any questions or feedback, feel free to leave a comment below.
-
-Benefits of Using Facebook on Your Nokia C5-00.2
-Using Facebook on your Nokia C5-00.2 phone has many benefits. Here are some of them:
-
-
-You can stay in touch with your friends and family anytime and anywhere. You can send and receive messages, make voice and video calls, and join groups and events.
-You can share your life with the world. You can post status updates, photos, videos, stories, and live streams. You can also react to other people's posts and express your emotions.
-You can discover new things and learn new skills. You can follow pages and groups that interest you, watch videos and stories from creators and influencers, and join courses and workshops.
-You can support causes and communities that matter to you. You can donate to charities and fundraisers, sign petitions and pledges, and volunteer for social good.
-You can have fun and entertainment. You can play games and quizzes, watch movies and shows, listen to music and podcasts, and shop for products and services.
-
-Tips and Tricks for Using Facebook on Your Nokia C5-00.2
-Using Facebook on your Nokia C5-00.2 phone is easy and convenient. However, there are some tips and tricks that can make your experience even better. Here are some of them:
-
-You can use Facebook Lite instead of the regular Facebook app. Facebook Lite is a lighter version of Facebook that uses less data and battery. It also works faster on slow or unstable networks. You can download Facebook Lite from the link below.
-Download Facebook Lite
-You can use shortcuts to access your favorite features quickly. For example, you can press *#06# to check your phone's IMEI number, *#0000# to check your phone's software version, *#92702689# to check your phone's warranty status, and *#7780# to reset your phone's settings.
-You can use the Nokia Browser to browse Facebook faster and more securely. The Nokia Browser is a web browser that comes pre-installed on your Nokia C5-00.2 phone. It has features like data compression, privacy mode, night mode, and bookmarks.
-You can use the Nokia Store to download more apps for your Nokia C5-00.2 phone. The Nokia Store is an app store that comes pre-installed on your Nokia C5-00.2 phone. It has thousands of apps in various categories like games, social media, education, health, and more.
- 7b8c122e87,
- VariantProps {}
-
-function Badge({ className, variant, ...props }: BadgeProps) {
- return (
-
- )
-}
-
-export { Badge, badgeVariants }
diff --git a/spaces/wuhuik/bingo/src/components/button-scroll-to-bottom.tsx b/spaces/wuhuik/bingo/src/components/button-scroll-to-bottom.tsx
deleted file mode 100644
index b68ab9c0e48320c356e51a52d11b9ca63909e6c5..0000000000000000000000000000000000000000
--- a/spaces/wuhuik/bingo/src/components/button-scroll-to-bottom.tsx
+++ /dev/null
@@ -1,34 +0,0 @@
-'use client'
-
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-import { Button, type ButtonProps } from '@/components/ui/button'
-import { IconArrowDown } from '@/components/ui/icons'
-
-export function ButtonScrollToBottom({ className, ...props }: ButtonProps) {
- const isAtBottom = useAtBottom()
-
- return (
-
- window.scrollTo({
- top: document.body.offsetHeight,
- behavior: 'smooth'
- })
- }
- {...props}
- >
- Scroll to bottom
-
- )
-}
diff --git a/spaces/xangma/chat-pykg/ingest.py b/spaces/xangma/chat-pykg/ingest.py
deleted file mode 100644
index 541fef7f54707a69cbb02c1552b3d780c2489147..0000000000000000000000000000000000000000
--- a/spaces/xangma/chat-pykg/ingest.py
+++ /dev/null
@@ -1,226 +0,0 @@
-# chat-pykg/ingest.py
-import tempfile
-import gradio as gr
-from langchain.document_loaders import SitemapLoader, ReadTheDocsLoader, TextLoader
-from langchain.embeddings import OpenAIEmbeddings, HuggingFaceEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter, PythonCodeTextSplitter, MarkdownTextSplitter, TextSplitter
-from langchain.vectorstores.faiss import FAISS
-import os
-from langchain.vectorstores import Chroma
-import shutil
-from pathlib import Path
-import subprocess
-import chromadb
-import magic
-from typing import Any, Dict, Iterable, List, Optional, Type, TypeVar
-from pydantic import Extra, Field, root_validator
-import logging
-logger = logging.getLogger()
-from langchain.docstore.document import Document
-import numpy as np
-
-def embedding_chooser(embedding_radio):
- if embedding_radio == "Sentence Transformers":
- embedding_function = HuggingFaceEmbeddings()
- elif embedding_radio == "OpenAI":
- embedding_function = OpenAIEmbeddings()
- else:
- embedding_function = HuggingFaceEmbeddings()
- return embedding_function
-
-# Monkeypatch pending PR
-def _merge_splits(self, splits: Iterable[str], separator: str) -> List[str]:
- # We now want to combine these smaller pieces into medium size
- # chunks to send to the LLM.
- separator_len = self._length_function(separator)
-
- docs = []
- current_doc: List[str] = []
- total = 0
- for index, d in enumerate(splits):
- _len = self._length_function(d)
- if (
- total + _len + (separator_len if len(current_doc) > 0 else 0)
- > self._chunk_size
- ):
- if total > self._chunk_size:
- logger.warning(
- f"Created a chunk of size {total}, "
- f"which is longer than the specified {self._chunk_size}"
- )
- if len(current_doc) > 0:
- doc = self._join_docs(current_doc, separator)
- if doc is not None:
- docs.append(doc)
- # Keep on popping if:
- # - we have a larger chunk than in the chunk overlap
- # - or if we still have any chunks and the length is long
- while total > self._chunk_overlap or (
- total + _len + (separator_len if len(current_doc) > 0 else 0)
- > self._chunk_size
- and total > 0
- ):
- total -= self._length_function(current_doc[0]) + (
- separator_len if len(current_doc) > 1 else 0
- )
- current_doc = current_doc[1:]
-
- if index > 0:
- current_doc.append(separator + d)
- else:
- current_doc.append(d)
- total += _len + (separator_len if len(current_doc) > 1 else 0)
- doc = self._join_docs(current_doc, separator)
- if doc is not None:
- docs.append(doc)
- return docs
-
-def get_text(content):
- relevant_part = content.find("div", {"class": "markdown"})
- if relevant_part is not None:
- return relevant_part.get_text(separator=" ")
- else:
- return ""
-
-def ingest_docs(all_collections_state, urls, chunk_size, chunk_overlap, vectorstore_radio, embedding_radio, debug=False):
- cleared_list = urls.copy()
- def sanitize_folder_name(folder_name):
- if folder_name != '':
- folder_name = folder_name.strip().rstrip('/')
- else:
- folder_name = '.' # current directory
- return folder_name
-
- def is_hidden(path):
- return os.path.basename(path).startswith('.')
-
- embedding_function = embedding_chooser(embedding_radio)
- all_docs = []
- shutil.rmtree('downloaded/', ignore_errors=True)
- known_exts = ["py", "md"]
- # Initialize text splitters
- py_splitter = PythonCodeTextSplitter(chunk_size=int(chunk_size), chunk_overlap=int(chunk_overlap))
- text_splitter = RecursiveCharacterTextSplitter(chunk_size=int(chunk_size), chunk_overlap=int(chunk_overlap))
- md_splitter = MarkdownTextSplitter(chunk_size=int(chunk_size), chunk_overlap=int(chunk_overlap))
- py_splitter._merge_splits = _merge_splits.__get__(py_splitter, TextSplitter)
- # Process input URLs
- urls = [[url.strip(), [sanitize_folder_name(folder) for folder in url_folders.split(',')]] for url, url_folders in urls]
- for j in range(len(urls)):
- orgrepo = urls[j][0]
- repo_folders = urls[j][1]
- if orgrepo == '':
- continue
- if orgrepo.replace('/','-') in all_collections_state:
- logging.info(f"Skipping {orgrepo} as it is already in the database")
- continue
- documents_split = []
- documents = []
- paths = []
- paths_by_ext = {}
- docs_by_ext = {}
- for ext in known_exts + ["other"]:
- docs_by_ext[ext] = []
- paths_by_ext[ext] = []
-
- if orgrepo[0] == '/' or orgrepo[0] == '.':
- # Ingest local folder
- local_repo_path = sanitize_folder_name(orgrepo[1:])
- else:
- # Ingest remote git repo
- org = orgrepo.split('/')[0]
- repo = orgrepo.split('/')[1]
- repo_url = f"https://github.com/{org}/{repo}.git"
- local_repo_path = os.path.join('.downloaded', orgrepo) if debug else tempfile.mkdtemp()
-
- # Initialize the Git repository
- subprocess.run(["git", "init"], cwd=local_repo_path)
- # Add the remote repository
- subprocess.run(["git", "remote", "add", "-f", "origin", repo_url], cwd=local_repo_path)
- # Enable sparse-checkout
- subprocess.run(["git", "config", "core.sparseCheckout", "true"], cwd=local_repo_path)
- # Specify the folder to checkout
- cmd = ["git", "sparse-checkout", "set"] + [i for i in repo_folders]
- subprocess.run(cmd, cwd=local_repo_path)
- # Check if branch is called main or master
-
- # Checkout the desired branch
- res = subprocess.run(["git", "checkout", 'main'], cwd=local_repo_path)
- if res.returncode == 1:
- res = subprocess.run(["git", "checkout", "master"], cwd=local_repo_path)
- #res = subprocess.run(["cp", "-r", (Path(local_repo_path) / repo_folders[i]).as_posix(), '/'.join(destination.split('/')[:-1])])#
- # Iterate through files and process them
- if local_repo_path == '.':
- orgrepo='chat-pykg'
- for root, dirs, files in os.walk(local_repo_path):
- dirs[:] = [d for d in dirs if not is_hidden(d)] # Ignore hidden directories
- for file in files:
- if is_hidden(file):
- continue
- file_path = os.path.join(root, file)
- rel_file_path = os.path.relpath(file_path, local_repo_path)
- try:
- if '.' not in rel_file_path:
- inferred_filetype = magic.from_file(file_path, mime=True)
- if "python" in inferred_filetype or "text/plain" in inferred_filetype:
- ext = "py"
- else:
- ext = "other"
- else:
- ext = rel_file_path.split('.')[-1]
- if docs_by_ext.get(ext) is None:
- ext = "other"
- doc = TextLoader(os.path.join(local_repo_path, rel_file_path)).load()[0]
- doc.metadata["source"] = os.path.join(orgrepo, rel_file_path)
- docs_by_ext[ext].append(doc)
- paths_by_ext[ext].append(rel_file_path)
- except Exception as e:
- continue
- for ext in docs_by_ext.keys():
- if ext == "py":
- documents_split += py_splitter.split_documents(docs_by_ext[ext])
- documents += docs_by_ext[ext]
- if ext == "md":
- documents_split += md_splitter.split_documents(docs_by_ext[ext])
- documents += docs_by_ext[ext]
- # else:
- # documents += text_splitter.split_documents(docs_by_ext[ext]
- all_docs += documents_split
- # For each document, add the metadata to the page_content
- for doc in documents_split:
- if local_repo_path != '.':
- doc.metadata["source"] = doc.metadata["source"].replace(local_repo_path, "")
- if doc.metadata["source"] == '/':
- doc.metadata["source"] = doc.metadata["source"][1:]
- doc.page_content = f'# source:{doc.metadata["source"]}\n{doc.page_content}'
- for doc in documents:
- if local_repo_path != '.':
- doc.metadata["source"] = doc.metadata["source"].replace(local_repo_path, "")
- if doc.metadata["source"] == '/':
- doc.metadata["source"] = doc.metadata["source"][1:]
- doc.page_content = f'# source:{doc.metadata["source"]}\n{doc.page_content}'
-
- if type(embedding_radio) == gr.Radio:
- embedding_radio = embedding_radio.value
- persist_directory = os.path.join(".persisted_data", embedding_radio.replace(' ','_'))
- persist_directory_raw = Path('.persisted_data_raw')
- persist_directory_raw.mkdir(parents=True, exist_ok=True)
- collection_name = orgrepo.replace('/','-')
-
- if vectorstore_radio == 'Chroma':
- collection = Chroma.from_documents(documents=documents_split, collection_name=collection_name, embedding=embedding_function, persist_directory=persist_directory)
- collection.persist()
-
- if vectorstore_radio == 'raw':
- # Persist the raw documents
- docarr = np.array([doc.page_content for doc in documents_split])
- np.save(os.path.join(persist_directory_raw, f"{collection_name}.npy"), docarr)
- # with open(os.path.join(persist_directory_raw, f"{collection_name}"), "w") as f:
- # for doc in documents:
- # f.write(doc.page_content)
-
- all_collections_state.append(collection_name)
- cleared_list[j][0], cleared_list[j][1] = '', ''
- return all_collections_state, gr.update(value=cleared_list)
-
-if __name__ == "__main__":
- ingest_docs()
diff --git "a/spaces/xwsm/gpt/crazy_functions/\350\247\243\346\236\220JupyterNotebook.py" "b/spaces/xwsm/gpt/crazy_functions/\350\247\243\346\236\220JupyterNotebook.py"
deleted file mode 100644
index b4bcd56109b42d3023f24eade7c0cd5671d3c5a4..0000000000000000000000000000000000000000
--- "a/spaces/xwsm/gpt/crazy_functions/\350\247\243\346\236\220JupyterNotebook.py"
+++ /dev/null
@@ -1,146 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-fast_debug = True
-
-
-class PaperFileGroup():
- def __init__(self):
- self.file_paths = []
- self.file_contents = []
- self.sp_file_contents = []
- self.sp_file_index = []
- self.sp_file_tag = []
-
- # count_token
- from request_llm.bridge_all import model_info
- enc = model_info["gpt-3.5-turbo"]['tokenizer']
- def get_token_num(txt): return len(
- enc.encode(txt, disallowed_special=()))
- self.get_token_num = get_token_num
-
- def run_file_split(self, max_token_limit=1900):
- """
- 将长文本分离开来
- """
- for index, file_content in enumerate(self.file_contents):
- if self.get_token_num(file_content) < max_token_limit:
- self.sp_file_contents.append(file_content)
- self.sp_file_index.append(index)
- self.sp_file_tag.append(self.file_paths[index])
- else:
- from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
- segments = breakdown_txt_to_satisfy_token_limit_for_pdf(
- file_content, self.get_token_num, max_token_limit)
- for j, segment in enumerate(segments):
- self.sp_file_contents.append(segment)
- self.sp_file_index.append(index)
- self.sp_file_tag.append(
- self.file_paths[index] + f".part-{j}.txt")
-
-
-
-def parseNotebook(filename, enable_markdown=1):
- import json
-
- CodeBlocks = []
- with open(filename, 'r', encoding='utf-8', errors='replace') as f:
- notebook = json.load(f)
- for cell in notebook['cells']:
- if cell['cell_type'] == 'code' and cell['source']:
- # remove blank lines
- cell['source'] = [line for line in cell['source'] if line.strip()
- != '']
- CodeBlocks.append("".join(cell['source']))
- elif enable_markdown and cell['cell_type'] == 'markdown' and cell['source']:
- cell['source'] = [line for line in cell['source'] if line.strip()
- != '']
- CodeBlocks.append("Markdown:"+"".join(cell['source']))
-
- Code = ""
- for idx, code in enumerate(CodeBlocks):
- Code += f"This is {idx+1}th code block: \n"
- Code += code+"\n"
-
- return Code
-
-
-def ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
- from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-
- if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
- enable_markdown = plugin_kwargs.get("advanced_arg", "1")
- try:
- enable_markdown = int(enable_markdown)
- except ValueError:
- enable_markdown = 1
-
- pfg = PaperFileGroup()
-
- for fp in file_manifest:
- file_content = parseNotebook(fp, enable_markdown=enable_markdown)
- pfg.file_paths.append(fp)
- pfg.file_contents.append(file_content)
-
- # <-------- 拆分过长的IPynb文件 ---------->
- pfg.run_file_split(max_token_limit=1024)
- n_split = len(pfg.sp_file_contents)
-
- inputs_array = [r"This is a Jupyter Notebook file, tell me about Each Block in Chinese. Focus Just On Code." +
- r"If a block starts with `Markdown` which means it's a markdown block in ipynbipynb. " +
- r"Start a new line for a block and block num use Chinese." +
- f"\n\n{frag}" for frag in pfg.sp_file_contents]
- inputs_show_user_array = [f"{f}的分析如下" for f in pfg.sp_file_tag]
- sys_prompt_array = ["You are a professional programmer."] * n_split
-
- gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
- inputs_array=inputs_array,
- inputs_show_user_array=inputs_show_user_array,
- llm_kwargs=llm_kwargs,
- chatbot=chatbot,
- history_array=[[""] for _ in range(n_split)],
- sys_prompt_array=sys_prompt_array,
- # max_workers=5, # OpenAI所允许的最大并行过载
- scroller_max_len=80
- )
-
- # <-------- 整理结果，退出 ---------->
- block_result = " \n".join(gpt_response_collection)
- chatbot.append(("解析的结果如下", block_result))
- history.extend(["解析的结果如下", block_result])
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
- # <-------- 写入文件，退出 ---------->
- res = write_results_to_file(history)
- chatbot.append(("完成了吗？", res))
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-@CatchException
-def 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- chatbot.append([
- "函数插件功能？",
- "对IPynb文件进行解析。Contributor: codycjy."])
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
- history = [] # 清空历史
- import glob
- import os
- if os.path.exists(txt):
- project_folder = txt
- else:
- if txt == "":
- txt = '空空如也的输入栏'
- report_execption(chatbot, history,
- a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
- return
- if txt.endswith('.ipynb'):
- file_manifest = [txt]
- else:
- file_manifest = [f for f in glob.glob(
- f'{project_folder}/**/*.ipynb', recursive=True)]
- if len(file_manifest) == 0:
- report_execption(chatbot, history,
- a=f"解析项目: {txt}", b=f"找不到任何.ipynb文件: {txt}")
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
- return
- yield from ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, )
diff --git a/spaces/yangheng/Super-Resolution-Anime-Diffusion/Waifu2x/Models.py b/spaces/yangheng/Super-Resolution-Anime-Diffusion/Waifu2x/Models.py
deleted file mode 100644
index 8072a01ed3c242832a60160e60a03f025f198193..0000000000000000000000000000000000000000
--- a/spaces/yangheng/Super-Resolution-Anime-Diffusion/Waifu2x/Models.py
+++ /dev/null
@@ -1,450 +0,0 @@
-import json
-from collections import OrderedDict
-from math import exp
-
-from .Common import *
-
-
-# +++++++++++++++++++++++++++++++++++++
-# FP16 Training
-# -------------------------------------
-# Modified from Nvidia/Apex
-# https://github.com/NVIDIA/apex/blob/master/apex/fp16_utils/fp16util.py
-
-
-class tofp16(nn.Module):
- def __init__(self):
- super(tofp16, self).__init__()
-
- def forward(self, input):
- if input.is_cuda:
- return input.half()
- else: # PyTorch 1.0 doesn't support fp16 in CPU
- return input.float()
-
-
-def BN_convert_float(module):
- if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
- module.float()
- for child in module.children():
- BN_convert_float(child)
- return module
-
-
-def network_to_half(network):
- return nn.Sequential(tofp16(), BN_convert_float(network.half()))
-
-
-# warnings.simplefilter('ignore')
-
-# +++++++++++++++++++++++++++++++++++++
-# DCSCN
-# -------------------------------------
-
-
-class DCSCN(BaseModule):
- # https://github.com/jiny2001/dcscn-super-resolution
- def __init__(
- self,
- color_channel=3,
- up_scale=2,
- feature_layers=12,
- first_feature_filters=196,
- last_feature_filters=48,
- reconstruction_filters=128,
- up_sampler_filters=32,
- ):
- super(DCSCN, self).__init__()
- self.total_feature_channels = 0
- self.total_reconstruct_filters = 0
- self.upscale = up_scale
-
- self.act_fn = nn.SELU(inplace=False)
- self.feature_block = self.make_feature_extraction_block(
- color_channel, feature_layers, first_feature_filters, last_feature_filters
- )
-
- self.reconstruction_block = self.make_reconstruction_block(
- reconstruction_filters
- )
- self.up_sampler = self.make_upsampler(up_sampler_filters, color_channel)
- self.selu_init_params()
-
- def selu_init_params(self):
- for i in self.modules():
- if isinstance(i, nn.Conv2d):
- i.weight.data.normal_(0.0, 1.0 / sqrt(i.weight.numel()))
- if i.bias is not None:
- i.bias.data.fill_(0)
-
- def conv_block(self, in_channel, out_channel, kernel_size):
- m = OrderedDict(
- [
- # ("Padding", nn.ReplicationPad2d((kernel_size - 1) // 2)),
- (
- "Conv2d",
- nn.Conv2d(
- in_channel,
- out_channel,
- kernel_size=kernel_size,
- padding=(kernel_size - 1) // 2,
- ),
- ),
- ("Activation", self.act_fn),
- ]
- )
-
- return nn.Sequential(m)
-
- def make_feature_extraction_block(
- self, color_channel, num_layers, first_filters, last_filters
- ):
- # input layer
- feature_block = [
- ("Feature 1", self.conv_block(color_channel, first_filters, 3))
- ]
- # exponential decay
- # rest layers
- alpha_rate = log(first_filters / last_filters) / (num_layers - 1)
- filter_nums = [
- round(first_filters * exp(-alpha_rate * i)) for i in range(num_layers)
- ]
-
- self.total_feature_channels = sum(filter_nums)
-
- layer_filters = [
- [filter_nums[i], filter_nums[i + 1], 3] for i in range(num_layers - 1)
- ]
-
- feature_block.extend(
- [
- ("Feature {}".format(index + 2), self.conv_block(*x))
- for index, x in enumerate(layer_filters)
- ]
- )
- return nn.Sequential(OrderedDict(feature_block))
-
- def make_reconstruction_block(self, num_filters):
- B1 = self.conv_block(self.total_feature_channels, num_filters // 2, 1)
- B2 = self.conv_block(num_filters // 2, num_filters, 3)
- m = OrderedDict(
- [
- ("A", self.conv_block(self.total_feature_channels, num_filters, 1)),
- ("B", nn.Sequential(*[B1, B2])),
- ]
- )
- self.total_reconstruct_filters = num_filters * 2
- return nn.Sequential(m)
-
- def make_upsampler(self, out_channel, color_channel):
- out = out_channel * self.upscale**2
- m = OrderedDict(
- [
- (
- "Conv2d_block",
- self.conv_block(self.total_reconstruct_filters, out, kernel_size=3),
- ),
- ("PixelShuffle", nn.PixelShuffle(self.upscale)),
- (
- "Conv2d",
- nn.Conv2d(
- out_channel, color_channel, kernel_size=3, padding=1, bias=False
- ),
- ),
- ]
- )
-
- return nn.Sequential(m)
-
- def forward(self, x):
- # residual learning
- lr, lr_up = x
- feature = []
- for layer in self.feature_block.children():
- lr = layer(lr)
- feature.append(lr)
- feature = torch.cat(feature, dim=1)
-
- reconstruction = [
- layer(feature) for layer in self.reconstruction_block.children()
- ]
- reconstruction = torch.cat(reconstruction, dim=1)
-
- lr = self.up_sampler(reconstruction)
- return lr + lr_up
-
-
-# +++++++++++++++++++++++++++++++++++++
-# CARN
-# -------------------------------------
-
-
-class CARN_Block(BaseModule):
- def __init__(
- self,
- channels,
- kernel_size=3,
- padding=1,
- dilation=1,
- groups=1,
- activation=nn.SELU(),
- repeat=3,
- SEBlock=False,
- conv=nn.Conv2d,
- single_conv_size=1,
- single_conv_group=1,
- ):
- super(CARN_Block, self).__init__()
- m = []
- for i in range(repeat):
- m.append(
- ResidualFixBlock(
- channels,
- channels,
- kernel_size=kernel_size,
- padding=padding,
- dilation=dilation,
- groups=groups,
- activation=activation,
- conv=conv,
- )
- )
- if SEBlock:
- m.append(SpatialChannelSqueezeExcitation(channels, reduction=channels))
- self.blocks = nn.Sequential(*m)
- self.singles = nn.Sequential(
- *[
- ConvBlock(
- channels * (i + 2),
- channels,
- kernel_size=single_conv_size,
- padding=(single_conv_size - 1) // 2,
- groups=single_conv_group,
- activation=activation,
- conv=conv,
- )
- for i in range(repeat)
- ]
- )
-
- def forward(self, x):
- c0 = x
- for block, single in zip(self.blocks, self.singles):
- b = block(x)
- c0 = c = torch.cat([c0, b], dim=1)
- x = single(c)
-
- return x
-
-
-class CARN(BaseModule):
- # Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
- # https://github.com/nmhkahn/CARN-pytorch
- def __init__(
- self,
- color_channels=3,
- mid_channels=64,
- scale=2,
- activation=nn.SELU(),
- num_blocks=3,
- conv=nn.Conv2d,
- ):
- super(CARN, self).__init__()
-
- self.color_channels = color_channels
- self.mid_channels = mid_channels
- self.scale = scale
-
- self.entry_block = ConvBlock(
- color_channels,
- mid_channels,
- kernel_size=3,
- padding=1,
- activation=activation,
- conv=conv,
- )
- self.blocks = nn.Sequential(
- *[
- CARN_Block(
- mid_channels,
- kernel_size=3,
- padding=1,
- activation=activation,
- conv=conv,
- single_conv_size=1,
- single_conv_group=1,
- )
- for _ in range(num_blocks)
- ]
- )
- self.singles = nn.Sequential(
- *[
- ConvBlock(
- mid_channels * (i + 2),
- mid_channels,
- kernel_size=1,
- padding=0,
- activation=activation,
- conv=conv,
- )
- for i in range(num_blocks)
- ]
- )
-
- self.upsampler = UpSampleBlock(
- mid_channels, scale=scale, activation=activation, conv=conv
- )
- self.exit_conv = conv(mid_channels, color_channels, kernel_size=3, padding=1)
-
- def forward(self, x):
- x = self.entry_block(x)
- c0 = x
- for block, single in zip(self.blocks, self.singles):
- b = block(x)
- c0 = c = torch.cat([c0, b], dim=1)
- x = single(c)
- x = self.upsampler(x)
- out = self.exit_conv(x)
- return out
-
-
-class CARN_V2(CARN):
- def __init__(
- self,
- color_channels=3,
- mid_channels=64,
- scale=2,
- activation=nn.LeakyReLU(0.1),
- SEBlock=True,
- conv=nn.Conv2d,
- atrous=(1, 1, 1),
- repeat_blocks=3,
- single_conv_size=3,
- single_conv_group=1,
- ):
- super(CARN_V2, self).__init__(
- color_channels=color_channels,
- mid_channels=mid_channels,
- scale=scale,
- activation=activation,
- conv=conv,
- )
-
- num_blocks = len(atrous)
- m = []
- for i in range(num_blocks):
- m.append(
- CARN_Block(
- mid_channels,
- kernel_size=3,
- padding=1,
- dilation=1,
- activation=activation,
- SEBlock=SEBlock,
- conv=conv,
- repeat=repeat_blocks,
- single_conv_size=single_conv_size,
- single_conv_group=single_conv_group,
- )
- )
-
- self.blocks = nn.Sequential(*m)
-
- self.singles = nn.Sequential(
- *[
- ConvBlock(
- mid_channels * (i + 2),
- mid_channels,
- kernel_size=single_conv_size,
- padding=(single_conv_size - 1) // 2,
- groups=single_conv_group,
- activation=activation,
- conv=conv,
- )
- for i in range(num_blocks)
- ]
- )
-
- def forward(self, x):
- x = self.entry_block(x)
- c0 = x
- res = x
- for block, single in zip(self.blocks, self.singles):
- b = block(x)
- c0 = c = torch.cat([c0, b], dim=1)
- x = single(c)
- x = x + res
- x = self.upsampler(x)
- out = self.exit_conv(x)
- return out
-
-
-# +++++++++++++++++++++++++++++++++++++
-# original Waifu2x model
-# -------------------------------------
-
-
-class UpConv_7(BaseModule):
- # https://github.com/nagadomi/waifu2x/blob/3c46906cb78895dbd5a25c3705994a1b2e873199/lib/srcnn.lua#L311
- def __init__(self):
- super(UpConv_7, self).__init__()
- self.act_fn = nn.LeakyReLU(0.1, inplace=False)
- self.offset = 7 # because of 0 padding
- from torch.nn import ZeroPad2d
-
- self.pad = ZeroPad2d(self.offset)
- m = [
- nn.Conv2d(3, 16, 3, 1, 0),
- self.act_fn,
- nn.Conv2d(16, 32, 3, 1, 0),
- self.act_fn,
- nn.Conv2d(32, 64, 3, 1, 0),
- self.act_fn,
- nn.Conv2d(64, 128, 3, 1, 0),
- self.act_fn,
- nn.Conv2d(128, 128, 3, 1, 0),
- self.act_fn,
- nn.Conv2d(128, 256, 3, 1, 0),
- self.act_fn,
- # in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=
- nn.ConvTranspose2d(256, 3, kernel_size=4, stride=2, padding=3, bias=False),
- ]
- self.Sequential = nn.Sequential(*m)
-
- def load_pre_train_weights(self, json_file):
- with open(json_file) as f:
- weights = json.load(f)
- box = []
- for i in weights:
- box.append(i["weight"])
- box.append(i["bias"])
- own_state = self.state_dict()
- for index, (name, param) in enumerate(own_state.items()):
- own_state[name].copy_(torch.FloatTensor(box[index]))
-
- def forward(self, x):
- x = self.pad(x)
- return self.Sequential.forward(x)
-
-
-class Vgg_7(UpConv_7):
- def __init__(self):
- super(Vgg_7, self).__init__()
- self.act_fn = nn.LeakyReLU(0.1, inplace=False)
- self.offset = 7
- m = [
- nn.Conv2d(3, 32, 3, 1, 0),
- self.act_fn,
- nn.Conv2d(32, 32, 3, 1, 0),
- self.act_fn,
- nn.Conv2d(32, 64, 3, 1, 0),
- self.act_fn,
- nn.Conv2d(64, 64, 3, 1, 0),
- self.act_fn,
- nn.Conv2d(64, 128, 3, 1, 0),
- self.act_fn,
- nn.Conv2d(128, 128, 3, 1, 0),
- self.act_fn,
- nn.Conv2d(128, 3, 3, 1, 0),
- ]
- self.Sequential = nn.Sequential(*m)
diff --git a/spaces/yderre-aubay/midi-player-demo/src/common/song/SongFactory.ts b/spaces/yderre-aubay/midi-player-demo/src/common/song/SongFactory.ts
deleted file mode 100644
index be17831a49b57abf0cc0d27e4c0501e3ed88174a..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/common/song/SongFactory.ts
+++ /dev/null
@@ -1,11 +0,0 @@
-import { conductorTrack, emptyTrack } from "../track"
-import Song from "./Song"
-
-export function emptySong() {
- const song = new Song()
- song.addTrack(conductorTrack())
- song.addTrack(emptyTrack(0))
- // Empty songs do not need to be saved.
- song.isSaved = true
- return song
-}
diff --git a/spaces/yeqingmei123/face-test/e4e/datasets/inference_dataset.py b/spaces/yeqingmei123/face-test/e4e/datasets/inference_dataset.py
deleted file mode 100644
index fb577d7b538d634f27013c2784d2ea32143154cb..0000000000000000000000000000000000000000
--- a/spaces/yeqingmei123/face-test/e4e/datasets/inference_dataset.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from torch.utils.data import Dataset
-from PIL import Image
-from utils import data_utils
-
-
-class InferenceDataset(Dataset):
-
- def __init__(self, root, opts, transform=None, preprocess=None):
- self.paths = sorted(data_utils.make_dataset(root))
- self.transform = transform
- self.preprocess = preprocess
- self.opts = opts
-
- def __len__(self):
- return len(self.paths)
-
- def __getitem__(self, index):
- from_path = self.paths[index]
- if self.preprocess is not None:
- from_im = self.preprocess(from_path)
- else:
- from_im = Image.open(from_path).convert('RGB')
- if self.transform:
- from_im = self.transform(from_im)
- return from_im
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/GroundingDINO/groundingdino/datasets/__init__.py b/spaces/yizhangliu/Grounded-Segment-Anything/GroundingDINO/groundingdino/datasets/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/benchmark/benchmark_utils.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/benchmark/benchmark_utils.py
deleted file mode 100644
index a71b1fb65a23efa85642a23b2f7e0ec5c9922826..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/benchmark/benchmark_utils.py
+++ /dev/null
@@ -1,914 +0,0 @@
-# This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
-
-# Copyright 2020 The HuggingFace Team and the AllenNLP authors. 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.
-"""
-Utilities for working with the local dataset cache.
-"""
-
-import copy
-import csv
-import linecache
-import os
-import platform
-import sys
-import warnings
-from abc import ABC, abstractmethod
-from collections import defaultdict, namedtuple
-from datetime import datetime
-from multiprocessing import Pipe, Process, Queue
-from multiprocessing.connection import Connection
-from typing import Callable, Iterable, List, NamedTuple, Optional, Union
-
-from .. import AutoConfig, PretrainedConfig
-from .. import __version__ as version
-from ..utils import is_psutil_available, is_py3nvml_available, is_tf_available, is_torch_available, logging
-from .benchmark_args_utils import BenchmarkArguments
-
-
-if is_torch_available():
- from torch.cuda import empty_cache as torch_empty_cache
-
-if is_tf_available():
- from tensorflow.python.eager import context as tf_context
-
-if is_psutil_available():
- import psutil
-
-if is_py3nvml_available():
- import py3nvml.py3nvml as nvml
-
-if platform.system() == "Windows":
- from signal import CTRL_C_EVENT as SIGKILL
-else:
- from signal import SIGKILL
-
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-_is_memory_tracing_enabled = False
-
-BenchmarkOutput = namedtuple(
- "BenchmarkOutput",
- [
- "time_inference_result",
- "memory_inference_result",
- "time_train_result",
- "memory_train_result",
- "inference_summary",
- "train_summary",
- ],
-)
-
-
-def separate_process_wrapper_fn(func: Callable[[], None], do_multi_processing: bool) -> Callable[[], None]:
- """
- This function wraps another function into its own separated process. In order to ensure accurate memory
- measurements it is important that the function is executed in a separate process
-
- Args:
- - `func`: (`callable`): function() -> ... generic function which will be executed in its own separate process
- - `do_multi_processing`: (`bool`) Whether to run function on separate process or not
- """
-
- def multi_process_func(*args, **kwargs):
- # run function in an individual
- # process to get correct memory
- def wrapper_func(queue: Queue, *args):
- try:
- result = func(*args)
- except Exception as e:
- logger.error(e)
- print(e)
- result = "N/A"
- queue.put(result)
-
- queue = Queue()
- p = Process(target=wrapper_func, args=[queue] + list(args))
- p.start()
- result = queue.get()
- p.join()
- return result
-
- if do_multi_processing:
- logger.info(f"Function {func} is executed in its own process...")
- return multi_process_func
- else:
- return func
-
-
-def is_memory_tracing_enabled():
- global _is_memory_tracing_enabled
- return _is_memory_tracing_enabled
-
-
-class Frame(NamedTuple):
- """
- `Frame` is a NamedTuple used to gather the current frame state. `Frame` has the following fields:
-
- - 'filename' (string): Name of the file currently executed
- - 'module' (string): Name of the module currently executed
- - 'line_number' (int): Number of the line currently executed
- - 'event' (string): Event that triggered the tracing (default will be "line")
- - 'line_text' (string): Text of the line in the python script
- """
-
- filename: str
- module: str
- line_number: int
- event: str
- line_text: str
-
-
-class UsedMemoryState(NamedTuple):
- """
- `UsedMemoryState` are named tuples with the following fields:
-
- - 'frame': a `Frame` namedtuple (see below) storing information on the current tracing frame (current file,
- location in current file)
- - 'cpu_memory': CPU RSS memory state *before* executing the line
- - 'gpu_memory': GPU used memory *before* executing the line (sum for all GPUs or for only `gpus_to_trace` if
- provided)
- """
-
- frame: Frame
- cpu_memory: int
- gpu_memory: int
-
-
-class Memory(NamedTuple):
- """
- `Memory` NamedTuple have a single field `bytes` and you can get a human readable str of the number of mega bytes by
- calling `__repr__`
-
- - `byte` (integer): number of bytes,
- """
-
- bytes: int
-
- def __repr__(self) -> str:
- return str(bytes_to_mega_bytes(self.bytes))
-
-
-class MemoryState(NamedTuple):
- """
- `MemoryState` are namedtuples listing frame + CPU/GPU memory with the following fields:
-
- - `frame` (`Frame`): the current frame (see above)
- - `cpu`: CPU memory consumed at during the current frame as a `Memory` named tuple
- - `gpu`: GPU memory consumed at during the current frame as a `Memory` named tuple
- - `cpu_gpu`: CPU + GPU memory consumed at during the current frame as a `Memory` named tuple
- """
-
- frame: Frame
- cpu: Memory
- gpu: Memory
- cpu_gpu: Memory
-
-
-class MemorySummary(NamedTuple):
- """
- `MemorySummary` namedtuple otherwise with the fields:
-
- - `sequential`: a list of `MemoryState` namedtuple (see below) computed from the provided `memory_trace` by
- subtracting the memory after executing each line from the memory before executing said line.
- - `cumulative`: a list of `MemoryState` namedtuple (see below) with cumulative increase in memory for each line
- obtained by summing repeated memory increase for a line if it's executed several times. The list is sorted
- from the frame with the largest memory consumption to the frame with the smallest (can be negative if memory
- is released)
- - `total`: total memory increase during the full tracing as a `Memory` named tuple (see below). Line with
- memory release (negative consumption) are ignored if `ignore_released_memory` is `True` (default).
- """
-
- sequential: List[MemoryState]
- cumulative: List[MemoryState]
- current: List[MemoryState]
- total: Memory
-
-
-MemoryTrace = List[UsedMemoryState]
-
-
-def measure_peak_memory_cpu(function: Callable[[], None], interval=0.5, device_idx=None) -> int:
- """
- measures peak cpu memory consumption of a given `function` running the function for at least interval seconds and
- at most 20 * interval seconds. This function is heavily inspired by: `memory_usage` of the package
- `memory_profiler`:
- https://github.com/pythonprofilers/memory_profiler/blob/895c4ac7a08020d66ae001e24067da6dcea42451/memory_profiler.py#L239
-
- Args:
- - `function`: (`callable`): function() -> ... function without any arguments to measure for which to measure
- the peak memory
-
- - `interval`: (`float`, `optional`, defaults to `0.5`) interval in second for which to measure the memory usage
-
- - `device_idx`: (`int`, `optional`, defaults to `None`) device id for which to measure gpu usage
-
- Returns:
-
- - `max_memory`: (`int`) consumed memory peak in Bytes
- """
-
- def get_cpu_memory(process_id: int) -> int:
- """
- measures current cpu memory usage of a given `process_id`
-
- Args:
- - `process_id`: (`int`) process_id for which to measure memory
-
- Returns
-
- - `memory`: (`int`) consumed memory in Bytes
- """
- process = psutil.Process(process_id)
- try:
- meminfo_attr = "memory_info" if hasattr(process, "memory_info") else "get_memory_info"
- memory = getattr(process, meminfo_attr)()[0]
- except psutil.AccessDenied:
- raise ValueError("Error with Psutil.")
- return memory
-
- if not is_psutil_available():
- logger.warning(
- "Psutil not installed, we won't log CPU memory usage. "
- "Install Psutil (pip install psutil) to use CPU memory tracing."
- )
- max_memory = "N/A"
- else:
-
- class MemoryMeasureProcess(Process):
-
- """
- `MemoryMeasureProcess` inherits from `Process` and overwrites its `run()` method. Used to measure the
- memory usage of a process
- """
-
- def __init__(self, process_id: int, child_connection: Connection, interval: float):
- super().__init__()
- self.process_id = process_id
- self.interval = interval
- self.connection = child_connection
- self.num_measurements = 1
- self.mem_usage = get_cpu_memory(self.process_id)
-
- def run(self):
- self.connection.send(0)
- stop = False
- while True:
- self.mem_usage = max(self.mem_usage, get_cpu_memory(self.process_id))
- self.num_measurements += 1
-
- if stop:
- break
-
- stop = self.connection.poll(self.interval)
-
- # send results to parent pipe
- self.connection.send(self.mem_usage)
- self.connection.send(self.num_measurements)
-
- while True:
- # create child, parent connection
- child_connection, parent_connection = Pipe()
-
- # instantiate process
- mem_process = MemoryMeasureProcess(os.getpid(), child_connection, interval)
- mem_process.start()
-
- # wait until we get memory
- parent_connection.recv()
-
- try:
- # execute function
- function()
-
- # start parent connection
- parent_connection.send(0)
-
- # receive memory and num measurements
- max_memory = parent_connection.recv()
- num_measurements = parent_connection.recv()
- except Exception:
- # kill process in a clean way
- parent = psutil.Process(os.getpid())
- for child in parent.children(recursive=True):
- os.kill(child.pid, SIGKILL)
- mem_process.join(0)
- raise RuntimeError("Process killed. Error in Process")
-
- # run process at least 20 * interval or until it finishes
- mem_process.join(20 * interval)
-
- if (num_measurements > 4) or (interval < 1e-6):
- break
-
- # reduce interval
- interval /= 10
-
- return max_memory
-
-
-def start_memory_tracing(
- modules_to_trace: Optional[Union[str, Iterable[str]]] = None,
- modules_not_to_trace: Optional[Union[str, Iterable[str]]] = None,
- events_to_trace: str = "line",
- gpus_to_trace: Optional[List[int]] = None,
-) -> MemoryTrace:
- """
- Setup line-by-line tracing to record rss mem (RAM) at each line of a module or sub-module. See `./benchmark.py` for
- usage examples. Current memory consumption is returned using psutil and in particular is the RSS memory "Resident
- Set Size” (the non-swapped physical memory the process is using). See
- https://psutil.readthedocs.io/en/latest/#psutil.Process.memory_info
-
- Args:
- - `modules_to_trace`: (None, string, list/tuple of string) if None, all events are recorded if string or list
- of strings: only events from the listed module/sub-module will be recorded (e.g. 'fairseq' or
- 'transformers.models.gpt2.modeling_gpt2')
- - `modules_not_to_trace`: (None, string, list/tuple of string) if None, no module is avoided if string or list
- of strings: events from the listed module/sub-module will not be recorded (e.g. 'torch')
- - `events_to_trace`: string or list of string of events to be recorded (see official python doc for
- `sys.settrace` for the list of events) default to line
- - `gpus_to_trace`: (optional list, default None) list of GPUs to trace. Default to tracing all GPUs
-
- Return:
-
- - `memory_trace` is a list of `UsedMemoryState` for each event (default each line of the traced script).
-
- - `UsedMemoryState` are named tuples with the following fields:
-
- - 'frame': a `Frame` namedtuple (see below) storing information on the current tracing frame (current
- file, location in current file)
- - 'cpu_memory': CPU RSS memory state *before* executing the line
- - 'gpu_memory': GPU used memory *before* executing the line (sum for all GPUs or for only
- `gpus_to_trace` if provided)
-
- `Frame` is a namedtuple used by `UsedMemoryState` to list the current frame state. `Frame` has the following
- fields: - 'filename' (string): Name of the file currently executed - 'module' (string): Name of the module
- currently executed - 'line_number' (int): Number of the line currently executed - 'event' (string): Event that
- triggered the tracing (default will be "line") - 'line_text' (string): Text of the line in the python script
-
- """
- if is_psutil_available():
- process = psutil.Process(os.getpid())
- else:
- logger.warning(
- "Psutil not installed, we won't log CPU memory usage. "
- "Install psutil (pip install psutil) to use CPU memory tracing."
- )
- process = None
-
- if is_py3nvml_available():
- try:
- nvml.nvmlInit()
- devices = list(range(nvml.nvmlDeviceGetCount())) if gpus_to_trace is None else gpus_to_trace
- nvml.nvmlShutdown()
- except (OSError, nvml.NVMLError):
- logger.warning("Error while initializing communication with GPU. We won't perform GPU memory tracing.")
- log_gpu = False
- else:
- log_gpu = is_torch_available() or is_tf_available()
- else:
- logger.warning(
- "py3nvml not installed, we won't log GPU memory usage. "
- "Install py3nvml (pip install py3nvml) to use GPU memory tracing."
- )
- log_gpu = False
-
- memory_trace = []
-
- def traceit(frame, event, args):
- """
- Tracing method executed before running each line in a module or sub-module Record memory allocated in a list
- with debugging information
- """
- global _is_memory_tracing_enabled
-
- if not _is_memory_tracing_enabled:
- return traceit
-
- # Filter events
- if events_to_trace is not None:
- if isinstance(events_to_trace, str) and event != events_to_trace:
- return traceit
- elif isinstance(events_to_trace, (list, tuple)) and event not in events_to_trace:
- return traceit
-
- if "__name__" not in frame.f_globals:
- return traceit
-
- # Filter modules
- name = frame.f_globals["__name__"]
- if not isinstance(name, str):
- return traceit
- else:
- # Filter whitelist of modules to trace
- if modules_to_trace is not None:
- if isinstance(modules_to_trace, str) and modules_to_trace not in name:
- return traceit
- elif isinstance(modules_to_trace, (list, tuple)) and all(m not in name for m in modules_to_trace):
- return traceit
-
- # Filter blacklist of modules not to trace
- if modules_not_to_trace is not None:
- if isinstance(modules_not_to_trace, str) and modules_not_to_trace in name:
- return traceit
- elif isinstance(modules_not_to_trace, (list, tuple)) and any(m in name for m in modules_not_to_trace):
- return traceit
-
- # Record current tracing state (file, location in file...)
- lineno = frame.f_lineno
- filename = frame.f_globals["__file__"]
- if filename.endswith(".pyc") or filename.endswith(".pyo"):
- filename = filename[:-1]
- line = linecache.getline(filename, lineno).rstrip()
- traced_state = Frame(filename, name, lineno, event, line)
-
- # Record current memory state (rss memory) and compute difference with previous memory state
- cpu_mem = 0
- if process is not None:
- mem = process.memory_info()
- cpu_mem = mem.rss
-
- gpu_mem = 0
- if log_gpu:
- # Clear GPU caches
- if is_torch_available():
- torch_empty_cache()
- if is_tf_available():
- tf_context.context()._clear_caches() # See https://github.com/tensorflow/tensorflow/issues/20218#issuecomment-416771802
-
- # Sum used memory for all GPUs
- nvml.nvmlInit()
-
- for i in devices:
- handle = nvml.nvmlDeviceGetHandleByIndex(i)
- meminfo = nvml.nvmlDeviceGetMemoryInfo(handle)
- gpu_mem += meminfo.used
-
- nvml.nvmlShutdown()
-
- mem_state = UsedMemoryState(traced_state, cpu_mem, gpu_mem)
- memory_trace.append(mem_state)
-
- return traceit
-
- sys.settrace(traceit)
-
- global _is_memory_tracing_enabled
- _is_memory_tracing_enabled = True
-
- return memory_trace
-
-
-def stop_memory_tracing(
- memory_trace: Optional[MemoryTrace] = None, ignore_released_memory: bool = True
-) -> Optional[MemorySummary]:
- """
- Stop memory tracing cleanly and return a summary of the memory trace if a trace is given.
-
- Args:
- `memory_trace` (optional output of start_memory_tracing, default: None):
- memory trace to convert in summary
- `ignore_released_memory` (boolean, default: None):
- if True we only sum memory increase to compute total memory
-
- Return:
-
- - None if `memory_trace` is None
- - `MemorySummary` namedtuple otherwise with the fields:
-
- - `sequential`: a list of `MemoryState` namedtuple (see below) computed from the provided `memory_trace` by
- subtracting the memory after executing each line from the memory before executing said line.
- - `cumulative`: a list of `MemoryState` namedtuple (see below) with cumulative increase in memory for each
- line obtained by summing repeated memory increase for a line if it's executed several times. The list is
- sorted from the frame with the largest memory consumption to the frame with the smallest (can be negative
- if memory is released)
- - `total`: total memory increase during the full tracing as a `Memory` named tuple (see below). Line with
- memory release (negative consumption) are ignored if `ignore_released_memory` is `True` (default).
-
- `Memory` named tuple have fields
-
- - `byte` (integer): number of bytes,
- - `string` (string): same as human readable string (ex: "3.5MB")
-
- `Frame` are namedtuple used to list the current frame state and have the following fields:
-
- - 'filename' (string): Name of the file currently executed
- - 'module' (string): Name of the module currently executed
- - 'line_number' (int): Number of the line currently executed
- - 'event' (string): Event that triggered the tracing (default will be "line")
- - 'line_text' (string): Text of the line in the python script
-
- `MemoryState` are namedtuples listing frame + CPU/GPU memory with the following fields:
-
- - `frame` (`Frame`): the current frame (see above)
- - `cpu`: CPU memory consumed at during the current frame as a `Memory` named tuple
- - `gpu`: GPU memory consumed at during the current frame as a `Memory` named tuple
- - `cpu_gpu`: CPU + GPU memory consumed at during the current frame as a `Memory` named tuple
- """
- global _is_memory_tracing_enabled
- _is_memory_tracing_enabled = False
-
- if memory_trace is not None and len(memory_trace) > 1:
- memory_diff_trace = []
- memory_curr_trace = []
-
- cumulative_memory_dict = defaultdict(lambda: [0, 0, 0])
-
- for (
- (frame, cpu_mem, gpu_mem),
- (next_frame, next_cpu_mem, next_gpu_mem),
- ) in zip(memory_trace[:-1], memory_trace[1:]):
- cpu_mem_inc = next_cpu_mem - cpu_mem
- gpu_mem_inc = next_gpu_mem - gpu_mem
- cpu_gpu_mem_inc = cpu_mem_inc + gpu_mem_inc
- memory_diff_trace.append(
- MemoryState(
- frame=frame,
- cpu=Memory(cpu_mem_inc),
- gpu=Memory(gpu_mem_inc),
- cpu_gpu=Memory(cpu_gpu_mem_inc),
- )
- )
-
- memory_curr_trace.append(
- MemoryState(
- frame=frame,
- cpu=Memory(next_cpu_mem),
- gpu=Memory(next_gpu_mem),
- cpu_gpu=Memory(next_gpu_mem + next_cpu_mem),
- )
- )
-
- cumulative_memory_dict[frame][0] += cpu_mem_inc
- cumulative_memory_dict[frame][1] += gpu_mem_inc
- cumulative_memory_dict[frame][2] += cpu_gpu_mem_inc
-
- cumulative_memory = sorted(
- cumulative_memory_dict.items(), key=lambda x: x[1][2], reverse=True
- ) # order by the total CPU + GPU memory increase
- cumulative_memory = [
- MemoryState(
- frame=frame,
- cpu=Memory(cpu_mem_inc),
- gpu=Memory(gpu_mem_inc),
- cpu_gpu=Memory(cpu_gpu_mem_inc),
- )
- for frame, (cpu_mem_inc, gpu_mem_inc, cpu_gpu_mem_inc) in cumulative_memory
- ]
-
- memory_curr_trace = sorted(memory_curr_trace, key=lambda x: x.cpu_gpu.bytes, reverse=True)
-
- if ignore_released_memory:
- total_memory = sum(max(0, step_trace.cpu_gpu.bytes) for step_trace in memory_diff_trace)
- else:
- total_memory = sum(step_trace.cpu_gpu.bytes for step_trace in memory_diff_trace)
-
- total_memory = Memory(total_memory)
-
- return MemorySummary(
- sequential=memory_diff_trace,
- cumulative=cumulative_memory,
- current=memory_curr_trace,
- total=total_memory,
- )
-
- return None
-
-
-def bytes_to_mega_bytes(memory_amount: int) -> int:
- """Utility to convert a number of bytes (int) into a number of mega bytes (int)"""
- return memory_amount >> 20
-
-
-class Benchmark(ABC):
- """
- Benchmarks is a simple but feature-complete benchmarking script to compare memory and time performance of models in
- Transformers.
- """
-
- args: BenchmarkArguments
- configs: PretrainedConfig
- framework: str
-
- def __init__(self, args: BenchmarkArguments = None, configs: PretrainedConfig = None):
- self.args = args
- if configs is None:
- self.config_dict = {
- model_name: AutoConfig.from_pretrained(model_name) for model_name in self.args.model_names
- }
- else:
- self.config_dict = dict(zip(self.args.model_names, configs))
-
- warnings.warn(
- f"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
- " are deprecated in general and it is advised to use external Benchmarking libraries "
- " to benchmark Transformer models.",
- FutureWarning,
- )
-
- if self.args.memory and os.getenv("TRANSFORMERS_USE_MULTIPROCESSING") == 0:
- logger.warning(
- "Memory consumption will not be measured accurately if `args.multi_process` is set to `False.` The"
- " flag 'TRANSFORMERS_USE_MULTIPROCESSING' should only be disabled for debugging / testing."
- )
-
- self._print_fn = None
- self._framework_version = None
- self._environment_info = None
-
- @property
- def print_fn(self):
- if self._print_fn is None:
- if self.args.log_print:
-
- def print_and_log(*args):
- with open(self.args.log_filename, "a") as log_file:
- log_file.write("".join(args) + "\n")
- print(*args)
-
- self._print_fn = print_and_log
- else:
- self._print_fn = print
- return self._print_fn
-
- @property
- @abstractmethod
- def framework_version(self):
- pass
-
- @abstractmethod
- def _inference_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float:
- pass
-
- @abstractmethod
- def _train_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float:
- pass
-
- @abstractmethod
- def _inference_memory(
- self, model_name: str, batch_size: int, sequence_length: int
- ) -> [Memory, Optional[MemorySummary]]:
- pass
-
- @abstractmethod
- def _train_memory(
- self, model_name: str, batch_size: int, sequence_length: int
- ) -> [Memory, Optional[MemorySummary]]:
- pass
-
- def inference_speed(self, *args, **kwargs) -> float:
- return separate_process_wrapper_fn(self._inference_speed, self.args.do_multi_processing)(*args, **kwargs)
-
- def train_speed(self, *args, **kwargs) -> float:
- return separate_process_wrapper_fn(self._train_speed, self.args.do_multi_processing)(*args, **kwargs)
-
- def inference_memory(self, *args, **kwargs) -> [Memory, Optional[MemorySummary]]:
- return separate_process_wrapper_fn(self._inference_memory, self.args.do_multi_processing)(*args, **kwargs)
-
- def train_memory(self, *args, **kwargs) -> [Memory, Optional[MemorySummary]]:
- return separate_process_wrapper_fn(self._train_memory, self.args.do_multi_processing)(*args, **kwargs)
-
- def run(self):
- result_dict = {model_name: {} for model_name in self.args.model_names}
- inference_result_time = copy.deepcopy(result_dict)
- inference_result_memory = copy.deepcopy(result_dict)
- train_result_time = copy.deepcopy(result_dict)
- train_result_memory = copy.deepcopy(result_dict)
-
- for c, model_name in enumerate(self.args.model_names):
- self.print_fn(f"{c + 1} / {len(self.args.model_names)}")
-
- model_dict = {
- "bs": self.args.batch_sizes,
- "ss": self.args.sequence_lengths,
- "result": {i: {} for i in self.args.batch_sizes},
- }
- inference_result_time[model_name] = copy.deepcopy(model_dict)
- inference_result_memory[model_name] = copy.deepcopy(model_dict)
- train_result_time[model_name] = copy.deepcopy(model_dict)
- train_result_memory[model_name] = copy.deepcopy(model_dict)
-
- inference_summary = train_summary = None
-
- for batch_size in self.args.batch_sizes:
- for sequence_length in self.args.sequence_lengths:
- if self.args.inference:
- if self.args.memory:
- memory, inference_summary = self.inference_memory(model_name, batch_size, sequence_length)
- inference_result_memory[model_name]["result"][batch_size][sequence_length] = memory
- if self.args.speed:
- time = self.inference_speed(model_name, batch_size, sequence_length)
- inference_result_time[model_name]["result"][batch_size][sequence_length] = time
-
- if self.args.training:
- if self.args.memory:
- memory, train_summary = self.train_memory(model_name, batch_size, sequence_length)
- train_result_memory[model_name]["result"][batch_size][sequence_length] = memory
- if self.args.speed:
- time = self.train_speed(model_name, batch_size, sequence_length)
- train_result_time[model_name]["result"][batch_size][sequence_length] = time
-
- if self.args.inference:
- if self.args.speed:
- self.print_fn("\n" + 20 * "=" + ("INFERENCE - SPEED - RESULT").center(40) + 20 * "=")
- self.print_results(inference_result_time, type_label="Time in s")
- self.save_to_csv(inference_result_time, self.args.inference_time_csv_file)
- if self.args.is_tpu:
- self.print_fn(
- "TPU was used for inference. Note that the time after compilation stabilized (after ~10"
- " inferences model.forward(..) calls) was measured."
- )
-
- if self.args.memory:
- self.print_fn("\n" + 20 * "=" + ("INFERENCE - MEMORY - RESULT").center(40) + 20 * "=")
- self.print_results(inference_result_memory, type_label="Memory in MB")
- self.save_to_csv(inference_result_memory, self.args.inference_memory_csv_file)
-
- if self.args.trace_memory_line_by_line:
- self.print_fn("\n" + 20 * "=" + ("INFERENCE - MEMOMRY - LINE BY LINE - SUMMARY").center(40) + 20 * "=")
- self.print_memory_trace_statistics(inference_summary)
-
- if self.args.training:
- if self.args.speed:
- self.print_fn("\n" + 20 * "=" + ("TRAIN - SPEED - RESULTS").center(40) + 20 * "=")
- self.print_results(train_result_time, "Time in s")
- self.save_to_csv(train_result_time, self.args.train_time_csv_file)
- if self.args.is_tpu:
- self.print_fn(
- "TPU was used for training. Note that the time after compilation stabilized (after ~10 train"
- " loss=model.forward(...) + loss.backward() calls) was measured."
- )
-
- if self.args.memory:
- self.print_fn("\n" + 20 * "=" + ("TRAIN - MEMORY - RESULTS").center(40) + 20 * "=")
- self.print_results(train_result_memory, type_label="Memory in MB")
- self.save_to_csv(train_result_memory, self.args.train_memory_csv_file)
-
- if self.args.trace_memory_line_by_line:
- self.print_fn("\n" + 20 * "=" + ("TRAIN - MEMOMRY - LINE BY LINE - SUMMARY").center(40) + 20 * "=")
- self.print_memory_trace_statistics(train_summary)
-
- if self.args.env_print:
- self.print_fn("\n" + 20 * "=" + ("ENVIRONMENT INFORMATION").center(40) + 20 * "=")
- self.print_fn("\n".join([f"- {prop}: {val}" for prop, val in self.environment_info.items()]) + "\n")
-
- if self.args.save_to_csv:
- with open(self.args.env_info_csv_file, mode="w", newline="") as csv_file:
- writer = csv.writer(csv_file)
- for key, value in self.environment_info.items():
- writer.writerow([key, value])
-
- return BenchmarkOutput(
- inference_result_time,
- inference_result_memory,
- train_result_time,
- train_result_memory,
- inference_summary,
- train_summary,
- )
-
- @property
- def environment_info(self):
- if self._environment_info is None:
- info = {}
- info["transformers_version"] = version
- info["framework"] = self.framework
- if self.framework == "PyTorch":
- info["use_torchscript"] = self.args.torchscript
- if self.framework == "TensorFlow":
- info["eager_mode"] = self.args.eager_mode
- info["use_xla"] = self.args.use_xla
- info["framework_version"] = self.framework_version
- info["python_version"] = platform.python_version()
- info["system"] = platform.system()
- info["cpu"] = platform.processor()
- info["architecture"] = platform.architecture()[0]
- info["date"] = datetime.date(datetime.now())
- info["time"] = datetime.time(datetime.now())
- info["fp16"] = self.args.fp16
- info["use_multiprocessing"] = self.args.do_multi_processing
- info["only_pretrain_model"] = self.args.only_pretrain_model
-
- if is_psutil_available():
- info["cpu_ram_mb"] = bytes_to_mega_bytes(psutil.virtual_memory().total)
- else:
- logger.warning(
- "Psutil not installed, we won't log available CPU memory. "
- "Install psutil (pip install psutil) to log available CPU memory."
- )
- info["cpu_ram_mb"] = "N/A"
-
- info["use_gpu"] = self.args.is_gpu
- if self.args.is_gpu:
- info["num_gpus"] = 1 # TODO(PVP) Currently only single GPU is supported
- if is_py3nvml_available():
- nvml.nvmlInit()
- handle = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
- info["gpu"] = nvml.nvmlDeviceGetName(handle)
- info["gpu_ram_mb"] = bytes_to_mega_bytes(nvml.nvmlDeviceGetMemoryInfo(handle).total)
- info["gpu_power_watts"] = nvml.nvmlDeviceGetPowerManagementLimit(handle) / 1000
- info["gpu_performance_state"] = nvml.nvmlDeviceGetPerformanceState(handle)
- nvml.nvmlShutdown()
- else:
- logger.warning(
- "py3nvml not installed, we won't log GPU memory usage. "
- "Install py3nvml (pip install py3nvml) to log information about GPU."
- )
- info["gpu"] = "N/A"
- info["gpu_ram_mb"] = "N/A"
- info["gpu_power_watts"] = "N/A"
- info["gpu_performance_state"] = "N/A"
-
- info["use_tpu"] = self.args.is_tpu
- # TODO(PVP): See if we can add more information about TPU
- # see: https://github.com/pytorch/xla/issues/2180
-
- self._environment_info = info
- return self._environment_info
-
- def print_results(self, result_dict, type_label):
- self.print_fn(80 * "-")
- self.print_fn(
- "Model Name".center(30) + "Batch Size".center(15) + "Seq Length".center(15) + type_label.center(15)
- )
- self.print_fn(80 * "-")
- for model_name in self.args.model_names:
- for batch_size in result_dict[model_name]["bs"]:
- for sequence_length in result_dict[model_name]["ss"]:
- result = result_dict[model_name]["result"][batch_size][sequence_length]
- if isinstance(result, float):
- result = round(1000 * result) / 1000
- result = "< 0.001" if result == 0.0 else str(result)
- else:
- result = str(result)
- self.print_fn(
- model_name[:30].center(30) + str(batch_size).center(15),
- str(sequence_length).center(15),
- result.center(15),
- )
- self.print_fn(80 * "-")
-
- def print_memory_trace_statistics(self, summary: MemorySummary):
- self.print_fn(
- "\nLine by line memory consumption:\n"
- + "\n".join(
- f"{state.frame.filename}:{state.frame.line_number}: mem {state.cpu_gpu}: {state.frame.line_text}"
- for state in summary.sequential
- )
- )
- self.print_fn(
- "\nLines with top memory consumption:\n"
- + "\n".join(
- f"=> {state.frame.filename}:{state.frame.line_number}: mem {state.cpu_gpu}: {state.frame.line_text}"
- for state in summary.cumulative[:6]
- )
- )
- self.print_fn(
- "\nLines with lowest memory consumption:\n"
- + "\n".join(
- f"=> {state.frame.filename}:{state.frame.line_number}: mem {state.cpu_gpu}: {state.frame.line_text}"
- for state in summary.cumulative[-6:]
- )
- )
- self.print_fn(f"\nTotal memory increase: {summary.total}")
-
- def save_to_csv(self, result_dict, filename):
- if not self.args.save_to_csv:
- return
- self.print_fn("Saving results to csv.")
- with open(filename, mode="w") as csv_file:
- if len(self.args.model_names) <= 0:
- raise ValueError(f"At least 1 model should be defined, but got {self.model_names}")
-
- fieldnames = ["model", "batch_size", "sequence_length"]
- writer = csv.DictWriter(csv_file, fieldnames=fieldnames + ["result"])
- writer.writeheader()
-
- for model_name in self.args.model_names:
- result_dict_model = result_dict[model_name]["result"]
- for bs in result_dict_model:
- for ss in result_dict_model[bs]:
- result_model = result_dict_model[bs][ss]
- writer.writerow(
- {
- "model": model_name,
- "batch_size": bs,
- "sequence_length": ss,
- "result": ("{}" if not isinstance(result_model, float) else "{:.4f}").format(
- result_model
- ),
- }
- )
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/pop2piano/processing_pop2piano.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/pop2piano/processing_pop2piano.py
deleted file mode 100644
index 5ea579111ddbcd226820a34a45c7bdd3276202a2..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/pop2piano/processing_pop2piano.py
+++ /dev/null
@@ -1,138 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Processor class for Pop2Piano."""
-
-import os
-from typing import List, Optional, Union
-
-import numpy as np
-
-from ...feature_extraction_utils import BatchFeature
-from ...processing_utils import ProcessorMixin
-from ...tokenization_utils import BatchEncoding, PaddingStrategy, TruncationStrategy
-from ...utils import TensorType
-
-
-class Pop2PianoProcessor(ProcessorMixin):
- r"""
- Constructs an Pop2Piano processor which wraps a Pop2Piano Feature Extractor and Pop2Piano Tokenizer into a single
- processor.
-
- [`Pop2PianoProcessor`] offers all the functionalities of [`Pop2PianoFeatureExtractor`] and [`Pop2PianoTokenizer`].
- See the docstring of [`~Pop2PianoProcessor.__call__`] and [`~Pop2PianoProcessor.decode`] for more information.
-
- Args:
- feature_extractor (`Pop2PianoFeatureExtractor`):
- An instance of [`Pop2PianoFeatureExtractor`]. The feature extractor is a required input.
- tokenizer (`Pop2PianoTokenizer`):
- An instance of ['Pop2PianoTokenizer`]. The tokenizer is a required input.
- """
- attributes = ["feature_extractor", "tokenizer"]
- feature_extractor_class = "Pop2PianoFeatureExtractor"
- tokenizer_class = "Pop2PianoTokenizer"
-
- def __init__(self, feature_extractor, tokenizer):
- super().__init__(feature_extractor, tokenizer)
-
- def __call__(
- self,
- audio: Union[np.ndarray, List[float], List[np.ndarray]] = None,
- sampling_rate: Union[int, List[int]] = None,
- steps_per_beat: int = 2,
- resample: Optional[bool] = True,
- notes: Union[List, TensorType] = None,
- padding: Union[bool, str, PaddingStrategy] = False,
- truncation: Union[bool, str, TruncationStrategy] = None,
- max_length: Optional[int] = None,
- pad_to_multiple_of: Optional[int] = None,
- verbose: bool = True,
- **kwargs,
- ) -> Union[BatchFeature, BatchEncoding]:
- """
- This method uses [`Pop2PianoFeatureExtractor.__call__`] method to prepare log-mel-spectrograms for the model,
- and [`Pop2PianoTokenizer.__call__`] to prepare token_ids from notes.
-
- Please refer to the docstring of the above two methods for more information.
- """
-
- # Since Feature Extractor needs both audio and sampling_rate and tokenizer needs both token_ids and
- # feature_extractor_output, we must check for both.
- if (audio is None and sampling_rate is None) and (notes is None):
- raise ValueError(
- "You have to specify at least audios and sampling_rate in order to use feature extractor or "
- "notes to use the tokenizer part."
- )
-
- if audio is not None and sampling_rate is not None:
- inputs = self.feature_extractor(
- audio=audio,
- sampling_rate=sampling_rate,
- steps_per_beat=steps_per_beat,
- resample=resample,
- **kwargs,
- )
- if notes is not None:
- encoded_token_ids = self.tokenizer(
- notes=notes,
- padding=padding,
- truncation=truncation,
- max_length=max_length,
- pad_to_multiple_of=pad_to_multiple_of,
- verbose=verbose,
- **kwargs,
- )
-
- if notes is None:
- return inputs
-
- elif audio is None or sampling_rate is None:
- return encoded_token_ids
-
- else:
- inputs["token_ids"] = encoded_token_ids["token_ids"]
- return inputs
-
- def batch_decode(
- self,
- token_ids,
- feature_extractor_output: BatchFeature,
- return_midi: bool = True,
- ) -> BatchEncoding:
- """
- This method uses [`Pop2PianoTokenizer.batch_decode`] method to convert model generated token_ids to midi_notes.
-
- Please refer to the docstring of the above two methods for more information.
- """
-
- return self.tokenizer.batch_decode(
- token_ids=token_ids, feature_extractor_output=feature_extractor_output, return_midi=return_midi
- )
-
- @property
- def model_input_names(self):
- tokenizer_input_names = self.tokenizer.model_input_names
- feature_extractor_input_names = self.feature_extractor.model_input_names
- return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))
-
- def save_pretrained(self, save_directory, **kwargs):
- if os.path.isfile(save_directory):
- raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
- os.makedirs(save_directory, exist_ok=True)
- return super().save_pretrained(save_directory, **kwargs)
-
- @classmethod
- def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
- args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, **kwargs)
- return cls(*args)
diff --git a/spaces/yl12053/so-vits-4.1-Kitasan-Black/spkmix.py b/spaces/yl12053/so-vits-4.1-Kitasan-Black/spkmix.py
deleted file mode 100644
index 1d266e017859aca3c48727c5acbef9c8da8c1411..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Kitasan-Black/spkmix.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# 角色混合轨道 编写规则：
-# 角色ID : [[起始时间1, 终止时间1, 起始数值1, 起始数值1], [起始时间2, 终止时间2, 起始数值2, 起始数值2]]
-# 起始时间和前一个的终止时间必须相同，第一个起始时间必须为0，最后一个终止时间必须为1 （时间的范围为0-1）
-# 全部角色必须填写，不使用的角色填[[0., 1., 0., 0.]]即可
-# 融合数值可以随便填，在指定的时间段内从起始数值线性变化为终止数值，内部会自动确保线性组合为1，可以放心使用
-
-spk_mix_map = {
- 0 : [[0., 0.5, 1, 0.5], [0.5, 1, 0.5, 1]],
- 1 : [[0., 0.35, 1, 0.5], [0.35, 0.75, 0.75, 1], [0.75, 1, 0.45, 1]],
- 2 : [[0., 0.35, 1, 0.5], [0.35, 0.75, 0.75, 1], [0.75, 1, 0.45, 1]]
-}
\ No newline at end of file
diff --git a/spaces/younus93/pdfgpt/README.md b/spaces/younus93/pdfgpt/README.md
deleted file mode 100644
index de5994b2fa8a0bab99c9e026eca5a237c9e1e3a1..0000000000000000000000000000000000000000
--- a/spaces/younus93/pdfgpt/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Pdfgpt
-emoji: ⚡
-colorFrom: purple
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-duplicated_from: ns2001/pdfgpt
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ysharma/xtts/README.md b/spaces/ysharma/xtts/README.md
deleted file mode 100644
index 20f45a2647f190ae09c44c61614c8e61ee4dea6f..0000000000000000000000000000000000000000
--- a/spaces/ysharma/xtts/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: XTTS
-emoji: 🐸
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.44.2
-app_file: app.py
-pinned: false
-models:
-- coqui/XTTS-v1
-duplicated_from: coqui/xtts
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/yueranseo/mygpt/assets/custom.js b/spaces/yueranseo/mygpt/assets/custom.js
deleted file mode 100644
index f013209931218fd054979e290706f1945de76856..0000000000000000000000000000000000000000
--- a/spaces/yueranseo/mygpt/assets/custom.js
+++ /dev/null
@@ -1,502 +0,0 @@
-
-// custom javascript here
-
-const MAX_HISTORY_LENGTH = 32;
-
-var key_down_history = [];
-var currentIndex = -1;
-var user_input_ta;
-
-var gradioContainer = null;
-var user_input_ta = null;
-var user_input_tb = null;
-var userInfoDiv = null;
-var appTitleDiv = null;
-var chatbot = null;
-var chatbotWrap = null;
-var apSwitch = null;
-var empty_botton = null;
-var messageBotDivs = null;
-var loginUserForm = null;
-var logginUser = null;
-
-var userLogged = false;
-var usernameGotten = false;
-var historyLoaded = false;
-
-var ga = document.getElementsByTagName("gradio-app");
-var targetNode = ga[0];
-var isInIframe = (window.self !== window.top);
-var language = navigator.language.slice(0,2);
-
-var forView_i18n = {
- 'zh': "仅供查看",
- 'en': "For viewing only",
- 'ja': "閲覧専用",
- 'fr': "Pour consultation seulement",
- 'es': "Solo para visualización",
-};
-
-// gradio 页面加载好了么??? 我能动你的元素了么??
-function gradioLoaded(mutations) {
- for (var i = 0; i < mutations.length; i++) {
- if (mutations[i].addedNodes.length) {
- loginUserForm = document.querySelector(".gradio-container > .main > .wrap > .panel > .form")
- gradioContainer = document.querySelector(".gradio-container");
- user_input_tb = document.getElementById('user_input_tb');
- userInfoDiv = document.getElementById("user_info");
- appTitleDiv = document.getElementById("app_title");
- chatbot = document.querySelector('#chuanhu_chatbot');
- chatbotWrap = document.querySelector('#chuanhu_chatbot > .wrap');
- apSwitch = document.querySelector('.apSwitch input[type="checkbox"]');
- empty_botton = document.getElementById("empty_btn")
-
- if (loginUserForm) {
- localStorage.setItem("userLogged", true);
- userLogged = true;
- }
-
- if (gradioContainer && apSwitch) { // gradioCainter 加载出来了没?
- adjustDarkMode();
- }
- if (user_input_tb) { // user_input_tb 加载出来了没?
- selectHistory();
- }
- if (userInfoDiv && appTitleDiv) { // userInfoDiv 和 appTitleDiv 加载出来了没?
- if (!usernameGotten) {
- getUserInfo();
- }
- setTimeout(showOrHideUserInfo(), 2000);
- }
- if (chatbot) { // chatbot 加载出来了没?
- setChatbotHeight();
- }
- if (chatbotWrap) {
- if (!historyLoaded) {
- loadHistoryHtml();
- }
- setChatbotScroll();
- }
- if (empty_botton) {
- emptyHistory();
- }
- }
- }
-}
-
-function webLocale() {
- console.log("webLocale", language);
- if (forView_i18n.hasOwnProperty(language)) {
- var forView = forView_i18n[language];
- var forViewStyle = document.createElement('style');
- forViewStyle.innerHTML = '.wrap>.history-message>:last-child::after { content: "' + forView + '"!important; }';
- document.head.appendChild(forViewStyle);
- // console.log("added forViewStyle", forView);
- }
-}
-
-function selectHistory() {
- user_input_ta = user_input_tb.querySelector("textarea");
- if (user_input_ta) {
- observer.disconnect(); // 停止监听
- // 在 textarea 上监听 keydown 事件
- user_input_ta.addEventListener("keydown", function (event) {
- var value = user_input_ta.value.trim();
- // 判断按下的是否为方向键
- if (event.code === 'ArrowUp' || event.code === 'ArrowDown') {
- // 如果按下的是方向键，且输入框中有内容，且历史记录中没有该内容，则不执行操作
- if (value && key_down_history.indexOf(value) === -1)
- return;
- // 对于需要响应的动作，阻止默认行为。
- event.preventDefault();
- var length = key_down_history.length;
- if (length === 0) {
- currentIndex = -1; // 如果历史记录为空，直接将当前选中的记录重置
- return;
- }
- if (currentIndex === -1) {
- currentIndex = length;
- }
- if (event.code === 'ArrowUp' && currentIndex > 0) {
- currentIndex--;
- user_input_ta.value = key_down_history[currentIndex];
- } else if (event.code === 'ArrowDown' && currentIndex < length - 1) {
- currentIndex++;
- user_input_ta.value = key_down_history[currentIndex];
- }
- user_input_ta.selectionStart = user_input_ta.value.length;
- user_input_ta.selectionEnd = user_input_ta.value.length;
- const input_event = new InputEvent("input", { bubbles: true, cancelable: true });
- user_input_ta.dispatchEvent(input_event);
- } else if (event.code === "Enter") {
- if (value) {
- currentIndex = -1;
- if (key_down_history.indexOf(value) === -1) {
- key_down_history.push(value);
- if (key_down_history.length > MAX_HISTORY_LENGTH) {
- key_down_history.shift();
- }
- }
- }
- }
- });
- }
-}
-
-var username = null;
-function getUserInfo() {
- if (usernameGotten) {
- return;
- }
- userLogged = localStorage.getItem('userLogged');
- if (userLogged) {
- username = userInfoDiv.innerText;
- if (username) {
- if (username.includes("getting user info…")) {
- setTimeout(getUserInfo, 500);
- return;
- } else if (username === " ") {
- localStorage.removeItem("username");
- localStorage.removeItem("userLogged")
- userLogged = false;
- usernameGotten = true;
- return;
- } else {
- username = username.match(/User:\s*(.*)/)[1] || username;
- localStorage.setItem("username", username);
- usernameGotten = true;
- clearHistoryHtml();
- }
- }
- }
-}
-
-function toggleUserInfoVisibility(shouldHide) {
- if (userInfoDiv) {
- if (shouldHide) {
- userInfoDiv.classList.add("hideK");
- } else {
- userInfoDiv.classList.remove("hideK");
- }
- }
-}
-function showOrHideUserInfo() {
- var sendBtn = document.getElementById("submit_btn");
-
- // Bind mouse/touch events to show/hide user info
- appTitleDiv.addEventListener("mouseenter", function () {
- toggleUserInfoVisibility(false);
- });
- userInfoDiv.addEventListener("mouseenter", function () {
- toggleUserInfoVisibility(false);
- });
- sendBtn.addEventListener("mouseenter", function () {
- toggleUserInfoVisibility(false);
- });
-
- appTitleDiv.addEventListener("mouseleave", function () {
- toggleUserInfoVisibility(true);
- });
- userInfoDiv.addEventListener("mouseleave", function () {
- toggleUserInfoVisibility(true);
- });
- sendBtn.addEventListener("mouseleave", function () {
- toggleUserInfoVisibility(true);
- });
-
- appTitleDiv.ontouchstart = function () {
- toggleUserInfoVisibility(false);
- };
- userInfoDiv.ontouchstart = function () {
- toggleUserInfoVisibility(false);
- };
- sendBtn.ontouchstart = function () {
- toggleUserInfoVisibility(false);
- };
-
- appTitleDiv.ontouchend = function () {
- setTimeout(function () {
- toggleUserInfoVisibility(true);
- }, 3000);
- };
- userInfoDiv.ontouchend = function () {
- setTimeout(function () {
- toggleUserInfoVisibility(true);
- }, 3000);
- };
- sendBtn.ontouchend = function () {
- setTimeout(function () {
- toggleUserInfoVisibility(true);
- }, 3000); // Delay 1 second to hide user info
- };
-
- // Hide user info after 2 second
- setTimeout(function () {
- toggleUserInfoVisibility(true);
- }, 2000);
-}
-
-function toggleDarkMode(isEnabled) {
- if (isEnabled) {
- document.body.classList.add("dark");
- document.body.style.setProperty("background-color", "var(--neutral-950)", "important");
- } else {
- document.body.classList.remove("dark");
- document.body.style.backgroundColor = "";
- }
-}
-function adjustDarkMode() {
- const darkModeQuery = window.matchMedia("(prefers-color-scheme: dark)");
-
- // 根据当前颜色模式设置初始状态
- apSwitch.checked = darkModeQuery.matches;
- toggleDarkMode(darkModeQuery.matches);
- // 监听颜色模式变化
- darkModeQuery.addEventListener("change", (e) => {
- apSwitch.checked = e.matches;
- toggleDarkMode(e.matches);
- });
- // apSwitch = document.querySelector('.apSwitch input[type="checkbox"]');
- apSwitch.addEventListener("change", (e) => {
- toggleDarkMode(e.target.checked);
- });
-}
-
-function setChatbotHeight() {
- const screenWidth = window.innerWidth;
- const statusDisplay = document.querySelector('#status_display');
- const statusDisplayHeight = statusDisplay ? statusDisplay.offsetHeight : 0;
- const wrap = chatbot.querySelector('.wrap');
- const vh = window.innerHeight * 0.01;
- document.documentElement.style.setProperty('--vh', `${vh}px`);
- if (isInIframe) {
- chatbot.style.height = `700px`;
- wrap.style.maxHeight = `calc(700px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`
- } else {
- if (screenWidth <= 320) {
- chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 150}px)`;
- wrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 150}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
- } else if (screenWidth <= 499) {
- chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 100}px)`;
- wrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 100}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
- } else {
- chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 160}px)`;
- wrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 160}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
- }
- }
-}
-function setChatbotScroll() {
- var scrollHeight = chatbotWrap.scrollHeight;
- chatbotWrap.scrollTo(0,scrollHeight)
-}
-var rangeInputs = null;
-var numberInputs = null;
-function setSlider() {
- rangeInputs = document.querySelectorAll('input[type="range"]');
- numberInputs = document.querySelectorAll('input[type="number"]')
- setSliderRange();
- rangeInputs.forEach(rangeInput => {
- rangeInput.addEventListener('input', setSliderRange);
- });
- numberInputs.forEach(numberInput => {
- numberInput.addEventListener('input', setSliderRange);
- })
-}
-function setSliderRange() {
- var range = document.querySelectorAll('input[type="range"]');
- range.forEach(range => {
- range.style.backgroundSize = (range.value - range.min) / (range.max - range.min) * 100 + '% 100%';
- });
-}
-
-function addChuanhuButton(botElement) {
- var rawMessage = null;
- var mdMessage = null;
- rawMessage = botElement.querySelector('.raw-message');
- mdMessage = botElement.querySelector('.md-message');
- if (!rawMessage) {
- var buttons = botElement.querySelectorAll('button.chuanhu-btn');
- for (var i = 0; i < buttons.length; i++) {
- buttons[i].parentNode.removeChild(buttons[i]);
- }
- return;
- }
- var copyButton = null;
- var toggleButton = null;
- copyButton = botElement.querySelector('button.copy-bot-btn');
- toggleButton = botElement.querySelector('button.toggle-md-btn');
- if (copyButton) copyButton.remove();
- if (toggleButton) toggleButton.remove();
-
- // Copy bot button
- var copyButton = document.createElement('button');
- copyButton.classList.add('chuanhu-btn');
- copyButton.classList.add('copy-bot-btn');
- copyButton.setAttribute('aria-label', 'Copy');
- copyButton.innerHTML = copyIcon;
- copyButton.addEventListener('click', () => {
- const textToCopy = rawMessage.innerText;
- navigator.clipboard
- .writeText(textToCopy)
- .then(() => {
- copyButton.innerHTML = copiedIcon;
- setTimeout(() => {
- copyButton.innerHTML = copyIcon;
- }, 1500);
- })
- .catch(() => {
- console.error("copy failed");
- });
- });
- botElement.appendChild(copyButton);
-
- // Toggle button
- var toggleButton = document.createElement('button');
- toggleButton.classList.add('chuanhu-btn');
- toggleButton.classList.add('toggle-md-btn');
- toggleButton.setAttribute('aria-label', 'Toggle');
- var renderMarkdown = mdMessage.classList.contains('hideM');
- toggleButton.innerHTML = renderMarkdown ? mdIcon : rawIcon;
- toggleButton.addEventListener('click', () => {
- renderMarkdown = mdMessage.classList.contains('hideM');
- if (renderMarkdown){
- renderMarkdownText(botElement);
- toggleButton.innerHTML=rawIcon;
- } else {
- removeMarkdownText(botElement);
- toggleButton.innerHTML=mdIcon;
- }
- });
- botElement.insertBefore(toggleButton, copyButton);
-}
-
-function renderMarkdownText(message) {
- var mdDiv = message.querySelector('.md-message');
- if (mdDiv) mdDiv.classList.remove('hideM');
- var rawDiv = message.querySelector('.raw-message');
- if (rawDiv) rawDiv.classList.add('hideM');
-}
-function removeMarkdownText(message) {
- var rawDiv = message.querySelector('.raw-message');
- if (rawDiv) rawDiv.classList.remove('hideM');
- var mdDiv = message.querySelector('.md-message');
- if (mdDiv) mdDiv.classList.add('hideM');
-}
-
-let timeoutId;
-let isThrottled = false;
-var mmutation
-// 监听所有元素中 bot message 的变化，为 bot 消息添加复制按钮。
-var mObserver = new MutationObserver(function (mutationsList) {
- for (mmutation of mutationsList) {
- if (mmutation.type === 'childList') {
- for (var node of mmutation.addedNodes) {
- if (node.nodeType === 1 && node.classList.contains('message') && node.getAttribute('data-testid') === 'bot') {
- saveHistoryHtml();
- document.querySelectorAll('#chuanhu_chatbot>.wrap>.message-wrap .message.bot').forEach(addChuanhuButton);
- }
- if (node.tagName === 'INPUT' && node.getAttribute('type') === 'range') {
- setSlider();
- }
- }
- for (var node of mmutation.removedNodes) {
- if (node.nodeType === 1 && node.classList.contains('message') && node.getAttribute('data-testid') === 'bot') {
- saveHistoryHtml();
- document.querySelectorAll('#chuanhu_chatbot>.wrap>.message-wrap .message.bot').forEach(addChuanhuButton);
- }
- }
- } else if (mmutation.type === 'attributes') {
- if (mmutation.target.nodeType === 1 && mmutation.target.classList.contains('message') && mmutation.target.getAttribute('data-testid') === 'bot') {
- if (isThrottled) break; // 为了防止重复不断疯狂渲染，加上等待_(:з」∠)_
- isThrottled = true;
- clearTimeout(timeoutId);
- timeoutId = setTimeout(() => {
- isThrottled = false;
- document.querySelectorAll('#chuanhu_chatbot>.wrap>.message-wrap .message.bot').forEach(addChuanhuButton);
- saveHistoryHtml();
- }, 500);
- }
- }
- }
-});
-mObserver.observe(document.documentElement, { attributes: true, childList: true, subtree: true });
-
-var loadhistorytime = 0; // for debugging
-function saveHistoryHtml() {
- var historyHtml = document.querySelector('#chuanhu_chatbot > .wrap');
- localStorage.setItem('chatHistory', historyHtml.innerHTML);
- // console.log("History Saved")
- historyLoaded = false;
-}
-function loadHistoryHtml() {
- var historyHtml = localStorage.getItem('chatHistory');
- if (!historyHtml) {
- historyLoaded = true;
- return; // no history, do nothing
- }
- userLogged = localStorage.getItem('userLogged');
- if (userLogged){
- historyLoaded = true;
- return; // logged in, do nothing
- }
- if (!historyLoaded) {
- var tempDiv = document.createElement('div');
- tempDiv.innerHTML = historyHtml;
- var buttons = tempDiv.querySelectorAll('button.chuanhu-btn');
- var gradioCopyButtons = tempDiv.querySelectorAll('button.copy_code_button');
- for (var i = 0; i < buttons.length; i++) {
- buttons[i].parentNode.removeChild(buttons[i]);
- }
- for (var i = 0; i < gradioCopyButtons.length; i++) {
- gradioCopyButtons[i].parentNode.removeChild(gradioCopyButtons[i]);
- }
- var fakeHistory = document.createElement('div');
- fakeHistory.classList.add('history-message');
- fakeHistory.innerHTML = tempDiv.innerHTML;
- webLocale();
- chatbotWrap.insertBefore(fakeHistory, chatbotWrap.firstChild);
- // var fakeHistory = document.createElement('div');
- // fakeHistory.classList.add('history-message');
- // fakeHistory.innerHTML = historyHtml;
- // chatbotWrap.insertBefore(fakeHistory, chatbotWrap.firstChild);
- historyLoaded = true;
- console.log("History Loaded");
- loadhistorytime += 1; // for debugging
- } else {
- historyLoaded = false;
- }
-}
-function clearHistoryHtml() {
- localStorage.removeItem("chatHistory");
- historyMessages = chatbotWrap.querySelector('.history-message');
- if (historyMessages) {
- chatbotWrap.removeChild(historyMessages);
- console.log("History Cleared");
- }
-}
-function emptyHistory() {
- empty_botton.addEventListener("click", function () {
- clearHistoryHtml();
- });
-}
-
-// 监视页面内部 DOM 变动
-var observer = new MutationObserver(function (mutations) {
- gradioLoaded(mutations);
-});
-observer.observe(targetNode, { childList: true, subtree: true });
-
-// 监视页面变化
-window.addEventListener("DOMContentLoaded", function () {
- isInIframe = (window.self !== window.top);
- historyLoaded = false;
-});
-window.addEventListener('resize', setChatbotHeight);
-window.addEventListener('scroll', setChatbotHeight);
-window.matchMedia("(prefers-color-scheme: dark)").addEventListener("change", adjustDarkMode);
-
-// button svg code
-const copyIcon = '
- if (crypto.timingSafeEqual) {
- return crypto.timingSafeEqual(a, b);
- }
- for (var i = 0; i < a.length; i++) {
- if (a[i] !== b[i]) {
- return false;
- }
- }
- return true;
-}
-
-function timeSafeCompare(a, b) {
- var sa = String(a);
- var sb = String(b);
- var key = crypto.pseudoRandomBytes(32);
- var ah = crypto.createHmac('sha256', key).update(sa).digest();
- var bh = crypto.createHmac('sha256', key).update(sb).digest();
-
- return bufferEqual(ah, bh) && a === b;
-}
-
-module.exports = timeSafeCompare;
diff --git a/spaces/zhangguofen/Real-CUGAN/app.py b/spaces/zhangguofen/Real-CUGAN/app.py
deleted file mode 100644
index 2439c5cec6b61e8a517f957daf710cbb6b5c3cf6..0000000000000000000000000000000000000000
--- a/spaces/zhangguofen/Real-CUGAN/app.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from upcunet_v3 import RealWaifuUpScaler
-import gradio as gr
-import time
-import logging
-import os
-from PIL import ImageOps
-import numpy as np
-import math
-
-
-def greet(input_img, input_model_name, input_tile_mode):
- # if input_img.size[0] * input_img.size[1] > 256 * 256:
- # y = int(math.sqrt(256*256/input_img.size[0]*input_img.size[1]))
- # x = int(input_img.size[0]/input_img.size[1]*y)
- # input_img = ImageOps.fit(input_img, (x, y))
- input_img = np.array(input_img)
- if input_model_name not in model_cache:
- t1 = time.time()
- upscaler = RealWaifuUpScaler(input_model_name[2], ModelPath + input_model_name, half=False, device="cpu")
- t2 = time.time()
- logger.info(f'load model time, {t2 - t1}')
- model_cache[input_model_name] = upscaler
- else:
- upscaler = model_cache[input_model_name]
- logger.info(f'load model from cache')
-
- start = time.time()
- result = upscaler(input_img, tile_mode=input_tile_mode)
- end = time.time()
- logger.info(f'input_model_name, {input_model_name}')
- logger.info(f'input_tile_mode, {input_tile_mode}')
- logger.info(f'input shape, {input_img.shape}')
- logger.info(f'output shape, {result.shape}')
- logger.info(f'speed time, {end - start}')
- return result
-
-
-if __name__ == '__main__':
- logging.basicConfig(level=logging.INFO, format="[%(asctime)s] [%(process)d] [%(levelname)s] %(message)s")
- logger = logging.getLogger()
-
- ModelPath = "weights_v3/"
- model_cache = {}
-
- input_model_name = gr.inputs.Dropdown(os.listdir(ModelPath), default="up2x-latest-denoise2x.pth", label='选择model')
- input_tile_mode = gr.inputs.Dropdown([0, 1, 2, 3, 4], default=2, label='选择tile_mode')
- input_img = gr.inputs.Image(label='image', type='pil')
-
- inputs = [input_img, input_model_name, input_tile_mode]
- outputs = "image"
- iface = gr.Interface(fn=greet,
- inputs=inputs,
- outputs=outputs,
- allow_screenshot=False,
- allow_flagging='never',
- examples=[['test-img.jpg', "up2x-latest-denoise2x.pth", 2]],
- article='[https://github.com/bilibili/ailab/tree/main/Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN) 
- 
- 
- 
