Akadi: Idle RPG is a mobile game developed by CatDecided, a Korean studio that specializes in creating idle RPG games. The game was released in March 2021 and has since gained a lot of positive reviews from players around the world. The game belongs to the idle RPG genre, which means that it does not require much active input from the player, as the game progresses automatically even when the player is offline. The player's role is mainly to manage their team of heroes, equip them with items, and use their skills strategically in battles.
- An APK mod is a modified version of an original APK file, which is the format of Android applications. An APK mod can alter or add features and resources to the original application, such as unlimited gems, gold, items, heroes, skills, etc. An APK mod can also remove or bypass restrictions and limitations that the original application may have, such as ads, in-app purchases, verification, etc. An APK mod works by replacing or overriding the original APK file on the device, or by creating a parallel space where the modified application can run without affecting the original one.
- If you want to download and install Akadi APK Mod on your device, you need to follow these steps carefully:
-There are many versions of Akadi APK Mod available online, each with different features and resources. Some of the most common ones are:
-Playing Akadi APK Mod is very easy and similar to playing the original game. Here is a quick overview of how to start and play Akadi APK Mod:
-If you want to enjoy Akadi APK Mod to the fullest, here are some tips and tricks that you can use:
-In conclusion, Akadi APK Mod is a modified version of Akadi: Idle RPG that lets you play with unlimited features and resources. It is a fun and addictive game that belongs to the idle RPG genre. It has many features that make it enjoyable, such as collecting and upgrading heroes, fighting against various enemies, and exploring a vast fantasy world. It also has some benefits and risks that you should be aware of before using it. If you want to download and install Akadi APK Mod on your device, you can follow our step-by-step guide above. If you want to play Akadi APK Mod with ease, you can follow our tips and tricks above. We hope that this article has helped you learn more about Akadi APK Mod and how to use it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
- A1. Akadi APK Mod is not an official application from the developer or the platform. It is a modified version of an original application that may contain malware or viruses that may harm or steal your device or data. Therefore, it is not completely safe to use Akadi APK Mod. However, you can reduce the risk of using Akadi APK Mod by downloading and installing it from a reliable source, checking the details and requirements of the mod, enabling the anti-ban system, and scanning your device regularly.
- A2. Yes, you can play Akadi APK Mod online with other players. Akadi APK Mod enables the online mode that allows you to compete against other players' teams in real-time battles in the arena mode, or team up with other players to fight against powerful bosses in the raid mode. However, you should be careful when playing online with Akadi APK Mod, as you may encounter some issues or problems, such as lag, disconnect, or ban. You should also respect the rules and etiquette of online gaming and avoid cheating or abusing the unlimited features and resources that Akadi APK Mod provides.
- A3. The best characters and items in Akadi APK Mod may vary depending on your preference, playstyle, strategy, etc. However, some of the most popular and powerful characters and items in Akadi APK Mod are:
-A4. You can update Akadi APK Mod to the latest version by following these steps:
-A5. You can find more information and support for Akadi APK Mod by using these sources:
-Do you love hunting and exploring the outdoors? Do you want to experience the thrill of shooting wild animals and surviving in the wilderness? If yes, then you should try American Marksman - a realistic and immersive hunting simulation game that will test your skills and challenge your limits. And if you want to make the game even more fun and exciting, you should download American Marksman MOD APK - a modified version of the original game that gives you unlimited resources, no ads, and more. In this article, we will tell you everything you need to know about American Marksman MOD APK, including its features, benefits, and installation process. Read on to find out more!
-American Marksman is a hunting and outdoor adventure game that lets you explore vast, open landscapes and hunt various animals, from deer and bears to wolves and cougars. You can also modify the terrain and weather to suit your hunting needs, such as creating hills, valleys, rivers, lakes, forests, deserts, snowfields, and more. You can also choose from different weapons, such as rifles, shotguns, bows, crossbows, pistols, knives, and more. You can also customize your character's appearance, clothing, equipment, and skills. The game has realistic physics, animations, sounds, and graphics that will make you feel like you are really in the wild.
- American Marksman is not just a shooting game. It is a hunting simulation game that requires you to use your senses, instincts, strategy, and patience to track down and hunt your prey. You have to pay attention to the wind direction, animal behavior, scent trails, footprints, blood trails, sounds, and more. You also have to be careful of your surroundings, as some animals may attack you or run away if they sense your presence. You also have to manage your stamina, hunger, thirst, health, and inventory. You can also use various items and tools to help you in your hunt, such as binoculars, scopes, cameras, traps, baits, calls, decoys, blinds, tents, vehicles, boats, helicopters, drones, and more.
- American Marksman is not just a hunting simulation game. It is also a sandbox game that gives you unlimited possibilities to create your own hunting scenarios and adventures. You can modify the terrain and weather to your liking, and create any landscape you can imagine. You can also spawn any animal you want, and adjust their population density, aggressiveness, size, and color. You can also set up different challenges and objectives for yourself, such as hunting a specific animal, hunting a certain number of animals, hunting within a time limit, hunting without being detected, hunting with a specific weapon, and more.
-American Marksman is not just a sandbox game. It is also a game with stunning graphics and sound effects that will make you feel like you are really in the wild. The game has high-quality 3D models and textures for the animals, weapons, items, and environments. The game also has realistic lighting and shadows that create a realistic atmosphere. The game also has immersive sound effects that capture the sounds of nature, such as the wind, rain, thunder, birds, insects, and more. The game also has realistic sound effects for the animals, such as their roars, growls, snorts, grunts, bleats, and more. The game also has realistic sound effects for the weapons, such as their shots, reloads, clicks, and more.
- American Marksman MOD APK is a modified version of the original game that gives you extra features and benefits that are not available in the official version. It is a free and easy way to download and install the game on your Android device without any hassle. It is also a safe and secure way to enjoy the game without any risks of viruses, malware, or bans.
- American Marksman MOD APK is a modified version of the original game that gives you extra features that make the game more fun and exciting. Some of these features are:
-American Marksman MOD APK is a free and easy way to download and install the game on your Android device without any hassle. You don't need to root your device or use any third-party apps to install the game. You just need to follow these simple steps:
-American Marksman MOD APK is a safe and secure way to enjoy the game without any risks of viruses, malware, or bans. The APK file is scanned and tested by our team to ensure its safety and quality. The MOD APK is also compatible with most Android devices and versions. You don't have to worry about any issues or problems while playing the game.
- American Marksman MOD APK has many features that make it one of the best hunting and outdoor adventure games available. Here are some of them:
- With American Marksman MOD APK, you can have unlimited money, gold, wood, and metal that you can use to buy and upgrade your weapons, items, equipment, skills, and more. You can also use them to modify the terrain and weather to your liking, and create any landscape you want. You don't have to worry about running out of resources or spending real money to enjoy the game.
- With American Marksman MOD APK, you can enjoy the game without any ads or in-app purchases that may interrupt or limit your gameplay. You can access all the features and content of the game without any restrictions or costs. You can have a smooth and satisfying gaming experience.
- With American Marksman MOD APK, you can customize the terrain and weather to your liking, and create any landscape you can imagine. You can change the elevation, water level, vegetation, soil type, and more. You can also change the temperature, humidity, wind speed, precipitation, cloud cover, and more. You can create hills, valleys, rivers, lakes, forests, deserts, snowfields, and more. You can also create different seasons, time of day, and weather conditions. You can have a unique and diverse hunting environment.
- With American you can update American Marksman MOD APK whenever there is a new version available. However, you may need to uninstall the previous version and install the new one manually. You can also check our website regularly for the latest updates and news.
-
Facebook is one of the most popular social media platforms in the world, with over 2.8 billion monthly active users. It allows you to share updates, photos, videos, and stories with your friends, family, and communities. You can also use Facebook to chat with your contacts, watch videos, play games, and buy or sell products.
-Apkshub is a website that provides free downloads of Android applications in APK format. APK stands for Android Package Kit, which is a file format that contains all the components of an app. You can install APK files on your device without using the Google Play Store, which may have some restrictions or limitations in your region.
-Facebook Apkshub is a modified version of the official Facebook app that you can download from Apkshub. It has some extra features and functions that are not available in the original app, such as downloading videos, hiding online status, customizing themes, and more. It also has a smaller size and faster performance than the official app.
-If you want to try Facebook Apkshub on your device, you need to follow these steps:
-After installing Facebook Apkshub on your device, you can use it just like the official app. Here are some tips on how to use it:
-Facebook Apkshub may seem like a better alternative to the official app, but it also has some drawbacks. Here are some of the pros and cons of using Facebook Apkshub:
-Facebook Apkshub is a modified version of the official Facebook app that you can download from Apkshub. It offers some extra features and functions that are not available in the original app, such as downloading videos, hiding online status, customizing themes, and more. It also has a smaller size and faster performance than the official app. However, it also has some disadvantages and risks, such as violating the terms of service and privacy policy of Facebook, not being compatible with some devices or updates, and containing malware or viruses. Therefore, you should use Facebook Apkshub at your own discretion and responsibility.
-If you want to try Facebook Apkshub on your device, you can follow the steps in this article to download and install it from Apkshub. You can also use it to log in or sign up for Facebook, share updates, photos, videos, and stories, connect with friends, family, and communities, and use Facebook Messenger, Watch, Gaming, and Marketplace. However, you should also be aware of the pros and cons of using Facebook Apkshub and decide whether it is worth it or not.
-Facebook Lite is an official version of Facebook that is designed for low-end devices or slow internet connections. It has a smaller size and consumes less data than the regular app. However, it also has fewer features and functions than the regular app. Facebook Apkshub is a modified version of the regular app that has more features and functions than the regular app. However, it is not authorized by Facebook and may have some drawbacks or risks.
-Facebook Apkshub is not safe to use because it is not authorized by Facebook and may violate the terms of service and privacy policy of the platform. It may also contain malware or viruses that can harm your device or steal your data. Therefore, you should use Facebook Apkshub at your own risk and responsibility.
-You can use Facebook Apkshub on your PC by using an Android emulator software that allows you to run Android apps on your computer. Some examples of Android emulators are BlueStacks, Nox Player, MEmu, etc. However, you should also be careful when using an emulator because it may slow down your PC or cause some issues.
-You can use both Facebook Apkshub and the official app on your device by installing them separately. However, you should not log in with the same account on both apps because it may cause some conflicts or errors. You should also avoid using both apps at the same time because it may consume more battery life or data.
197e85843dIf you are interested in 3D modeling, animation, and rendering, you might have heard of Alias Maya. It is one of the most popular and powerful software tools for creating stunning 3D graphics and visual effects. Whether you are a hobbyist or a professional, you can use Alias Maya to bring your imagination to life.
-But how can you get your hands on this amazing software? And how can you learn to use it effectively? In this article, we will show you how to download Alias Maya 6.0 for free from Archive.org, how to install it on your computer, how to use it for 3D modeling, animation, and rendering, and some tips and tricks to get the most out of it. Let's get started!
-Alias Maya 6.0 is an older version of the software that was released in 2004 by Alias|Wavefront, a company that was later acquired by Autodesk. Although it is not the latest version, it still has many features and functions that can help you create amazing 3D graphics and visual effects.
-Unfortunately, Alias Maya 6.0 is not available for purchase or download from the official website anymore. However, you can still find it on Archive.org, a website that preserves digital content from various sources. Here are the steps to download Alias Maya 6.0 for free from Archive.org:
-Once you have downloaded and extracted Alias Maya 6.0 from Archive.org, you can proceed to install it on your computer. Here are the steps to install Alias Maya 6.0 on your computer:
-Congratulations! You have successfully installed Alias Maya 6.0 on your computer. You can now launch it from the Start menu or from the desktop shortcut.
-Now that you have installed Alias Maya 6.0 on your computer, you can start using it for 3D modeling, animation, and rendering. Alias Maya 6.0 has a user-friendly interface that allows you to create and manipulate 3D objects, scenes, and animations with ease. Here are some basic steps to use Alias Maya 6.0 for 3D modeling, animation, and rendering:
-These are just some of the basic steps to use Alias Maya 6.0 for 3D modeling, animation, and rendering. Of course, there are many more features and functions that you can explore and learn as you become more familiar with Alias Maya 6.0.
Alias Maya 6.0 is a powerful and versatile software that can help you create amazing 3D graphics and visual effects. However, it can also be challenging and complex to master. Here are some tips and tricks to get the most out of Alias Maya 6.0:
-These are just some of the tips and tricks to get the most out of Alias Maya 6.0. Of course, there are many more that you can discover and learn as you practice and experiment with Alias Maya 6.0.
-In this article, we have shown you how to download Alias Maya 6.0 for free from Archive.org, how to install it on your computer, how to use it for 3D modeling, animation, and rendering, and some tips and tricks to get the most out of it. We hope that this article has been helpful and informative for you.
-If you are interested in learning more about Alias Maya 6.0, we recommend that you check out some of the online tutorials, courses, books, and forums that are available on the internet. You can also join some of the online communities and groups that are dedicated to Alias Maya users and enthusiasts. You can learn from other people's experiences, share your own work, ask questions, get feedback, and make new friends.
-Alias Maya 6.0 is a great software that can help you unleash your creativity and express yourself in 3D. Whether you want to create realistic or stylized graphics, simple or complex animations, static or dynamic scenes, Alias Maya 6.0 can help you achieve your goals. So what are you waiting for? Download Alias Maya 6.0 today and start creating your own 3D masterpiece!
-
-- Is Alias Maya 6.0 compatible with Windows 10?
-Yes, Alias Maya 6.0 is compatible with Windows 10 as well as Windows XP, Windows Vista, Windows 7, Windows 8, and Windows 8.1.
-- Is Alias Maya 6.0 safe to download from Archive.org?
-Yes, Archive.org is a reputable website that preserves digital content from various sources. The file that contains Alias Maya 6.0 is scanned for viruses and malware before being uploaded to Archive.org. However, you should always be careful when downloading files from unknown sources and scan them with your own antivirus software before opening them.
-- Do I need a license or a registration code to use Alias Maya 6.0?
-Yes, you need a serial number to use Alias Maya 6.0. You can find the serial number in a file called "serial.txt" in the same folder as the "setup.exe" file. You will need to enter the serial number during the installation process and activate it online or by phone.
-- What are the system requirements for Alias Maya 6.0?
-The minimum system requirements for Alias Maya 6.0 are as follows:
-
-| Operating System | Windows XP, Windows Vista, Windows 7, Windows 8, Windows 8.1, or Windows 10 |
-| Processor | Pentium III or higher |
-| Memory | 512 MB RAM or higher |
-| Hard Disk Space | 450 MB free disk space or higher |
-| Graphics Card | OpenGL-compatible graphics card with 32 MB VRAM or higher |
-| Display Resolution | 1024 x 768 pixels or higher |
-| Internet Connection | Required for activation and updates |
-
-The recommended system requirements for Alias Maya 6.0 are as follows:
-
-| Operating System | Windows XP, Windows Vista, Windows 7, Windows 8, Windows 8.1, or Windows 10 |
-| Processor | Pentium 4 or higher |
-| Memory | 1 GB RAM or higher |
-| Hard Disk Space | 1 GB free disk space or higher |
-| Graphics Card | NVIDIA Quadro FX or ATI FireGL graphics card with 128 MB VRAM or higher |
-| Display Resolution | 1280 x 1024 pixels or higher |
-| Internet Connection | Required for activation and updates |
-
-- What are the main differences between Alias Maya 6.0 and the latest version of Maya?
-The latest version of Maya as of June 2023 is Maya 2023, which is developed and distributed by Autodesk. It has many new features and improvements compared to Alias Maya 6.0, such as:
-
-- A redesigned user interface that is more intuitive and customizable.
-- A new animation system that is faster and more flexible.
-- A new rendering engine that is more realistic and efficient.
-- A new modeling toolkit that is more powerful and versatile.
-- A new dynamics and effects system that is more interactive and realistic.
-- A new scripting and programming environment that is more robust and extensible.
-- A new collaboration and cloud integration system that is more convenient and secure.
-- A new learning and support system that is more accessible and comprehensive.
-
-However, the latest version of Maya also has some drawbacks compared to Alias Maya 6.0, such as:
-
-- A higher price tag that may not be affordable for some users.
-- A higher system requirement that may not be compatible with some computers.
-- A steeper learning curve that may not be suitable for some beginners.
-- A different workflow and interface that may not be familiar to some users.
-- A potential compatibility issue with some older files and plugins.
-
- The choice between Alias Maya 6.0 and the latest version of Maya depends on your personal preference, budget, skill level, project needs, and computer specifications. You can try both versions and see which one works better for you.
- - Where can I find more resources and tutorials for Alias Maya 6.0?
- If you want to learn more about Alias Maya 6.0, you can find many resources and tutorials online. Here are some of the best ones:
-
- - The official Alias Maya 6.0 documentation, which contains detailed information about the features and functions of Alias Maya 6.0.
- - The official Alias Maya 6.0 tutorials, which contain step-by-step instructions on how to use Alias Maya 6.0 for various tasks and projects.
- - The official Alias Maya 6.0 forums, which contain discussions, questions, answers, tips, tricks, feedback, and support from other Alias Maya users and experts.
- - The unofficial Alias Maya 6.0 tutorials, which contain various video and text tutorials on how to use Alias Maya 6.0 for different purposes and genres.
- - The unofficial Alias Maya 6.0 books, which contain comprehensive and in-depth guides on how to master Alias Maya 6.0 for various levels and topics.
- - The unofficial Alias Maya 6.0 blogs, which contain articles, reviews, news, updates, and opinions on Alias Maya 6.0 and related topics.
- - The unofficial Alias Maya 6.0 podcasts, which contain audio and video interviews, discussions, stories, and tips on Alias Maya 6.0 and related topics.
- - The unofficial Alias Maya 6.0 courses, which contain online and offline classes, workshops, and lessons on how to learn and improve your skills in Alias Maya 6.0.
-
- These are just some of the resources and tutorials for Alias Maya 6.0 that you can find online. Of course, there are many more that you can search and explore on your own.
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-How to Download Save Data for Resident Evil 4 Aether 2
- Resident Evil 4 Aether 2 is a modded version of the popular survival horror game Resident Evil 4, which adds a new dimension called the Aether, filled with floating islands, biomes, dungeons, and loot. If you want to enjoy this mod without having to start from scratch, you might want to download save data from other players who have completed the game or unlocked certain features. In this article, we will explain what Resident Evil 4 Aether 2 is, why you might want to download save data, and how to do it safely and easily.
- What is Resident Evil 4 Aether 2?
- Resident Evil 4 Aether 2 is a combination of two mods: Resident Evil 4 and Aether 2. Let's take a look at each mod separately.
-download save data resident evil 4 aether 2
DOWNLOAD >>> https://urlca.com/2uObJR
- Resident Evil 4
- Resident Evil 4 is a survival horror game developed and published by Capcom for the GameCube in 2005. It is the sixth main installment in the Resident Evil series, and follows the story of Leon S. Kennedy, a special agent who is sent to rescue the US president's daughter, Ashley Graham, who has been kidnapped by a religious cult in rural Spain. Leon has to fight his way through hordes of enemies infected by a mind-controlling parasite, and reunite with the spy Ada Wong. The game features a dynamic camera system and action-oriented gameplay, and is widely considered as one of the best video games of all time.
- Aether 2
- Aether 2 is a mod for Minecraft that adds a new dimension called the Aether, a dimension serving as the polar opposite of The Nether. The Aether is made of floating islands, similar to The End, and contains plenty of biomes, dungeons, and loot. The mod also introduces new mobs, blocks, items, and mechanics, such as Aerclouds, Continuum Orbs, Skyroot Crafting Tables, and more. The mod is a complete redo of the original Aether mod, and is still in development.
- Why Download Save Data?
- Downloading save data for Resident Evil 4 Aether 2 can have some advantages and disadvantages. Here are some of them:
- Benefits of Save Data
-
-- You can skip the parts of the game that you find boring or difficult.
-- You can access features that are otherwise locked or hard to obtain, such as weapons, costumes, modes, etc.
-- You can explore the Aether dimension without having to craft portals or gather materials.
-- You can enjoy the game at your own pace and style.
-
- Risks of Save Data
-
-- You might miss out on some of the fun and challenge of playing the game from scratch.
-- You might encounter compatibility issues or bugs with different versions of the game or the mod.
-- You might download corrupted or malicious files that can harm your device or compromise your security.
-- You might violate the terms and conditions of the game or the mod developers.
-
- How to Download Save Data for Resident Evil 4 Aether 2
- If you decide to download save data for Resident Evil 4 Aether 2, you will need to follow some steps and precautions. Here are some of them:
- Requirements
-
-- A device that can run Minecraft and Resident Evil 4 (PC recommended).
-- A copy of Minecraft installed on your device.
-- A copy of Resident Evil 4 installed on your device.
-- The Aether 2 mod downloaded and installed on your device.
-- A reliable source of save data for Resident Evil 4 Aether 2.
-
- Sources of Save Data
- There are many websites and forums that offer save data for Resident Evil 4 Aether 2, but not all of them are trustworthy or compatible. You should always do some research and check the reviews and ratings of the source before downloading anything. Some of the factors that you should consider are:
-
-- The version of the game and the mod that the save data is compatible with.
-- The content and features that the save data includes or unlocks.
-- The language and region of the save data.
-- The file size and format of the save data.
-- The safety and security of the download link and the file.
-
- Some examples of sources that you can try are:
-
-- [Nexus Mods]: This is a popular website that hosts mods and save data for various games, including Resident Evil 4 and Minecraft. You can browse through different categories and filters, and download files with a free account. You can also read the comments and ratings of other users, and contact the mod or save data authors if you have any questions or issues.
-- [GameFAQs]: This is another popular website that provides guides, walkthroughs, cheats, and save data for various games, including Resident Evil 4. You can search for the game and select the platform that you are using, and then choose the save data option. You can also read the descriptions and instructions of each file, and see how many users have downloaded or rated it.
-- [YouTube]: This is a video-sharing platform that hosts many videos related to gaming, including tutorials, reviews, gameplay, and more. You can search for videos that show how to download or install save data for Resident Evil 4 Aether 2, and follow the links or instructions provided by the video creators. You can also watch the videos to see how the save data works or looks like, and leave comments or questions if you have any.
-
- Steps to Download and Install Save Data
- Once you have found a source of save data that suits your needs and preferences, you will need to download and install it on your device. The exact steps may vary depending on the source, the file, and your device, but here are some general steps that you can follow:
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-
-- Download the save data file from the source to your device. Make sure that you have enough space and a stable internet connection.
-- Extract or unzip the file if it is compressed or archived. You may need a software like WinRAR or 7-Zip to do this.
-- Locate the folder where your Resident Evil 4 game is installed on your device. It is usually in Program Files (x86) > Capcom > Resident Evil 4.
-- Backup your original save data files in case something goes wrong or you want to revert to them later. You can copy them to another folder or an external drive.
-- Copy or move the downloaded save data files to the folder where your Resident Evil 4 game is installed. Overwrite or replace any existing files if prompted.
-- Launch your Resident Evil 4 game and check if the save data works as expected. You may need to select a different profile or slot to load the save data.
-
- Conclusion
- In this article, we have explained what Resident Evil 4 Aether 2 is, why you might want to download save data for it, and how to do it safely and easily. We hope that this article has been helpful and informative for you, and that you enjoy playing this modded version of one of the best survival horror games ever made.
- Summary of the Article
-
-- Resident Evil 4 Aether 2 is a modded version of Resident Evil 4 that adds a new dimension called the Aether.
-- Downloading save data for Resident Evil 4 Aether 2 can have some benefits and risks, depending on your preferences and goals.
-- To download save data for Resident Evil 4 Aether 2, you will need to have some requirements, find a reliable source of save data, and follow some steps to download and install it on your device.
-
- Call to Action
- If you liked this article, please share it with your friends who might be interested in playing Resident Evil 4 Aether 2. You can also leave a comment below and let us know what you think of this mod or this article. If you have any questions or suggestions, feel free to contact us anytime. Thank you for reading and happy gaming!
- FAQs
- Here are some frequently asked questions and answers about Resident Evil 4 Aether 2 and save data:
- Q: How do I get the Aether 2 mod for Resident Evil 4?
-A: You can get the Aether 2 mod for Resident Evil 4 from the official website of the mod developers, [Aether II]. You will need to download the mod file and install it on your device, following the instructions provided by the developers. You will also need to have Minecraft installed on your device, as the mod is based on it.
- Q: Is Resident Evil 4 Aether 2 compatible with other mods or versions of Resident Evil 4?
-A: Resident Evil 4 Aether 2 is compatible with most mods and versions of Resident Evil 4, as long as they do not conflict with the core files or features of the game or the mod. However, some mods or versions may cause glitches or errors, so you should always backup your original files and test them before playing.
- Q: Can I use my own save data for Resident Evil 4 Aether 2?
-A: Yes, you can use your own save data for Resident Evil 4 Aether 2, as long as it is compatible with the game and the mod. You can also create your own save data by playing the game normally and saving your progress. However, if you want to access certain features or items that are exclusive to the mod, you may need to download save data from other sources.
- Q: Is downloading save data for Resident Evil 4 Aether 2 legal or ethical?
-A: Downloading save data for Resident Evil 4 Aether 2 is not illegal, as long as you do not distribute or sell it for profit. However, it may be considered unethical by some players or developers, as it may affect the original gameplay experience or design of the game or the mod. You should always respect the rights and wishes of the creators and owners of the game and the mod, and follow their terms and conditions.
- Q: What are some tips or tricks for playing Resident Evil 4 Aether 2?
-A: Here are some tips or tricks for playing Resident Evil 4 Aether 2:
-
-- Use the Continuum Orbs to teleport between different islands in the Aether dimension.
-- Use the Skyroot Crafting Tables to craft items that are exclusive to the mod, such as Aerclouds, Zanite Armor, etc.
-- Use the Gravitite Armor to fly in the Aether dimension.
-- Use the Golden Feather to negate fall damage in any dimension.
-- Use the Phoenix Bow to shoot flaming arrows that can ignite enemies or blocks.
-
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-
-Fastest Way to Download Bitcoin Blockchain
-Bitcoin is a decentralized digital currency that operates on a peer-to-peer network of nodes. The bitcoin blockchain is a shared public ledger that records all the transactions that have ever occurred on the network. The bitcoin blockchain is essential for verifying the validity and authenticity of transactions and preventing double-spending and fraud.
-However, downloading the bitcoin blockchain can be a daunting task for many users, especially beginners. The bitcoin blockchain is constantly growing in size and complexity, and as of June 2023, it has reached over 488 GB. Downloading the bitcoin blockchain can take a lot of time, bandwidth, disk space, and computing power. Moreover, some users may face censorship or surveillance issues when trying to access the bitcoin network in certain regions or countries.
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-On the other hand, downloading the bitcoin blockchain can also bring many benefits for users who want to run a full node and contribute to the security and decentralization of the network. A full node is a node that validates transactions and blocks according to the consensus rules of the network. A full node can also provide better privacy, autonomy, and reliability for users who want to transact with bitcoin without relying on third-party services or intermediaries.
-So, how can you download the bitcoin blockchain faster and easier? In this article, we will explore some of the best ways and tips to do so. We will focus on two main methods: using Bitcoin Core, the official and most popular bitcoin client, and using alternative clients that offer faster or lighter synchronization options. We will also provide some useful links and resources for further reading and learning.
- How to Download the Bitcoin Blockchain Faster with Bitcoin Core
-Bitcoin Core is the original and most widely used bitcoin client that implements a full node. Bitcoin Core allows users to download, validate, and store the entire bitcoin blockchain on their local device. Bitcoin Core also provides a graphical user interface (GUI) and a command-line interface (CLI) for users to interact with the network.
-However, Bitcoin Core can also be slow and resource-intensive when it comes to downloading the bitcoin blockchain. Bitcoin Core does not trust any information from other nodes and verifies everything itself. This means that it has to download and validate every transaction and block in the history of the network, which can take hours or even days depending on your internet speed and hardware specifications.
-Fortunately, there are some ways to speed up this process and make it more efficient. Here are some tips:
- How to Increase the Database Cache Size to Speed Up the Validation Process
-One of the factors that affects the speed of downloading the bitcoin blockchain is the database cache size. The database cache size is the amount of memory that Bitcoin Core uses to store frequently accessed data, such as unspent transaction outputs (UTXOs) and block headers. The larger the database cache size, the faster Bitcoin Core can access and process data, which reduces disk reads and writes and speeds up the validation process.
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-The default database cache size in Bitcoin Core is 300 MB, but you can increase it to a higher value depending on your available memory. To do so, you need to edit the bitcoin.conf file, which is a configuration file that stores various settings for Bitcoin Core. You can find this file in:
-
-- Windows: /appdata/roaming/bitcoin/bitcoin.conf
-- Linux: $USER/.bitcoin/bitcoin.conf
-- MacOS: $HOME/Library/Application Support/Bitcoin/bitcoin.conf
-
-To increase the database cache size, you need to add the following line to the bitcoin.conf file:
-dbcache=SIZE
-where SIZE is the desired value in megabytes. For example, if you want to set the database cache size to 2 GB, you need to add:
-dbcache=2000
-After editing the bitcoin.conf file, you need to restart Bitcoin Core for the changes to take effect. You can also adjust the database cache size from the GUI by going to Settings > Options > Main and moving the slider under "Size of database cache".
-A larger database cache size can significantly improve the performance of Bitcoin Core, but it also consumes more memory and may affect other applications running on your device. Therefore, you should choose a value that is appropriate for your device and your needs.
- How to Use bootstrap.dat File to Skip the Initial Download
-Another way to download the bitcoin blockchain faster with Bitcoin Core is to use a bootstrap.dat file. A bootstrap.dat file is a file that contains a copy of the bitcoin blockchain up to a certain point in time. By using a bootstrap.dat file, you can skip the initial download of the bitcoin blockchain from other nodes and only download the new blocks that have been added since the file was created.
-To use a bootstrap.dat file, you need to follow these steps:
-
-- Download a bootstrap.dat file from a trusted source. You can find some links to download bootstrap.dat files from various sources here. Make sure that the file is up-to-date and verified by checking its hash value.
-- Place the bootstrap.dat file in the same folder where Bitcoin Core stores the bitcoin blockchain data. You can find this folder in:
-
-- Windows: /appdata/roaming/bitcoin/blocks
-- Linux: $USER/.bitcoin/blocks
-- MacOS: $HOME/Library/Application Support/Bitcoin/blocks
-
-- Start Bitcoin Core and wait for it to import the bootstrap.dat file. This may take some time depending on the size of the file and your hardware specifications. You can monitor the progress of the import process from the debug window or the debug.log file.
-- Once the import process is finished, Bitcoin Core will rename the bootstrap.dat file to bootstrap.dat.old and start downloading the new blocks from other nodes. You can delete or move the bootstrap.dat.old file to free up some disk space.
-
-Using a bootstrap.dat file can save you some time and bandwidth when downloading the bitcoin blockchain with Bitcoin Core, but it also has some drawbacks. For example, you need to trust the source of the bootstrap.dat file and make sure that it is not corrupted or tampered with. Moreover, you still need to validate all the transactions and blocks in the bootstrap.dat file, which can take longer than downloading them from other nodes.
- How to Download the Bitcoin Blockchain Faster with Alternative Clients
-If you do not want to use Bitcoin Core or you want to try some alternative clients that offer faster or lighter synchronization options, you have some choices. There are many different bitcoin clients that implement different types of nodes, such as pruned nodes, light nodes, or SPV nodes. These nodes do not download or store the entire bitcoin blockchain, but only a subset of it or none at all. Instead, they rely on other nodes or servers to provide them with the necessary information to verify transactions and blocks.
-However, using alternative clients also comes with some trade-offs. For example, you may sacrifice some privacy, security, or functionality when using these clients. You may also depend on third-party services or intermediaries that may not be trustworthy or reliable. Therefore, you should carefully weigh the pros and cons of each client before choosing one.
-Here are some examples of alternative clients that offer faster or lighter synchronization options:
- How to Use Electrum, a Lightweight Client that Does Not Require Downloading the Whole Blockchain
-Electrum is one of the most popular and oldest bitcoin clients that does not require downloading the whole blockchain. Electrum is a lightweight client that uses a technique called Simplified Payment Verification (SPV) to verify transactions and blocks. SPV is a method that allows a node to verify transactions without having the full blockchain, but only by downloading and storing the block headers, which are much smaller in size. Electrum also connects to a network of servers, called Electrum servers, that provide it with the necessary information to verify transactions and blocks.
-Using Electrum can save you a lot of time, bandwidth, disk space, and computing power when downloading the bitcoin blockchain. Electrum can synchronize with the network in minutes and allow you to transact with bitcoin quickly and easily. Electrum also provides a user-friendly interface and some advanced features, such as multisig, hardware wallet integration, cold storage, and custom fees.
-However, using Electrum also has some drawbacks. For example, you need to trust the Electrum servers to provide you with accurate and honest information. If the Electrum servers are compromised or malicious, they may feed you false or outdated data, which can affect your transactions and your security. Moreover, you may leak some privacy information to the Electrum servers, such as your IP address, your transaction history, or your wallet balance. Therefore, you should choose an Electrum server that is reputable and trustworthy, or run your own Electrum server if possible.
-To use Electrum, you need to download and install the Electrum software from the official website. You can choose between a desktop version or a mobile version for Android devices. You can also use Electrum with Tor or a VPN to enhance your privacy and anonymity.
- How to Use Neutrino, a Privacy-Preserving Client that Uses Compact Block Filters
-Neutrino is a new and experimental bitcoin client that aims to provide faster and more private synchronization than SPV clients. Neutrino uses a technique called compact block filters to verify transactions and blocks. Compact block filters are data structures that allow a node to filter out the transactions and blocks that are relevant to its wallet without downloading the whole blockchain or revealing its addresses to other nodes. Neutrino connects to a network of nodes that support the compact block filters protocol (BIP 157/158) and downloads the filters from them.
-Using Neutrino can offer some advantages over SPV clients. For example, Neutrino can provide better privacy, as it does not need to disclose its addresses or transaction history to other nodes. Neutrino can also provide better security, as it does not need to trust other nodes to provide it with accurate and honest information. Neutrino can verify transactions and blocks by itself using the compact block filters.
-However, using Neutrino also has some challenges. For example, Neutrino is still in development and testing, and it may not be stable or compatible with all devices or platforms. Neutrino also requires more bandwidth and disk space than SPV clients, as it has to download and store all the compact block filters, which are larger than block headers. Moreover, Neutrino may not have many nodes that support the compact block filters protocol, which may limit its connectivity and availability.
-To use Neutrino, you need to download and install the Neutrino software from the official GitHub repository. You can choose between a desktop version or a mobile version for Android devices. You can also use Neutrino with Tor or a VPN to enhance your privacy and anonymity.
- Conclusion
-In this article, we have explored some of the fastest ways to download the bitcoin blockchain. We have focused on two main methods: using Bitcoin Core, the official and most popular bitcoin client that implements a full node; and using alternative clients that offer faster or lighter synchronization options, such as Electrum and Neutrino.
-We have also provided some tips and tricks to speed up the download process with Bitcoin Core, such as increasing the database cache size or using a bootstrap.dat file. We have also discussed some of the pros and cons of each method, such as privacy, security, functionality, and resource consumption.
-We hope that this article has been helpful and informative for you. If you want to learn more about bitcoin and blockchain technology, you can check out some of these resources:
-
-- [Bitcoin.org]: The official website of bitcoin that provides basic information, guides, resources, and links for bitcoin users.
-- [Bitcoin Wiki]: A comprehensive wiki that covers various topics related to bitcoin technology, terminology, protocol, software, services, history, and more.
-- [Bitcoin Core]: The official website of Bitcoin Core that provides downloads, documentation, support, and news for Bitcoin Core users and developers.
-- [Electrum]: The official website of Electrum that provides downloads, documentation, support, and news for Electrum users and developers.
-- [Neutrino]: The official GitHub repository of Neutrino that provides downloads, documentation, support, and news for Neutrino users and developers.
-
-We also invite you to share your feedback and experience with us. Have you tried any of the methods we have mentioned in this article? How did they work for you? Do you have any other tips or suggestions to download the bitcoin blockchain faster? Let us know in the comments below!
- FAQs
-Here are some of the frequently asked questions (FAQs) about downloading the bitcoin blockchain:
- How long does it take to download the bitcoin blockchain?
-The answer to this question depends on many factors, such as your internet speed, hardware specifications, software settings, network conditions, and more. However, as a general estimate, it can take anywhere from a few hours to several days to download the bitcoin blockchain with Bitcoin Core. It can take much less time with alternative clients that do not download the whole blockchain, such as Electrum or Neutrino.
- How much disk space does the bitcoin blockchain take?
-The answer to this question also depends on many factors, such as the type of node you are running, the software settings, the pruning options, and more. However, as a general estimate, the bitcoin blockchain takes about 488 GB of disk space as of June 2023. It can take much less disk space with alternative clients that do not store the whole blockchain, such as Electrum or Neutrino.
- How can I prune the bitcoin blockchain to save disk space?
-Pruning is a technique that allows a node to delete old or irrelevant data from the bitcoin blockchain and only keep a subset of it. Pruning can save disk space and reduce resource consumption without affecting the functionality or security of the node. However, pruning also has some limitations, such as not being able to serve historical blocks or transactions to other nodes.
-To prune the bitcoin blockchain with Bitcoin Core, you need to edit the bitcoin.conf file and add the following line:
-prune=SIZE
-where SIZE is the desired value in megabytes. For example, if you want to prune the bitcoin blockchain to 10 GB, you need to add:
-prune=10000
-After editing the bitcoin.conf file, you need to restart Bitcoin Core for the changes to take effect. You can also enable pruning from the GUI by going to Settings > Options > Main and checking the box under "Reduce storage".
- How can I verify the integrity of the bitcoin blockchain?
-Verifying the integrity of the bitcoin blockchain is important to ensure that you have a valid and authentic copy of it. To verify the integrity of the bitcoin blockchain with Bitcoin Core, you need to use a tool called Bitcoin Core Checkpoints. Bitcoin Core Checkpoints is a feature that allows Bitcoin Core to skip the verification of some blocks that have been pre-verified by trusted sources. This can speed up the synchronization process and prevent some attacks on the network.
-To use Bitcoin Core Checkpoints, you need to download and install Bitcoin Core Checkpoints from here. You can then run Bitcoin Core Checkpoints from the command line with the following syntax:
-bitcoin-checkpoints [options] [checkpoint-file]
-where [options] are optional parameters that modify the behavior of Bitcoin Core Checkpoints, such as -v for verbose mode or -d for debug mode; and [checkpoint-file] is the name of the checkpoint file that contains the pre-verified blocks. You can find some checkpoint files for different versions of Bitcoin Core here.
-Bitcoin Core Checkpoints will then compare the blocks in your bitcoin blockchain with the blocks in the checkpoint file and report any discrepancies or errors. If Bitcoin Core Checkpoints finds any problems, you may need to re-download or re-validate the bitcoin blockchain.
- How can I backup and restore the bitcoin blockchain?
-Backing up and restoring the bitcoin blockchain can be useful in case of data loss, corruption, or migration. To backup and restore the bitcoin blockchain with Bitcoin Core, you need to copy and paste the folder where Bitcoin Core stores the bitcoin blockchain data. You can find this folder in:
-
-- Windows: /appdata/roaming/bitcoin
-- Linux: $USER/.bitcoin
-- MacOS: $HOME/Library/Application Support/Bitcoin
-
-To backup the bitcoin blockchain, you need to copy the whole folder and save it to a secure and reliable location, such as an external hard drive or a cloud storage service. To restore the bitcoin blockchain, you need to paste the whole folder to the same location on your new device and overwrite any existing files. You may also need to update or reconfigure some settings in the bitcoin.conf file or the GUI.
-
-This is the end of the article. I hope you enjoyed reading it and learned something new. Thank you for your attention and have a great day!
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-Messenger APK HappyMod: A Modded Version of Messenger with Extra Features
-If you are looking for a way to enhance your messaging experience with your friends and family, you might want to try out messenger apk happymod. This is a modified version of the official Messenger app from Meta that offers some additional features and customizations that are not available in the original app. In this article, we will tell you what messenger apk happymod is, what are its benefits, how to download and install it, what are some user reviews and ratings, and what are some alternatives to it.
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-Messenger apk happymod is a modded version of Messenger, a popular messaging and video calling app that works across Facebook, Instagram, Portal, and Oculus. Messenger apk happymod is created by using a tool called HappyMod, which is an Android-based app search engine that allows you to download modified or mod versions of apps and games. HappyMod provides many versions mod of one game or app, and users give them the using experience percent, so you can download the high score one.
-The main benefit of using messenger apk happymod is that it gives you access to some features that are not available in the official Messenger app, such as custom reactions, animated effects, app lock, payments, business, and more. These features can make your conversations more fun, secure, convenient, and productive. You can also enjoy the core features of Messenger, such as cross-app communication, watch together, replies and forwarding, and more.
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-You can send and receive money securely with Messenger. You can link your debit card, credit card, or PayPal account to Messenger and use it to pay friends or businesses. You can also request money from others or split bills with groups. To use payments, tap the plus icon in a chat, then tap Pay or Request. You can also access your payment history and settings from your profile picture.
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-- Go to happymod.com and search for messenger mod apk.
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-- Open the downloaded file and install it. You may need to grant some permissions for the app to work properly.
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-That's it! You can now enjoy messenger apk happymod on your device.
- Reviews and ratings of messenger apk happymod
-Messenger apk happymod has received mixed reviews and ratings from users who have tried it out. Here are some of the pros and cons of using messenger apk happymod:
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-| - It offers more features and customizations than the official Messenger app. | - It may not be compatible with some devices or Android versions. |
-| - It is easy to download and install from happymod.com. | - It may not be updated regularly or in sync with the official Messenger app. |
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- Conclusion
-Messenger apk happymod is a modded version of Messenger that offers some extra features and customizations that are not available in the official app. It is created by using HappyMod, an Android-based app search engine that allows you to download mod versions of apps and games. Some of the features of messenger apk happymod are cross-app communication, watch together, custom reactions, animated effects, replies and forwarding, app lock, payments, business, and more. However, messenger apk happymod also has some drawbacks, such as compatibility issues, update delays, security risks, or privacy concerns.
-If you want to try out messenger apk happymod, you can download and install it from happymod.com by following the steps we have provided in this article. Alternatively, you can also check out some other options that we have suggested, such as F-Droid, Aurora Store, Xmodgames, Cheat Engine, or FtiOS app. These are some of the apps that allow you to download or modify apps and games on your device.
-We hope this article has helped you learn more about messenger apk happymod and its alternatives. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
- FAQs
-What is happymod?
-HappyMod is an Android-based app search engine that allows you to download modified or mod versions of apps and games. HappyMod provides many versions mod of one game or app, and users give them the using experience percent.
-Is messenger apk happymod safe to use?
-Messenger apk happymod is not verified by Meta or Google Play Protect, so it may pose some security risks or privacy issues for your device or data. You should only download messenger apk happymod from trusted sources and at your own risk.
-How can I update messenger apk happymod?
-You can update messenger apk happymod by visiting happymod.com and downloading the latest version of the mod apk file. You may need to uninstall the previous version before installing the new one.
-What are the disadvantages of using messenger apk happymod?
-Some of the disadvantages of using messenger apk happymod are:
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-- It may not be compatible with some devices or Android versions.
-- It may not be updated regularly or in sync with the official Messenger app.
-- It may interfere with some functions or features of the official Messenger app.
-- It may violate the terms of service or policies of Meta or Google Play Store.
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-Can I use messenger apk happymod on my PC?
-You cannot use messenger apk happymod directly on your PC, but you can use an Android emulator to run it on your PC. An Android emulator is a software that simulates an Android device on your PC and lets you run Android apps and games on it. Some of the popular Android emulators are BlueStacks, NoxPlayer, LDPlayer, etc. To use messenger apk happymod on your PC, you need to download and install an Android emulator on your PC, then download and install messenger apk happymod from happymod.com on the emulator.
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Whats New in Stick War Legacy APK New Version? Find Out Here!.md b/spaces/congsaPfin/Manga-OCR/logs/Whats New in Stick War Legacy APK New Version? Find Out Here!.md
deleted file mode 100644
index 41774a720d7b78f7da1289f6625445e7898f2011..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Whats New in Stick War Legacy APK New Version? Find Out Here!.md
+++ /dev/null
@@ -1,98 +0,0 @@
-
-Stick War Legacy APK New Version: What You Need to Know
-If you are a fan of stick figure games, you might have heard of Stick War Legacy, one of the most popular and highest rated web games of all time. This game is now available on mobile devices, and it has recently been updated with new features and improvements. In this article, we will tell you everything you need to know about Stick War Legacy APK new version, including what it is, what's new in it, how to download and install it, and some FAQs.
- Introduction
-What is Stick War Legacy?
-Stick War Legacy is a strategy game that lets you control your army of stickmen in various modes and scenarios. You can play as each unit, or command them in formations. You can build units, mine gold, learn different skills, and destroy the enemy statue. The game has a classic campaign mode, where you can follow the story of the Order Empire and its struggle against other nations. The game also has other modes, such as missions, tournament, and endless deads.
-stick war legacy apk new version
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-What's new in the latest version?
-The latest version of Stick War Legacy APK is 2021.1.4, which was released on May 17, 2021. This version has several new features and improvements, such as:
-
-- New levels released every Friday in missions mode
-- New skins and weapons for all characters
-- New game types and challenges in tournament mode
-- New zombies and bosses in endless deads mode
-- Improved graphics and animations
-- Bug fixes and performance enhancements
-
- Features of Stick War Legacy APK New Version
-Missions Mode
-Missions mode is a new feature that was added in the latest version of Stick War Legacy APK. In this mode, you can play new levels that are released every Friday. Each level has a different objective and difficulty level. You can earn crowns for completing each level, and unlock rewards such as skins and weapons. Missions mode is a great way to test your skills and have fun.
-Skins and Weapons
-Skins and weapons are another new feature that was added in the latest version of Stick War Legacy APK. You can now customize your stickmen with different skins and weapons, each with their own unique perks. For example, you can equip your swordwrath with a katana that increases their speed, or your spearton with a shield that blocks arrows. You can unlock skins and weapons by playing missions mode or tournament mode.
-Tournament Mode
-Tournament mode is a feature that lets you compete against other players in various game types and challenges. You can choose from different difficulty levels, such as normal, hard, or insane. You can also choose from different game types, such as win before sunset, triple barricaded gold, deathmatch, forward statue, or mini bosses. You can earn crowns for winning each match, and climb the leaderboard to win the crown of Inamorta.
-Endless Deads Mode
-Endless deads mode is a feature that lets you survive against waves of zombies and bosses. You can choose from different difficulty levels, such as easy, normal, hard, or insane. You can also choose from different maps, such as forest ambush, desert siege, or ice holdout. You can earn gold for killing zombies and bosses, and use it to upgrade your units and defenses. Endless deads mode is a great way to challenge yourself and have fun.
- How to Download and Install Stick War Legacy APK New Version
Requirements
-To download and install Stick War Legacy APK new version, you need to have the following requirements:
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-
-- An Android device that runs on Android 4.4 or higher
-- At least 100 MB of free storage space
-- A stable internet connection
-- A permission to install apps from unknown sources
-
-Steps
-To download and install Stick War Legacy APK new version, you need to follow these steps:
-
-- Go to the official website of Stick War Legacy APK and click on the download button.
-- Wait for the download to finish and locate the APK file in your device's file manager.
-- Tap on the APK file and allow the installation from unknown sources if prompted.
-- Wait for the installation to complete and launch the game from your app drawer.
-- Enjoy playing Stick War Legacy APK new version with all the new features and improvements.
-
- Conclusion
-Summary
-Stick War Legacy APK new version is a strategy game that lets you control your army of stickmen in various modes and scenarios. You can play as each unit, or command them in formations. You can build units, mine gold, learn different skills, and destroy the enemy statue. The game has a classic campaign mode, where you can follow the story of the Order Empire and its struggle against other nations. The game also has other modes, such as missions, tournament, and endless deads. The game has been updated with new features and improvements, such as new levels, skins, weapons, game types, zombies, bosses, graphics, animations, bug fixes, and performance enhancements. You can download and install Stick War Legacy APK new version by following the steps mentioned above.
-FAQs
-Here are some frequently asked questions about Stick War Legacy APK new version:
-
-- Is Stick War Legacy APK new version safe to download and install?
Yes, Stick War Legacy APK new version is safe to download and install. It does not contain any viruses or malware. However, you should always download it from the official website or a trusted source.
-- Is Stick War Legacy APK new version free to play?
Yes, Stick War Legacy APK new version is free to play. However, it contains some in-app purchases that can enhance your gaming experience.
-- Can I play Stick War Legacy APK new version offline?
Yes, you can play Stick War Legacy APK new version offline. However, you need an internet connection to download and install it, and to access some features such as tournament mode and leaderboards.
-- Can I play Stick War Legacy APK new version on PC?
No, Stick War Legacy APK new version is only available for Android devices. However, you can use an Android emulator to play it on PC.
-- How can I contact the developers of Stick War Legacy APK new version?
You can contact the developers of Stick War Legacy APK new version by sending an email to support@maxgames.com or by visiting their website.
-
- : https://news.yahoo.com/nuclear-fusion-breakthrough-reactor-runs-130157687.html : https://www.thesun.co.uk/news/17143468/holy-grail-fusion-experiments-breakthrough-race-unlimited-energy/ : https://www.the-sun.com/news/4381435/holy-grail-fusion-experiments-breakthrough-race-unlimited-energy/ : https://www.newscientist.com/article/2336385-korean-nuclear-fusion-reactor-achieves-100-millionc-for-30-seconds/ : https://en.wikipedia.org/wiki/Sun : https://en.wikipedia.org/wiki/Solar_core : https://nssdc.gsfc.nasa.gov/planetary/factsheet/sunfact.html : https://stick-war-legacy.en.aptoide.com/app : https://www.maxgames.com/ 197e85843d
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diff --git a/spaces/contluForse/HuggingGPT/assets/Explode Arena 320x240 S60v3 Sis Cracked Might Modelisation A.md b/spaces/contluForse/HuggingGPT/assets/Explode Arena 320x240 S60v3 Sis Cracked Might Modelisation A.md
deleted file mode 100644
index c52af8fa5f6c6699ed2a5534fccdd86dbd74ecd3..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Explode Arena 320x240 S60v3 Sis Cracked Might Modelisation A.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Explode Arena 320x240 S60v3 Sis Cracked might modelisation a
DOWNLOAD ››› https://ssurll.com/2uzwc6
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cooelf/Multimodal-CoT/timm/models/coat.py b/spaces/cooelf/Multimodal-CoT/timm/models/coat.py
deleted file mode 100644
index f071715a347120ce0ca4710eceddda61de28ce8f..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/models/coat.py
+++ /dev/null
@@ -1,660 +0,0 @@
-"""
-CoaT architecture.
-
-Paper: Co-Scale Conv-Attentional Image Transformers - https://arxiv.org/abs/2104.06399
-
-Official CoaT code at: https://github.com/mlpc-ucsd/CoaT
-
-Modified from timm/models/vision_transformer.py
-"""
-from copy import deepcopy
-from functools import partial
-from typing import Tuple, List
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import build_model_with_cfg, overlay_external_default_cfg
-from .layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_
-from .registry import register_model
-
-
-__all__ = [
- "coat_tiny",
- "coat_mini",
- "coat_lite_tiny",
- "coat_lite_mini",
- "coat_lite_small"
-]
-
-
-def _cfg_coat(url='', **kwargs):
- return {
- 'url': url,
- 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
- 'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
- 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
- 'first_conv': 'patch_embed1.proj', 'classifier': 'head',
- **kwargs
- }
-
-
-default_cfgs = {
- 'coat_tiny': _cfg_coat(
- url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-coat-weights/coat_tiny-473c2a20.pth'
- ),
- 'coat_mini': _cfg_coat(
- url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-coat-weights/coat_mini-2c6baf49.pth'
- ),
- 'coat_lite_tiny': _cfg_coat(
- url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-coat-weights/coat_lite_tiny-461b07a7.pth'
- ),
- 'coat_lite_mini': _cfg_coat(
- url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-coat-weights/coat_lite_mini-d7842000.pth'
- ),
- 'coat_lite_small': _cfg_coat(
- url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-coat-weights/coat_lite_small-fea1d5a1.pth'
- ),
-}
-
-
-class ConvRelPosEnc(nn.Module):
- """ Convolutional relative position encoding. """
- def __init__(self, Ch, h, window):
- """
- Initialization.
- Ch: Channels per head.
- h: Number of heads.
- window: Window size(s) in convolutional relative positional encoding. It can have two forms:
- 1. An integer of window size, which assigns all attention heads with the same window s
- size in ConvRelPosEnc.
- 2. A dict mapping window size to #attention head splits (
- e.g. {window size 1: #attention head split 1, window size 2: #attention head split 2})
- It will apply different window size to the attention head splits.
- """
- super().__init__()
-
- if isinstance(window, int):
- # Set the same window size for all attention heads.
- window = {window: h}
- self.window = window
- elif isinstance(window, dict):
- self.window = window
- else:
- raise ValueError()
-
- self.conv_list = nn.ModuleList()
- self.head_splits = []
- for cur_window, cur_head_split in window.items():
- dilation = 1
- # Determine padding size.
- # Ref: https://discuss.pytorch.org/t/how-to-keep-the-shape-of-input-and-output-same-when-dilation-conv/14338
- padding_size = (cur_window + (cur_window - 1) * (dilation - 1)) // 2
- cur_conv = nn.Conv2d(cur_head_split*Ch, cur_head_split*Ch,
- kernel_size=(cur_window, cur_window),
- padding=(padding_size, padding_size),
- dilation=(dilation, dilation),
- groups=cur_head_split*Ch,
- )
- self.conv_list.append(cur_conv)
- self.head_splits.append(cur_head_split)
- self.channel_splits = [x*Ch for x in self.head_splits]
-
- def forward(self, q, v, size: Tuple[int, int]):
- B, h, N, Ch = q.shape
- H, W = size
- assert N == 1 + H * W
-
- # Convolutional relative position encoding.
- q_img = q[:, :, 1:, :] # [B, h, H*W, Ch]
- v_img = v[:, :, 1:, :] # [B, h, H*W, Ch]
-
- v_img = v_img.transpose(-1, -2).reshape(B, h * Ch, H, W)
- v_img_list = torch.split(v_img, self.channel_splits, dim=1) # Split according to channels
- conv_v_img_list = []
- for i, conv in enumerate(self.conv_list):
- conv_v_img_list.append(conv(v_img_list[i]))
- conv_v_img = torch.cat(conv_v_img_list, dim=1)
- conv_v_img = conv_v_img.reshape(B, h, Ch, H * W).transpose(-1, -2)
-
- EV_hat = q_img * conv_v_img
- EV_hat = F.pad(EV_hat, (0, 0, 1, 0, 0, 0)) # [B, h, N, Ch].
- return EV_hat
-
-
-class FactorAtt_ConvRelPosEnc(nn.Module):
- """ Factorized attention with convolutional relative position encoding class. """
- def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0., shared_crpe=None):
- super().__init__()
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = head_dim ** -0.5
-
- self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
- self.attn_drop = nn.Dropout(attn_drop) # Note: attn_drop is actually not used.
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
-
- # Shared convolutional relative position encoding.
- self.crpe = shared_crpe
-
- def forward(self, x, size: Tuple[int, int]):
- B, N, C = x.shape
-
- # Generate Q, K, V.
- 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] # [B, h, N, Ch]
-
- # Factorized attention.
- k_softmax = k.softmax(dim=2)
- factor_att = k_softmax.transpose(-1, -2) @ v
- factor_att = q @ factor_att
-
- # Convolutional relative position encoding.
- crpe = self.crpe(q, v, size=size) # [B, h, N, Ch]
-
- # Merge and reshape.
- x = self.scale * factor_att + crpe
- x = x.transpose(1, 2).reshape(B, N, C) # [B, h, N, Ch] -> [B, N, h, Ch] -> [B, N, C]
-
- # Output projection.
- x = self.proj(x)
- x = self.proj_drop(x)
-
- return x
-
-
-class ConvPosEnc(nn.Module):
- """ Convolutional Position Encoding.
- Note: This module is similar to the conditional position encoding in CPVT.
- """
- def __init__(self, dim, k=3):
- super(ConvPosEnc, self).__init__()
- self.proj = nn.Conv2d(dim, dim, k, 1, k//2, groups=dim)
-
- def forward(self, x, size: Tuple[int, int]):
- B, N, C = x.shape
- H, W = size
- assert N == 1 + H * W
-
- # Extract CLS token and image tokens.
- cls_token, img_tokens = x[:, :1], x[:, 1:] # [B, 1, C], [B, H*W, C]
-
- # Depthwise convolution.
- feat = img_tokens.transpose(1, 2).view(B, C, H, W)
- x = self.proj(feat) + feat
- x = x.flatten(2).transpose(1, 2)
-
- # Combine with CLS token.
- x = torch.cat((cls_token, x), dim=1)
-
- return x
-
-
-class SerialBlock(nn.Module):
- """ Serial block class.
- Note: In this implementation, each serial block only contains a conv-attention and a FFN (MLP) module. """
- def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
- drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, shared_cpe=None, shared_crpe=None):
- super().__init__()
-
- # Conv-Attention.
- self.cpe = shared_cpe
-
- self.norm1 = norm_layer(dim)
- self.factoratt_crpe = FactorAtt_ConvRelPosEnc(
- dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, shared_crpe=shared_crpe)
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-
- # MLP.
- 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, size: Tuple[int, int]):
- # Conv-Attention.
- x = self.cpe(x, size)
- cur = self.norm1(x)
- cur = self.factoratt_crpe(cur, size)
- x = x + self.drop_path(cur)
-
- # MLP.
- cur = self.norm2(x)
- cur = self.mlp(cur)
- x = x + self.drop_path(cur)
-
- return x
-
-
-class ParallelBlock(nn.Module):
- """ Parallel block class. """
- def __init__(self, dims, num_heads, mlp_ratios=[], qkv_bias=False, drop=0., attn_drop=0.,
- drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, shared_crpes=None):
- super().__init__()
-
- # Conv-Attention.
- self.norm12 = norm_layer(dims[1])
- self.norm13 = norm_layer(dims[2])
- self.norm14 = norm_layer(dims[3])
- self.factoratt_crpe2 = FactorAtt_ConvRelPosEnc(
- dims[1], num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
- shared_crpe=shared_crpes[1]
- )
- self.factoratt_crpe3 = FactorAtt_ConvRelPosEnc(
- dims[2], num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
- shared_crpe=shared_crpes[2]
- )
- self.factoratt_crpe4 = FactorAtt_ConvRelPosEnc(
- dims[3], num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
- shared_crpe=shared_crpes[3]
- )
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-
- # MLP.
- self.norm22 = norm_layer(dims[1])
- self.norm23 = norm_layer(dims[2])
- self.norm24 = norm_layer(dims[3])
- # In parallel block, we assume dimensions are the same and share the linear transformation.
- assert dims[1] == dims[2] == dims[3]
- assert mlp_ratios[1] == mlp_ratios[2] == mlp_ratios[3]
- mlp_hidden_dim = int(dims[1] * mlp_ratios[1])
- self.mlp2 = self.mlp3 = self.mlp4 = Mlp(
- in_features=dims[1], hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
- def upsample(self, x, factor: float, size: Tuple[int, int]):
- """ Feature map up-sampling. """
- return self.interpolate(x, scale_factor=factor, size=size)
-
- def downsample(self, x, factor: float, size: Tuple[int, int]):
- """ Feature map down-sampling. """
- return self.interpolate(x, scale_factor=1.0/factor, size=size)
-
- def interpolate(self, x, scale_factor: float, size: Tuple[int, int]):
- """ Feature map interpolation. """
- B, N, C = x.shape
- H, W = size
- assert N == 1 + H * W
-
- cls_token = x[:, :1, :]
- img_tokens = x[:, 1:, :]
-
- img_tokens = img_tokens.transpose(1, 2).reshape(B, C, H, W)
- img_tokens = F.interpolate(
- img_tokens, scale_factor=scale_factor, recompute_scale_factor=False, mode='bilinear', align_corners=False)
- img_tokens = img_tokens.reshape(B, C, -1).transpose(1, 2)
-
- out = torch.cat((cls_token, img_tokens), dim=1)
-
- return out
-
- def forward(self, x1, x2, x3, x4, sizes: List[Tuple[int, int]]):
- _, S2, S3, S4 = sizes
- cur2 = self.norm12(x2)
- cur3 = self.norm13(x3)
- cur4 = self.norm14(x4)
- cur2 = self.factoratt_crpe2(cur2, size=S2)
- cur3 = self.factoratt_crpe3(cur3, size=S3)
- cur4 = self.factoratt_crpe4(cur4, size=S4)
- upsample3_2 = self.upsample(cur3, factor=2., size=S3)
- upsample4_3 = self.upsample(cur4, factor=2., size=S4)
- upsample4_2 = self.upsample(cur4, factor=4., size=S4)
- downsample2_3 = self.downsample(cur2, factor=2., size=S2)
- downsample3_4 = self.downsample(cur3, factor=2., size=S3)
- downsample2_4 = self.downsample(cur2, factor=4., size=S2)
- cur2 = cur2 + upsample3_2 + upsample4_2
- cur3 = cur3 + upsample4_3 + downsample2_3
- cur4 = cur4 + downsample3_4 + downsample2_4
- x2 = x2 + self.drop_path(cur2)
- x3 = x3 + self.drop_path(cur3)
- x4 = x4 + self.drop_path(cur4)
-
- # MLP.
- cur2 = self.norm22(x2)
- cur3 = self.norm23(x3)
- cur4 = self.norm24(x4)
- cur2 = self.mlp2(cur2)
- cur3 = self.mlp3(cur3)
- cur4 = self.mlp4(cur4)
- x2 = x2 + self.drop_path(cur2)
- x3 = x3 + self.drop_path(cur3)
- x4 = x4 + self.drop_path(cur4)
-
- return x1, x2, x3, x4
-
-
-class CoaT(nn.Module):
- """ CoaT class. """
- def __init__(
- self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=(0, 0, 0, 0),
- serial_depths=(0, 0, 0, 0), parallel_depth=0, num_heads=0, mlp_ratios=(0, 0, 0, 0), qkv_bias=True,
- drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=partial(nn.LayerNorm, eps=1e-6),
- return_interm_layers=False, out_features=None, crpe_window=None, **kwargs):
- super().__init__()
- crpe_window = crpe_window or {3: 2, 5: 3, 7: 3}
- self.return_interm_layers = return_interm_layers
- self.out_features = out_features
- self.embed_dims = embed_dims
- self.num_features = embed_dims[-1]
- self.num_classes = num_classes
-
- # Patch embeddings.
- img_size = to_2tuple(img_size)
- self.patch_embed1 = PatchEmbed(
- img_size=img_size, patch_size=patch_size, in_chans=in_chans,
- embed_dim=embed_dims[0], norm_layer=nn.LayerNorm)
- self.patch_embed2 = PatchEmbed(
- img_size=[x // 4 for x in img_size], patch_size=2, in_chans=embed_dims[0],
- embed_dim=embed_dims[1], norm_layer=nn.LayerNorm)
- self.patch_embed3 = PatchEmbed(
- img_size=[x // 8 for x in img_size], patch_size=2, in_chans=embed_dims[1],
- embed_dim=embed_dims[2], norm_layer=nn.LayerNorm)
- self.patch_embed4 = PatchEmbed(
- img_size=[x // 16 for x in img_size], patch_size=2, in_chans=embed_dims[2],
- embed_dim=embed_dims[3], norm_layer=nn.LayerNorm)
-
- # Class tokens.
- self.cls_token1 = nn.Parameter(torch.zeros(1, 1, embed_dims[0]))
- self.cls_token2 = nn.Parameter(torch.zeros(1, 1, embed_dims[1]))
- self.cls_token3 = nn.Parameter(torch.zeros(1, 1, embed_dims[2]))
- self.cls_token4 = nn.Parameter(torch.zeros(1, 1, embed_dims[3]))
-
- # Convolutional position encodings.
- self.cpe1 = ConvPosEnc(dim=embed_dims[0], k=3)
- self.cpe2 = ConvPosEnc(dim=embed_dims[1], k=3)
- self.cpe3 = ConvPosEnc(dim=embed_dims[2], k=3)
- self.cpe4 = ConvPosEnc(dim=embed_dims[3], k=3)
-
- # Convolutional relative position encodings.
- self.crpe1 = ConvRelPosEnc(Ch=embed_dims[0] // num_heads, h=num_heads, window=crpe_window)
- self.crpe2 = ConvRelPosEnc(Ch=embed_dims[1] // num_heads, h=num_heads, window=crpe_window)
- self.crpe3 = ConvRelPosEnc(Ch=embed_dims[2] // num_heads, h=num_heads, window=crpe_window)
- self.crpe4 = ConvRelPosEnc(Ch=embed_dims[3] // num_heads, h=num_heads, window=crpe_window)
-
- # Disable stochastic depth.
- dpr = drop_path_rate
- assert dpr == 0.0
-
- # Serial blocks 1.
- self.serial_blocks1 = nn.ModuleList([
- SerialBlock(
- dim=embed_dims[0], num_heads=num_heads, mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias,
- drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer,
- shared_cpe=self.cpe1, shared_crpe=self.crpe1
- )
- for _ in range(serial_depths[0])]
- )
-
- # Serial blocks 2.
- self.serial_blocks2 = nn.ModuleList([
- SerialBlock(
- dim=embed_dims[1], num_heads=num_heads, mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias,
- drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer,
- shared_cpe=self.cpe2, shared_crpe=self.crpe2
- )
- for _ in range(serial_depths[1])]
- )
-
- # Serial blocks 3.
- self.serial_blocks3 = nn.ModuleList([
- SerialBlock(
- dim=embed_dims[2], num_heads=num_heads, mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias,
- drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer,
- shared_cpe=self.cpe3, shared_crpe=self.crpe3
- )
- for _ in range(serial_depths[2])]
- )
-
- # Serial blocks 4.
- self.serial_blocks4 = nn.ModuleList([
- SerialBlock(
- dim=embed_dims[3], num_heads=num_heads, mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias,
- drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer,
- shared_cpe=self.cpe4, shared_crpe=self.crpe4
- )
- for _ in range(serial_depths[3])]
- )
-
- # Parallel blocks.
- self.parallel_depth = parallel_depth
- if self.parallel_depth > 0:
- self.parallel_blocks = nn.ModuleList([
- ParallelBlock(
- dims=embed_dims, num_heads=num_heads, mlp_ratios=mlp_ratios, qkv_bias=qkv_bias,
- drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer,
- shared_crpes=(self.crpe1, self.crpe2, self.crpe3, self.crpe4)
- )
- for _ in range(parallel_depth)]
- )
- else:
- self.parallel_blocks = None
-
- # Classification head(s).
- if not self.return_interm_layers:
- if self.parallel_blocks is not None:
- self.norm2 = norm_layer(embed_dims[1])
- self.norm3 = norm_layer(embed_dims[2])
- else:
- self.norm2 = self.norm3 = None
- self.norm4 = norm_layer(embed_dims[3])
-
- if self.parallel_depth > 0:
- # CoaT series: Aggregate features of last three scales for classification.
- assert embed_dims[1] == embed_dims[2] == embed_dims[3]
- self.aggregate = torch.nn.Conv1d(in_channels=3, out_channels=1, kernel_size=1)
- self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
- else:
- # CoaT-Lite series: Use feature of last scale for classification.
- self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
-
- # Initialize weights.
- trunc_normal_(self.cls_token1, std=.02)
- trunc_normal_(self.cls_token2, std=.02)
- trunc_normal_(self.cls_token3, std=.02)
- trunc_normal_(self.cls_token4, 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)
-
- @torch.jit.ignore
- def no_weight_decay(self):
- return {'cls_token1', 'cls_token2', 'cls_token3', 'cls_token4'}
-
- def get_classifier(self):
- return self.head
-
- def reset_classifier(self, num_classes, global_pool=''):
- self.num_classes = num_classes
- self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
-
- def insert_cls(self, x, cls_token):
- """ Insert CLS token. """
- cls_tokens = cls_token.expand(x.shape[0], -1, -1)
- x = torch.cat((cls_tokens, x), dim=1)
- return x
-
- def remove_cls(self, x):
- """ Remove CLS token. """
- return x[:, 1:, :]
-
- def forward_features(self, x0):
- B = x0.shape[0]
-
- # Serial blocks 1.
- x1 = self.patch_embed1(x0)
- H1, W1 = self.patch_embed1.grid_size
- x1 = self.insert_cls(x1, self.cls_token1)
- for blk in self.serial_blocks1:
- x1 = blk(x1, size=(H1, W1))
- x1_nocls = self.remove_cls(x1)
- x1_nocls = x1_nocls.reshape(B, H1, W1, -1).permute(0, 3, 1, 2).contiguous()
-
- # Serial blocks 2.
- x2 = self.patch_embed2(x1_nocls)
- H2, W2 = self.patch_embed2.grid_size
- x2 = self.insert_cls(x2, self.cls_token2)
- for blk in self.serial_blocks2:
- x2 = blk(x2, size=(H2, W2))
- x2_nocls = self.remove_cls(x2)
- x2_nocls = x2_nocls.reshape(B, H2, W2, -1).permute(0, 3, 1, 2).contiguous()
-
- # Serial blocks 3.
- x3 = self.patch_embed3(x2_nocls)
- H3, W3 = self.patch_embed3.grid_size
- x3 = self.insert_cls(x3, self.cls_token3)
- for blk in self.serial_blocks3:
- x3 = blk(x3, size=(H3, W3))
- x3_nocls = self.remove_cls(x3)
- x3_nocls = x3_nocls.reshape(B, H3, W3, -1).permute(0, 3, 1, 2).contiguous()
-
- # Serial blocks 4.
- x4 = self.patch_embed4(x3_nocls)
- H4, W4 = self.patch_embed4.grid_size
- x4 = self.insert_cls(x4, self.cls_token4)
- for blk in self.serial_blocks4:
- x4 = blk(x4, size=(H4, W4))
- x4_nocls = self.remove_cls(x4)
- x4_nocls = x4_nocls.reshape(B, H4, W4, -1).permute(0, 3, 1, 2).contiguous()
-
- # Only serial blocks: Early return.
- if self.parallel_blocks is None:
- if not torch.jit.is_scripting() and self.return_interm_layers:
- # Return intermediate features for down-stream tasks (e.g. Deformable DETR and Detectron2).
- feat_out = {}
- if 'x1_nocls' in self.out_features:
- feat_out['x1_nocls'] = x1_nocls
- if 'x2_nocls' in self.out_features:
- feat_out['x2_nocls'] = x2_nocls
- if 'x3_nocls' in self.out_features:
- feat_out['x3_nocls'] = x3_nocls
- if 'x4_nocls' in self.out_features:
- feat_out['x4_nocls'] = x4_nocls
- return feat_out
- else:
- # Return features for classification.
- x4 = self.norm4(x4)
- x4_cls = x4[:, 0]
- return x4_cls
-
- # Parallel blocks.
- for blk in self.parallel_blocks:
- x2, x3, x4 = self.cpe2(x2, (H2, W2)), self.cpe3(x3, (H3, W3)), self.cpe4(x4, (H4, W4))
- x1, x2, x3, x4 = blk(x1, x2, x3, x4, sizes=[(H1, W1), (H2, W2), (H3, W3), (H4, W4)])
-
- if not torch.jit.is_scripting() and self.return_interm_layers:
- # Return intermediate features for down-stream tasks (e.g. Deformable DETR and Detectron2).
- feat_out = {}
- if 'x1_nocls' in self.out_features:
- x1_nocls = self.remove_cls(x1)
- x1_nocls = x1_nocls.reshape(B, H1, W1, -1).permute(0, 3, 1, 2).contiguous()
- feat_out['x1_nocls'] = x1_nocls
- if 'x2_nocls' in self.out_features:
- x2_nocls = self.remove_cls(x2)
- x2_nocls = x2_nocls.reshape(B, H2, W2, -1).permute(0, 3, 1, 2).contiguous()
- feat_out['x2_nocls'] = x2_nocls
- if 'x3_nocls' in self.out_features:
- x3_nocls = self.remove_cls(x3)
- x3_nocls = x3_nocls.reshape(B, H3, W3, -1).permute(0, 3, 1, 2).contiguous()
- feat_out['x3_nocls'] = x3_nocls
- if 'x4_nocls' in self.out_features:
- x4_nocls = self.remove_cls(x4)
- x4_nocls = x4_nocls.reshape(B, H4, W4, -1).permute(0, 3, 1, 2).contiguous()
- feat_out['x4_nocls'] = x4_nocls
- return feat_out
- else:
- x2 = self.norm2(x2)
- x3 = self.norm3(x3)
- x4 = self.norm4(x4)
- x2_cls = x2[:, :1] # [B, 1, C]
- x3_cls = x3[:, :1]
- x4_cls = x4[:, :1]
- merged_cls = torch.cat((x2_cls, x3_cls, x4_cls), dim=1) # [B, 3, C]
- merged_cls = self.aggregate(merged_cls).squeeze(dim=1) # Shape: [B, C]
- return merged_cls
-
- def forward(self, x):
- if self.return_interm_layers:
- # Return intermediate features (for down-stream tasks).
- return self.forward_features(x)
- else:
- # Return features for classification.
- x = self.forward_features(x)
- x = self.head(x)
- return x
-
-
-def checkpoint_filter_fn(state_dict, model):
- out_dict = {}
- for k, v in state_dict.items():
- # original model had unused norm layers, removing them requires filtering pretrained checkpoints
- if k.startswith('norm1') or \
- (model.norm2 is None and k.startswith('norm2')) or \
- (model.norm3 is None and k.startswith('norm3')):
- continue
- out_dict[k] = v
- return out_dict
-
-
-def _create_coat(variant, pretrained=False, default_cfg=None, **kwargs):
- if kwargs.get('features_only', None):
- raise RuntimeError('features_only not implemented for Vision Transformer models.')
-
- model = build_model_with_cfg(
- CoaT, variant, pretrained,
- default_cfg=default_cfgs[variant],
- pretrained_filter_fn=checkpoint_filter_fn,
- **kwargs)
- return model
-
-
-@register_model
-def coat_tiny(pretrained=False, **kwargs):
- model_cfg = dict(
- patch_size=4, embed_dims=[152, 152, 152, 152], serial_depths=[2, 2, 2, 2], parallel_depth=6,
- num_heads=8, mlp_ratios=[4, 4, 4, 4], **kwargs)
- model = _create_coat('coat_tiny', pretrained=pretrained, **model_cfg)
- return model
-
-
-@register_model
-def coat_mini(pretrained=False, **kwargs):
- model_cfg = dict(
- patch_size=4, embed_dims=[152, 216, 216, 216], serial_depths=[2, 2, 2, 2], parallel_depth=6,
- num_heads=8, mlp_ratios=[4, 4, 4, 4], **kwargs)
- model = _create_coat('coat_mini', pretrained=pretrained, **model_cfg)
- return model
-
-
-@register_model
-def coat_lite_tiny(pretrained=False, **kwargs):
- model_cfg = dict(
- patch_size=4, embed_dims=[64, 128, 256, 320], serial_depths=[2, 2, 2, 2], parallel_depth=0,
- num_heads=8, mlp_ratios=[8, 8, 4, 4], **kwargs)
- model = _create_coat('coat_lite_tiny', pretrained=pretrained, **model_cfg)
- return model
-
-
-@register_model
-def coat_lite_mini(pretrained=False, **kwargs):
- model_cfg = dict(
- patch_size=4, embed_dims=[64, 128, 320, 512], serial_depths=[2, 2, 2, 2], parallel_depth=0,
- num_heads=8, mlp_ratios=[8, 8, 4, 4], **kwargs)
- model = _create_coat('coat_lite_mini', pretrained=pretrained, **model_cfg)
- return model
-
-
-@register_model
-def coat_lite_small(pretrained=False, **kwargs):
- model_cfg = dict(
- patch_size=4, embed_dims=[64, 128, 320, 512], serial_depths=[3, 4, 6, 3], parallel_depth=0,
- num_heads=8, mlp_ratios=[8, 8, 4, 4], **kwargs)
- model = _create_coat('coat_lite_small', pretrained=pretrained, **model_cfg)
- return model
\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/parallel/distributed.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/parallel/distributed.py
deleted file mode 100644
index 929c7a451a7443d715ab0cceef530c53eff44cb9..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/parallel/distributed.py
+++ /dev/null
@@ -1,112 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from torch.nn.parallel.distributed import (DistributedDataParallel,
- _find_tensors)
-
-from annotator.mmpkg.mmcv import print_log
-from annotator.mmpkg.mmcv.utils import TORCH_VERSION, digit_version
-from .scatter_gather import scatter_kwargs
-
-
-class MMDistributedDataParallel(DistributedDataParallel):
- """The DDP module that supports DataContainer.
-
- MMDDP has two main differences with PyTorch DDP:
-
- - It supports a custom type :class:`DataContainer` which allows more
- flexible control of input data.
- - It implement two APIs ``train_step()`` and ``val_step()``.
- """
-
- def to_kwargs(self, inputs, kwargs, device_id):
- # Use `self.to_kwargs` instead of `self.scatter` in pytorch1.8
- # to move all tensors to device_id
- return scatter_kwargs(inputs, kwargs, [device_id], dim=self.dim)
-
- def scatter(self, inputs, kwargs, device_ids):
- return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim)
-
- def train_step(self, *inputs, **kwargs):
- """train_step() API for module wrapped by DistributedDataParallel.
-
- This method is basically the same as
- ``DistributedDataParallel.forward()``, while replacing
- ``self.module.forward()`` with ``self.module.train_step()``.
- It is compatible with PyTorch 1.1 - 1.5.
- """
-
- # In PyTorch >= 1.7, ``reducer._rebuild_buckets()`` is moved from the
- # end of backward to the beginning of forward.
- if ('parrots' not in TORCH_VERSION
- and digit_version(TORCH_VERSION) >= digit_version('1.7')
- and self.reducer._rebuild_buckets()):
- print_log(
- 'Reducer buckets have been rebuilt in this iteration.',
- logger='mmcv')
-
- if getattr(self, 'require_forward_param_sync', True):
- self._sync_params()
- if self.device_ids:
- inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
- if len(self.device_ids) == 1:
- output = self.module.train_step(*inputs[0], **kwargs[0])
- else:
- outputs = self.parallel_apply(
- self._module_copies[:len(inputs)], inputs, kwargs)
- output = self.gather(outputs, self.output_device)
- else:
- output = self.module.train_step(*inputs, **kwargs)
-
- if torch.is_grad_enabled() and getattr(
- self, 'require_backward_grad_sync', True):
- if self.find_unused_parameters:
- self.reducer.prepare_for_backward(list(_find_tensors(output)))
- else:
- self.reducer.prepare_for_backward([])
- else:
- if ('parrots' not in TORCH_VERSION
- and digit_version(TORCH_VERSION) > digit_version('1.2')):
- self.require_forward_param_sync = False
- return output
-
- def val_step(self, *inputs, **kwargs):
- """val_step() API for module wrapped by DistributedDataParallel.
-
- This method is basically the same as
- ``DistributedDataParallel.forward()``, while replacing
- ``self.module.forward()`` with ``self.module.val_step()``.
- It is compatible with PyTorch 1.1 - 1.5.
- """
- # In PyTorch >= 1.7, ``reducer._rebuild_buckets()`` is moved from the
- # end of backward to the beginning of forward.
- if ('parrots' not in TORCH_VERSION
- and digit_version(TORCH_VERSION) >= digit_version('1.7')
- and self.reducer._rebuild_buckets()):
- print_log(
- 'Reducer buckets have been rebuilt in this iteration.',
- logger='mmcv')
-
- if getattr(self, 'require_forward_param_sync', True):
- self._sync_params()
- if self.device_ids:
- inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
- if len(self.device_ids) == 1:
- output = self.module.val_step(*inputs[0], **kwargs[0])
- else:
- outputs = self.parallel_apply(
- self._module_copies[:len(inputs)], inputs, kwargs)
- output = self.gather(outputs, self.output_device)
- else:
- output = self.module.val_step(*inputs, **kwargs)
-
- if torch.is_grad_enabled() and getattr(
- self, 'require_backward_grad_sync', True):
- if self.find_unused_parameters:
- self.reducer.prepare_for_backward(list(_find_tensors(output)))
- else:
- self.reducer.prepare_for_backward([])
- else:
- if ('parrots' not in TORCH_VERSION
- and digit_version(TORCH_VERSION) > digit_version('1.2')):
- self.require_forward_param_sync = False
- return output
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/decode_heads/dnl_head.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/decode_heads/dnl_head.py
deleted file mode 100644
index b3bb1de1499ad043cc51b2269b4d970d07c16076..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/decode_heads/dnl_head.py
+++ /dev/null
@@ -1,131 +0,0 @@
-import torch
-from annotator.mmpkg.mmcv.cnn import NonLocal2d
-from torch import nn
-
-from ..builder import HEADS
-from .fcn_head import FCNHead
-
-
-class DisentangledNonLocal2d(NonLocal2d):
- """Disentangled Non-Local Blocks.
-
- Args:
- temperature (float): Temperature to adjust attention. Default: 0.05
- """
-
- def __init__(self, *arg, temperature, **kwargs):
- super().__init__(*arg, **kwargs)
- self.temperature = temperature
- self.conv_mask = nn.Conv2d(self.in_channels, 1, kernel_size=1)
-
- def embedded_gaussian(self, theta_x, phi_x):
- """Embedded gaussian with temperature."""
-
- # NonLocal2d pairwise_weight: [N, HxW, HxW]
- pairwise_weight = torch.matmul(theta_x, phi_x)
- if self.use_scale:
- # theta_x.shape[-1] is `self.inter_channels`
- pairwise_weight /= theta_x.shape[-1]**0.5
- pairwise_weight /= self.temperature
- pairwise_weight = pairwise_weight.softmax(dim=-1)
- return pairwise_weight
-
- def forward(self, x):
- # x: [N, C, H, W]
- n = x.size(0)
-
- # g_x: [N, HxW, C]
- g_x = self.g(x).view(n, self.inter_channels, -1)
- g_x = g_x.permute(0, 2, 1)
-
- # theta_x: [N, HxW, C], phi_x: [N, C, HxW]
- if self.mode == 'gaussian':
- theta_x = x.view(n, self.in_channels, -1)
- theta_x = theta_x.permute(0, 2, 1)
- if self.sub_sample:
- phi_x = self.phi(x).view(n, self.in_channels, -1)
- else:
- phi_x = x.view(n, self.in_channels, -1)
- elif self.mode == 'concatenation':
- theta_x = self.theta(x).view(n, self.inter_channels, -1, 1)
- phi_x = self.phi(x).view(n, self.inter_channels, 1, -1)
- else:
- theta_x = self.theta(x).view(n, self.inter_channels, -1)
- theta_x = theta_x.permute(0, 2, 1)
- phi_x = self.phi(x).view(n, self.inter_channels, -1)
-
- # subtract mean
- theta_x -= theta_x.mean(dim=-2, keepdim=True)
- phi_x -= phi_x.mean(dim=-1, keepdim=True)
-
- pairwise_func = getattr(self, self.mode)
- # pairwise_weight: [N, HxW, HxW]
- pairwise_weight = pairwise_func(theta_x, phi_x)
-
- # y: [N, HxW, C]
- y = torch.matmul(pairwise_weight, g_x)
- # y: [N, C, H, W]
- y = y.permute(0, 2, 1).contiguous().reshape(n, self.inter_channels,
- *x.size()[2:])
-
- # unary_mask: [N, 1, HxW]
- unary_mask = self.conv_mask(x)
- unary_mask = unary_mask.view(n, 1, -1)
- unary_mask = unary_mask.softmax(dim=-1)
- # unary_x: [N, 1, C]
- unary_x = torch.matmul(unary_mask, g_x)
- # unary_x: [N, C, 1, 1]
- unary_x = unary_x.permute(0, 2, 1).contiguous().reshape(
- n, self.inter_channels, 1, 1)
-
- output = x + self.conv_out(y + unary_x)
-
- return output
-
-
-@HEADS.register_module()
-class DNLHead(FCNHead):
- """Disentangled Non-Local Neural Networks.
-
- This head is the implementation of `DNLNet
- `_.
-
- Args:
- reduction (int): Reduction factor of projection transform. Default: 2.
- use_scale (bool): Whether to scale pairwise_weight by
- sqrt(1/inter_channels). Default: False.
- mode (str): The nonlocal mode. Options are 'embedded_gaussian',
- 'dot_product'. Default: 'embedded_gaussian.'.
- temperature (float): Temperature to adjust attention. Default: 0.05
- """
-
- def __init__(self,
- reduction=2,
- use_scale=True,
- mode='embedded_gaussian',
- temperature=0.05,
- **kwargs):
- super(DNLHead, self).__init__(num_convs=2, **kwargs)
- self.reduction = reduction
- self.use_scale = use_scale
- self.mode = mode
- self.temperature = temperature
- self.dnl_block = DisentangledNonLocal2d(
- in_channels=self.channels,
- reduction=self.reduction,
- use_scale=self.use_scale,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- mode=self.mode,
- temperature=self.temperature)
-
- def forward(self, inputs):
- """Forward function."""
- x = self._transform_inputs(inputs)
- output = self.convs[0](x)
- output = self.dnl_block(output)
- output = self.convs[1](output)
- if self.concat_input:
- output = self.conv_cat(torch.cat([x, output], dim=1))
- output = self.cls_seg(output)
- return output
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/utils/video_visualizer.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/utils/video_visualizer.py
deleted file mode 100644
index 591c3ad3d551c421e923378fbc48fb44facc7257..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/utils/video_visualizer.py
+++ /dev/null
@@ -1,287 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-from typing import List
-import pycocotools.mask as mask_util
-
-from annotator.oneformer.detectron2.structures import Instances
-from annotator.oneformer.detectron2.utils.visualizer import (
- ColorMode,
- Visualizer,
- _create_text_labels,
- _PanopticPrediction,
-)
-
-from .colormap import random_color, random_colors
-
-
-class _DetectedInstance:
- """
- Used to store data about detected objects in video frame,
- in order to transfer color to objects in the future frames.
-
- Attributes:
- label (int):
- bbox (tuple[float]):
- mask_rle (dict):
- color (tuple[float]): RGB colors in range (0, 1)
- ttl (int): time-to-live for the instance. For example, if ttl=2,
- the instance color can be transferred to objects in the next two frames.
- """
-
- __slots__ = ["label", "bbox", "mask_rle", "color", "ttl"]
-
- def __init__(self, label, bbox, mask_rle, color, ttl):
- self.label = label
- self.bbox = bbox
- self.mask_rle = mask_rle
- self.color = color
- self.ttl = ttl
-
-
-class VideoVisualizer:
- def __init__(self, metadata, instance_mode=ColorMode.IMAGE):
- """
- Args:
- metadata (MetadataCatalog): image metadata.
- """
- self.metadata = metadata
- self._old_instances = []
- assert instance_mode in [
- ColorMode.IMAGE,
- ColorMode.IMAGE_BW,
- ], "Other mode not supported yet."
- self._instance_mode = instance_mode
- self._max_num_instances = self.metadata.get("max_num_instances", 74)
- self._assigned_colors = {}
- self._color_pool = random_colors(self._max_num_instances, rgb=True, maximum=1)
- self._color_idx_set = set(range(len(self._color_pool)))
-
- def draw_instance_predictions(self, frame, predictions):
- """
- Draw instance-level prediction results on an image.
-
- Args:
- frame (ndarray): an RGB image of shape (H, W, C), in the range [0, 255].
- predictions (Instances): the output of an instance detection/segmentation
- model. Following fields will be used to draw:
- "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle").
-
- Returns:
- output (VisImage): image object with visualizations.
- """
- frame_visualizer = Visualizer(frame, self.metadata)
- num_instances = len(predictions)
- if num_instances == 0:
- return frame_visualizer.output
-
- boxes = predictions.pred_boxes.tensor.numpy() if predictions.has("pred_boxes") else None
- scores = predictions.scores if predictions.has("scores") else None
- classes = predictions.pred_classes.numpy() if predictions.has("pred_classes") else None
- keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None
- colors = predictions.COLOR if predictions.has("COLOR") else [None] * len(predictions)
- periods = predictions.ID_period if predictions.has("ID_period") else None
- period_threshold = self.metadata.get("period_threshold", 0)
- visibilities = (
- [True] * len(predictions)
- if periods is None
- else [x > period_threshold for x in periods]
- )
-
- if predictions.has("pred_masks"):
- masks = predictions.pred_masks
- # mask IOU is not yet enabled
- # masks_rles = mask_util.encode(np.asarray(masks.permute(1, 2, 0), order="F"))
- # assert len(masks_rles) == num_instances
- else:
- masks = None
-
- if not predictions.has("COLOR"):
- if predictions.has("ID"):
- colors = self._assign_colors_by_id(predictions)
- else:
- # ToDo: clean old assign color method and use a default tracker to assign id
- detected = [
- _DetectedInstance(classes[i], boxes[i], mask_rle=None, color=colors[i], ttl=8)
- for i in range(num_instances)
- ]
- colors = self._assign_colors(detected)
-
- labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None))
-
- if self._instance_mode == ColorMode.IMAGE_BW:
- # any() returns uint8 tensor
- frame_visualizer.output.reset_image(
- frame_visualizer._create_grayscale_image(
- (masks.any(dim=0) > 0).numpy() if masks is not None else None
- )
- )
- alpha = 0.3
- else:
- alpha = 0.5
-
- labels = (
- None
- if labels is None
- else [y[0] for y in filter(lambda x: x[1], zip(labels, visibilities))]
- ) # noqa
- assigned_colors = (
- None
- if colors is None
- else [y[0] for y in filter(lambda x: x[1], zip(colors, visibilities))]
- ) # noqa
- frame_visualizer.overlay_instances(
- boxes=None if masks is not None else boxes[visibilities], # boxes are a bit distracting
- masks=None if masks is None else masks[visibilities],
- labels=labels,
- keypoints=None if keypoints is None else keypoints[visibilities],
- assigned_colors=assigned_colors,
- alpha=alpha,
- )
-
- return frame_visualizer.output
-
- def draw_sem_seg(self, frame, sem_seg, area_threshold=None):
- """
- Args:
- sem_seg (ndarray or Tensor): semantic segmentation of shape (H, W),
- each value is the integer label.
- area_threshold (Optional[int]): only draw segmentations larger than the threshold
- """
- # don't need to do anything special
- frame_visualizer = Visualizer(frame, self.metadata)
- frame_visualizer.draw_sem_seg(sem_seg, area_threshold=None)
- return frame_visualizer.output
-
- def draw_panoptic_seg_predictions(
- self, frame, panoptic_seg, segments_info, area_threshold=None, alpha=0.5
- ):
- frame_visualizer = Visualizer(frame, self.metadata)
- pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata)
-
- if self._instance_mode == ColorMode.IMAGE_BW:
- frame_visualizer.output.reset_image(
- frame_visualizer._create_grayscale_image(pred.non_empty_mask())
- )
-
- # draw mask for all semantic segments first i.e. "stuff"
- for mask, sinfo in pred.semantic_masks():
- category_idx = sinfo["category_id"]
- try:
- mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]]
- except AttributeError:
- mask_color = None
-
- frame_visualizer.draw_binary_mask(
- mask,
- color=mask_color,
- text=self.metadata.stuff_classes[category_idx],
- alpha=alpha,
- area_threshold=area_threshold,
- )
-
- all_instances = list(pred.instance_masks())
- if len(all_instances) == 0:
- return frame_visualizer.output
- # draw mask for all instances second
- masks, sinfo = list(zip(*all_instances))
- num_instances = len(masks)
- masks_rles = mask_util.encode(
- np.asarray(np.asarray(masks).transpose(1, 2, 0), dtype=np.uint8, order="F")
- )
- assert len(masks_rles) == num_instances
-
- category_ids = [x["category_id"] for x in sinfo]
- detected = [
- _DetectedInstance(category_ids[i], bbox=None, mask_rle=masks_rles[i], color=None, ttl=8)
- for i in range(num_instances)
- ]
- colors = self._assign_colors(detected)
- labels = [self.metadata.thing_classes[k] for k in category_ids]
-
- frame_visualizer.overlay_instances(
- boxes=None,
- masks=masks,
- labels=labels,
- keypoints=None,
- assigned_colors=colors,
- alpha=alpha,
- )
- return frame_visualizer.output
-
- def _assign_colors(self, instances):
- """
- Naive tracking heuristics to assign same color to the same instance,
- will update the internal state of tracked instances.
-
- Returns:
- list[tuple[float]]: list of colors.
- """
-
- # Compute iou with either boxes or masks:
- is_crowd = np.zeros((len(instances),), dtype=bool)
- if instances[0].bbox is None:
- assert instances[0].mask_rle is not None
- # use mask iou only when box iou is None
- # because box seems good enough
- rles_old = [x.mask_rle for x in self._old_instances]
- rles_new = [x.mask_rle for x in instances]
- ious = mask_util.iou(rles_old, rles_new, is_crowd)
- threshold = 0.5
- else:
- boxes_old = [x.bbox for x in self._old_instances]
- boxes_new = [x.bbox for x in instances]
- ious = mask_util.iou(boxes_old, boxes_new, is_crowd)
- threshold = 0.6
- if len(ious) == 0:
- ious = np.zeros((len(self._old_instances), len(instances)), dtype="float32")
-
- # Only allow matching instances of the same label:
- for old_idx, old in enumerate(self._old_instances):
- for new_idx, new in enumerate(instances):
- if old.label != new.label:
- ious[old_idx, new_idx] = 0
-
- matched_new_per_old = np.asarray(ious).argmax(axis=1)
- max_iou_per_old = np.asarray(ious).max(axis=1)
-
- # Try to find match for each old instance:
- extra_instances = []
- for idx, inst in enumerate(self._old_instances):
- if max_iou_per_old[idx] > threshold:
- newidx = matched_new_per_old[idx]
- if instances[newidx].color is None:
- instances[newidx].color = inst.color
- continue
- # If an old instance does not match any new instances,
- # keep it for the next frame in case it is just missed by the detector
- inst.ttl -= 1
- if inst.ttl > 0:
- extra_instances.append(inst)
-
- # Assign random color to newly-detected instances:
- for inst in instances:
- if inst.color is None:
- inst.color = random_color(rgb=True, maximum=1)
- self._old_instances = instances[:] + extra_instances
- return [d.color for d in instances]
-
- def _assign_colors_by_id(self, instances: Instances) -> List:
- colors = []
- untracked_ids = set(self._assigned_colors.keys())
- for id in instances.ID:
- if id in self._assigned_colors:
- colors.append(self._color_pool[self._assigned_colors[id]])
- untracked_ids.remove(id)
- else:
- assert (
- len(self._color_idx_set) >= 1
- ), f"Number of id exceeded maximum, \
- max = {self._max_num_instances}"
- idx = self._color_idx_set.pop()
- color = self._color_pool[idx]
- self._assigned_colors[id] = idx
- colors.append(color)
- for id in untracked_ids:
- self._color_idx_set.add(self._assigned_colors[id])
- del self._assigned_colors[id]
- return colors
diff --git a/spaces/csuer/vits/transforms.py b/spaces/csuer/vits/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/csuer/vits/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
-
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {
- 'tails': tails,
- 'tail_bound': tail_bound
- }
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(
- inputs[..., None] >= bin_locations,
- dim=-1
- ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails='linear',
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == 'linear':
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError('{} tails are not implemented.'.format(tails))
-
- outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative
- )
-
- return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0., right=1., bottom=0., top=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError('Input to a transform is not within its domain')
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError('Minimal bin width too large for the number of bins')
- if min_bin_height * num_bins > 1.0:
- raise ValueError('Minimal bin height too large for the number of bins')
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (((inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta)
- + input_heights * (input_delta - input_derivatives)))
- b = (input_heights * input_derivatives
- - (inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta))
- c = - input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (input_delta * theta.pow(2)
- + input_derivatives * theta_one_minus_theta)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/cynika/NFT_avatar/nft.py b/spaces/cynika/NFT_avatar/nft.py
deleted file mode 100644
index 0ee426cd08344bb5458c244848c8018ff9009bef..0000000000000000000000000000000000000000
--- a/spaces/cynika/NFT_avatar/nft.py
+++ /dev/null
@@ -1,140 +0,0 @@
-import time
-import requests
-from hashlib import md5
-from typing import Union
-from urllib.parse import urlencode
-from requests_toolbelt.multipart.encoder import MultipartEncoder
-import imghdr
-
-
-class Crypto:
- APPKEY = '4409e2ce8ffd12b8'
- APPSECRET = '59b43e04ad6965f34319062b478f83dd'
-
- @staticmethod
- def md5(data: Union[str, bytes]) -> str:
- '''generates md5 hex dump of `str` or `bytes`'''
- if type(data) == str:
- return md5(data.encode()).hexdigest()
- return md5(data).hexdigest()
-
- @staticmethod
- def sign(data: Union[str, dict]) -> str:
- '''salted sign funtion for `dict`(converts to qs then parse) & `str`'''
- if isinstance(data, dict):
- _str = urlencode(data)
- elif type(data) != str:
- raise TypeError
- return Crypto.md5(_str + Crypto.APPSECRET)
-
-
-class SingableDict(dict):
- @property
- def sorted(self):
- '''returns a alphabetically sorted version of `self`'''
- return dict(sorted(self.items()))
-
- @property
- def signed(self):
- '''returns our sorted self with calculated `sign` as a new key-value pair at the end'''
- _sorted = self.sorted
- return {**_sorted, 'sign': Crypto.sign(_sorted)}
-
-
-def get_image_type(file_path):
- with open(file_path, 'rb') as f:
- data = f.read()
- return imghdr.what(None, data)
-
-
-def get_have_card_id_list(UID, ACCESS_KEY, sid):
- url = "https://api.bilibili.com/x/vas/nftcard/cardlist"
- params = SingableDict(
- {
- "access_key": ACCESS_KEY,
- "act_id": sid, # 这里默认已经是三体数字藏品卡14，github下载的默认是4，也就是胶囊卡
- "appkey": "4409e2ce8ffd12b8",
- "disable_rcmd": "0",
- "ruid": UID,
- "statistics": "{\"appId\":1,\"platform\":3,\"version\":\"7.9.0\",\"abtest\":\"\"}",
- "ts": int(time.time()),
- }
- ).signed
- response = requests.request("GET", url, params=params)
- data = response.json()
- if data['code'] != 0:
- print(f"获取卡片信息出错，下面是 api 返回：\n{data}")
- return
- # print(data) # 查询已添加无需再填
- have_card_id_list = {}
- try:
- for round in data['data']['round_list']:
- for card in round['card_list']:
- if card['card_id_list']:
- print(f"找到付费卡 card_id: {card['card_id_list'][0]['card_id']}\n这个id属于{card['card_name']}")
- have_card_id_list.update({card['card_name']: card['card_id_list'][0]['card_id']})
- if data["data"]["pre_list"]:
- for i in data["data"]["pre_list"]:
- if card_id_list := i.get("card_id_list"):
- for j in card_id_list:
- if card_id := j.get("card_id"):
- print(f"找到预约卡card_id: {card_id}\n这个 id 属于{i.get('card_name')}")
- have_card_id_list.update({i.get('card_name'): card_id})
- except Exception as e:
- print(f"处理卡牌列表出错{e.args[0]}，下面是 api 返回：\n{data}")
- return {}
- if have_card_id_list:
- print(f"已经找到卡片")
- return have_card_id_list
- else:
- print("没有找到可用的卡片")
- return {}
-
-
-def set_face(card_id, ACCESS_KEY, img_data):
- api = "https://api.bilibili.com/x/member/app/face/digitalKit/update"
- params = SingableDict(
- {
- "access_key": ACCESS_KEY,
- "appkey": "4409e2ce8ffd12b8",
- "build": "7090300",
- "c_locale": "zh_CN",
- "channel": "xiaomi",
- "disable_rcmd": "0",
- "mobi_app": "android",
- "platform": "android",
- "s_locale": "zh_CN",
- "statistics": "{\"appId\":1,\"platform\":3,\"version\":\"7.9.0\",\"abtest\":\"\"}",
- "ts": int(time.time()),
- }
- ).signed
- m = MultipartEncoder(
- fields={
- 'digital_kit_id': str(card_id),
- 'face': ('face', img_data, 'application/octet-stream'),
- }
- )
- headers = {
- "Content-Type": m.content_type,
- }
- response = requests.request("POST", api, data=m, headers=headers, params=params)
- if response.json()['code'] != 0:
- return False, response.json()
- return True, '设置头像成功, 请等待审核'
-
-
-def having_card_id_list(uid, key, sid):
- uid = int(uid)
- access_key = str(key)
- sid = str(sid)
-
- had_card_id_list = get_have_card_id_list(uid, access_key, sid) # 根据你所取得的卡card_id进行更改
- if had_card_id_list:
- return True, had_card_id_list, "已找到拥有卡牌"
- return False, {}, "没找到任何card_id ,请确认当前卡组已拥有卡片"
-
-
-def card_id_set_ava(card_id, key, img_data):
- access_key = str(key)
- result, code = set_face(card_id, access_key, img_data)
- return result, code
diff --git a/spaces/dachenchen/real/ChuanhuChatbot.py b/spaces/dachenchen/real/ChuanhuChatbot.py
deleted file mode 100644
index 6b1a08a8b1dbb5a60cc11d3b47ffc85a329492a2..0000000000000000000000000000000000000000
--- a/spaces/dachenchen/real/ChuanhuChatbot.py
+++ /dev/null
@@ -1,159 +0,0 @@
-import gradio as gr
-# import openai
-import os
-import sys
-import argparse
-from utils import *
-from presets import *
-
-
-my_api_key = "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" # 在这里输入你的 API 密钥
-
-#if we are running in Docker
-if os.environ.get('dockerrun') == 'yes':
- dockerflag = True
-else:
- dockerflag = False
-
-authflag = False
-
-if dockerflag:
- my_api_key = os.environ.get('my_api_key')
- if my_api_key == "empty":
- print("Please give a api key!")
- sys.exit(1)
- #auth
- username = os.environ.get('USERNAME')
- password = os.environ.get('PASSWORD')
- if not (isinstance(username, type(None)) or isinstance(password, type(None))):
- authflag = True
-else:
- if not my_api_key and os.path.exists("api_key.txt") and os.path.getsize("api_key.txt"):
- with open("api_key.txt", "r") as f:
- my_api_key = f.read().strip()
- if os.path.exists("auth.json"):
- with open("auth.json", "r") as f:
- auth = json.load(f)
- username = auth["username"]
- password = auth["password"]
- if username != "" and password != "":
- authflag = True
-
-gr.Chatbot.postprocess = postprocess
-
-with gr.Blocks(css=customCSS) as demo:
- gr.HTML(title)
- with gr.Row():
- keyTxt = gr.Textbox(show_label=False, placeholder=f"在这里输入你的OpenAI API-key...",
- value=my_api_key, type="password", visible=not HIDE_MY_KEY).style(container=True)
- use_streaming_checkbox = gr.Checkbox(label="实时传输回答", value=True, visible=enable_streaming_option)
- chatbot = gr.Chatbot() # .style(color_map=("#1D51EE", "#585A5B"))
- history = gr.State([])
- token_count = gr.State([])
- promptTemplates = gr.State(load_template(get_template_names(plain=True)[0], mode=2))
- TRUECOMSTANT = gr.State(True)
- FALSECONSTANT = gr.State(False)
- topic = gr.State("未命名对话历史记录")
-
- with gr.Row():
- with gr.Column(scale=12):
- user_input = gr.Textbox(show_label=False, placeholder="在这里输入").style(
- container=False)
- with gr.Column(min_width=50, scale=1):
- submitBtn = gr.Button("🚀", variant="primary")
- with gr.Row():
- emptyBtn = gr.Button("🧹 新的对话")
- retryBtn = gr.Button("🔄 重新生成")
- delLastBtn = gr.Button("🗑️ 删除最近一条对话")
- reduceTokenBtn = gr.Button("♻️ 总结对话")
- status_display = gr.Markdown("status: ready")
- systemPromptTxt = gr.Textbox(show_label=True, placeholder=f"在这里输入System Prompt...",
- label="System prompt", value=initial_prompt).style(container=True)
- with gr.Accordion(label="加载Prompt模板", open=False):
- with gr.Column():
- with gr.Row():
- with gr.Column(scale=6):
- templateFileSelectDropdown = gr.Dropdown(label="选择Prompt模板集合文件", choices=get_template_names(plain=True), multiselect=False, value=get_template_names(plain=True)[0])
- with gr.Column(scale=1):
- templateRefreshBtn = gr.Button("🔄 刷新")
- templaeFileReadBtn = gr.Button("📂 读入模板")
- with gr.Row():
- with gr.Column(scale=6):
- templateSelectDropdown = gr.Dropdown(label="从Prompt模板中加载", choices=load_template(get_template_names(plain=True)[0], mode=1), multiselect=False, value=load_template(get_template_names(plain=True)[0], mode=1)[0])
- with gr.Column(scale=1):
- templateApplyBtn = gr.Button("⬇️ 应用")
- with gr.Accordion(label="保存/加载对话历史记录", open=False):
- with gr.Column():
- with gr.Row():
- with gr.Column(scale=6):
- saveFileName = gr.Textbox(
- show_label=True, placeholder=f"在这里输入保存的文件名...", label="设置保存文件名", value="对话历史记录").style(container=True)
- with gr.Column(scale=1):
- saveHistoryBtn = gr.Button("💾 保存对话")
- with gr.Row():
- with gr.Column(scale=6):
- historyFileSelectDropdown = gr.Dropdown(label="从列表中加载对话", choices=get_history_names(plain=True), multiselect=False, value=get_history_names(plain=True)[0])
- with gr.Column(scale=1):
- historyRefreshBtn = gr.Button("🔄 刷新")
- historyReadBtn = gr.Button("📂 读入对话")
- #inputs, top_p, temperature, top_k, repetition_penalty
- with gr.Accordion("参数", open=False):
- top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.05,
- interactive=True, label="Top-p (nucleus sampling)",)
- temperature = gr.Slider(minimum=-0, maximum=5.0, value=1.0,
- step=0.1, interactive=True, label="Temperature",)
- #top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
- #repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
- gr.Markdown(description)
-
-
- user_input.submit(predict, [keyTxt, systemPromptTxt, history, user_input, chatbot, token_count, top_p, temperature, use_streaming_checkbox], [chatbot, history, status_display, token_count], show_progress=True)
- user_input.submit(reset_textbox, [], [user_input])
-
- submitBtn.click(predict, [keyTxt, systemPromptTxt, history, user_input, chatbot, token_count, top_p, temperature, use_streaming_checkbox], [chatbot, history, status_display, token_count], show_progress=True)
- submitBtn.click(reset_textbox, [], [user_input])
-
- emptyBtn.click(reset_state, outputs=[chatbot, history, token_count, status_display], show_progress=True)
-
- retryBtn.click(retry, [keyTxt, systemPromptTxt, history, chatbot, token_count, top_p, temperature, use_streaming_checkbox], [chatbot, history, status_display, token_count], show_progress=True)
-
- delLastBtn.click(delete_last_conversation, [chatbot, history, token_count, use_streaming_checkbox], [
- chatbot, history, token_count, status_display], show_progress=True)
-
- reduceTokenBtn.click(reduce_token_size, [keyTxt, systemPromptTxt, history, chatbot, token_count, top_p, temperature, use_streaming_checkbox], [chatbot, history, status_display, token_count], show_progress=True)
-
- saveHistoryBtn.click(save_chat_history, [
- saveFileName, systemPromptTxt, history, chatbot], None, show_progress=True)
-
- saveHistoryBtn.click(get_history_names, None, [historyFileSelectDropdown])
-
- historyRefreshBtn.click(get_history_names, None, [historyFileSelectDropdown])
-
- historyReadBtn.click(load_chat_history, [historyFileSelectDropdown, systemPromptTxt, history, chatbot], [saveFileName, systemPromptTxt, history, chatbot], show_progress=True)
-
- templateRefreshBtn.click(get_template_names, None, [templateFileSelectDropdown])
-
- templaeFileReadBtn.click(load_template, [templateFileSelectDropdown], [promptTemplates, templateSelectDropdown], show_progress=True)
-
- templateApplyBtn.click(get_template_content, [promptTemplates, templateSelectDropdown, systemPromptTxt], [systemPromptTxt], show_progress=True)
-
-print("温馨提示：访问 http://localhost:7860 查看界面")
-# 默认开启本地服务器，默认可以直接从IP访问，默认不创建公开分享链接
-demo.title = "大陈陈ChatGPT 🚀"
-
-if __name__ == "__main__":
- #if running in Docker
- if dockerflag:
- if authflag:
- demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=(username, password))
- else:
- demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False)
- #if not running in Docker
- else:
- if authflag:
- demo.queue().launch(share=False, auth=(username, password))
- else:
- demo.queue().launch(share=False) # 改为 share=True 可以创建公开分享链接
- #demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False) # 可自定义端口
- #demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=("在这里填写用户名", "在这里填写密码")) # 可设置用户名与密码
- #demo.queue().launch(auth=("在这里填写用户名", "在这里填写密码")) # 适合Nginx反向代理
diff --git a/spaces/dafqi/indo_twitter_sentiment_app/script/plotting.py b/spaces/dafqi/indo_twitter_sentiment_app/script/plotting.py
deleted file mode 100644
index 753deae878fab9a08f8190eb2b7cff41cc7d87ab..0000000000000000000000000000000000000000
--- a/spaces/dafqi/indo_twitter_sentiment_app/script/plotting.py
+++ /dev/null
@@ -1,116 +0,0 @@
-import itertools
-import numpy as np
-from typing import List
-
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-
-
-def visualize_barchart(topic_model,
- topics: List[int] = None,
- top_n_topics: int = 8,
- n_words: int = 5,
- custom_labels: bool = False,
- title: str = "Kata Kunci tiap Topic",
- width: int = 250,
- height: int = 250) -> go.Figure:
- """ Visualize a barchart of selected topics
- Arguments:
- topic_model: A fitted BERTopic instance.
- topics: A selection of topics to visualize.
- top_n_topics: Only select the top n most frequent topics.
- n_words: Number of words to show in a topic
- custom_labels: Whether to use custom topic labels that were defined using
- `topic_model.set_topic_labels`.
- title: Title of the plot.
- width: The width of each figure.
- height: The height of each figure.
- Returns:
- fig: A plotly figure
- Examples:
- To visualize the barchart of selected topics
- simply run:
- ```python
- topic_model.visualize_barchart()
- ```
- Or if you want to save the resulting figure:
- ```python
- fig = topic_model.visualize_barchart()
- fig.write_html("path/to/file.html")
- ```
-
- """
- colors = itertools.cycle(['#636EFA', '#EF553B', '#00CC96', '#AB63FA', '#FFA15A', '#19D3F3', '#FF6692', '#B6E880', '#FF97FF', '#FECB52'])
-
- # Select topics based on top_n and topics args
- freq_df = topic_model.get_topic_freq()
- if len(freq_df) > 1:
- freq_df = freq_df.loc[freq_df.Topic != -1, :]
- if topics is not None:
- topics = list(topics)
- elif top_n_topics is not None:
- topics = sorted(freq_df.Topic.to_list()[:top_n_topics])
- else:
- topics = sorted(freq_df.Topic.to_list()[0:6])
-
- # Initialize figure
- if topic_model.custom_labels_ is not None and custom_labels:
- subplot_titles = [topic_model.custom_labels_[topic + topic_model._outliers] for topic in topics]
- else:
- subplot_titles = [f"Topic {topic}" for topic in topics]
- columns = 3
- rows = int(np.ceil(len(topics) / columns))
- fig = make_subplots(rows=rows,
- cols=columns,
- shared_xaxes=False,
- horizontal_spacing=.1,
- vertical_spacing=.4 / rows if rows > 1 else 0,
- subplot_titles=subplot_titles)
-
- # Add barchart for each topic
- row = 1
- column = 1
- for topic in topics:
- words = [word + " " for word, _ in topic_model.get_topic(topic)][:n_words][::-1]
- scores = [score for _, score in topic_model.get_topic(topic)][:n_words][::-1]
-
- fig.add_trace(
- go.Bar(x=scores,
- y=words,
- orientation='h',
- marker_color=next(colors)),
- row=row, col=column)
-
- if column == columns:
- column = 1
- row += 1
- else:
- column += 1
-
- # Stylize graph
- fig.update_layout(
-
- showlegend=False,
- title={
- 'text': f"{title}",
- 'xanchor': 'center',
- 'yanchor': 'top',
- 'font': dict(
- size=22,
- color="Black")
- },
- width=width*3,
- height=height*rows if rows > 1 else height * 1.3,
- hoverlabel=dict(
- bgcolor="white",
- font_size=13,
- font_family="Rockwell"
- ),
- margin=dict(l=40, r=40)
- )
-
- fig.update_xaxes(showgrid=True)
- fig.update_yaxes(showgrid=True)
-
- return fig
\ No newline at end of file
diff --git a/spaces/darkstorm2150/Stable-Diffusion-Protogen-x3.4-webui/app.py b/spaces/darkstorm2150/Stable-Diffusion-Protogen-x3.4-webui/app.py
deleted file mode 100644
index be111e59a9c0f40769c871659999c100caa38561..0000000000000000000000000000000000000000
--- a/spaces/darkstorm2150/Stable-Diffusion-Protogen-x3.4-webui/app.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import os
-from subprocess import getoutput
-
-os.system(f"git clone -b v1.5 https://github.com/camenduru/stable-diffusion-webui /home/user/app/stable-diffusion-webui")
-os.chdir("/home/user/app/stable-diffusion-webui")
-
-os.system(f"wget -q https://github.com/camenduru/webui/raw/main/env_patch.py -O /home/user/app/env_patch.py")
-os.system(f"sed -i '$a fastapi==0.90.0' /home/user/app/stable-diffusion-webui/requirements_versions.txt")
-os.system(f"sed -i -e '/import image_from_url_text/r /home/user/app/env_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(modelmerger_interface, \"Checkpoint Merger\", \"modelmerger\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(train_interface, \"Train\", \"ti\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/extensions_interface, \"Extensions\", \"extensions\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/settings_interface, \"Settings\", \"settings\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f'''sed -i -e "s/document.getElementsByTagName('gradio-app')\[0\].shadowRoot/!!document.getElementsByTagName('gradio-app')[0].shadowRoot ? document.getElementsByTagName('gradio-app')[0].shadowRoot : document/g" /home/user/app/stable-diffusion-webui/script.js''')
-os.system(f"sed -i -e 's/ show_progress=False,/ show_progress=True,/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/shared.demo.launch/shared.demo.queue().launch/g' /home/user/app/stable-diffusion-webui/webui.py")
-os.system(f"sed -i -e 's/ outputs=\[/queue=False, &/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/ queue=False, / /g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-
-# ----------------------------Please duplicate this space and delete this block if you don't want to see the extra header----------------------------
-os.system(f"wget -q https://raw.githubusercontent.com/darkstorm2150/webui/main/OpenGen_header_patch.py -O /home/user/app/header_patch.py")
-os.system(f"sed -i -e '/demo:/r /home/user/app/header_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-# ---------------------------------------------------------------------------------------------------------------------------------------------------
-
-if "IS_SHARED_UI" in os.environ:
- os.system(f"rm -rfv /home/user/app/stable-diffusion-webui/scripts/")
-
- os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-config.json -O /home/user/app/shared-config.json")
- os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-ui-config.json -O /home/user/app/shared-ui-config.json")
-
- os.system(f"wget -q {os.getenv('MODEL_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('MODEL_NAME')}")
- os.system(f"wget -q {os.getenv('VAE_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('VAE_NAME')}")
- os.system(f"wget -q {os.getenv('YAML_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('YAML_NAME')}")
-
- os.system(f"python launch.py --disable-console-progressbars --enable-console-prompts --ui-config-file /home/user/app/shared-ui-config.json --ui-settings-file /home/user/app/shared-config.json --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding")
-else:
- os.system(f"rm -rfv /home/user/app/stable-diffusion-webui/scripts/")
-
- os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-config.json -O /home/user/app/shared-config.json")
- os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-ui-config.json -O /home/user/app/shared-ui-config.json")
-
- # Please duplicate this space and delete # character in front of the custom script you want to use or add here more custom scripts with same structure os.system(f"wget -q https://CUSTOM_SCRIPT_URL -O /home/user/app/stable-diffusion-webui/scripts/CUSTOM_SCRIPT_NAME.py")
- #os.system(f"wget -q https://gist.github.com/camenduru/9ec5f8141db9902e375967e93250860f/raw/d0bcf01786f20107c329c03f8968584ee67be12a/run_n_times.py -O /home/user/app/stable-diffusion-webui/scripts/run_n_times.py")
-
- # Please duplicate this space and delete # character in front of the extension you want to use or add here more extensions with same structure os.system(f"git clone https://EXTENSION_GIT_URL /home/user/app/stable-diffusion-webui/extensions/EXTENSION_NAME")
- #os.system(f"git clone https://github.com/camenduru/stable-diffusion-webui-artists-to-study /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-artists-to-study")
- #os.system(f"git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-images-browser")
- #os.system(f"git clone https://github.com/deforum-art/deforum-for-automatic1111-webui /home/user/app/stable-diffusion-webui/extensions/deforum-for-automatic1111-webui")
-
- # Please duplicate this space and delete # character in front of the model you want to use or add here more ckpts with same structure os.system(f"wget -q https://CKPT_URL -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/CKPT_NAME.ckpt")
- #os.system(f"wget -q https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-diffusion-v3.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/arcane-diffusion-v3.ckpt")
- #os.system(f"wget -q https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/Cyberpunk-Anime-Diffusion.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Cyberpunk-Anime-Diffusion.ckpt")
- #os.system(f"wget -q https://huggingface.co/prompthero/midjourney-v4-diffusion/resolve/main/mdjrny-v4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/mdjrny-v4.ckpt")
- #os.system(f"wget -q https://huggingface.co/nitrosocke/mo-di-diffusion/resolve/main/moDi-v1-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/moDi-v1-pruned.ckpt")
- #os.system(f"wget -q https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model/resolve/main/PaperCut_v1.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/PaperCut_v1.ckpt")
- #os.system(f"wget -q https://huggingface.co/lilpotat/sa/resolve/main/samdoesarts_style.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/samdoesarts_style.ckpt")
- #os.system(f"wget -q https://huggingface.co/hakurei/waifu-diffusion-v1-3/resolve/main/wd-v1-3-float32.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/wd-v1-3-float32.ckpt")
- #os.system(f"wget -q https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-4.ckpt")
- #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.ckpt")
- #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-5-inpainting.ckpt")
-
- #os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.ckpt")
- #os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0.vae.pt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.vae.pt")
-
- #os.system(f"wget -q https://huggingface.co/stabilityai/stable-diffusion-2/resolve/main/768-v-ema.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.ckpt")
- #os.system(f"wget -q https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.yaml")
-
- # ----------------------------Protogen Models----------------------------
- #os.system(f"wget -q https://huggingface.co/darkstorm2150/Protogen_v2.2_Official_Release/resolve/main/Protogen_V2.2.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Protogen_V2.2.safetensors")
- os.system(f"wget -q https://huggingface.co/darkstorm2150/Protogen_x3.4_Official_Release/resolve/main/ProtoGen_X3.4.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/ProtoGen_X3.4.safetensors")
- #os.system(f"wget -q https://huggingface.co/darkstorm2150/Protogen_v5.3_Official_Release/resolve/main/ProtoGen_X5.3.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/ProtoGen_X5.3.safetensors")
- #os.system(f"wget -q https://huggingface.co/darkstorm2150/Protogen_v5.8_Official_Release/resolve/main/ProtoGen_X5.8.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/ProtoGen_X5.8.safetensors")
- #os.system(f"wget -q https://huggingface.co/darkstorm2150/Protogen_Dragon_Official_Release/resolve/main/ProtoGen_Dragon.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/ProtoGen_Dragon.safetensors")
- # ----------------------------Protogen Models----------------------------
- #os.system(f"python launch.py --force-enable-xformers --ui-config-file /home/user/app/ui-config.json --ui-settings-file /home/user/app/config.json --disable-console-progressbars --enable-console-prompts --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding --api --skip-torch-cuda-test")
- os.system(f"python launch.py --disable-console-progressbars --enable-console-prompts --ui-config-file /home/user/app/shared-ui-config.json --ui-settings-file /home/user/app/shared-config.json --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding")
\ No newline at end of file
diff --git a/spaces/daspartho/prompt-extend/README.md b/spaces/daspartho/prompt-extend/README.md
deleted file mode 100644
index d8e38ea3a526ab1f57292d4b527d8300c8a4d55c..0000000000000000000000000000000000000000
--- a/spaces/daspartho/prompt-extend/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Prompt Extend
-emoji: ✍️
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.8.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/dawdqd/ChuanhuChatGPT/web_assets/javascript/chat-history.js b/spaces/dawdqd/ChuanhuChatGPT/web_assets/javascript/chat-history.js
deleted file mode 100644
index 1bc05355025a1a9abf83f51ff94b6815c604a6d6..0000000000000000000000000000000000000000
--- a/spaces/dawdqd/ChuanhuChatGPT/web_assets/javascript/chat-history.js
+++ /dev/null
@@ -1,54 +0,0 @@
-
-var historyLoaded = false;
-var loadhistorytime = 0; // for debugging
-
-
-function saveHistoryHtml() {
- var historyHtml = document.querySelector('#chuanhu-chatbot>.wrapper>.wrap');
- if (!historyHtml) return; // no history, do nothing
- localStorage.setItem('chatHistory', historyHtml.innerHTML);
- // console.log("History Saved")
- historyLoaded = false;
-}
-
-function loadHistoryHtml() {
- var historyHtml = localStorage.getItem('chatHistory');
- const tempDiv = document.createElement('div');
- tempDiv.innerHTML = historyHtml;
- if (!historyHtml || tempDiv.innerText.trim() === "") {
- historyLoaded = true;
- return; // no history, do nothing
- }
- userLogged = localStorage.getItem('userLogged');
- if (userLogged){
- historyLoaded = true;
- return; // logged in, do nothing
- }
- if (!historyLoaded) {
- var fakeHistory = document.createElement('div');
- fakeHistory.classList.add('history-message');
- fakeHistory.innerHTML = tempDiv.innerHTML;
- const forViewStyle = document.createElement('style');
- forViewStyle.innerHTML = '.wrapper>.wrap>.history-message>:last-child::after { content: "' + i18n(forView_i18n) + '"!important; }';
- document.head.appendChild(forViewStyle);
- 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");
- }
-}
diff --git a/spaces/dawood/audioldm-text-to-audio-generation/audioldm/clap/training/zero_shot.py b/spaces/dawood/audioldm-text-to-audio-generation/audioldm/clap/training/zero_shot.py
deleted file mode 100644
index 28b8fccc1af17fc69002857a7f529ac041c374f2..0000000000000000000000000000000000000000
--- a/spaces/dawood/audioldm-text-to-audio-generation/audioldm/clap/training/zero_shot.py
+++ /dev/null
@@ -1,95 +0,0 @@
-# NOTE: This script is currently not supported for CLAP.
-import logging
-from contextlib import suppress
-
-import torch
-import torch.nn.functional as F
-from tqdm import tqdm
-
-from open_clip import tokenize
-from .imagenet_zeroshot_data import imagenet_classnames, openai_imagenet_template
-
-
-def zero_shot_classifier(model, classnames, templates, args):
- with torch.no_grad():
- zeroshot_weights = []
- for classname in tqdm(classnames):
- texts = [template(classname) for template in templates] # format with class
- texts = tokenize(texts).to(args.device) # tokenize
- if args.distributed and not args.horovod:
- class_embeddings = model.module.encode_text(texts)
- else:
- class_embeddings = model.encode_text(texts)
- class_embedding = F.normalize(class_embeddings, dim=-1).mean(dim=0)
- class_embedding /= class_embedding.norm()
- zeroshot_weights.append(class_embedding)
- zeroshot_weights = torch.stack(zeroshot_weights, dim=1).to(args.device)
- return zeroshot_weights
-
-
-def accuracy(output, target, topk=(1,)):
- pred = output.topk(max(topk), 1, True, True)[1].t()
- correct = pred.eq(target.view(1, -1).expand_as(pred))
- return [
- float(correct[:k].reshape(-1).float().sum(0, keepdim=True).cpu().numpy())
- for k in topk
- ]
-
-
-def run(model, classifier, dataloader, args):
- autocast = torch.cuda.amp.autocast if args.precision == "amp" else suppress
- with torch.no_grad():
- top1, top5, n = 0.0, 0.0, 0.0
- for images, target in tqdm(dataloader, unit_scale=args.batch_size):
- images = images.to(args.device)
- target = target.to(args.device)
-
- with autocast():
- # predict
- if args.distributed and not args.horovod:
- image_features = model.module.encode_image(images)
- else:
- image_features = model.encode_image(images)
- image_features = F.normalize(image_features, dim=-1)
- logits = 100.0 * image_features @ classifier
-
- # measure accuracy
- acc1, acc5 = accuracy(logits, target, topk=(1, 5))
- top1 += acc1
- top5 += acc5
- n += images.size(0)
-
- top1 = top1 / n
- top5 = top5 / n
- return top1, top5
-
-
-def zero_shot_eval(model, data, epoch, args):
- if "imagenet-val" not in data and "imagenet-v2" not in data:
- return {}
- if args.zeroshot_frequency == 0:
- return {}
- if (epoch % args.zeroshot_frequency) != 0 and epoch != args.epochs:
- return {}
-
- logging.info("Starting zero-shot imagenet.")
-
- logging.info("Building zero-shot classifier")
- classifier = zero_shot_classifier(
- model, imagenet_classnames, openai_imagenet_template, args
- )
-
- logging.info("Using classifier")
- results = {}
- if "imagenet-val" in data:
- top1, top5 = run(model, classifier, data["imagenet-val"].dataloader, args)
- results["imagenet-zeroshot-val-top1"] = top1
- results["imagenet-zeroshot-val-top5"] = top5
- if "imagenet-v2" in data:
- top1, top5 = run(model, classifier, data["imagenet-v2"].dataloader, args)
- results["imagenetv2-zeroshot-val-top1"] = top1
- results["imagenetv2-zeroshot-val-top5"] = top5
-
- logging.info("Finished zero-shot imagenet.")
-
- return results
diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/detection/yolov5face/utils/__init__.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/detection/yolov5face/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/dbredvick/whisper-webui/src/vadParallel.py b/spaces/dbredvick/whisper-webui/src/vadParallel.py
deleted file mode 100644
index 55066bbe0bf6cc8ffaf5e4ce5e2723d7e681ff2e..0000000000000000000000000000000000000000
--- a/spaces/dbredvick/whisper-webui/src/vadParallel.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import multiprocessing
-import threading
-import time
-from src.vad import AbstractTranscription, TranscriptionConfig
-from src.whisperContainer import WhisperCallback
-
-from multiprocessing import Pool
-
-from typing import List
-import os
-
-
-class ParallelContext:
- def __init__(self, num_processes: int = None, auto_cleanup_timeout_seconds: float = None):
- self.num_processes = num_processes
- self.auto_cleanup_timeout_seconds = auto_cleanup_timeout_seconds
- self.lock = threading.Lock()
-
- self.ref_count = 0
- self.pool = None
- self.cleanup_timer = None
-
- def get_pool(self):
- # Initialize pool lazily
- if (self.pool is None):
- context = multiprocessing.get_context('spawn')
- self.pool = context.Pool(self.num_processes)
-
- self.ref_count = self.ref_count + 1
-
- if (self.auto_cleanup_timeout_seconds is not None):
- self._stop_auto_cleanup()
-
- return self.pool
-
- def return_pool(self, pool):
- if (self.pool == pool and self.ref_count > 0):
- self.ref_count = self.ref_count - 1
-
- if (self.ref_count == 0):
- if (self.auto_cleanup_timeout_seconds is not None):
- self._start_auto_cleanup()
-
- def _start_auto_cleanup(self):
- if (self.cleanup_timer is not None):
- self.cleanup_timer.cancel()
- self.cleanup_timer = threading.Timer(self.auto_cleanup_timeout_seconds, self._execute_cleanup)
- self.cleanup_timer.start()
-
- print("Started auto cleanup of pool in " + str(self.auto_cleanup_timeout_seconds) + " seconds")
-
- def _stop_auto_cleanup(self):
- if (self.cleanup_timer is not None):
- self.cleanup_timer.cancel()
- self.cleanup_timer = None
-
- print("Stopped auto cleanup of pool")
-
- def _execute_cleanup(self):
- print("Executing cleanup of pool")
-
- if (self.ref_count == 0):
- self.close()
-
- def close(self):
- self._stop_auto_cleanup()
-
- if (self.pool is not None):
- print("Closing pool of " + str(self.num_processes) + " processes")
- self.pool.close()
- self.pool.join()
- self.pool = None
-
-class ParallelTranscriptionConfig(TranscriptionConfig):
- def __init__(self, device_id: str, override_timestamps, initial_segment_index, copy: TranscriptionConfig = None):
- super().__init__(copy.non_speech_strategy, copy.segment_padding_left, copy.segment_padding_right, copy.max_silent_period, copy.max_merge_size, copy.max_prompt_window, initial_segment_index)
- self.device_id = device_id
- self.override_timestamps = override_timestamps
-
-class ParallelTranscription(AbstractTranscription):
- def __init__(self, sampling_rate: int = 16000):
- super().__init__(sampling_rate=sampling_rate)
-
-
- def transcribe_parallel(self, transcription: AbstractTranscription, audio: str, whisperCallable: WhisperCallback, config: TranscriptionConfig, devices: List[str], parallel_context: ParallelContext = None):
- # First, get the timestamps for the original audio
- merged = transcription.get_merged_timestamps(audio, config)
-
- # Split into a list for each device
- # TODO: Split by time instead of by number of chunks
- merged_split = list(self._split(merged, len(devices)))
-
- # Parameters that will be passed to the transcribe function
- parameters = []
- segment_index = config.initial_segment_index
-
- for i in range(len(merged_split)):
- device_segment_list = list(merged_split[i])
- device_id = devices[i]
-
- if (len(device_segment_list) <= 0):
- continue
-
- print("Device " + device_id + " (index " + str(i) + ") has " + str(len(device_segment_list)) + " segments")
-
- # Create a new config with the given device ID
- device_config = ParallelTranscriptionConfig(devices[i], device_segment_list, segment_index, config)
- segment_index += len(device_segment_list)
-
- parameters.append([audio, whisperCallable, device_config]);
-
- merged = {
- 'text': '',
- 'segments': [],
- 'language': None
- }
-
- created_context = False
-
- # Spawn a separate process for each device
- try:
- if (parallel_context is None):
- parallel_context = ParallelContext(len(devices))
- created_context = True
-
- # Get a pool of processes
- pool = parallel_context.get_pool()
-
- # Run the transcription in parallel
- results = pool.starmap(self.transcribe, parameters)
-
- for result in results:
- # Merge the results
- if (result['text'] is not None):
- merged['text'] += result['text']
- if (result['segments'] is not None):
- merged['segments'].extend(result['segments'])
- if (result['language'] is not None):
- merged['language'] = result['language']
-
- finally:
- # Return the pool to the context
- if (parallel_context is not None):
- parallel_context.return_pool(pool)
- # Always close the context if we created it
- if (created_context):
- parallel_context.close()
-
- return merged
-
- def get_transcribe_timestamps(self, audio: str, config: ParallelTranscriptionConfig):
- return []
-
- def get_merged_timestamps(self, audio: str, config: ParallelTranscriptionConfig):
- # Override timestamps that will be processed
- if (config.override_timestamps is not None):
- print("Using override timestamps of size " + str(len(config.override_timestamps)))
- return config.override_timestamps
- return super().get_merged_timestamps(audio, config)
-
- def transcribe(self, audio: str, whisperCallable: WhisperCallback, config: ParallelTranscriptionConfig):
- # Override device ID
- if (config.device_id is not None):
- print("Using device " + config.device_id)
- os.environ["CUDA_VISIBLE_DEVICES"] = config.device_id
- return super().transcribe(audio, whisperCallable, config)
-
- def _split(self, a, n):
- """Split a list into n approximately equal parts."""
- k, m = divmod(len(a), n)
- return (a[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(n))
-
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/altair/expr/core.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/altair/expr/core.py
deleted file mode 100644
index 9cc258c8b723613453d4033c85035e335a537318..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/altair/expr/core.py
+++ /dev/null
@@ -1,234 +0,0 @@
-from ..utils import SchemaBase
-
-
-class DatumType:
- """An object to assist in building Vega-Lite Expressions"""
-
- def __repr__(self):
- return "datum"
-
- def __getattr__(self, attr):
- if attr.startswith("__") and attr.endswith("__"):
- raise AttributeError(attr)
- return GetAttrExpression("datum", attr)
-
- def __getitem__(self, attr):
- return GetItemExpression("datum", attr)
-
- def __call__(self, datum, **kwargs):
- """Specify a datum for use in an encoding"""
- return dict(datum=datum, **kwargs)
-
-
-datum = DatumType()
-
-
-def _js_repr(val):
- """Return a javascript-safe string representation of val"""
- if val is True:
- return "true"
- elif val is False:
- return "false"
- elif val is None:
- return "null"
- elif isinstance(val, OperatorMixin):
- return val._to_expr()
- else:
- return repr(val)
-
-
-# Designed to work with Expression and VariableParameter
-class OperatorMixin:
- def _to_expr(self):
- return repr(self)
-
- def _from_expr(self, expr):
- return expr
-
- def __add__(self, other):
- comp_value = BinaryExpression("+", self, other)
- return self._from_expr(comp_value)
-
- def __radd__(self, other):
- comp_value = BinaryExpression("+", other, self)
- return self._from_expr(comp_value)
-
- def __sub__(self, other):
- comp_value = BinaryExpression("-", self, other)
- return self._from_expr(comp_value)
-
- def __rsub__(self, other):
- comp_value = BinaryExpression("-", other, self)
- return self._from_expr(comp_value)
-
- def __mul__(self, other):
- comp_value = BinaryExpression("*", self, other)
- return self._from_expr(comp_value)
-
- def __rmul__(self, other):
- comp_value = BinaryExpression("*", other, self)
- return self._from_expr(comp_value)
-
- def __truediv__(self, other):
- comp_value = BinaryExpression("/", self, other)
- return self._from_expr(comp_value)
-
- def __rtruediv__(self, other):
- comp_value = BinaryExpression("/", other, self)
- return self._from_expr(comp_value)
-
- __div__ = __truediv__
-
- __rdiv__ = __rtruediv__
-
- def __mod__(self, other):
- comp_value = BinaryExpression("%", self, other)
- return self._from_expr(comp_value)
-
- def __rmod__(self, other):
- comp_value = BinaryExpression("%", other, self)
- return self._from_expr(comp_value)
-
- def __pow__(self, other):
- # "**" Javascript operator is not supported in all browsers
- comp_value = FunctionExpression("pow", (self, other))
- return self._from_expr(comp_value)
-
- def __rpow__(self, other):
- # "**" Javascript operator is not supported in all browsers
- comp_value = FunctionExpression("pow", (other, self))
- return self._from_expr(comp_value)
-
- def __neg__(self):
- comp_value = UnaryExpression("-", self)
- return self._from_expr(comp_value)
-
- def __pos__(self):
- comp_value = UnaryExpression("+", self)
- return self._from_expr(comp_value)
-
- # comparison operators
-
- def __eq__(self, other):
- comp_value = BinaryExpression("===", self, other)
- return self._from_expr(comp_value)
-
- def __ne__(self, other):
- comp_value = BinaryExpression("!==", self, other)
- return self._from_expr(comp_value)
-
- def __gt__(self, other):
- comp_value = BinaryExpression(">", self, other)
- return self._from_expr(comp_value)
-
- def __lt__(self, other):
- comp_value = BinaryExpression("<", self, other)
- return self._from_expr(comp_value)
-
- def __ge__(self, other):
- comp_value = BinaryExpression(">=", self, other)
- return self._from_expr(comp_value)
-
- def __le__(self, other):
- comp_value = BinaryExpression("<=", self, other)
- return self._from_expr(comp_value)
-
- def __abs__(self):
- comp_value = FunctionExpression("abs", (self,))
- return self._from_expr(comp_value)
-
- # logical operators
-
- def __and__(self, other):
- comp_value = BinaryExpression("&&", self, other)
- return self._from_expr(comp_value)
-
- def __rand__(self, other):
- comp_value = BinaryExpression("&&", other, self)
- return self._from_expr(comp_value)
-
- def __or__(self, other):
- comp_value = BinaryExpression("||", self, other)
- return self._from_expr(comp_value)
-
- def __ror__(self, other):
- comp_value = BinaryExpression("||", other, self)
- return self._from_expr(comp_value)
-
- def __invert__(self):
- comp_value = UnaryExpression("!", self)
- return self._from_expr(comp_value)
-
-
-class Expression(OperatorMixin, SchemaBase):
- """Expression
-
- Base object for enabling build-up of Javascript expressions using
- a Python syntax. Calling ``repr(obj)`` will return a Javascript
- representation of the object and the operations it encodes.
- """
-
- _schema = {"type": "string"}
-
- def to_dict(self, *args, **kwargs):
- return repr(self)
-
- def __setattr__(self, attr, val):
- # We don't need the setattr magic defined in SchemaBase
- return object.__setattr__(self, attr, val)
-
- # item access
- def __getitem__(self, val):
- return GetItemExpression(self, val)
-
-
-class UnaryExpression(Expression):
- def __init__(self, op, val):
- super(UnaryExpression, self).__init__(op=op, val=val)
-
- def __repr__(self):
- return "({op}{val})".format(op=self.op, val=_js_repr(self.val))
-
-
-class BinaryExpression(Expression):
- def __init__(self, op, lhs, rhs):
- super(BinaryExpression, self).__init__(op=op, lhs=lhs, rhs=rhs)
-
- def __repr__(self):
- return "({lhs} {op} {rhs})".format(
- op=self.op, lhs=_js_repr(self.lhs), rhs=_js_repr(self.rhs)
- )
-
-
-class FunctionExpression(Expression):
- def __init__(self, name, args):
- super(FunctionExpression, self).__init__(name=name, args=args)
-
- def __repr__(self):
- args = ",".join(_js_repr(arg) for arg in self.args)
- return "{name}({args})".format(name=self.name, args=args)
-
-
-class ConstExpression(Expression):
- def __init__(self, name, doc):
- self.__doc__ = """{}: {}""".format(name, doc)
- super(ConstExpression, self).__init__(name=name, doc=doc)
-
- def __repr__(self):
- return str(self.name)
-
-
-class GetAttrExpression(Expression):
- def __init__(self, group, name):
- super(GetAttrExpression, self).__init__(group=group, name=name)
-
- def __repr__(self):
- return "{}.{}".format(self.group, self.name)
-
-
-class GetItemExpression(Expression):
- def __init__(self, group, name):
- super(GetItemExpression, self).__init__(group=group, name=name)
-
- def __repr__(self):
- return "{}[{!r}]".format(self.group, self.name)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/altair/utils/save.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/altair/utils/save.py
deleted file mode 100644
index 90d36f14bc5ebf5cb1e07cb469191ed21e4b3f4b..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/altair/utils/save.py
+++ /dev/null
@@ -1,176 +0,0 @@
-import json
-import pathlib
-import warnings
-
-from .mimebundle import spec_to_mimebundle
-from ..vegalite.v5.data import data_transformers
-
-
-def write_file_or_filename(fp, content, mode="w", encoding=None):
- """Write content to fp, whether fp is a string, a pathlib Path or a
- file-like object"""
- if isinstance(fp, str) or isinstance(fp, pathlib.PurePath):
- with open(file=fp, mode=mode, encoding=encoding) as f:
- f.write(content)
- else:
- fp.write(content)
-
-
-def set_inspect_format_argument(format, fp, inline):
- """Inspect the format argument in the save function"""
- if format is None:
- if isinstance(fp, str):
- format = fp.split(".")[-1]
- elif isinstance(fp, pathlib.PurePath):
- format = fp.suffix.lstrip(".")
- else:
- raise ValueError(
- "must specify file format: "
- "['png', 'svg', 'pdf', 'html', 'json', 'vega']"
- )
-
- if format != "html" and inline:
- warnings.warn("inline argument ignored for non HTML formats.", stacklevel=1)
-
- return format
-
-
-def set_inspect_mode_argument(mode, embed_options, spec, vegalite_version):
- """Inspect the mode argument in the save function"""
- if mode is None:
- if "mode" in embed_options:
- mode = embed_options["mode"]
- elif "$schema" in spec:
- mode = spec["$schema"].split("/")[-2]
- else:
- mode = "vega-lite"
-
- if mode != "vega-lite":
- raise ValueError("mode must be 'vega-lite', " "not '{}'".format(mode))
-
- if mode == "vega-lite" and vegalite_version is None:
- raise ValueError("must specify vega-lite version")
-
- return mode
-
-
-def save(
- chart,
- fp,
- vega_version,
- vegaembed_version,
- format=None,
- mode=None,
- vegalite_version=None,
- embed_options=None,
- json_kwds=None,
- webdriver=None,
- scale_factor=1,
- engine=None,
- inline=False,
- **kwargs,
-):
- """Save a chart to file in a variety of formats
-
- Supported formats are [json, html, png, svg, pdf]
-
- Parameters
- ----------
- chart : alt.Chart
- the chart instance to save
- fp : string filename, pathlib.Path or file-like object
- file to which to write the chart.
- format : string (optional)
- the format to write: one of ['json', 'html', 'png', 'svg', 'pdf'].
- If not specified, the format will be determined from the filename.
- mode : string (optional)
- Must be 'vega-lite'. If not specified, then infer the mode from
- the '$schema' property of the spec, or the ``opt`` dictionary.
- If it's not specified in either of those places, then use 'vega-lite'.
- vega_version : string (optional)
- For html output, the version of vega.js to use
- vegalite_version : string (optional)
- For html output, the version of vegalite.js to use
- vegaembed_version : string (optional)
- For html output, the version of vegaembed.js to use
- embed_options : dict (optional)
- The vegaEmbed options dictionary. Default is {}
- (See https://github.com/vega/vega-embed for details)
- json_kwds : dict (optional)
- Additional keyword arguments are passed to the output method
- associated with the specified format.
- webdriver : string {'chrome' | 'firefox'} (optional)
- Webdriver to use for png or svg output
- scale_factor : float (optional)
- scale_factor to use to change size/resolution of png or svg output
- engine: string {'vl-convert', 'altair_saver'}
- the conversion engine to use for 'png', 'svg', and 'pdf' formats
- inline: bool (optional)
- If False (default), the required JavaScript libraries are loaded
- from a CDN location in the resulting html file.
- If True, the required JavaScript libraries are inlined into the resulting
- html file so that it will work without an internet connection.
- The altair_viewer package is required if True.
- **kwargs :
- additional kwargs passed to spec_to_mimebundle.
- """
- if json_kwds is None:
- json_kwds = {}
-
- if embed_options is None:
- embed_options = {}
-
- format = set_inspect_format_argument(format, fp, inline)
-
- # Temporarily turn off any data transformers so that all data is inlined
- # when calling chart.to_dict. This is relevant for vl-convert which cannot access
- # local json files which could be created by a json data transformer. Furthermore,
- # we don't exit the with statement until this function completed due to the issue
- # described at https://github.com/vega/vl-convert/issues/31
- with data_transformers.enable("default"), data_transformers.disable_max_rows():
- spec = chart.to_dict()
-
- mode = set_inspect_mode_argument(mode, embed_options, spec, vegalite_version)
-
- if format == "json":
- json_spec = json.dumps(spec, **json_kwds)
- write_file_or_filename(fp, json_spec, mode="w")
- elif format == "html":
- if inline:
- kwargs["template"] = "inline"
- mimebundle = spec_to_mimebundle(
- spec=spec,
- format=format,
- mode=mode,
- vega_version=vega_version,
- vegalite_version=vegalite_version,
- vegaembed_version=vegaembed_version,
- embed_options=embed_options,
- json_kwds=json_kwds,
- **kwargs,
- )
- write_file_or_filename(fp, mimebundle["text/html"], mode="w")
- elif format in ["png", "svg", "pdf", "vega"]:
- mimebundle = spec_to_mimebundle(
- spec=spec,
- format=format,
- mode=mode,
- vega_version=vega_version,
- vegalite_version=vegalite_version,
- vegaembed_version=vegaembed_version,
- webdriver=webdriver,
- scale_factor=scale_factor,
- engine=engine,
- **kwargs,
- )
- if format == "png":
- write_file_or_filename(fp, mimebundle["image/png"], mode="wb")
- elif format == "pdf":
- write_file_or_filename(fp, mimebundle["application/pdf"], mode="wb")
- else:
- encoding = kwargs.get("encoding", "utf-8")
- write_file_or_filename(
- fp, mimebundle["image/svg+xml"], mode="w", encoding=encoding
- )
- else:
- raise ValueError("Unsupported format: '{}'".format(format))
diff --git a/spaces/declare-lab/tango/audioldm/audio/tools.py b/spaces/declare-lab/tango/audioldm/audio/tools.py
deleted file mode 100644
index d641a982664b6673822c8528a1929c593f011b11..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/audioldm/audio/tools.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import torch
-import numpy as np
-import torchaudio
-
-
-def get_mel_from_wav(audio, _stft):
- audio = torch.clip(torch.FloatTensor(audio).unsqueeze(0), -1, 1)
- audio = torch.autograd.Variable(audio, requires_grad=False)
- melspec, log_magnitudes_stft, energy = _stft.mel_spectrogram(audio)
- melspec = torch.squeeze(melspec, 0).numpy().astype(np.float32)
- log_magnitudes_stft = (
- torch.squeeze(log_magnitudes_stft, 0).numpy().astype(np.float32)
- )
- energy = torch.squeeze(energy, 0).numpy().astype(np.float32)
- return melspec, log_magnitudes_stft, energy
-
-
-def _pad_spec(fbank, target_length=1024):
- n_frames = fbank.shape[0]
- p = target_length - n_frames
- # cut and pad
- if p > 0:
- m = torch.nn.ZeroPad2d((0, 0, 0, p))
- fbank = m(fbank)
- elif p < 0:
- fbank = fbank[0:target_length, :]
-
- if fbank.size(-1) % 2 != 0:
- fbank = fbank[..., :-1]
-
- return fbank
-
-
-def pad_wav(waveform, segment_length):
- waveform_length = waveform.shape[-1]
- assert waveform_length > 100, "Waveform is too short, %s" % waveform_length
- if segment_length is None or waveform_length == segment_length:
- return waveform
- elif waveform_length > segment_length:
- return waveform[:segment_length]
- elif waveform_length < segment_length:
- temp_wav = np.zeros((1, segment_length))
- temp_wav[:, :waveform_length] = waveform
- return temp_wav
-
-def normalize_wav(waveform):
- waveform = waveform - np.mean(waveform)
- waveform = waveform / (np.max(np.abs(waveform)) + 1e-8)
- return waveform * 0.5
-
-
-def read_wav_file(filename, segment_length):
- # waveform, sr = librosa.load(filename, sr=None, mono=True) # 4 times slower
- waveform, sr = torchaudio.load(filename) # Faster!!!
- waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=16000)
- waveform = waveform.numpy()[0, ...]
- waveform = normalize_wav(waveform)
- waveform = waveform[None, ...]
- waveform = pad_wav(waveform, segment_length)
-
- waveform = waveform / np.max(np.abs(waveform))
- waveform = 0.5 * waveform
-
- return waveform
-
-
-def wav_to_fbank(filename, target_length=1024, fn_STFT=None):
- assert fn_STFT is not None
-
- # mixup
- waveform = read_wav_file(filename, target_length * 160) # hop size is 160
-
- waveform = waveform[0, ...]
- waveform = torch.FloatTensor(waveform)
-
- fbank, log_magnitudes_stft, energy = get_mel_from_wav(waveform, fn_STFT)
-
- fbank = torch.FloatTensor(fbank.T)
- log_magnitudes_stft = torch.FloatTensor(log_magnitudes_stft.T)
-
- fbank, log_magnitudes_stft = _pad_spec(fbank, target_length), _pad_spec(
- log_magnitudes_stft, target_length
- )
-
- return fbank, log_magnitudes_stft, waveform
diff --git a/spaces/deepkyu/multilingual-font-style-transfer/app.sh b/spaces/deepkyu/multilingual-font-style-transfer/app.sh
deleted file mode 100644
index a1bf5d3f16a2a2240293ca4650d099d8f4053256..0000000000000000000000000000000000000000
--- a/spaces/deepkyu/multilingual-font-style-transfer/app.sh
+++ /dev/null
@@ -1,3 +0,0 @@
-export MODEL_CHECKPOINT_PATH=https://www.dropbox.com/scl/fi/zzkqe07g8gs5j21fmxsep/epoch-269-step-347490.ckpt?rlkey=goejowz4axcq5f5xxm69gadv6 && \
-export NOTO_SANS_ZIP_PATH=https://www.dropbox.com/scl/fi/f7iclf71sh2luhpkbutjh/NotoSans.zip?rlkey=rr026nl5sj7i8xjp65s9377ni && \
-python app.py
\ No newline at end of file
diff --git a/spaces/deepwisdom/MetaGPT/metagpt/prompts/structure_goal.py b/spaces/deepwisdom/MetaGPT/metagpt/prompts/structure_goal.py
deleted file mode 100644
index e4b1a3beee0c1207acbbb097405c7398c51e484b..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/metagpt/prompts/structure_goal.py
+++ /dev/null
@@ -1,46 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time : 2023/5/30 09:51
-@Author : alexanderwu
-@File : structure_goal.py
-"""
-
-GOAL_SYSTEM = """SYSTEM:
-You are an assistant for the game Minecraft.
-I will give you some target object and some knowledge related to the object. Please write the obtaining of the object as a goal in the standard form.
-The standard form of the goal is as follows:
-{
-"object": "the name of the target object",
-"count": "the target quantity",
-"material": "the materials required for this goal, a dictionary in the form {material_name: material_quantity}. If no material is required, set it to None",
-"tool": "the tool used for this goal. If multiple tools can be used for this goal, only write the most basic one. If no tool is required, set it to None",
-"info": "the knowledge related to this goal"
-}
-The information I will give you:
-Target object: the name and the quantity of the target object
-Knowledge: some knowledge related to the object.
-Requirements:
-1. You must generate the goal based on the provided knowledge instead of purely depending on your own knowledge.
-2. The "info" should be as compact as possible, at most 3 sentences. The knowledge I give you may be raw texts from Wiki documents. Please extract and summarize important information instead of directly copying all the texts.
-Goal Example:
-{
-"object": "iron_ore",
-"count": 1,
-"material": None,
-"tool": "stone_pickaxe",
-"info": "iron ore is obtained by mining iron ore. iron ore is most found in level 53. iron ore can only be mined with a stone pickaxe or better; using a wooden or gold pickaxe will yield nothing."
-}
-{
-"object": "wooden_pickaxe",
-"count": 1,
-"material": {"planks": 3, "stick": 2},
-"tool": "crafting_table",
-"info": "wooden pickaxe can be crafted with 3 planks and 2 stick as the material and crafting table as the tool."
-}
-"""
-
-GOAL_USER = """USER:
-Target object: {object quantity} {object name}
-Knowledge: {related knowledge}
-"""
diff --git a/spaces/diacanFperku/AutoGPT/Danfoss Mct 10 Crack [NEW].md b/spaces/diacanFperku/AutoGPT/Danfoss Mct 10 Crack [NEW].md
deleted file mode 100644
index 37a6e950f5d58aa44a969b05f357aa96b246478e..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Danfoss Mct 10 Crack [NEW].md
+++ /dev/null
@@ -1,10 +0,0 @@
-Danfoss Mct 10 Crack
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-... VLT® Software Customizer: download VLT® MCT 10 4.00 (or higher), don't forget to uninstall the old one... Free download.Download video editor in Russian for free for Windows, Mac or ...
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diff --git a/spaces/diacanFperku/AutoGPT/Jcreator Full Version Free Download Cracked Software REPACK.md b/spaces/diacanFperku/AutoGPT/Jcreator Full Version Free Download Cracked Software REPACK.md
deleted file mode 100644
index adf3800558e5361f7a0948dada1c49322723d885..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Jcreator Full Version Free Download Cracked Software REPACK.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Jcreator Full Version Free Download Cracked Software
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diff --git a/spaces/diacanFperku/AutoGPT/OPTIPLANNING.md b/spaces/diacanFperku/AutoGPT/OPTIPLANNING.md
deleted file mode 100644
index 9cccb5a89a5f6a0f2b4e587af1c551f7c5ca2f55..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/OPTIPLANNING.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-SELCO OPTIPLANNING. OptiPlanning изъятие от. OptiPlanning for Cut Optimization. By Oxana Petrova. 1, Copyright Author. Aug 3, 2010. I used OptiPlanning with the SELCO workpiece feeding series PW60 and PW70 (but I think they all work the same way). The process always goes like this: the operator places the material in the hold tray, if he opens the hold tray, it.
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diff --git a/spaces/diacanFperku/AutoGPT/Phim Sex Online Lau Xanh ((FULL)).md b/spaces/diacanFperku/AutoGPT/Phim Sex Online Lau Xanh ((FULL)).md
deleted file mode 100644
index 3272f5bbf81e96286e05561617368b3b95dbf527..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Phim Sex Online Lau Xanh ((FULL)).md
+++ /dev/null
@@ -1,6 +0,0 @@
-phim sex online lau xanh
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diff --git a/spaces/diagaiwei/ir_chinese_medqa/colbert/indexing/collection_indexer.py b/spaces/diagaiwei/ir_chinese_medqa/colbert/indexing/collection_indexer.py
deleted file mode 100644
index 50102f6042fd499f2819375190d747d67e3b7258..0000000000000000000000000000000000000000
--- a/spaces/diagaiwei/ir_chinese_medqa/colbert/indexing/collection_indexer.py
+++ /dev/null
@@ -1,472 +0,0 @@
-import os
-import tqdm
-import time
-import ujson
-import torch
-import random
-try:
- import faiss
-except ImportError as e:
- print("WARNING: faiss must be imported for indexing")
-
-import numpy as np
-import torch.multiprocessing as mp
-from colbert.infra.config.config import ColBERTConfig
-
-import colbert.utils.distributed as distributed
-
-from colbert.infra.run import Run
-from colbert.infra.launcher import print_memory_stats
-from colbert.modeling.checkpoint import Checkpoint
-from colbert.data.collection import Collection
-
-from colbert.indexing.collection_encoder import CollectionEncoder
-from colbert.indexing.index_saver import IndexSaver
-from colbert.indexing.utils import optimize_ivf
-from colbert.utils.utils import flatten, print_message
-
-from colbert.indexing.codecs.residual import ResidualCodec
-
-
-def encode(config, collection, shared_lists, shared_queues):
- encoder = CollectionIndexer(config=config, collection=collection)
- encoder.run(shared_lists)
-
-
-class CollectionIndexer():
- def __init__(self, config: ColBERTConfig, collection):
- self.config = config
- self.rank, self.nranks = self.config.rank, self.config.nranks
-
- self.use_gpu = self.config.total_visible_gpus > 0
-
- if self.config.rank == 0:
- self.config.help()
-
- self.collection = Collection.cast(collection)
- self.checkpoint = Checkpoint(self.config.checkpoint, colbert_config=self.config)
- if self.use_gpu:
- self.checkpoint = self.checkpoint.cuda()
-
- self.encoder = CollectionEncoder(config, self.checkpoint)
- self.saver = IndexSaver(config)
-
- print_memory_stats(f'RANK:{self.rank}')
-
- def run(self, shared_lists):
- with torch.inference_mode():
- self.setup()
- distributed.barrier(self.rank)
- print_memory_stats(f'RANK:{self.rank}')
-
- if not self.config.resume or not self.saver.try_load_codec():
- self.train(shared_lists)
- distributed.barrier(self.rank)
- print_memory_stats(f'RANK:{self.rank}')
-
- self.index()
- distributed.barrier(self.rank)
- print_memory_stats(f'RANK:{self.rank}')
-
- self.finalize()
- distributed.barrier(self.rank)
- print_memory_stats(f'RANK:{self.rank}')
-
- def setup(self):
- if self.config.resume:
- if self._try_load_plan():
- Run().print_main(f"#> Loaded plan from {self.plan_path}:")
- Run().print_main(f"#> num_chunks = {self.num_chunks}")
- Run().print_main(f"#> num_partitions = {self.num_chunks}")
- Run().print_main(f"#> num_embeddings_est = {self.num_embeddings_est}")
- Run().print_main(f"#> avg_doclen_est = {self.avg_doclen_est}")
- return
-
- self.num_chunks = int(np.ceil(len(self.collection) / self.collection.get_chunksize()))
-
- sampled_pids = self._sample_pids()
- avg_doclen_est = self._sample_embeddings(sampled_pids)
-
- # Select the number of partitions
- num_passages = len(self.collection)
- self.num_embeddings_est = num_passages * avg_doclen_est
- self.num_partitions = int(2 ** np.floor(np.log2(16 * np.sqrt(self.num_embeddings_est))))
-
- Run().print_main(f'Creaing {self.num_partitions:,} partitions.')
- Run().print_main(f'*Estimated* {int(self.num_embeddings_est):,} embeddings.')
-
- self._save_plan()
-
- def _sample_pids(self):
- num_passages = len(self.collection)
-
- # Simple alternative: < 100k: 100%, < 1M: 15%, < 10M: 7%, < 100M: 3%, > 100M: 1%
- # Keep in mind that, say, 15% still means at least 100k.
- # So the formula is max(100% * min(total, 100k), 15% * min(total, 1M), ...)
- # Then we subsample the vectors to 100 * num_partitions
-
- typical_doclen = 120 # let's keep sampling independent of the actual doc_maxlen
- sampled_pids = 16 * np.sqrt(typical_doclen * num_passages)
- # sampled_pids = int(2 ** np.floor(np.log2(1 + sampled_pids)))
- sampled_pids = min(1 + int(sampled_pids), num_passages)
-
- sampled_pids = random.sample(range(num_passages), sampled_pids)
- Run().print_main(f"# of sampled PIDs = {len(sampled_pids)} \t sampled_pids[:3] = {sampled_pids[:3]}")
-
- return set(sampled_pids)
-
- def _sample_embeddings(self, sampled_pids):
- local_pids = self.collection.enumerate(rank=self.rank)
- local_sample = [passage for pid, passage in local_pids if pid in sampled_pids]
-
- local_sample_embs, doclens = self.encoder.encode_passages(local_sample)
-
- if torch.cuda.is_available():
- self.num_sample_embs = torch.tensor([local_sample_embs.size(0)]).cuda()
- torch.distributed.all_reduce(self.num_sample_embs)
-
- avg_doclen_est = sum(doclens) / len(doclens) if doclens else 0
- avg_doclen_est = torch.tensor([avg_doclen_est]).cuda()
- torch.distributed.all_reduce(avg_doclen_est)
-
- nonzero_ranks = torch.tensor([float(len(local_sample) > 0)]).cuda()
- torch.distributed.all_reduce(nonzero_ranks)
- else:
- if torch.distributed.is_initialized():
- self.num_sample_embs = torch.tensor([local_sample_embs.size(0)]).cpu()
- torch.distributed.all_reduce(self.num_sample_embs)
-
- avg_doclen_est = sum(doclens) / len(doclens) if doclens else 0
- avg_doclen_est = torch.tensor([avg_doclen_est]).cpu()
- torch.distributed.all_reduce(avg_doclen_est)
-
- nonzero_ranks = torch.tensor([float(len(local_sample) > 0)]).cpu()
- torch.distributed.all_reduce(nonzero_ranks)
- else:
- self.num_sample_embs = torch.tensor([local_sample_embs.size(0)]).cpu()
-
- avg_doclen_est = sum(doclens) / len(doclens) if doclens else 0
- avg_doclen_est = torch.tensor([avg_doclen_est]).cpu()
-
- nonzero_ranks = torch.tensor([float(len(local_sample) > 0)]).cpu()
-
- avg_doclen_est = avg_doclen_est.item() / nonzero_ranks.item()
- self.avg_doclen_est = avg_doclen_est
-
- Run().print(f'avg_doclen_est = {avg_doclen_est} \t len(local_sample) = {len(local_sample):,}')
-
- torch.save(local_sample_embs.half(), os.path.join(self.config.index_path_, f'sample.{self.rank}.pt'))
-
- return avg_doclen_est
-
- def _try_load_plan(self):
- config = self.config
- self.plan_path = os.path.join(config.index_path_, 'plan.json')
- if os.path.exists(self.plan_path):
- with open(self.plan_path, 'r') as f:
- try:
- plan = ujson.load(f)
- except Exception as e:
- return False
- if not ('num_chunks' in plan and
- 'num_partitions' in plan and
- 'num_embeddings_est' in plan and
- 'avg_doclen_est' in plan):
- return False
-
- # TODO: Verify config matches
- self.num_chunks = plan['num_chunks']
- self.num_partitions = plan['num_partitions']
- self.num_embeddings_est = plan['num_embeddings_est']
- self.avg_doclen_est = plan['avg_doclen_est']
-
- return True
- else:
- return False
-
- def _save_plan(self):
- if self.rank < 1:
- config = self.config
- self.plan_path = os.path.join(config.index_path_, 'plan.json')
- Run().print("#> Saving the indexing plan to", self.plan_path, "..")
-
- with open(self.plan_path, 'w') as f:
- d = {'config': config.export()}
- d['num_chunks'] = self.num_chunks
- d['num_partitions'] = self.num_partitions
- d['num_embeddings_est'] = self.num_embeddings_est
- d['avg_doclen_est'] = self.avg_doclen_est
-
- f.write(ujson.dumps(d, indent=4) + '\n')
-
-
- def train(self, shared_lists):
- if self.rank > 0:
- return
-
- sample, heldout = self._concatenate_and_split_sample()
-
- centroids = self._train_kmeans(sample, shared_lists)
-
- print_memory_stats(f'RANK:{self.rank}')
- del sample
-
- bucket_cutoffs, bucket_weights, avg_residual = self._compute_avg_residual(centroids, heldout)
-
- print_message(f'avg_residual = {avg_residual}')
-
- codec = ResidualCodec(config=self.config, centroids=centroids, avg_residual=avg_residual,
- bucket_cutoffs=bucket_cutoffs, bucket_weights=bucket_weights)
- self.saver.save_codec(codec)
-
- def _concatenate_and_split_sample(self):
- print_memory_stats(f'***1*** \t RANK:{self.rank}')
-
- # TODO: Allocate a float16 array. Load the samples from disk, copy to array.
- sample = torch.empty(self.num_sample_embs, self.config.dim, dtype=torch.float16)
-
- offset = 0
- for r in range(self.nranks):
- sub_sample_path = os.path.join(self.config.index_path_, f'sample.{r}.pt')
- sub_sample = torch.load(sub_sample_path)
- os.remove(sub_sample_path)
-
- endpos = offset + sub_sample.size(0)
- sample[offset:endpos] = sub_sample
- offset = endpos
-
- assert endpos == sample.size(0), (endpos, sample.size())
-
- print_memory_stats(f'***2*** \t RANK:{self.rank}')
-
- # Shuffle and split out a 5% "heldout" sub-sample [up to 50k elements]
- sample = sample[torch.randperm(sample.size(0))]
-
- print_memory_stats(f'***3*** \t RANK:{self.rank}')
-
- heldout_fraction = 0.05
- heldout_size = int(min(heldout_fraction * sample.size(0), 50_000))
- sample, sample_heldout = sample.split([sample.size(0) - heldout_size, heldout_size], dim=0)
-
- print_memory_stats(f'***4*** \t RANK:{self.rank}')
-
- return sample, sample_heldout
-
- def _train_kmeans(self, sample, shared_lists):
- if self.use_gpu:
- torch.cuda.empty_cache()
-
- do_fork_for_faiss = False # set to True to free faiss GPU-0 memory at the cost of one more copy of `sample`.
-
- args_ = [self.config.dim, self.num_partitions, self.config.kmeans_niters]
-
- if do_fork_for_faiss:
- # For this to work reliably, write the sample to disk. Pickle may not handle >4GB of data.
- # Delete the sample file after work is done.
-
- shared_lists[0][0] = sample
- return_value_queue = mp.Queue()
-
- args_ = args_ + [shared_lists, return_value_queue]
- proc = mp.Process(target=compute_faiss_kmeans, args=args_)
-
- proc.start()
- centroids = return_value_queue.get()
- proc.join()
-
- else:
- args_ = args_ + [[[sample]]]
- centroids = compute_faiss_kmeans(*args_)
-
- centroids = torch.nn.functional.normalize(centroids, dim=-1)
- if self.use_gpu:
- centroids = centroids.half()
- else:
- centroids = centroids.float()
-
- return centroids
-
- def _compute_avg_residual(self, centroids, heldout):
- compressor = ResidualCodec(config=self.config, centroids=centroids, avg_residual=None)
-
- heldout_reconstruct = compressor.compress_into_codes(heldout, out_device='cuda' if self.use_gpu else 'cpu')
- heldout_reconstruct = compressor.lookup_centroids(heldout_reconstruct, out_device='cuda' if self.use_gpu else 'cpu')
- if self.use_gpu:
- heldout_avg_residual = heldout.cuda() - heldout_reconstruct
- else:
- heldout_avg_residual = heldout - heldout_reconstruct
-
- avg_residual = torch.abs(heldout_avg_residual).mean(dim=0).cpu()
- print([round(x, 3) for x in avg_residual.squeeze().tolist()])
-
- num_options = 2 ** self.config.nbits
- quantiles = torch.arange(0, num_options, device=heldout_avg_residual.device) * (1 / num_options)
- bucket_cutoffs_quantiles, bucket_weights_quantiles = quantiles[1:], quantiles + (0.5 / num_options)
-
- bucket_cutoffs = heldout_avg_residual.float().quantile(bucket_cutoffs_quantiles)
- bucket_weights = heldout_avg_residual.float().quantile(bucket_weights_quantiles)
-
- print_message(
- f"#> Got bucket_cutoffs_quantiles = {bucket_cutoffs_quantiles} and bucket_weights_quantiles = {bucket_weights_quantiles}")
- print_message(f"#> Got bucket_cutoffs = {bucket_cutoffs} and bucket_weights = {bucket_weights}")
-
- return bucket_cutoffs, bucket_weights, avg_residual.mean()
-
- # EVENTAULLY: Compare the above with non-heldout sample. If too different, we can do better!
- # sample = sample[subsample_idxs]
- # sample_reconstruct = get_centroids_for(centroids, sample)
- # sample_avg_residual = (sample - sample_reconstruct).mean(dim=0)
-
- def index(self):
- with self.saver.thread():
- batches = self.collection.enumerate_batches(rank=self.rank)
- for chunk_idx, offset, passages in tqdm.tqdm(batches, disable=self.rank > 0):
- if self.config.resume and self.saver.check_chunk_exists(chunk_idx):
- Run().print_main(f"#> Found chunk {chunk_idx} in the index already, skipping encoding...")
- continue
- embs, doclens = self.encoder.encode_passages(passages)
- if self.use_gpu:
- assert embs.dtype == torch.float16
- else:
- assert embs.dtype == torch.float32
- embs = embs.half()
-
- Run().print_main(f"#> Saving chunk {chunk_idx}: \t {len(passages):,} passages "
- f"and {embs.size(0):,} embeddings. From #{offset:,} onward.")
-
- self.saver.save_chunk(chunk_idx, offset, embs, doclens)
- del embs, doclens
-
- def finalize(self):
- if self.rank > 0:
- return
-
- self._check_all_files_are_saved()
- self._collect_embedding_id_offset()
-
- self._build_ivf()
- self._update_metadata()
-
- def _check_all_files_are_saved(self):
- Run().print_main("#> Checking all files were saved...")
- success = True
- for chunk_idx in range(self.num_chunks):
- if not self.saver.check_chunk_exists(chunk_idx):
- success = False
- Run().print_main(f"#> ERROR: Could not find chunk {chunk_idx}!")
- #TODO: Fail here?
- if success:
- Run().print_main("Found all files!")
-
- def _collect_embedding_id_offset(self):
- passage_offset = 0
- embedding_offset = 0
-
- self.embedding_offsets = []
-
- for chunk_idx in range(self.num_chunks):
- metadata_path = os.path.join(self.config.index_path_, f'{chunk_idx}.metadata.json')
-
- with open(metadata_path) as f:
- chunk_metadata = ujson.load(f)
-
- chunk_metadata['embedding_offset'] = embedding_offset
- self.embedding_offsets.append(embedding_offset)
-
- assert chunk_metadata['passage_offset'] == passage_offset, (chunk_idx, passage_offset, chunk_metadata)
-
- passage_offset += chunk_metadata['num_passages']
- embedding_offset += chunk_metadata['num_embeddings']
-
- with open(metadata_path, 'w') as f:
- f.write(ujson.dumps(chunk_metadata, indent=4) + '\n')
-
- self.num_embeddings = embedding_offset
- assert len(self.embedding_offsets) == self.num_chunks
-
- def _build_ivf(self):
- # Maybe we should several small IVFs? Every 250M embeddings, so that's every 1 GB.
- # It would save *memory* here and *disk space* regarding the int64.
- # But we'd have to decide how many IVFs to use during retrieval: many (loop) or one?
- # A loop seems nice if we can find a size that's large enough for speed yet small enough to fit on GPU!
- # Then it would help nicely for batching later: 1GB.
-
- Run().print_main("#> Building IVF...")
-
- codes = torch.empty(self.num_embeddings,)
- print_memory_stats(f'RANK:{self.rank}')
-
- Run().print_main("#> Loading codes...")
-
- for chunk_idx in tqdm.tqdm(range(self.num_chunks)):
- offset = self.embedding_offsets[chunk_idx]
- chunk_codes = ResidualCodec.Embeddings.load_codes(self.config.index_path_, chunk_idx)
-
- codes[offset:offset+chunk_codes.size(0)] = chunk_codes
-
- assert offset+chunk_codes.size(0) == codes.size(0), (offset, chunk_codes.size(0), codes.size())
-
-
- Run().print_main(f"Sorting codes...")
-
- print_memory_stats(f'RANK:{self.rank}')
-
- codes = codes.sort()
- ivf, values = codes.indices, codes.values
-
- print_memory_stats(f'RANK:{self.rank}')
-
- Run().print_main(f"Getting unique codes...")
-
- partitions, ivf_lengths = values.unique_consecutive(return_counts=True)
-
- # All partitions should be non-empty. (We can use torch.histc otherwise.)
- assert partitions.size(0) == self.num_partitions, (partitions.size(), self.num_partitions)
-
- print_memory_stats(f'RANK:{self.rank}')
-
- _, _ = optimize_ivf(ivf, ivf_lengths, self.config.index_path_)
-
- def _update_metadata(self):
- config = self.config
- self.metadata_path = os.path.join(config.index_path_, 'metadata.json')
- Run().print("#> Saving the indexing metadata to", self.metadata_path, "..")
-
- with open(self.metadata_path, 'w') as f:
- d = {'config': config.export()}
- d['num_chunks'] = self.num_chunks
- d['num_partitions'] = self.num_partitions
- d['num_embeddings'] = self.num_embeddings
- d['avg_doclen'] = self.num_embeddings / len(self.collection)
-
- f.write(ujson.dumps(d, indent=4) + '\n')
-
-
-def compute_faiss_kmeans(dim, num_partitions, kmeans_niters, shared_lists, return_value_queue=None):
- use_gpu = torch.cuda.is_available()
- kmeans = faiss.Kmeans(dim, num_partitions, niter=kmeans_niters, gpu=use_gpu, verbose=True, seed=123)
-
- sample = shared_lists[0][0]
- sample = sample.float().numpy()
-
- kmeans.train(sample)
-
- centroids = torch.from_numpy(kmeans.centroids)
-
- print_memory_stats(f'RANK:0*')
-
- if return_value_queue is not None:
- return_value_queue.put(centroids)
-
- return centroids
-
-
-"""
-TODOs:
-
-1. Notice we're using self.config.bsize.
-
-2. Consider saving/using heldout_avg_residual as a vector --- that is, using 128 averages!
-
-3. Consider the operations with .cuda() tensors. Are all of them good for OOM?
-"""
diff --git a/spaces/digitalxingtong/Un-Bert-Vits2/app.py b/spaces/digitalxingtong/Un-Bert-Vits2/app.py
deleted file mode 100644
index 8bff81e53152543fce275291e896a42370f1eb62..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Un-Bert-Vits2/app.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import sys, os
-
-if sys.platform == "darwin":
- os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-
-import logging
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-
-logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s")
-
-logger = logging.getLogger(__name__)
-
-import torch
-import argparse
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-import gradio as gr
-import webbrowser
-
-
-net_g = None
-
-
-def get_text(text, language_str, hps):
- norm_text, phone, tone, word2ph = clean_text(text, language_str)
- phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
- if hps.data.add_blank:
- phone = commons.intersperse(phone, 0)
- tone = commons.intersperse(tone, 0)
- language = commons.intersperse(language, 0)
- for i in range(len(word2ph)):
- word2ph[i] = word2ph[i] * 2
- word2ph[0] += 1
- bert = get_bert(norm_text, word2ph, language_str)
- del word2ph
-
- assert bert.shape[-1] == len(phone)
-
- phone = torch.LongTensor(phone)
- tone = torch.LongTensor(tone)
- language = torch.LongTensor(language)
-
- return bert, phone, tone, language
-import soundfile as sf
-def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
- global net_g
- bert, phones, tones, lang_ids = get_text(text, "ZH", hps)
- with torch.no_grad():
- x_tst=phones.to(device).unsqueeze(0)
- tones=tones.to(device).unsqueeze(0)
- lang_ids=lang_ids.to(device).unsqueeze(0)
- bert = bert.to(device).unsqueeze(0)
- x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
- del phones
- speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
- audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio
- , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
- del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
- sf.write("tmp.wav", audio, 44100)
- return audio
-def convert_wav_to_ogg(wav_file):
- os.makedirs('out', exist_ok=True)
- filename = os.path.splitext(os.path.basename(wav_file.name))[0]
- output_path_ogg = os.path.join('out', f"out.ogg")
-
- renamed_input_path = os.path.join('in', f"in.wav")
- os.makedirs('in', exist_ok=True)
- os.rename(wav_file.name, renamed_input_path)
- command = ["ffmpeg", "-i", renamed_input_path, "-acodec", "libopus", "-y", output_path_ogg]
- os.system(" ".join(command))
- return output_path_ogg
-def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale):
- with torch.no_grad():
- audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker)
- with open('tmp.wav', 'rb') as wav_file:
- newogg = convert_wav_to_ogg(wav_file)
- return "Success", (hps.data.sampling_rate, audio),newogg
-
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--model_dir", default="./logs/un/un.pth", help="path of your model")
- parser.add_argument("--config_dir", default="./configs/config.json", help="path of your config file")
- parser.add_argument("--share", default=False, help="make link public")
- parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log")
-
- args = parser.parse_args()
- if args.debug:
- logger.info("Enable DEBUG-LEVEL log")
- logging.basicConfig(level=logging.DEBUG)
- hps = utils.get_hparams_from_file(args.config_dir)
- device = "cuda:0" if torch.cuda.is_available() else "cpu"
- '''
- device = (
- "cuda:0"
- if torch.cuda.is_available()
- else (
- "mps"
- if sys.platform == "darwin" and torch.backends.mps.is_available()
- else "cpu"
- )
- )
- '''
- net_g = SynthesizerTrn(
- len(symbols),
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- n_speakers=hps.data.n_speakers,
- **hps.model).to(device)
- _ = net_g.eval()
-
- _ = utils.load_checkpoint(args.model_dir, net_g, None, skip_optimizer=True)
-
- speaker_ids = hps.data.spk2id
- speakers = list(speaker_ids.keys())
- with gr.Blocks() as app:
- with gr.Row():
- with gr.Column():
-
-
- gr.Markdown(value="""
- 柚恩 Bert-Vits2在线语音生成\n
- 1、模型作者：数字星瞳企划 https://t.me/xingtong25680 \n
- \n
- 2、原项目地址：https://github.com/Stardust-minus/Bert-VITS2\n
- 3、使用此模型进行二创请注明AI生成，以及该项目地址。\n
- 4、如果想生成超长txt文本的音频请使用colab。 https://colab.research.google.com/drive/13ek8_j1aknr-pbjj3NXxSM4vBIsracU3?usp=drive_link\n
-
- """)
- text = gr.TextArea(label="Text", placeholder="Input Text Here",
- value="这里是数字星瞳企画，请在电报搜索星瞳全拼加二五六八零，获取最新更新进展。")
- speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker')
- sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.01, label='语调变化')
- noise_scale = gr.Slider(minimum=0.1, maximum=1.5, value=0.6, step=0.01, label='感情变化')
- noise_scale_w = gr.Slider(minimum=0.1, maximum=1.4, value=0.8, step=0.01, label='音节发音长度变化')
- length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.01, label='语速')
- btn = gr.Button("开启AI语音之旅吧!", variant="primary")
- with gr.Column():
- text_output = gr.Textbox(label="Message")
- audio_output = gr.Audio(label="Output Audio")
- ogg_output = gr.File(label="Converted OGG file")
- gr.Markdown(value="""
- 模型汇总：\n
- 星瞳 https://huggingface.co/spaces/digitalxingtong/Xingtong-Bert-Vits2 \n
- 星瞳 朗读专用 https://huggingface.co/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2 \n
- 星瞳 长文本专用 https://huggingface.co/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2 \n
- 甜甜叫花鸡 https://huggingface.co/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2 \n
- 七海 https://huggingface.co/spaces/digitalxingtong/Nanami-Bert-Vits2 \n
- 东雪莲 https://huggingface.co/spaces/digitalxingtong/Azuma-Bert-Vits2 \n
- 嘉然 https://huggingface.co/spaces/digitalxingtong/Jiaran-Bert-Vits2 \n
- 乃琳 https://huggingface.co/spaces/digitalxingtong/Eileen-Bert-Vits2 \n
- 恬豆 https://huggingface.co/spaces/digitalxingtong/Dou-Bert-Vits2 \n
- 奶绿 杂谈 https://huggingface.co/spaces/digitalxingtong/Nailv-Bert-Vits2 \n
- 奶绿 朗读 https://huggingface.co/spaces/digitalxingtong/Nailv-read-Bert-Vits2 \n
- 露早 https://huggingface.co/spaces/digitalxingtong/Luzao-Bert-Vits2 \n
- 柚恩 https://huggingface.co/spaces/digitalxingtong/Un-Bert-Vits2 \n
- 米诺 https://huggingface.co/spaces/digitalxingtong/Minuo-Bert-Vits2 \n
- 扇宝 https://huggingface.co/spaces/digitalxingtong/Shanbao-Bert-Vits2 \n
- 牧牧白 https://huggingface.co/spaces/digitalxingtong/Miiu-Bert-Vits2 \n
- 吉诺儿kino https://huggingface.co/spaces/digitalxingtong/Kino-Bert-Vits2 \n
- 九夏 https://huggingface.co/spaces/digitalxingtong/Jiuxia-Bert-Vits2 \n
- 卡缇娅 https://huggingface.co/spaces/digitalxingtong/Yaya-Bert-Vits2 \n
- 理想_ideal https://huggingface.co/spaces/digitalxingtong/Lixiang-Bert-Vits2 \n
- 阿梓 https://huggingface.co/spaces/digitalxingtong/Azusa-Bert-Vits2 \n
- 鹿鸣 https://huggingface.co/spaces/digitalxingtong/Luming-Bert-Vits2 \n
- 永雏塔菲 https://huggingface.co/spaces/digitalxingtong/Taffy-Bert-VITS2 \n
- """)
- btn.click(tts_fn,
- inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale],
- outputs=[text_output, audio_output,ogg_output])
-
-
- app.launch(show_error=True)
diff --git a/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2/monotonic_align/setup.py b/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2/monotonic_align/setup.py
deleted file mode 100644
index 30c224807a70faa9df9c9eb75f8e80c8c867b16b..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2/monotonic_align/setup.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from distutils.core import setup
-from Cython.Build import cythonize
-import numpy
-
-setup(
- name = 'monotonic_align',
- ext_modules = cythonize("core.pyx"),
- include_dirs=[numpy.get_include()]
-)
diff --git a/spaces/dinhminh20521597/OCR_DEMO/configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py b/spaces/dinhminh20521597/OCR_DEMO/configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py
deleted file mode 100644
index 045e89a3bb1fa44ff33da1d2b8b32b42e396c58b..0000000000000000000000000000000000000000
--- a/spaces/dinhminh20521597/OCR_DEMO/configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py
+++ /dev/null
@@ -1,33 +0,0 @@
-_base_ = [
- '../../_base_/default_runtime.py',
- '../../_base_/schedules/schedule_sgd_1200e.py',
- '../../_base_/det_models/textsnake_r50_fpn_unet.py',
- '../../_base_/det_datasets/ctw1500.py',
- '../../_base_/det_pipelines/textsnake_pipeline.py'
-]
-
-train_list = {{_base_.train_list}}
-test_list = {{_base_.test_list}}
-
-train_pipeline = {{_base_.train_pipeline}}
-test_pipeline = {{_base_.test_pipeline}}
-
-data = dict(
- samples_per_gpu=4,
- workers_per_gpu=4,
- val_dataloader=dict(samples_per_gpu=1),
- test_dataloader=dict(samples_per_gpu=1),
- train=dict(
- type='UniformConcatDataset',
- datasets=train_list,
- pipeline=train_pipeline),
- val=dict(
- type='UniformConcatDataset',
- datasets=test_list,
- pipeline=test_pipeline),
- test=dict(
- type='UniformConcatDataset',
- datasets=test_list,
- pipeline=test_pipeline))
-
-evaluation = dict(interval=10, metric='hmean-iou')
diff --git a/spaces/dolphinfusion/dolphinfusion-diffusion/README.md b/spaces/dolphinfusion/dolphinfusion-diffusion/README.md
deleted file mode 100644
index 60b562b9658f2310849f25a5e2dc5c2fa906cc53..0000000000000000000000000000000000000000
--- a/spaces/dolphinfusion/dolphinfusion-diffusion/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: DolphinFusion Global
-emoji: 🌊🐬
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 3.40.1
-app_file: gen.dolphin.script.py
-pinned: true
----
-
diff --git a/spaces/dorkai/singpt/extensions/send_pictures/script.py b/spaces/dorkai/singpt/extensions/send_pictures/script.py
deleted file mode 100644
index b0c356329a51edf026f7223a0ee7e5427d8751ce..0000000000000000000000000000000000000000
--- a/spaces/dorkai/singpt/extensions/send_pictures/script.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import base64
-from io import BytesIO
-
-import gradio as gr
-import torch
-from transformers import BlipForConditionalGeneration, BlipProcessor
-
-import modules.chat as chat
-import modules.shared as shared
-
-# If 'state' is True, will hijack the next chat generation with
-# custom input text given by 'value' in the format [text, visible_text]
-input_hijack = {
- 'state': False,
- 'value': ["", ""]
-}
-
-processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
-model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float32).to("cpu")
-
-def caption_image(raw_image):
- inputs = processor(raw_image.convert('RGB'), return_tensors="pt").to("cpu", torch.float32)
- out = model.generate(**inputs, max_new_tokens=100)
- return processor.decode(out[0], skip_special_tokens=True)
-
-def generate_chat_picture(picture, name1, name2):
- text = f'*{name1} sends {name2} a picture that contains the following: "{caption_image(picture)}"*'
- buffer = BytesIO()
- picture.save(buffer, format="JPEG")
- img_str = base64.b64encode(buffer.getvalue()).decode('utf-8')
- visible_text = f'
'
- return text, visible_text
-
-def ui():
- picture_select = gr.Image(label='Send a picture', type='pil')
-
- function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper'
-
- # Prepare the hijack with custom inputs
- picture_select.upload(lambda picture, name1, name2: input_hijack.update({"state": True, "value": generate_chat_picture(picture, name1, name2)}), [picture_select, shared.gradio['name1'], shared.gradio['name2']], None)
-
- # Call the generation function
- picture_select.upload(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)
-
- # Clear the picture from the upload field
- picture_select.upload(lambda : None, [], [picture_select], show_progress=False)
diff --git a/spaces/ds520/bingo/src/components/learn-more.tsx b/spaces/ds520/bingo/src/components/learn-more.tsx
deleted file mode 100644
index a64459ee7900a612292e117a6bda96ee9260990f..0000000000000000000000000000000000000000
--- a/spaces/ds520/bingo/src/components/learn-more.tsx
+++ /dev/null
@@ -1,39 +0,0 @@
-import React from 'react'
-import { SourceAttribution } from '@/lib/bots/bing/types'
-
-export interface LearnMoreProps {
- sourceAttributions?: SourceAttribution[]
-}
-
-export function LearnMore({ sourceAttributions }: LearnMoreProps) {
- if (!sourceAttributions?.length) {
- return null
- }
-
- return (
-
-
了解详细信息:
-
-
- {sourceAttributions.map((attribution, index) => {
- const { providerDisplayName, seeMoreUrl } = attribution
- const { host } = new URL(seeMoreUrl)
- return (
-
- {index + 1}. {host}
-
- )
- })}
-
-
-
CG Status Video Download Love: How to Express Your Feelings with Chhattisgarhi Songs
- Do you want to impress your lover with some romantic and catchy Chhattisgarhi songs? Do you want to share your emotions and feelings with your partner through WhatsApp status videos? If yes, then you are in the right place. In this article, we will tell you everything you need to know about CG status video download love. We will explain what are CG status videos, how to download them, and how to choose the best one for your love. So, let's get started.
- What are CG Status Videos?
- CG status videos are short video clips that feature Chhattisgarhi songs, which are the folk songs of the Indian state of Chhattisgarh. These songs are sung in the local language of Chhattisgarhi, which is a dialect of Hindi. CG status videos are popular among the people of Chhattisgarh and other neighboring states, as they express their culture, tradition, and identity.
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- Chhattisgarhi songs have a long history that dates back to ancient times. They are influenced by various musical genres, such as tribal, classical, devotional, and modern. Some of the famous singers of Chhattisgarhi songs are Alka Chandrakar, Mamta Chandrakar, Anuj Sharma, Dilip Shadangi, and Sunil Soni. Chhattisgarhi songs are popular not only in India but also in other countries, such as Nepal, Bangladesh, Pakistan, and Sri Lanka.
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- CG status videos have many features and benefits that make them appealing to the users. Some of them are:
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-- They are short and sweet, usually ranging from 15 seconds to 30 seconds.
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- How to Download CG Status Videos for WhatsApp?
- If you want to download CG status videos for WhatsApp, you have two options: websites or apps. Both have their own advantages and disadvantages. Let's see them in detail.
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- There are many websites and apps that offer CG status videos for download. However, not all of them are reliable and safe. Some may contain viruses, malware, or spam. Some may have low-quality or outdated videos. Some may have limited or restricted content. Therefore, you need to be careful while choosing the best website or app for downloading CG status videos. Here are some of the best websites and apps that we recommend:
-
-| Website/App | Description |
|---|
-| [CG Song] | A website that provides CG songs and videos for download. It has a dedicated section for CG love status videos for WhatsApp in various formats and resolutions. |
-| [CG Video Status] | An app that allows users to download and share CG status videos for WhatsApp and other social media platforms. It has a huge collection of CG love status videos for WhatsApp in different genres and languages. |
-| [CG Status] | An app that offers CG status videos for WhatsApp and other social media platforms. It has a wide range of CG love status videos for WhatsApp in different categories and moods. |
-
- The steps to download and share CG status videos
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-- Open the website or app that you want to use for downloading CG status videos.
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-- Preview the video and check its quality, size, and duration.
-- Click on the download button and choose the format and resolution that you prefer.
-- Wait for the download to complete and save the video on your device.
-- Open your WhatsApp and go to your status section.
-- Tap on the add button and select the video that you downloaded from your gallery.
-- Edit the video if you want to add any text, sticker, or effect.
-- Tap on the send button and share your CG love status video with your contacts.
-
- How to Choose the Best CG Status Video for Your Love?
- Now that you know how to download and share CG status videos, you might be wondering how to choose the best one for your love. After all, you want to make your partner feel special and happy with your gesture. Well, don't worry. We have some tips and tricks for you to select the most suitable CG love status video for your love. Here they are:
- The different types of CG love status videos
- CG love status videos come in different types, depending on the theme, tone, and message of the song. Some of the common types are:
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-- Romantic: These are the videos that feature romantic songs that express your love, affection, and admiration for your partner. They are ideal for celebrating occasions like Valentine's Day, anniversary, or birthday.
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-- Funny: These are the videos that feature funny songs that make your partner laugh, smile, or chuckle. They are perfect for cheering up your partner, teasing them, or making them feel good.
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- The tips and tricks to select the most suitable CG love status video
- Here are some tips and tricks that you can use to select the most suitable CG love status video for your partner:
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-- Know your partner's preferences: The first thing you need to do is to know what kind of songs your partner likes. Do they prefer romantic, sad, funny, or inspirational songs? Do they like slow, fast, or upbeat songs? Do they like traditional, modern, or fusion songs? Knowing their preferences will help you choose a video that matches their taste and mood.
-- Know your partner's personality: The next thing you need to do is to know what kind of personality your partner has. Are they shy, bold, sweet, or naughty? Are they emotional, rational, optimistic, or pessimistic? Are they adventurous, cautious, creative, or practical? Knowing their personality will help you choose a video that reflects their traits and qualities.
-- Know your relationship status: The last thing you need to do is to know what kind of relationship you have with your partner. Are you dating, engaged, married, or separated? Are you happy, sad, angry, or confused? Are you loyal, faithful, honest, or cheating? Knowing your relationship status will help you choose a video that conveys your feelings and intentions.
-
- Conclusion
- In conclusion, CG status video download love is a great way to express your feelings with Chhattisgarhi songs. CG status videos are short and catchy video clips that feature Chhattisgarhi songs, which are the folk songs of the Indian state of Chhattisgarh. They are popular among the people of Chhattisgarh and other neighboring states, as they reflect their culture, tradition, and identity. You can download and share CG status videos for WhatsApp from various websites and apps, such as Pinterest, CG Song, CG Video Status, and CG Status. You can also choose the best CG love status video for your partner by knowing their preferences, personality, and relationship status. So, what are you waiting for? Go ahead and download your favorite CG love status video and make your partner feel special and happy.
- FAQs
- Here are some of the frequently asked questions about CG status video download love:
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-- What is the meaning of CG?
-CG stands for Chhattisgarh, which is a state in central India. It is known for its rich cultural heritage, natural beauty, and mineral resources.
-- What is the difference between CG status videos and other status videos?
-CG status videos are different from other status videos in terms of the language, music, and content. CG status videos use Chhattisgarhi language, which is a dialect of Hindi. They also use Chhattisgarhi music, which is influenced by various musical genres, such as tribal, classical, devotional, and modern. Moreover, they use Chhattisgarhi content, which showcases the culture, tradition, and identity of the people of Chhattisgarh.
-- How can I download CG status videos without watermark?
-If you want to download CG status videos without watermark, you need to use a website or app that does not add any watermark to the videos. Some of the websites and apps that offer watermark-free CG status videos are [CG Song], [CG Video Status], and [CG Status].
-- How can I make my own CG status video?
-If you want to make your own CG status video, you need to have a video editing software or app that allows you to add Chhattisgarhi songs to your video clips. Some of the video editing software and apps that you can use are [Filmora], [Kinemaster], and [VivaVideo].
-- Where can I find more information about Chhattisgarhi songs?
-If you want to find more information about Chhattisgarhi songs, you can visit some of the websites that provide information about Chhattisgarhi music, such as [Chhattisgarh Music], [CG Music World], and [CG Song Lyrics].
-
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Demon Slayer 3 Temporada Legendado A Caada ao Demnio de Yoshiwara.md b/spaces/fatiXbelha/sd/Demon Slayer 3 Temporada Legendado A Caada ao Demnio de Yoshiwara.md
deleted file mode 100644
index 53e6553ab74150fc419323f1583e1c144ddb8d27..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Demon Slayer 3 Temporada Legendado A Caada ao Demnio de Yoshiwara.md
+++ /dev/null
@@ -1,115 +0,0 @@
-
-Demon Slayer 3ª temporada: How to download and watch the anime series
- If you are a fan of anime, you have probably heard of Demon Slayer, one of the most popular and successful manga and anime series in recent years. The series has captivated millions of fans around the world with its thrilling story, stunning animation, and memorable characters. But if you have not watched it yet, or if you want to catch up with the latest episodes, you might be wondering how to download and watch Demon Slayer 3ª temporada, the third season of the anime. In this article, we will tell you everything you need to know about Demon Slayer 3ª temporada, including what it is about, when and where to watch it, and how to download it legally and safely.
- What is Demon Slayer about?
- Demon Slayer is a manga series written and illustrated by Koyoharu Gotouge. It was serialized in Shueisha's Weekly Shonen Jump magazine from February 2016 to May 2020, with 23 volumes collected in tankobon format. It has been published in English by Viz Media and simultaneously published by Shueisha on their Manga Plus platform.
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- The manga series has been adapted into an anime television series by Ufotable, which aired from April to September 2019. A sequel film, Demon Slayer: Kimetsu no Yaiba – The Movie: Mugen Train, was released in October 2020 and became the highest-grossing anime film and Japanese film of all time. An 18-episode second season of the anime series aired from October 2021 to February 2022. It featured one original episode, re-edited the Mugen Train film into six episodes, and then covered the "Entertainment District" arc from the manga in 11 episodes. A compilation film, Demon Slayer: Kimetsu no Yaiba – To the Swordsmith Village, was released in February 2023, while a third season, covering the "Swordsmith Village" arc, aired from April to June 2023. A fourth season covering the "Hashira Training" arc has been announced.
- But what is Demon Slayer about? Here is a brief synopsis of the manga and anime:
- A brief synopsis of the manga and anime
- Demon Slayer follows the story of Tanjiro Kamado, a kind-hearted and intelligent boy who lives with his family in the mountains. He became his family's breadwinner after his father's death, traveling to the nearby village to sell charcoal. Everything changed when he came home one day to discover his family was attacked and slaughtered by a demon. Worse still, the sole survivor is his sister Nezuko, who has been turned into a bloodthirsty demon. Consumed by rage and hatred, Tanjiro swears to avenge his family and stay by his only remaining sibling. Alongside the mysterious group calling themselves the Demon Slayer Corps, Tanjiro will do whatever it takes to slay the demons and protect the remnants of his beloved sister's humanity.
- The main characters and their roles
- Some of the main characters in Demon Slayer are:
-
-- Tanjiro Kamado: The protagonist of the series. He is a kind-hearted boy who becomes a demon slayer after his family is killed by a demon. He wields a black Nichirin Blade that can change its form depending on his breathing technique. He also has the ability to sense the emotions of others and communicate with demons.
-- Nezuko Kamado: Tanjiro's younger sister and the deuteragonist of the series. She is turned into a demon by Muzan Kibutsuji, the first and most powerful demon in existence. Unlike most demons, she retains some of her human traits, such as her love for her brother and her refusal to eat humans. She can also shrink or grow her size at will and use her blood as a weapon. She usually travels inside a wooden box that Tanjiro carries on his back.
-- Zenitsu Agatsuma: A cowardly and nervous boy who becomes a demon slayer and a companion of Tanjiro. He wields a yellow Nichirin Blade that can produce lightning-fast attacks. He is also a skilled musician who can play the shamisen. He has a crush on Nezuko and often tries to impress her.
-- Inosuke Hashibira: A wild and aggressive boy who becomes a demon slayer and a companion of Tanjiro. He was raised by boars in the mountains and wears a boar's head as a mask. He wields two jagged Nichirin Blades that he can use for dual-wielding attacks. He is also very strong and flexible, able to climb trees and walls with ease. He often competes with Tanjiro and Zenitsu for glory and recognition.
-- Giyu Tomioka: A calm and stoic man who is one of the Hashira, the elite demon slayers of the Demon Slayer Corps. He wields a blue Nichirin Blade that can create water-based attacks. He is the one who saved Tanjiro and Nezuko from a demon attack and encouraged Tanjiro to join the Demon Slayer Corps. He also has a soft spot for Nezuko and often defends her from those who want to kill her.
-- Shinobu Kocho: A cheerful and elegant woman who is one of the Hashira, the elite demon slayers of the Demon Slayer Corps. She wields a thin Nichirin Blade that can inject poison into demons. She is also an expert in medicine and insect breeding, using various insects as her allies in battle. She has a deep hatred for demons, especially those who killed her sister, but she also believes in finding a cure for them.
-- Kanao Tsuyuri: A quiet and emotionless girl who is Shinobu's adoptive sister and apprentice. She wields a pink Nichirin Blade that can change its color depending on her mood. She is also very skilled in combat, being able to use various breathing techniques and enhanced senses. She has difficulty making decisions on her own, often relying on a coin toss to choose her actions.
-
- What is Demon Slayer 3ª temporada?
- Demon Slayer 3ª temporada is the third season of the anime adaptation of Demon Slayer manga series. It covers the "Swordsmith Village" arc, which spans from chapters 100 to 127 of the manga.
- The plot of the third season
- The plot of Demon Slayer 3ª temporada follows Tanjiro, Nezuko, Zenitsu, and Inosuke as they travel to the Swordsmith Village, where they hope to repair their broken Nichirin Blades and learn more about their origins. There, they meet the swordsmiths who forge the blades for the demon slayers, as well as some new allies and enemies. They also encounter a new threat: Upper Rank 4 of the Twelve Kizuki, Hantengu, a powerful demon who can split into multiple forms based on his emotions.
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- Meanwhile, Giyu and Shinobu are sent on a mission to investigate a mysterious mansion where several demon slayers have gone missing. There, they face off against Upper Rank 2 of the Twelve Kizuki, Doma, a cold-hearted demon who can manipulate ice and blood.
- As the battles rage on, secrets are revealed, bonds are tested, and lives are changed forever.
- The release date and streaming platforms
- Demon Slayer 3ª temporada aired from April 4 to June 27, 2023 on Tokyo MX, GTV, GYT, BS11, Aniplex Channel, AbemaTV, d Anime Store, Hulu Japan, Amazon Prime Video Japan, U-NEXT Japan, Netflix Japan, Crunchyroll Japan, Funimation Japan, Animax Asia (Southeast Asia), Wakanim (Europe), Funimation (North America), Crunchyroll (North America), AnimeLab (Australia), Netflix (Australia), and iQIYI (China). It consisted of 13 episodes, each with a runtime of 24 minutes. The opening theme song was "Homura" by LiSA, while the ending theme song was "Shirogane" by Aimer.
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- If you want to download and watch Demon Slayer 3ª temporada, you have several options to choose from. However, not all of them are legal and safe. Some websites or apps may offer you free or cheap downloads or streams, but they may also expose you to malware, viruses, or legal issues. Therefore, we recommend you to use only the official and licensed platforms that have the rights to distribute the anime series. Here are some of them:
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-- Aniplex Channel: This is the official streaming platform of Aniplex, the producer and distributor of Demon Slayer anime series. You can watch all the episodes of Demon Slayer 3ª temporada on Aniplex Channel for free, with ads. You can also download the episodes for offline viewing, but you need to pay a fee for each episode or buy a subscription plan. Aniplex Channel is available in Japan and some other regions.
-- Netflix: This is one of the most popular and widely available streaming platforms in the world. You can watch all the episodes of Demon Slayer 3ª temporada on Netflix with a subscription plan that varies depending on your region and device. You can also download the episodes for offline viewing on your mobile device or tablet. Netflix is available in most countries and regions, except for China, Crimea, North Korea, and Syria.
-- Crunchyroll: This is one of the largest and most popular streaming platforms for anime and manga fans. You can watch all the episodes of Demon Slayer 3ª temporada on Crunchyroll with a subscription plan that costs $7.99 per month or $79.99 per year. You can also watch some episodes for free, with ads and a one-week delay. You can also download the episodes for offline viewing on your mobile device or tablet with a subscription plan that costs $9.99 per month or $99.99 per year. Crunchyroll is available in most countries and regions, except for Japan, China, France, Germany, Italy, and some others.
-- Funimation: This is another popular streaming platform for anime and manga fans. You can watch all the episodes of Demon Slayer 3ª temporada on Funimation with a subscription plan that costs $5.99 per month or $59.99 per year. You can also watch some episodes for free, with ads and a two-week delay. You can also download the episodes for offline viewing on your mobile device or tablet with a subscription plan that costs $7.99 per month or $79.99 per year. Funimation is available in North America, United Kingdom, Ireland, Australia, New Zealand, Mexico, Brazil, Colombia, Peru, Chile, and some other regions.
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- These are some of the legal and safe ways to download and watch Demon Slayer 3ª temporada. However, you should always check the availability and compatibility of the platforms in your region and device before using them. You should also respect the intellectual property rights of the creators and distributors of the anime series and avoid using any illegal or pirated sources.
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- Downloading and watching Demon Slayer 3ª temporada can offer you many benefits, such as:
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-- Enjoying the anime at your own pace and convenience: By downloading the episodes, you can watch them anytime and anywhere you want, without worrying about internet connection, buffering, or ads. You can also pause, rewind, or skip the scenes as you wish.
-- Supporting the anime industry and the creators: By using the official and licensed platforms, you are contributing to the revenue and popularity of the anime series, which can help the anime industry and the creators to produce more quality content in the future.
-- Learning more about Japanese culture and language: By watching the anime, you can immerse yourself in the rich and diverse world of Japanese culture and language. You can learn more about the history, mythology, folklore, art, music, cuisine, and customs of Japan, as well as pick up some useful words and phrases in Japanese.
-- Having fun and being entertained: By watching the anime, you can experience a thrilling and emotional story that will keep you hooked from start to finish. You can also enjoy the stunning animation, the catchy soundtrack, and the memorable characters that will make you laugh, cry, cheer, and feel inspired.
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- Demon Slayer 3ª temporada is the third season of the anime adaptation of Demon Slayer manga series. It covers the "Swordsmith Village" arc, which follows Tanjiro and his friends as they travel to the Swordsmith Village to repair their blades and face a new enemy. The anime series aired from April to June 2023 on various streaming platforms that you can use to download and watch it legally and safely. By doing so, you can enjoy the anime at your own pace and convenience, support the anime industry and the creators, learn more about Japanese culture and language, and have fun and be entertained.
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- If you are a fan of Demon Slayer or if you are curious about it, we encourage you to download and watch Demon Slayer 3ª temporada as soon as possible. You will not regret it. It is one of the best anime series of all time, with a captivating story, stunning animation, memorable characters, catchy soundtrack, rich culture, and more. It will make you feel a range of emotions that will stay with you for a long time. So what are you waiting for? Download and watch Demon Slayer 3ª temporada today!
- Frequently Asked Questions
- Here are some frequently asked questions about Demon Slayer 3ª temporada:
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-- Q: How many episodes are there in Demon Slayer 3ª temporada?
-- A: There are 13 episodes in Demon Slayer 3ª temporada.
-- Q: Where can I read Demon Slayer manga?
-- A: You can read Demon Slayer manga on Manga Plus or Viz Media websites or apps.
-- Q: Is there a fourth season of Demon Slayer anime?
-- A: Yes, there is a fourth season of Demon Slayer anime that has been announced. It will cover the "Hashira Training" arc from the manga.
-- Q: Who is the strongest demon slayer in Demon Slayer?
A: The strongest demon slayer in Demon Slayer is Yoriichi Tsugikuni, the first user of the Sun Breathing technique and the ancestor of Tanjiro. He was able to defeat Muzan Kibutsuji, the first and most powerful demon, but failed to kill him due to Muzan's regeneration ability.
-- Q: What is the meaning of the title Demon Slayer?
-- A: The meaning of the title Demon Slayer is twofold. On one hand, it refers to the demon slayers, the warriors who fight against the demons and protect humanity. On the other hand, it refers to the demons themselves, who were once humans but were slayed by their own desires and corrupted by Muzan Kibutsuji.
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- I hope you enjoyed this article and learned something new. Thank you for reading and have a great day!
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diff --git a/spaces/fatiXbelha/sd/Download Drive Zone APK and Enjoy the Ultimate Car Driving Simulator.md b/spaces/fatiXbelha/sd/Download Drive Zone APK and Enjoy the Ultimate Car Driving Simulator.md
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diff --git a/spaces/fatiXbelha/sd/Download MuAwaY Global and Play for Free on Any Device.md b/spaces/fatiXbelha/sd/Download MuAwaY Global and Play for Free on Any Device.md
deleted file mode 100644
index 80720f1e2545599de7a797e20ab6436a2ca2bf3e..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download MuAwaY Global and Play for Free on Any Device.md
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@@ -1,98 +0,0 @@
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diff --git a/spaces/fatiXbelha/sd/Episode Choose Your Story Hack How to Get Free Passes Gems without Downloading Cheats.md b/spaces/fatiXbelha/sd/Episode Choose Your Story Hack How to Get Free Passes Gems without Downloading Cheats.md
deleted file mode 100644
index 1f70d25a579c670cf4d0d440e13e6faca310d6f6..0000000000000000000000000000000000000000
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-- Choose a reputable and reliable source for the cheats. You can check the reviews, ratings, comments, and feedback from other users to verify the quality and legitimacy of the cheats.
-- Scan the files or apps for viruses or malware before downloading or installing them. You can use a trusted antivirus or anti-malware software to do this.
-- Backup your device and your app data before using the cheats. You can use a cloud service or an external storage device to do this.
-- Follow the instructions carefully and correctly when using the cheats. You can read the FAQs, guides, tutorials, or manuals provided by the cheat developers to learn how to use the cheats properly.
-- Use the cheats sparingly and moderately. You can avoid using the cheats too often or too excessively to reduce the risk of getting detected or banned by the developers.
-
- Conclusion
- Episode Choose Your Story is a fun and engaging app that allows you to live your stories with love, romance, adventure, and drama. However, if you want to enjoy the app more fully, you might need more passes and gems to unlock more episodes, choices, outfits, and features. You can get free passes and gems legally by rating and reviewing the app, watching ads and completing offers, referring friends and joining contests. You can also use cheats to get unlimited free passes and gems faster and easier, but you should be aware of the risks and use them safely. We hope this article has helped you learn more about Episode Choose Your Story cheats download. Happy reading!
- FAQs
- Here are some frequently asked questions related to Episode Choose Your Story cheats download:
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-- Q: Are Episode Choose Your Story cheats legal?
A: Episode Choose Your Story cheats are not legal, as they violate the terms of service of the app. Using cheats can result in your account being banned or suspended from the app.
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A: Episode Choose Your Story cheats are not safe, as they might contain viruses or malware that can harm your device or steal your personal information. They might also be scams or phishing sites that can hack your account or charge you money without your consent.
-- Q: How do I get more passes and gems without using cheats?
A: You can get more passes and gems without using cheats by rating and reviewing the app, watching ads and completing offers, referring friends and joining contests. These are legal and safe ways to earn more passes and gems in the app.
-- Q: How do I write my own story on Episode Choose Your Story?
A: You can write your own story on Episode Choose Your Story by using the Episode Writer Portal on their website. You can create your own characters, scenes, dialogues, choices, animations, sounds, etc. You can also publish your story on the app and get feedback from other readers.
-- Q: How do I contact Episode Choose Your Story support?
A: You can contact Episode Choose Your Story support by filling out a form on their website or by emailing them at support@episodeinteractive.com. You can also visit their help center or their forums for more information.
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diff --git a/spaces/fclong/summary/fengshen/data/universal_datamodule/universal_sampler.py b/spaces/fclong/summary/fengshen/data/universal_datamodule/universal_sampler.py
deleted file mode 100644
index 86db3016d0f9795f5c8e501da2ff55c6e34e7222..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/data/universal_datamodule/universal_sampler.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# coding=utf-8
-# Copyright (c) 2020, NVIDIA CORPORATION. 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.
-
-"""Dataloaders."""
-
-
-import torch
-
-
-class PretrainingSampler:
-
- def __init__(self, total_samples, consumed_samples, micro_batch_size,
- data_parallel_rank, data_parallel_size, drop_last=True):
- # Keep a copy of input params for later use.
- self.total_samples = total_samples
- self.consumed_samples = consumed_samples
- self.micro_batch_size = micro_batch_size
- self.data_parallel_rank = data_parallel_rank
- self.micro_batch_times_data_parallel_size = \
- self.micro_batch_size * data_parallel_size
- self.drop_last = drop_last
-
- # Sanity checks.
- assert self.total_samples > 0, \
- 'no sample to consume: {}'.format(self.total_samples)
- assert self.consumed_samples < self.total_samples, \
- 'no samples left to consume: {}, {}'.format(self.consumed_samples,
- self.total_samples)
- assert self.micro_batch_size > 0
- assert data_parallel_size > 0
- assert self.data_parallel_rank < data_parallel_size, \
- 'data_parallel_rank should be smaller than data size: {}, ' \
- '{}'.format(self.data_parallel_rank, data_parallel_size)
-
- def __len__(self):
- return self.total_samples // self.micro_batch_times_data_parallel_size
-
- def get_start_end_idx(self):
- start_idx = self.data_parallel_rank * self.micro_batch_size
- end_idx = start_idx + self.micro_batch_size
- return start_idx, end_idx
-
- def __iter__(self):
- batch = []
- # Last batch will be dropped if drop_last is not set False
- for idx in range(self.consumed_samples, self.total_samples):
- batch.append(idx)
- if len(batch) == self.micro_batch_times_data_parallel_size:
- start_idx, end_idx = self.get_start_end_idx()
- yield batch[start_idx:end_idx]
- batch = []
-
- # Check the last partial batch and see drop_last is set
- if len(batch) > 0 and not self.drop_last:
- start_idx, end_idx = self.get_start_end_idx()
- yield batch[start_idx:end_idx]
-
-
-class PretrainingRandomSampler:
-
- def __init__(self, total_samples, consumed_samples, micro_batch_size,
- data_parallel_rank, data_parallel_size, epoch):
- # Keep a copy of input params for later use.
- self.total_samples = total_samples
- self.consumed_samples = consumed_samples
- self.micro_batch_size = micro_batch_size
- self.data_parallel_rank = data_parallel_rank
- self.data_parallel_size = data_parallel_size
- self.micro_batch_times_data_parallel_size = \
- self.micro_batch_size * data_parallel_size
- self.last_batch_size = \
- self.total_samples % self.micro_batch_times_data_parallel_size
- self.epoch = epoch
-
- # Sanity checks.
- assert self.total_samples > 0, \
- 'no sample to consume: {}'.format(self.total_samples)
- assert self.micro_batch_size > 0
- assert data_parallel_size > 0
- assert self.data_parallel_rank < data_parallel_size, \
- 'data_parallel_rank should be smaller than data size: {}, ' \
- '{}'.format(self.data_parallel_rank, data_parallel_size)
-
- def __len__(self):
- return self.total_samples // self.micro_batch_times_data_parallel_size
-
- def __iter__(self):
- active_total_samples = self.total_samples - self.last_batch_size
- current_epoch_samples = self.consumed_samples % active_total_samples
- assert current_epoch_samples % self.micro_batch_times_data_parallel_size == 0
-
- # data sharding and random sampling
- bucket_size = (self.total_samples // self.micro_batch_times_data_parallel_size) \
- * self.micro_batch_size
- bucket_offset = current_epoch_samples // self.data_parallel_size
- start_idx = self.data_parallel_rank * bucket_size
-
- g = torch.Generator()
- g.manual_seed(self.epoch)
- random_idx = torch.randperm(bucket_size, generator=g).tolist()
- idx_range = [start_idx + x for x in random_idx[bucket_offset:]]
-
- batch = []
- # Last batch if not complete will be dropped.
- for idx in idx_range:
- batch.append(idx)
- if len(batch) == self.micro_batch_size:
- self.consumed_samples += self.micro_batch_times_data_parallel_size
- yield batch
- batch = []
-
- def set_epoch(self, epoch):
- self.epoch = epoch
diff --git a/spaces/fengmuxi/ChatGpt-Web/app/api/user/logout/route.ts b/spaces/fengmuxi/ChatGpt-Web/app/api/user/logout/route.ts
deleted file mode 100644
index 4b0daa2c24096bbd7b354c61dfdaa46df877e235..0000000000000000000000000000000000000000
--- a/spaces/fengmuxi/ChatGpt-Web/app/api/user/logout/route.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-import { NextRequest, NextResponse } from "next/server";
-import { auth, getIP } from "../../auth";
-
-export async function POST(req: NextRequest) {
- try {
- const authResult = auth(req);
- if (authResult.error) {
- return NextResponse.json(authResult, {
- status: 401,
- });
- }
- const token=req.headers.get("auth") ?? ""
- let res=await fetch("https://eladmin.dwzynj.top/auth/logout", {
- method: "DELETE",
- headers:{
- "Authorization":token,
- "UserIp": String(getIP(req))
- }
- })
- if(res.status==401){
- let msg={
- flag:false,
- msg:"未登录！"
- }
- // console.log(res.status)
- return new Response(JSON.stringify(msg))
- }
- let msg=await res.json()
- console.log(msg)
- return new Response(JSON.stringify(msg))
- } catch (e) {
- console.error("[eladmin] ", e);
- return new Response(JSON.stringify(e));
- }
-}
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Desert Car Racing A Free and Awesome 3D Game for Android Lovers.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Desert Car Racing A Free and Awesome 3D Game for Android Lovers.md
deleted file mode 100644
index ddde812bcdcb816461055c358a49a5156a9cebb2..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Desert Car Racing A Free and Awesome 3D Game for Android Lovers.md
+++ /dev/null
@@ -1,45 +0,0 @@
-
-, , ![]()
, , - , etc. to format the article. 6. I will write " Here are the two tables I created: | Outline of the Article | Article with HTML Formatting | | --- | --- |
Desert Super Car Racing APK: A Review
| | H2: Introduction | Introduction
Do you love racing games? Do you want to experience the thrill of driving super cars in a desert environment? If yes, then you should try Desert Super Car Racing APK, a free Android game that lets you race against other players or against the clock in various desert tracks.
| | H2: Features of Desert Super Car Racing APK | Features of Desert Super Car Racing APK
Desert Super Car Racing APK is a game that offers many features to make your racing experience more enjoyable and challenging. Here are some of them:
-desert super car racing apk
Download File ○○○ https://gohhs.com/2uPv7g
- Multiple cars to choose from: You can select from different super cars, each with its own speed, handling, and design. You can also customize your car's color, wheels, and stickers.
- Various desert tracks: You can race in different desert locations, such as sand dunes, rocky hills, oasis, and pyramids. Each track has its own obstacles, curves, and jumps.
- Different game modes: You can play in single-player mode or multiplayer mode. In single-player mode, you can race against the clock or against AI opponents. In multiplayer mode, you can race against other players online or locally via Wi-Fi or Bluetooth.
- Realistic graphics and sound effects: The game has high-quality graphics and sound effects that make you feel like you are really driving a super car in a desert. You can see the dust, smoke, and shadows on the screen, and hear the engine roar, tires screech, and wind blow.
| | H2: How to Download and Install Desert Super Car Racing APK | How to Download and Install Desert Super Car Racing APK
If you want to play Desert Super Car Racing APK on your Android device, you need to follow these steps:
- Go to [Desert Car Racing APK (Game) - Free Download - APKCombo](^1^) and click on the "Download APK" button.
- Wait for the download to finish and then open the file.
- Allow the installation of unknown sources if prompted.
- Follow the instructions on the screen to install the game.
- Launch the game and enjoy!
| | H2: Pros and Cons of Desert Super Car Racing APK | Pros and Cons of Desert Super Car Racing APK
Like any other game, Desert Super Car Racing APK has its advantages and disadvantages. Here are some of them:
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| Pros | Cons |
|---|
| - Free to play | - Contains ads |
| - Easy to control | - Requires internet connection for multiplayer mode |
| - Fun and addictive | - May drain battery quickly |
| | H2: Tips and Tricks for Playing Desert Super Car Racing APK | Tips and Tricks for Playing Desert Super Car Racing APK
If you
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\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Lite ROM The Best Custom ROM for Your Smartphone.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Lite ROM The Best Custom ROM for Your Smartphone.md
deleted file mode 100644
index 511cc7ed776b52af17e70e429c8d828d40c5154c..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Lite ROM The Best Custom ROM for Your Smartphone.md
+++ /dev/null
@@ -1,130 +0,0 @@
-
-Download Lite ROM: What Is It and Why You Should Try It
-If you are an Android user who loves to customize your device and get the most out of it, you might have heard of custom ROMs. Custom ROMs are modified versions of the Android operating system that offer various features, enhancements, and optimizations that are not available in the stock ROM. However, not all custom ROMs are created equal. Some of them are heavy, bloated, and slow, while others are light, fast, and smooth. In this article, we will focus on the latter category, which is known as lite ROMs. We will explain what a lite ROM is, what are its benefits, how to download and install it on your Android device, and what are the best lite ROMs for Android in 2023. We will also discuss the risks and drawbacks of using a lite ROM, so you can make an informed decision before flashing one.
- What Is a Lite ROM?
-A lite ROM is a custom ROM that is designed to be as lightweight, minimal, and efficient as possible. It aims to provide a fast, smooth, and stable Android experience without compromising on performance, battery life, or security. A lite ROM usually removes or replaces the unnecessary or redundant apps, services, features, and bloatware that come with the stock ROM or the manufacturer's skin. It also optimizes the system settings, kernel, memory management, and power consumption to improve the overall speed and responsiveness of the device.
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- Definition and Features of a Lite ROM
-A lite ROM can be defined as an aftermarket firmware production based on the Android source code provided by Google or other developers. It is a third-party operating system that replaces the factory-installed stock ROM or skin on your phone. A lite ROM typically has the following features:
-
-- It is based on AOSP (Android Open Source Project) or other popular custom ROMs like LineageOS or Pixel Experience.
-- It has a clean, simple, and stock-like user interface without any unnecessary modifications or customizations.
-- It has minimal or no pre-installed apps or bloatware that consume system resources or storage space.
-- It has essential features and functions that enhance the usability and functionality of the device.
-- It has regular updates and security patches to keep the device up-to-date and secure.
-- It has support for various devices from different manufacturers and models.
-
- Benefits of Using a Lite ROM
-Using a lite ROM can have several benefits for your Android device. Some of them are:
-
-- It can improve the performance and speed of your device by removing the bloatware and optimizing the system settings.
-- It can extend the battery life of your device by reducing the power consumption and background processes.
-- It can free up storage space on your device by removing the unnecessary apps and files.
-- It can update your device to the latest version of Android or add new features that are not available in the stock ROM.
-- It can give you more control and customization options over your device by allowing you to root it, install custom kernels, mods, themes, etc.
-- It can revive your old or unsupported device by providing it with a new lease of life.
-
- How to Download and Install a Lite ROM on Your Android Device
-If you are interested in trying out a lite ROM on your Android device, you will need to download and install it on your device. However, before you do that, you need to be aware of the requirements and precautions that are involved in flashing a custom ROM. Flashing a custom ROM is not a simple or risk-free process, and it can potentially damage or brick your device if done incorrectly. Therefore, you should only proceed if you are confident and experienced in doing so, or if you are willing to take the responsibility for any consequences that may arise. Here are some of the things you need to do before flashing a lite ROM on your device:
- Requirements and Precautions Before Flashing a Lite ROM
-
-- Make sure your device is compatible with the lite ROM you want to flash. You can check the official website or forum of the ROM developer to see the list of supported devices and models.
-- Make sure your device has an unlocked bootloader and a custom recovery installed. An unlocked bootloader allows you to flash custom ROMs and other modifications on your device, while a custom recovery is a software that lets you backup, restore, and install ROMs and other files on your device. You can find guides on how to unlock the bootloader and install a custom recovery for your device online.
-- Make sure your device has enough battery charge (at least 50%) and enough storage space (at least 2 GB) before flashing the lite ROM. A low battery or insufficient storage can cause errors or interruptions during the flashing process, which can lead to bricking or corruption of your device.
-- Make sure you backup all your important data and files on your device before flashing the lite ROM. Flashing a custom ROM will erase all the data and files on your device, including your contacts, messages, photos, videos, apps, settings, etc. You can use the backup feature of your custom recovery or other apps to backup your data and files.
-- Make sure you download the correct and latest version of the lite ROM for your device from a trusted source. You can find the download links for the lite ROMs on their official websites or forums. You should also download the Google Apps (GApps) package if you want to use Google services and apps on your device. GApps are not included in most custom ROMs due to licensing issues, so you need to flash them separately after flashing the ROM.
-- Make sure you follow the instructions and guidelines provided by the ROM developer carefully and correctly when flashing the lite ROM. Different ROMs may have different installation methods and steps, so you should always read and follow the instructions carefully. Do not skip any steps or do anything that is not mentioned in the instructions.
-
- Steps to Download and Install a Lite ROM
-Once you have met all the requirements and precautions mentioned above, you are ready to download and install a lite ROM on your device. Here are the general steps to do so:
-
-- Download the lite ROM zip file and the GApps zip file (if needed) for your device from their respective sources and save them on your computer or on your device's internal storage or SD card.
-- Turn off your device and boot it into recovery mode by pressing and holding a combination of buttons (usually volume up + power or volume down + power) until you see the recovery screen.
-- In the recovery menu, select "Wipe" or "Advanced Wipe" and wipe the system, data, cache, and Dalvik cache partitions. This will erase all the data and files on your device and prepare it for flashing the new ROM.
-- Select "Install" or "Install Zip" and navigate to the location where you saved the lite ROM zip file. Select it and swipe to confirm flashing. Wait for the flashing process to complete.
-- If you want to flash GApps as well, repeat step 4 with the GApps zip file.
-- Once both files are flashed, select "Reboot System" or "Reboot" to restart your device.
-
-Congratulations! You have successfully downloaded and installed a lite ROM on your Android device. You can now enjoy a fast, smooth, and stable Android experience with minimal bloatware and maximum performance.
- Best Lite ROMs for Android in 2023
-There are many lite ROMs available for Android devices in 2023, but not all of them are equally good or suitable for every user. Depending on your preferences, needs, and expectations, you may find some lite ROMs better than others. To help you choose the best lite ROM for your device, we have compiled a list of some of the most popular and well-reviewed lite ROMs for Android in 2023. Here they are:
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- LineageOS
-LineageOS is one of the oldest and most popular custom ROM for Android devices. It is based on AOSP and offers a pure and clean Android experience with minimal modifications and customizations. It has a wide range of features and functions that enhance the usability and functionality of the device, such as privacy guard, live display, root access, OTA updates, etc. It also has a large and active community of developers and users who provide support, feedback, and updates for various devices and models. LineageOS is a lite ROM that is fast, smooth, stable, and secure. It is suitable for users who want a stock-like Android experience with some added features and options.
- Pixel Experience
-Pixel Experience is a custom ROM that aims to bring the Google Pixel experience to other Android devices. It is based on AOSP and includes all the features, apps, and services that are found on the Pixel devices, such as the Pixel Launcher, Google Camera, Google Assistant, Google Photos, etc. It also has some additional features and optimizations that improve the performance, battery life, and stability of the device. Pixel Experience is a lite ROM that is simple, elegant, and smooth. It is suitable for users who want a Pixel-like Android experience with minimal bloatware and maximum performance.
- Bliss ROM
-Bliss ROM is a custom ROM that is designed to provide a blissful Android experience to the users. It is based on AOSP and other popular custom ROMs like LineageOS and Pixel Experience. It has a unique and beautiful user interface that can be customized with various themes, icons, fonts, wallpapers, etc. It also has a plethora of features and functions that enhance the usability and functionality of the device, such as gesture navigation, smart pixels, ambient display, gaming mode, etc. Bliss ROM is a lite ROM that is colorful, fun, and smooth. It is suitable for users who want a customizable Android experience with lots of features and options.
- Risks and Drawbacks of Using a Lite ROM
-While using a lite ROM can have many benefits for your Android device, it can also have some risks and drawbacks that you should be aware of before flashing one. Some of them are:
- Potential Bricking and Warranty Voiding
-Flashing a custom ROM can potentially brick your device if done incorrectly or if something goes wrong during the process. Bricking means that your device becomes unusable or unresponsive due to software or hardware damage. If your device gets bricked, you may not be able to recover it or fix it easily. You may need to use special tools or methods to unbrick it or take it to a professional service center for repair. Moreover, flashing a custom ROM can void your device's warranty if it is still valid. This means that you will not be able to claim any warranty service or support from the manufacturer or the seller if your device gets damaged or malfunctioning due to flashing a custom ROM.
- Compatibility and Stability Issues
-Flashing a custom ROM can cause compatibility and stability issues on your device if it is not well-developed or well-tested for your device model or variant. Compatibility issues mean that some features or functions of the custom ROM may not work properly or at all on your device due to hardware or software differences or limitations. Stability issues mean that your device may experience crashes, freezes, reboots, lags, bugs, glitches, or errors due to faulty or incomplete coding or optimization of the custom ROM. These issues can affect the performance, usability, and functionality of your device negatively.
- Missing Features and Customizations
-Flashing a custom ROM can make you miss some features and customizations that are present in the stock ROM or the manufacturer's skin on your device. These features and customizations may be useful or important for you depending on your preferences, needs, and expectations. For example, you may miss some camera modes or settings, some gesture controls or shortcuts, some security options or enhancements, some display modes or adjustments, etc. that are exclusive to the stock ROM or the manufacturer's skin. You may not be able to find or use these features and customizations on the custom ROM, or they may not work as well as they do on the stock ROM.
- Conclusion
-A lite ROM is a custom ROM that is designed to be as lightweight, minimal, and efficient as possible. It can improve the performance, battery life, and storage space of your Android device by removing the bloatware and optimizing the system settings. It can also update your device to the latest version of Android or add new features that are not available in the stock ROM. However, flashing a lite ROM can also have some risks and drawbacks, such as potential bricking and warranty voiding, compatibility and stability issues, and missing features and customizations. Therefore, you should weigh the pros and cons of using a lite ROM before flashing one on your device.
- FAQs
-Here are some of the frequently asked questions about downloading and installing a lite ROM on your Android device:
-
-- What is the difference between a lite ROM and a stock ROM?
-A stock ROM is the official operating system that comes pre-installed on your device by the manufacturer or the carrier. A lite ROM is a custom operating system that replaces the stock ROM on your device by flashing it through a custom recovery. A lite ROM is usually based on AOSP or other popular custom ROMs like LineageOS or Pixel Experience. A lite ROM is lighter, faster, and smoother than a stock ROM, but it may also have some risks and drawbacks.
-- How do I know if my device is compatible with a lite ROM?
-You can check the official website or forum of the lite ROM developer to see the list of supported devices and models. You can also search online for reviews, feedback, or guides from other users who have flashed the lite ROM on your device or a similar device. You should always make sure that you download the correct and latest version of the lite ROM for your device model or variant.
-- How do I backup my data and files before flashing a lite ROM?
-You can use the backup feature of your custom recovery or other apps to backup your data and files before flashing a lite ROM. You should backup all your important data and files, such as contacts, messages, photos, videos, apps, settings, etc. You should also save your backup files on your computer or on an external storage device, such as an SD card or a USB drive. You should not rely on your device's internal storage or cloud storage for backup, as they may get erased or inaccessible during or after flashing.
-- How do I restore my data and files after flashing a lite ROM?
-You can use the restore feature of your custom recovery or other apps to restore your data and files after flashing a lite ROM. You should restore your backup files from your computer or from an external storage device, such as an SD card or a USB drive. You should not restore your backup files from your device's internal storage or cloud storage, as they may be corrupted or incompatible with the new ROM.
-- How do I update my lite ROM to the latest version?
-You can update your lite ROM to the latest version by downloading the update zip file from the official website or forum of the lite ROM developer and flashing it through your custom recovery. You should always check for updates regularly and download them from trusted sources. You should also backup your data and files before updating your lite ROM, as some updates may require a clean flash or a wipe of data.
-
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/fffiloni/Video-Matting-Anything/GroundingDINO/groundingdino/util/visualizer.py b/spaces/fffiloni/Video-Matting-Anything/GroundingDINO/groundingdino/util/visualizer.py
deleted file mode 100644
index 7a1b7b101e9b73f75f9136bc67f2063c7c1cf1c1..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/Video-Matting-Anything/GroundingDINO/groundingdino/util/visualizer.py
+++ /dev/null
@@ -1,318 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-@File : visualizer.py
-@Time : 2022/04/05 11:39:33
-@Author : Shilong Liu
-@Contact : slongliu86@gmail.com
-"""
-
-import datetime
-import os
-
-import cv2
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-from matplotlib import transforms
-from matplotlib.collections import PatchCollection
-from matplotlib.patches import Polygon
-from pycocotools import mask as maskUtils
-
-
-def renorm(
- img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-) -> torch.FloatTensor:
- # img: tensor(3,H,W) or tensor(B,3,H,W)
- # return: same as img
- assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim()
- if img.dim() == 3:
- assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % (
- img.size(0),
- str(img.size()),
- )
- img_perm = img.permute(1, 2, 0)
- mean = torch.Tensor(mean)
- std = torch.Tensor(std)
- img_res = img_perm * std + mean
- return img_res.permute(2, 0, 1)
- else: # img.dim() == 4
- assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % (
- img.size(1),
- str(img.size()),
- )
- img_perm = img.permute(0, 2, 3, 1)
- mean = torch.Tensor(mean)
- std = torch.Tensor(std)
- img_res = img_perm * std + mean
- return img_res.permute(0, 3, 1, 2)
-
-
-class ColorMap:
- def __init__(self, basergb=[255, 255, 0]):
- self.basergb = np.array(basergb)
-
- def __call__(self, attnmap):
- # attnmap: h, w. np.uint8.
- # return: h, w, 4. np.uint8.
- assert attnmap.dtype == np.uint8
- h, w = attnmap.shape
- res = self.basergb.copy()
- res = res[None][None].repeat(h, 0).repeat(w, 1) # h, w, 3
- attn1 = attnmap.copy()[..., None] # h, w, 1
- res = np.concatenate((res, attn1), axis=-1).astype(np.uint8)
- return res
-
-
-def rainbow_text(x, y, ls, lc, **kw):
- """
- Take a list of strings ``ls`` and colors ``lc`` and place them next to each
- other, with text ls[i] being shown in color lc[i].
-
- This example shows how to do both vertical and horizontal text, and will
- pass all keyword arguments to plt.text, so you can set the font size,
- family, etc.
- """
- t = plt.gca().transData
- fig = plt.gcf()
- plt.show()
-
- # horizontal version
- for s, c in zip(ls, lc):
- text = plt.text(x, y, " " + s + " ", color=c, transform=t, **kw)
- text.draw(fig.canvas.get_renderer())
- ex = text.get_window_extent()
- t = transforms.offset_copy(text._transform, x=ex.width, units="dots")
-
- # #vertical version
- # for s,c in zip(ls,lc):
- # text = plt.text(x,y," "+s+" ",color=c, transform=t,
- # rotation=90,va='bottom',ha='center',**kw)
- # text.draw(fig.canvas.get_renderer())
- # ex = text.get_window_extent()
- # t = transforms.offset_copy(text._transform, y=ex.height, units='dots')
-
-
-class COCOVisualizer:
- def __init__(self, coco=None, tokenlizer=None) -> None:
- self.coco = coco
-
- def visualize(self, img, tgt, caption=None, dpi=180, savedir="vis"):
- """
- img: tensor(3, H, W)
- tgt: make sure they are all on cpu.
- must have items: 'image_id', 'boxes', 'size'
- """
- plt.figure(dpi=dpi)
- plt.rcParams["font.size"] = "5"
- ax = plt.gca()
- img = renorm(img).permute(1, 2, 0)
- # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
- # import ipdb; ipdb.set_trace()
- ax.imshow(img)
-
- self.addtgt(tgt)
-
- if tgt is None:
- image_id = 0
- elif "image_id" not in tgt:
- image_id = 0
- else:
- image_id = tgt["image_id"]
-
- if caption is None:
- savename = "{}/{}-{}.png".format(
- savedir, int(image_id), str(datetime.datetime.now()).replace(" ", "-")
- )
- else:
- savename = "{}/{}-{}-{}.png".format(
- savedir, caption, int(image_id), str(datetime.datetime.now()).replace(" ", "-")
- )
- print("savename: {}".format(savename))
- os.makedirs(os.path.dirname(savename), exist_ok=True)
- plt.savefig(savename)
- plt.close()
-
- def addtgt(self, tgt):
- """ """
- if tgt is None or not "boxes" in tgt:
- ax = plt.gca()
-
- if "caption" in tgt:
- ax.set_title(tgt["caption"], wrap=True)
-
- ax.set_axis_off()
- return
-
- ax = plt.gca()
- H, W = tgt["size"]
- numbox = tgt["boxes"].shape[0]
-
- color = []
- polygons = []
- boxes = []
- for box in tgt["boxes"].cpu():
- unnormbbox = box * torch.Tensor([W, H, W, H])
- unnormbbox[:2] -= unnormbbox[2:] / 2
- [bbox_x, bbox_y, bbox_w, bbox_h] = unnormbbox.tolist()
- boxes.append([bbox_x, bbox_y, bbox_w, bbox_h])
- poly = [
- [bbox_x, bbox_y],
- [bbox_x, bbox_y + bbox_h],
- [bbox_x + bbox_w, bbox_y + bbox_h],
- [bbox_x + bbox_w, bbox_y],
- ]
- np_poly = np.array(poly).reshape((4, 2))
- polygons.append(Polygon(np_poly))
- c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
- color.append(c)
-
- p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.1)
- ax.add_collection(p)
- p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2)
- ax.add_collection(p)
-
- if "strings_positive" in tgt and len(tgt["strings_positive"]) > 0:
- assert (
- len(tgt["strings_positive"]) == numbox
- ), f"{len(tgt['strings_positive'])} = {numbox}, "
- for idx, strlist in enumerate(tgt["strings_positive"]):
- cate_id = int(tgt["labels"][idx])
- _string = str(cate_id) + ":" + " ".join(strlist)
- bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]
- # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})
- ax.text(
- bbox_x,
- bbox_y,
- _string,
- color="black",
- bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1},
- )
-
- if "box_label" in tgt:
- assert len(tgt["box_label"]) == numbox, f"{len(tgt['box_label'])} = {numbox}, "
- for idx, bl in enumerate(tgt["box_label"]):
- _string = str(bl)
- bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]
- # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})
- ax.text(
- bbox_x,
- bbox_y,
- _string,
- color="black",
- bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1},
- )
-
- if "caption" in tgt:
- ax.set_title(tgt["caption"], wrap=True)
- # plt.figure()
- # rainbow_text(0.0,0.0,"all unicorns poop rainbows ! ! !".split(),
- # ['red', 'orange', 'brown', 'green', 'blue', 'purple', 'black'])
-
- if "attn" in tgt:
- # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
- # import ipdb; ipdb.set_trace()
- if isinstance(tgt["attn"], tuple):
- tgt["attn"] = [tgt["attn"]]
- for item in tgt["attn"]:
- attn_map, basergb = item
- attn_map = (attn_map - attn_map.min()) / (attn_map.max() - attn_map.min() + 1e-3)
- attn_map = (attn_map * 255).astype(np.uint8)
- cm = ColorMap(basergb)
- heatmap = cm(attn_map)
- ax.imshow(heatmap)
- ax.set_axis_off()
-
- def showAnns(self, anns, draw_bbox=False):
- """
- Display the specified annotations.
- :param anns (array of object): annotations to display
- :return: None
- """
- if len(anns) == 0:
- return 0
- if "segmentation" in anns[0] or "keypoints" in anns[0]:
- datasetType = "instances"
- elif "caption" in anns[0]:
- datasetType = "captions"
- else:
- raise Exception("datasetType not supported")
- if datasetType == "instances":
- ax = plt.gca()
- ax.set_autoscale_on(False)
- polygons = []
- color = []
- for ann in anns:
- c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
- if "segmentation" in ann:
- if type(ann["segmentation"]) == list:
- # polygon
- for seg in ann["segmentation"]:
- poly = np.array(seg).reshape((int(len(seg) / 2), 2))
- polygons.append(Polygon(poly))
- color.append(c)
- else:
- # mask
- t = self.imgs[ann["image_id"]]
- if type(ann["segmentation"]["counts"]) == list:
- rle = maskUtils.frPyObjects(
- [ann["segmentation"]], t["height"], t["width"]
- )
- else:
- rle = [ann["segmentation"]]
- m = maskUtils.decode(rle)
- img = np.ones((m.shape[0], m.shape[1], 3))
- if ann["iscrowd"] == 1:
- color_mask = np.array([2.0, 166.0, 101.0]) / 255
- if ann["iscrowd"] == 0:
- color_mask = np.random.random((1, 3)).tolist()[0]
- for i in range(3):
- img[:, :, i] = color_mask[i]
- ax.imshow(np.dstack((img, m * 0.5)))
- if "keypoints" in ann and type(ann["keypoints"]) == list:
- # turn skeleton into zero-based index
- sks = np.array(self.loadCats(ann["category_id"])[0]["skeleton"]) - 1
- kp = np.array(ann["keypoints"])
- x = kp[0::3]
- y = kp[1::3]
- v = kp[2::3]
- for sk in sks:
- if np.all(v[sk] > 0):
- plt.plot(x[sk], y[sk], linewidth=3, color=c)
- plt.plot(
- x[v > 0],
- y[v > 0],
- "o",
- markersize=8,
- markerfacecolor=c,
- markeredgecolor="k",
- markeredgewidth=2,
- )
- plt.plot(
- x[v > 1],
- y[v > 1],
- "o",
- markersize=8,
- markerfacecolor=c,
- markeredgecolor=c,
- markeredgewidth=2,
- )
-
- if draw_bbox:
- [bbox_x, bbox_y, bbox_w, bbox_h] = ann["bbox"]
- poly = [
- [bbox_x, bbox_y],
- [bbox_x, bbox_y + bbox_h],
- [bbox_x + bbox_w, bbox_y + bbox_h],
- [bbox_x + bbox_w, bbox_y],
- ]
- np_poly = np.array(poly).reshape((4, 2))
- polygons.append(Polygon(np_poly))
- color.append(c)
-
- # p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
- # ax.add_collection(p)
- p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2)
- ax.add_collection(p)
- elif datasetType == "captions":
- for ann in anns:
- print(ann["caption"])
diff --git a/spaces/fffiloni/audioldm-text-to-audio-generation-copy/audioldm/clap/open_clip/openai.py b/spaces/fffiloni/audioldm-text-to-audio-generation-copy/audioldm/clap/open_clip/openai.py
deleted file mode 100644
index 3f4eb8b55fe960e1792b3da804b60b3d8f70fe26..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/audioldm-text-to-audio-generation-copy/audioldm/clap/open_clip/openai.py
+++ /dev/null
@@ -1,156 +0,0 @@
-""" OpenAI pretrained model functions
-
-Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
-"""
-
-import os
-import warnings
-from typing import Union, List
-
-import torch
-
-from .model import build_model_from_openai_state_dict
-from .pretrained import (
- get_pretrained_url,
- list_pretrained_tag_models,
- download_pretrained,
-)
-
-__all__ = ["list_openai_models", "load_openai_model"]
-
-
-def list_openai_models() -> List[str]:
- """Returns the names of available CLIP models"""
- return list_pretrained_tag_models("openai")
-
-
-def load_openai_model(
- name: str,
- model_cfg,
- device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu",
- jit=True,
- cache_dir=os.path.expanduser("~/.cache/clip"),
- enable_fusion: bool = False,
- fusion_type: str = "None",
-):
- """Load a CLIP model, preserve its text pretrained part, and set in the CLAP model
-
- Parameters
- ----------
- name : str
- A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
- device : Union[str, torch.device]
- The device to put the loaded model
- jit : bool
- Whether to load the optimized JIT model (default) or more hackable non-JIT model.
-
- Returns
- -------
- model : torch.nn.Module
- The CLAP model
- preprocess : Callable[[PIL.Image], torch.Tensor]
- A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
- """
- if get_pretrained_url(name, "openai"):
- model_path = download_pretrained(
- get_pretrained_url(name, "openai"), root=cache_dir
- )
- elif os.path.isfile(name):
- model_path = name
- else:
- raise RuntimeError(
- f"Model {name} not found; available models = {list_openai_models()}"
- )
-
- try:
- # loading JIT archive
- model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval()
- state_dict = None
- except RuntimeError:
- # loading saved state dict
- if jit:
- warnings.warn(
- f"File {model_path} is not a JIT archive. Loading as a state dict instead"
- )
- jit = False
- state_dict = torch.load(model_path, map_location="cpu")
-
- if not jit:
- try:
- model = build_model_from_openai_state_dict(
- state_dict or model.state_dict(), model_cfg, enable_fusion, fusion_type
- ).to(device)
- except KeyError:
- sd = {k[7:]: v for k, v in state_dict["state_dict"].items()}
- model = build_model_from_openai_state_dict(
- sd, model_cfg, enable_fusion, fusion_type
- ).to(device)
-
- if str(device) == "cpu":
- model.float()
- return model
-
- # patch the device names
- device_holder = torch.jit.trace(
- lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]
- )
- device_node = [
- n
- for n in device_holder.graph.findAllNodes("prim::Constant")
- if "Device" in repr(n)
- ][-1]
-
- def patch_device(module):
- try:
- graphs = [module.graph] if hasattr(module, "graph") else []
- except RuntimeError:
- graphs = []
-
- if hasattr(module, "forward1"):
- graphs.append(module.forward1.graph)
-
- for graph in graphs:
- for node in graph.findAllNodes("prim::Constant"):
- if "value" in node.attributeNames() and str(node["value"]).startswith(
- "cuda"
- ):
- node.copyAttributes(device_node)
-
- model.apply(patch_device)
- patch_device(model.encode_audio)
- patch_device(model.encode_text)
-
- # patch dtype to float32 on CPU
- if str(device) == "cpu":
- float_holder = torch.jit.trace(
- lambda: torch.ones([]).float(), example_inputs=[]
- )
- float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
- float_node = float_input.node()
-
- def patch_float(module):
- try:
- graphs = [module.graph] if hasattr(module, "graph") else []
- except RuntimeError:
- graphs = []
-
- if hasattr(module, "forward1"):
- graphs.append(module.forward1.graph)
-
- for graph in graphs:
- for node in graph.findAllNodes("aten::to"):
- inputs = list(node.inputs())
- for i in [
- 1,
- 2,
- ]: # dtype can be the second or third argument to aten::to()
- if inputs[i].node()["value"] == 5:
- inputs[i].node().copyAttributes(float_node)
-
- model.apply(patch_float)
- patch_float(model.encode_audio)
- patch_float(model.encode_text)
- model.float()
-
- model.audio_branch.audio_length = model.audio_cfg.audio_length
- return model
diff --git a/spaces/fffiloni/lama-video-watermark-remover/saicinpainting/training/losses/__init__.py b/spaces/fffiloni/lama-video-watermark-remover/saicinpainting/training/losses/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/fgbwyude/ChuanhuChatGPT/assets/Kelpy-Codos.js b/spaces/fgbwyude/ChuanhuChatGPT/assets/Kelpy-Codos.js
deleted file mode 100644
index cfbaeedb4f371dfb5fe157db545b364046fca3e1..0000000000000000000000000000000000000000
--- a/spaces/fgbwyude/ChuanhuChatGPT/assets/Kelpy-Codos.js
+++ /dev/null
@@ -1,76 +0,0 @@
-// ==UserScript==
-// @name Kelpy Codos
-// @namespace https://github.com/Keldos-Li/Kelpy-Codos
-// @version 1.0.5
-// @author Keldos; https://keldos.me/
-// @description Add copy button to PRE tags before CODE tag, for Chuanhu ChatGPT especially.
-// Based on Chuanhu ChatGPT version: ac04408 (2023-3-22)
-// @license GPL-3.0
-// @grant none
-// ==/UserScript==
-
-(function () {
- 'use strict';
-
- function addCopyButton(pre) {
- var code = pre.querySelector('code');
- if (!code) {
- return; // 如果没有找到 元素，则不添加按钮
- }
- var firstChild = code.firstChild;
- if (!firstChild) {
- return; // 如果 元素没有子节点，则不添加按钮
- }
- var button = document.createElement('button');
- button.textContent = '\uD83D\uDCCE'; // 使用 📎 符号作为“复制”按钮的文本
- button.style.position = 'relative';
- button.style.float = 'right';
- button.style.fontSize = '1em'; // 可选：调整按钮大小
- button.style.background = 'none'; // 可选：去掉背景颜色
- button.style.border = 'none'; // 可选：去掉边框
- button.style.cursor = 'pointer'; // 可选：显示指针样式
- button.addEventListener('click', function () {
- var range = document.createRange();
- range.selectNodeContents(code);
- range.setStartBefore(firstChild); // 将范围设置为第一个子节点之前
- var selection = window.getSelection();
- selection.removeAllRanges();
- selection.addRange(range);
-
- try {
- var success = document.execCommand('copy');
- if (success) {
- button.textContent = '\u2714';
- setTimeout(function () {
- button.textContent = '\uD83D\uDCCE'; // 恢复按钮为“复制”
- }, 2000);
- } else {
- button.textContent = '\u2716';
- }
- } catch (e) {
- console.error(e);
- button.textContent = '\u2716';
- }
-
- selection.removeAllRanges();
- });
- code.insertBefore(button, firstChild); // 将按钮插入到第一个子元素之前
- }
-
- function handleNewElements(mutationsList, observer) {
- for (var mutation of mutationsList) {
- if (mutation.type === 'childList') {
- for (var node of mutation.addedNodes) {
- if (node.nodeName === 'PRE') {
- addCopyButton(node);
- }
- }
- }
- }
- }
-
- var observer = new MutationObserver(handleNewElements);
- observer.observe(document.documentElement, { childList: true, subtree: true });
-
- document.querySelectorAll('pre').forEach(addCopyButton);
-})();
diff --git a/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_26.py b/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_26.py
deleted file mode 100644
index 908b5a2b9ce6ecb9cdc1ccd5680094f828ec674c..0000000000000000000000000000000000000000
--- a/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_26.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import re
-
-def is_spam(text: str) -> bool:
- # Check for spam keywords and patterns
- spam_keywords = ['광고', '거부', '클릭', '해지', '이벤트', '공짜', '하세요', '무료', '최고', '상위', '증권사', '특별', '혜택', '무료거부', '입장코드', '특별정보방', '여의도', '입장', '금전']
-
- # Check for URL patterns
- url_pattern = re.compile(r'(http|https)://\S+')
-
- # Check for phone number patterns
- phone_pattern = re.compile(r'\d{2,4}-\d{3,4}-\d{4}')
-
- # Check for non-normal characters
- non_normal_chars = re.compile(r'[^가-힣a-zA-Z0-9.,?!:;\-\s]+')
-
- # Count the number of spam indicators
- spam_count = 0
-
- # Check for spam keywords
- for keyword in spam_keywords:
- if keyword in text:
- spam_count += 1
-
- # Check for URL patterns
- if url_pattern.search(text) is not None:
- spam_count += 1
-
- # Check for phone number patterns
- if phone_pattern.search(text) is not None:
- spam_count += 1
-
- # Check for non-normal characters
- if non_normal_chars.search(text) is not None:
- spam_count += 1
-
- # If more than 1 spam indicators are detected, classify the message as spam
- if spam_count >= 2:
- return True
-
- return False
\ No newline at end of file
diff --git a/spaces/fisehara/openai-whisper-base/app.py b/spaces/fisehara/openai-whisper-base/app.py
deleted file mode 100644
index ddedf9ed0e7c4809c5dea4b633a52d5975f8f4c4..0000000000000000000000000000000000000000
--- a/spaces/fisehara/openai-whisper-base/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/openai/whisper-base").launch()
\ No newline at end of file
diff --git a/spaces/flax-community/DietNerf-Demo/app.py b/spaces/flax-community/DietNerf-Demo/app.py
deleted file mode 100644
index 6a0c88a3cfab647e63119690c61265e3bc2c6296..0000000000000000000000000000000000000000
--- a/spaces/flax-community/DietNerf-Demo/app.py
+++ /dev/null
@@ -1,177 +0,0 @@
-import json
-import math
-import random
-import os
-
-import gdown
-import streamlit as st
-
-
-from demo.src.models import load_trained_model
-from demo.src.utils import predict_to_image, render_predict_from_pose
-
-st.set_page_config(page_title="DietNeRF")
-
-with open("config.json") as f:
- cfg = json.loads(f.read())
-
-MODEL_DIR = "models"
-
-SCENES_LIST = ["Mic", "Chair", "Lego", "Drums", "Ship", "Hotdog"]
-# random_index = random.randint(0, len(SCENES_LIST) - 1)
-
-
-def select_model(obj_select):
- DIET_NERF_MODEL_NAME = cfg[obj_select]["DIET_NERF_MODEL_NAME"]
- DIET_NERF_FILE_ID = cfg[obj_select]["DIET_NERF_FILE_ID"]
-
- NERF_MODEL_NAME = cfg[obj_select]["NERF_MODEL_NAME"]
- NERF_FILE_ID = cfg[obj_select]["NERF_FILE_ID"]
-
- return DIET_NERF_MODEL_NAME, DIET_NERF_FILE_ID, NERF_MODEL_NAME, NERF_FILE_ID
-
-
-pi = math.pi
-
-st.title("DietNeRF")
-st.sidebar.markdown(
- """
-
-
- 
-
-""",
- unsafe_allow_html=True,
-)
-st.sidebar.markdown(
- """
-
-GitHub | Project Report
-
- """,
- unsafe_allow_html=True,
-)
-st.sidebar.header("SELECT YOUR VIEW DIRECTION")
-theta = st.sidebar.slider(
- "Rotation (Left to Right)",
- min_value=-pi,
- max_value=pi,
- step=0.5,
- value=0.0,
- help="Rotational angle in Vertical direction (Theta)",
-)
-phi = st.sidebar.slider(
- "Rotation (Bottom to Top)",
- min_value=0.0,
- max_value=0.5 * pi,
- step=0.1,
- value=1.0,
- help="Rotational angle in Horizontal direction (Phi)",
-)
-radius = st.sidebar.slider(
- "Distance (Close to Far)",
- min_value=2.0,
- max_value=6.0,
- step=1.0,
- value=3.0,
- help="Distance between object and the viewer (Radius)",
-)
-caption = (
- "`DietNeRF` achieves state-of-the-art few-shot learning capacity in 3D model reconstruction. "
- "Thanks to the 2D supervision by `CLIP (aka. Semantic Consisteny Loss)`, "
- "it can render novel and challenging views with `ONLY 8 training images`, "
- "**outperforming** original [NeRF](https://www.matthewtancik.com/nerf)!"
-)
-st.markdown(caption)
-st.markdown(
- "> 📒 **NOTE**: To get a detailed comparison of differences in results between `DietNeRF` and `NeRF`, you can take a look at the "
- "[Experimental Results](https://www.notion.so/DietNeRF-Putting-NeRF-on-a-Diet-4aeddae95d054f1d91686f02bdb74745#0f6bc8f1008d4765b9b4635999626d4b) "
- "section in our project report."
-)
-
-
-obj_select = st.selectbox("Select a Scene", SCENES_LIST, index=0)
-DIET_NERF_MODEL_NAME, DIET_NERF_FILE_ID, NERF_MODEL_NAME, NERF_FILE_ID = select_model(obj_select)
-
-
-@st.cache(show_spinner=False)
-def download_diet_nerf_model():
- os.makedirs(MODEL_DIR, exist_ok=True)
- diet_nerf_model_path = os.path.join(MODEL_DIR, DIET_NERF_MODEL_NAME)
- url = f"https://drive.google.com/uc?id={DIET_NERF_FILE_ID}"
- gdown.download(url, diet_nerf_model_path, quiet=False)
- print(f"Model downloaded from google drive: {diet_nerf_model_path}")
-
-
-# @st.cache(show_spinner=False)
-# def download_nerf_model():
-# nerf_model_path = os.path.join(MODEL_DIR, NERF_MODEL_NAME)
-# url = f"https://drive.google.com/uc?id={NERF_FILE_ID}"
-# gdown.download(url, nerf_model_path, quiet=False)
-# print(f"Model downloaded from google drive: {nerf_model_path}")
-
-
-@st.cache(show_spinner=False, allow_output_mutation=True)
-def fetch_diet_nerf_model():
- model, state = load_trained_model(MODEL_DIR, DIET_NERF_MODEL_NAME)
- return model, state
-
-
-# @st.cache(show_spinner=False, allow_output_mutation=True)
-# def fetch_nerf_model():
-# model, state = load_trained_model(MODEL_DIR, NERF_MODEL_NAME)
-# return model, state
-
-
-diet_nerf_model_path = os.path.join(MODEL_DIR, DIET_NERF_MODEL_NAME)
-if not os.path.isfile(diet_nerf_model_path):
- download_diet_nerf_model()
-
-# nerf_model_path = os.path.join(MODEL_DIR, NERF_MODEL_NAME)
-# if not os.path.isfile(nerf_model_path):
-# download_nerf_model()
-
-diet_nerf_model, diet_nerf_state = fetch_diet_nerf_model()
-# nerf_model, nerf_state = fetch_nerf_model()
-
-st.markdown("")
-
-with st.spinner("Rendering view..."):
- with st.spinner(
- ":information_source: **INFO**: It may take around 30-50 seconds to render the view. "
- "In the meantime, why don't you take a look at our "
- "[project report](https://www.notion.so/DietNeRF-Putting-NeRF-on-a-Diet-4aeddae95d054f1d91686f02bdb74745), "
- "if you haven't already :slightly_smiling_face:"
- ):
- dn_pred_color, _ = render_predict_from_pose(diet_nerf_state, theta, phi, radius)
- dn_im = predict_to_image(dn_pred_color)
- # dn_w, _ = dn_im.size
- # dn_new_w = int(2 * dn_w)
- # dn_im = dn_im.resize(size=(dn_new_w, dn_new_w))
-
- # n_pred_color, _ = render_predict_from_pose(nerf_state, theta, phi, radius)
- # n_im = predict_to_image(n_pred_color)
- # n_w, _ = n_im.size
- # n_new_w = int(2 * n_w)
- # n_im = n_im.resize(size=(n_new_w, n_new_w))
-
- # diet_nerf_col, nerf_col = st.beta_columns([1, 1])
- st.markdown(
- "> 📒 **NOTE**: The rendered view does not fully reflect the true quality of the view generated by the model "
- "because it has been downsampled to speedup the process."
- )
- st.markdown(f"""Rendered view for {obj_select}
""", unsafe_allow_html=True)
- st.image(dn_im, use_column_width=True)
-
- # nerf_col.markdown("""NeRF
""", unsafe_allow_html=True)
- # nerf_col.image(n_im, use_column_width=True)
-
- # st.markdown(
- # "> 📒 NOTE: The views may look similar to you but see the "
- # "[Experimental Results](https://www.notion.so/DietNeRF-Putting-NeRF-on-a-Diet-4aeddae95d054f1d91686f02bdb74745#0f6bc8f1008d4765b9b4635999626d4b) "
- # "section in our report to get a detailed comparison of differences between `DietNeRF` and `NeRF`."
- # )
diff --git a/spaces/florim/MedGPT/autogpt/__main__.py b/spaces/florim/MedGPT/autogpt/__main__.py
deleted file mode 100644
index 128f9eea4900429e88276abdde3419b806001ac7..0000000000000000000000000000000000000000
--- a/spaces/florim/MedGPT/autogpt/__main__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""Auto-GPT: A GPT powered AI Assistant"""
-import autogpt.cli
-
-if __name__ == "__main__":
- autogpt.cli.main()
diff --git a/spaces/flowers-team/SocialAISchool/param_tree_demo.py b/spaces/flowers-team/SocialAISchool/param_tree_demo.py
deleted file mode 100644
index a352f6faf4e28f74eff6103c1ad7726d8135f2be..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/param_tree_demo.py
+++ /dev/null
@@ -1,234 +0,0 @@
-import streamlit as st
-import copy
-import streamlit.components.v1 as components
-import streamlit.caching as caching
-import time
-import argparse
-import numpy as np
-import gym
-import gym_minigrid
-from gym_minigrid.wrappers import *
-from gym_minigrid.window import Window
-import matplotlib.pyplot as plt
-from gym_minigrid.social_ai_envs.socialaigrammar import SocialAIGrammar, SocialAIActions, SocialAIActionSpace
-
-default_params = {
- "Pointing": 0,
- "Emulation": 1,
- "Language_grounding": 2,
- "Pragmatic_frame_complexity": 1,
-}
-
-class InteractiveACL:
-
- def choose(self, node, chosen_parameters):
-
- options = [n.label for n in node.children]
-
- box_name = f'{node.label} ({node.id})'
- ret = st.sidebar.selectbox(
- box_name,
- options,
- index=default_params.get(node.label, 0)
- )
-
- for ind, (c, c_lab) in enumerate(zip(node.children, options)):
- if c_lab == ret:
- return c
-
- def get_info(self):
- return {}
-
-@st.cache(allow_output_mutation=True, suppress_st_warning=True)
-def load_env():
- env = gym.make("SocialAI-SocialAIParamEnv-v1")
- env.curriculum=InteractiveACL()
-
- return env
-
-
-
-
-st.title("SocialAI interactive demo")
-
-
-env = load_env()
-
-st.subheader("Primitive actions")
-
-# moving buttons
-columns = st.columns([1]*(len(SocialAIActions)+1))
-action_names = [a.name for a in list(SocialAIActions)] + ["no_op"]
-# keys = ["Left arrow", "Right arrow", "Up arrow", "t", "q", "Shift"]
-keys = ["a", "d", "w", "t", "q", "Shift"]
-
-# actions = [st.button(a.name) for a in list(SocialAIActions)] + [st.button("none")]
-actions = []
-for a_name, col, key in zip(action_names, columns, keys):
- with col:
- actions.append(st.button(a_name+f" ({key})", help=f"Shortcut: {key}"))
-
-
-st.subheader("Speaking actions")
-# talking buttons
-t, w, b = st.columns([1, 1, 1])
-
-changes = [False, False]
-
-with t:
- templ = st.selectbox("Template", options=SocialAIGrammar.templates, index=1)
-with w:
- word = st.selectbox("Word", options=SocialAIGrammar.things, index=0)
-
-speak = st.button("Speak (s)", help="Shortcut s")
-
-# utterance change detection
-utt_changed = False
-
-if "template" in st.session_state:
- utt_changed = st.session_state.template != templ
-
-if "word" in st.session_state:
- utt_changed = utt_changed or st.session_state.word != word
-
-st.session_state["template"] = templ
-st.session_state["word"] = word
-
-st.sidebar.subheader("Select the parameters:")
-
-play = st.button("Play (Enter)", help="Generate the env. Shortcut: Enter")
-
-components.html(
- """
-
-""",
- height=0,
- width=0,
-)
-
-# no action
-done_ind = len(actions) - 2
-actions[done_ind] = False
-
-# was agent controlled
-no_action = not any(actions) and not speak
-
-done = False
-info = None
-
-if not no_action or play or utt_changed:
- # agent is controlled
- if any(actions):
- p_act = np.argmax(actions)
- if p_act == len(actions) - 1:
- p_act = np.nan
-
- action = [p_act, np.nan, np.nan]
-
- elif speak:
- templ_ind = SocialAIGrammar.templates.index(templ)
- word_ind = SocialAIGrammar.things.index(word)
- action = [np.nan, templ_ind, word_ind]
-
- else:
- action = None
-
- if action:
- obs, reward, done, info = env.step(action)
-
- env.render(mode='human')
- st.pyplot(env.window.fig)
-
-
-# if done or no_action:
-if done or (no_action and not play and not utt_changed):
- env.reset()
-
-else:
- env.parameter_tree.sample_env_params(ACL=env.curriculum)
-
-
-with st.expander("Parametric tree", True):
- # draw tree
- current_param_labels = env.current_env.parameters if env.current_env.parameters else {}
- folded_nodes = [
- "Information_seeking",
- "Collaboration",
- "OthersPerceptionInference"
- ]
- # print(current_param_labels["Env_type"])
- folded_nodes.remove(current_param_labels["Env_type"])
- env.parameter_tree.draw_tree(
- filename="viz/streamlit_temp_tree",
- ignore_labels=["Num_of_colors"],
- selected_parameters=current_param_labels,
- folded_nodes=folded_nodes,
- # save=False
- )
- # st.graphviz_chart(env.parameter_tree.tree)
- st.image("viz/streamlit_temp_tree.png")
-
-# if not no_action or play or utt_changed:
-# # agent is controlled
-# if any(actions):
-# p_act = np.argmax(actions)
-# if p_act == len(actions) - 1:
-# p_act = np.nan
-#
-# action = [p_act, np.nan, np.nan]
-#
-# elif speak:
-# templ_ind = SocialAIGrammar.templates.index(templ)
-# word_ind = SocialAIGrammar.things.index(word)
-# action = [np.nan, templ_ind, word_ind]
-#
-# else:
-# action = None
-#
-# if action:
-# obs, reward, done, info = env.step(action)
-#
-# env.render(mode='human')
-# st.pyplot(env.window.fig)
diff --git a/spaces/freddyaboulton/gradio_foliumtest/__init__.py b/spaces/freddyaboulton/gradio_foliumtest/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/futuristicdude/andite-anything-v4.0/README.md b/spaces/futuristicdude/andite-anything-v4.0/README.md
deleted file mode 100644
index 7437f6acc015b82be0c5e0cb10b65bf7587d9478..0000000000000000000000000000000000000000
--- a/spaces/futuristicdude/andite-anything-v4.0/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Andite Anything V4.0
-emoji: 👁
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/runner/hooks/sync_buffer.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/runner/hooks/sync_buffer.py
deleted file mode 100644
index 6376b7ff894280cb2782243b25e8973650591577..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/runner/hooks/sync_buffer.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from ..dist_utils import allreduce_params
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class SyncBuffersHook(Hook):
- """Synchronize model buffers such as running_mean and running_var in BN at
- the end of each epoch.
-
- Args:
- distributed (bool): Whether distributed training is used. It is
- effective only for distributed training. Defaults to True.
- """
-
- def __init__(self, distributed=True):
- self.distributed = distributed
-
- def after_epoch(self, runner):
- """All-reduce model buffers at the end of each epoch."""
- if self.distributed:
- allreduce_params(runner.model.buffers())
diff --git a/spaces/gilbertb/ChatGPTwithAPI/README.md b/spaces/gilbertb/ChatGPTwithAPI/README.md
deleted file mode 100644
index 5e9db9ee137f91124dc76c9ed996db9fff3477d5..0000000000000000000000000000000000000000
--- a/spaces/gilbertb/ChatGPTwithAPI/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: ChatGPTwithAPI
-emoji: 🚀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.20.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ysharma/ChatGPTwithAPI
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/glfpes/stabilityai-stable-diffusion-2-1/README.md b/spaces/glfpes/stabilityai-stable-diffusion-2-1/README.md
deleted file mode 100644
index 65ec37fb67a36bff9c895a4bd5a89a3fc1ef0598..0000000000000000000000000000000000000000
--- a/spaces/glfpes/stabilityai-stable-diffusion-2-1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Stabilityai Stable Diffusion 2 1
-emoji: 😻
-colorFrom: yellow
-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/gptbase/GPTBase/app.py b/spaces/gptbase/GPTBase/app.py
deleted file mode 100644
index c593a31cca30ba23168d7a9db2a13d67186c8651..0000000000000000000000000000000000000000
--- a/spaces/gptbase/GPTBase/app.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import requests
-import streamlit as st
-import json
-from streamlit_chat import message
-from utils import upload_file
-
-st.title("GPTBase")
-
-def clear_submit():
- st.session_state["submit"] = False
-
-def set_api_key(api_key: str):
- st.session_state["API_KEY"] = api_key
-
-def set_ai_id(ai_id: str):
- st.session_state["ai_id"] = ai_id
-
-# Sidebar
-with st.sidebar:
- user_secret = st.text_input(
- "API Key",
- type="password",
- placeholder="Paste your API key here (ak-...)",
- help="You can get your API key from https://gptbase.ai/api-keys.",
- value=st.session_state.get("API_KEY", ""),
- )
- if user_secret:
- set_api_key(user_secret)
-
- user_ai_id = st.text_input(
- "AI Id",
- placeholder="Paste your AI Id here",
- value=st.session_state.get("ai_id", ""),
- )
- if user_ai_id:
- set_ai_id(user_ai_id)
-
- uploaded_file = st.file_uploader(
- "Upload a pdf, docx, or txt file",
- type=["pdf", "docx", "txt"],
- help="Scanned documents are not supported yet!",
- on_change=clear_submit,
- )
- api_key = st.session_state.get("API_KEY")
- ai_id = st.session_state.get("ai_id")
- if not api_key or not ai_id:
- st.warning("Please enter your API key and AI Id to upload a file.")
-
- if uploaded_file is not None and "API_KEY" in st.session_state and "ai_id" in st.session_state:
- res = upload_file(uploaded_file, api_key, ai_id)
- if not res:
- st.text("Upload failed, please try again")
- # if not st.session_state.get("is_upload"):
- # file = {"file": uploaded_file}
- # url = f'https://mygpt.felo.me/api/v1/ais/{ai_id}/files'
- # headers = {"Authorization": f"Bearer {api_key}"}
- # try:
- # response = requests.post(url, headers=headers, files=file)
- # # print("upload")
- # except requests.exceptions.HTTPError as err:
- # st.text("Upload failed, please try again")
-
- # st.session_state['is_upload'] = True
-
-tab1, tab2 = st.tabs(["Introduce", "Ask AI"])
-with tab1:
- st.markdown("### Use steps")
- st.write('1.Please fill in your API Key and AI Id.')
- st.write('2.Upload the file you want to get the answer.')
- st.write('3.You can ask questions about all the files you have uploaded.')
- st.markdown("""---""")
- st.write('If you have any questions or suggestions, please visit: https://twitter.com/GptBase')
-
-with tab2:
- if not api_key or not ai_id:
- st.write('Please fill in your API Key and AI Id.')
- st.header("Ask AI some questions about the file you uploaded:")
-
- def get_text():
- input_text = st.text_area("You:", on_change=clear_submit)
- return input_text
- user_input = get_text()
- button = st.button("Submit")
- if button or st.session_state.get("submit"):
- if api_key and ai_id and user_input:
- st.session_state["submit"] = True
- question_url = f'https://gptbase.ai/api/v1/question/{ai_id}'
- headers = {"Authorization": f"Bearer {api_key}"}
- data = {"question": user_input}
- # response = requests.post(question_url, headers=headers, data=json.dumps(data))
- try:
- response = requests.post(question_url, headers=headers, data=json.dumps(data))
- if response.status_code == 200:
- result = response.json()
- message(result['answer'])
- else:
- print(f"Failed to ask, please try again.")
- response.raise_for_status() # Raises an HTTPError if the response contains an error status code.
- except requests.exceptions.HTTPError as err:
- print(f"Failed to ask, please try again.:{err}")
- except requests.exceptions.RequestException as e:
- print(e)
diff --git a/spaces/gradio/HuBERT/fairseq/criterions/model_criterion.py b/spaces/gradio/HuBERT/fairseq/criterions/model_criterion.py
deleted file mode 100644
index 30350f13b1c00498de6784579250d6b342ced7dd..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/criterions/model_criterion.py
+++ /dev/null
@@ -1,138 +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 dataclasses import dataclass, field
-from typing import Dict, List
-
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class ModelCriterionConfig(FairseqDataclass):
- loss_weights: Dict[str, float] = field(
- default_factory=dict,
- metadata={"help": "weights for the loss terms"},
- )
- log_keys: List[str] = field(
- default_factory=list,
- metadata={"help": "additional output keys to log"},
- )
-
-
-@register_criterion("model", dataclass=ModelCriterionConfig)
-class ModelCriterion(FairseqCriterion):
- """
- This criterion relies on the model to supply losses.
- The losses should be a dictionary of name -> scalar returned by
- the model either by including it in the net_output dict or by
- implementing a get_losses(net_output, sample) method. The final loss is
- a scaled sum of all losses according to weights in loss_weights.
- If no weights are provided, then all losses are scaled by 1.0.
-
- The losses will be automatically logged. Additional keys from
- net_output dict can be logged via the log_keys parameter.
- """
-
- def __init__(self, task, loss_weights=None, log_keys=None):
- super().__init__(task)
- self.loss_weights = loss_weights
- self.log_keys = log_keys
-
- def forward(self, model, sample, reduce=True):
- net_output = model(**sample["net_input"])
-
- sample_size = net_output["sample_size"]
- scaled_losses = {}
-
- if hasattr(model, "get_losses"):
- losses = model.get_losses(net_output, sample)
- elif isinstance(net_output, dict) and "losses" in net_output:
- losses = net_output["losses"]
- else:
- raise Exception("Could not retrieve losses")
-
- for lk, p in losses.items():
- try:
- coef = 1.0 if len(self.loss_weights) == 0 else self.loss_weights[lk]
- except KeyError:
- logger.error(
- f"weight for loss {lk} is not in loss_weights ({self.loss_weights})"
- )
- raise
- if coef != 0 and p is not None:
- scaled_losses[lk] = coef * p.float()
-
- loss = sum(scaled_losses.values())
- if reduce and loss.numel() > 1:
- loss = loss.sum()
-
- logging_output = {
- "loss": loss.data,
- "ntokens": sample_size,
- "nsentences": sample["id"].numel(),
- "sample_size": sample_size,
- "_world_size": 1,
- }
-
- for lk in self.log_keys:
- if lk in net_output and net_output[lk] is not None:
- logging_output[lk] = float(net_output[lk])
-
- if len(scaled_losses) > 1:
- for lk, l in scaled_losses.items():
- logging_output[f"loss_{lk}"] = l.item()
-
- return loss, sample_size, logging_output
-
- @staticmethod
- def reduce_metrics(logging_outputs) -> None:
- """Aggregate logging outputs from data parallel training."""
- loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs))
- ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs))
- nsentences = utils.item(
- sum(log.get("nsentences", 0) for log in logging_outputs)
- )
- sample_size = utils.item(
- sum(log.get("sample_size", 0) for log in logging_outputs)
- )
-
- metrics.log_scalar("loss", loss_sum / sample_size, sample_size, round=3)
- metrics.log_scalar("ntokens", ntokens)
- metrics.log_scalar("nsentences", nsentences)
-
- builtin_keys = {
- "loss",
- "ntokens",
- "nsentences",
- "sample_size",
- "_world_size",
- }
-
- world_size = utils.item(
- sum(log.get("_world_size", 0) for log in logging_outputs)
- )
-
- for k in logging_outputs[0]:
- if k not in builtin_keys:
- val = sum(log.get(k, 0) for log in logging_outputs)
- if k.startswith("loss_"):
- metrics.log_scalar(k, val / sample_size, sample_size, round=3)
- else:
- metrics.log_scalar(k, val / world_size, round=3)
-
- @staticmethod
- def logging_outputs_can_be_summed() -> bool:
- """
- Whether the logging outputs returned by `forward` can be summed
- across workers prior to calling `reduce_metrics`. Setting this
- to True will improves distributed training speed.
- """
- return True
diff --git a/spaces/gradio/HuBERT/fairseq/data/list_dataset.py b/spaces/gradio/HuBERT/fairseq/data/list_dataset.py
deleted file mode 100644
index 12f00aa43661d6bad701c9e72653ba8779136906..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/data/list_dataset.py
+++ /dev/null
@@ -1,32 +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.
-
-from . import BaseWrapperDataset
-
-
-class ListDataset(BaseWrapperDataset):
- def __init__(self, dataset, sizes=None):
- super().__init__(dataset)
- self._sizes = sizes
-
- def __iter__(self):
- for x in self.dataset:
- yield x
-
- def collater(self, samples):
- return samples
-
- @property
- def sizes(self):
- return self._sizes
-
- def num_tokens(self, index):
- return self.sizes[index]
-
- def size(self, index):
- return self.sizes[index]
-
- def set_epoch(self, epoch):
- pass
diff --git a/spaces/greymatter72/goofyai-3d_render_style_xl/README.md b/spaces/greymatter72/goofyai-3d_render_style_xl/README.md
deleted file mode 100644
index 87f4c9e67d4ba3c2490576bf30f4c1a0a4c82308..0000000000000000000000000000000000000000
--- a/spaces/greymatter72/goofyai-3d_render_style_xl/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Goofyai-3d Render Style Xl
-emoji: 🌖
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.45.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/gwang-kim/DATID-3D/eg3d/docs/visualizer_guide.md b/spaces/gwang-kim/DATID-3D/eg3d/docs/visualizer_guide.md
deleted file mode 100644
index 9cb2edae7f9ae3348cc1a7ae670f7a85e935114e..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/eg3d/docs/visualizer_guide.md
+++ /dev/null
@@ -1,66 +0,0 @@
-## Guide to the Visualizer
-
-
-
-We include a 3D visualizer that is based on the amazing tool introduced in StyleGAN3. The following document describes important options and sliders of the visualizer UI.
-
-TLDR:
-1. Press the "Pickle/Recent" button to select a pretrained EG3D model.
-2. Click and drag the "Latent/Drag" button to sweep latent codes and change the scene identity.
-3. Click and drag the rendering on the right to move the camera.
-
----
-
-## Network & Latent
-
-### Pickle
-Specify the path of the model checkpoint to visualize. You have a few options:
-1. Drag and drop the .pkl file from your file browser into the visualizer window
-1. Type the path (or url) of your .pkl file into the text field
-1. Press the recent box to access a list of recently used checkpoints
-
-### Pose
-Control the pitch and yaw of the camera by clicking and dragging the rendering on the right. By default, the camera rotates on a sphere with fixed radius, pointed at the origin.
-
-### FOV
-Control the field of view of the camera with this slider to zoom the camera in and out. For FFHQ, 18 degrees is about right; for ShapeNet, use a FOV of 45 degrees.
-
-### Cond Pose
-The pose with which we condition the generator (see Generator Pose Conditioning in Sec. 4.4). By default, we condition on the fixed frontal camera pose. For models trained without generator pose conditioning, this will have no effect.
-
-### Render Type
-Toggle between the final super-resolved output (RGB image), a depth map (Depth image) or the raw neural rendering without super resolution (Neural rendering).
-
-### Depth Sample Multiplier / Depth Sample Importance Multiplier
-Adjust the number of depth samples taken per ray. By increasing the number of depth samples, we reduce flickering artifacts caused by depth aliasing, which leads to more temporally-consistent videos. However, the tradeoff is slower rendering and slightly blurrier images. At 1X / 1X, render in the visualizer with the same number of depth samples as at training; at 2X / 2X, take double the uniformly spaced and double the importance samples per ray. As an example: we train FFHQ with 48 uniformly spaced depth samples and 48 importance samples per ray. Using 2X / 2X, we instead take 96 uniformly spaced depth samples and 96 importance samples (192 total).
-
-### Latent
-The seed for the latent code, *z*, that is the input to the generator. Click and drag the "drag" button to sweep between scene identities. Press the "Anim" checkbox to play an animation sweeping through latent codes.
-
-### Stylemix
-The seed for a second latent code for style mixing. Check the boxes on the right to select which layers should be conditioned by this second code.
-
-### Truncate
-Apply the truncation trick in *w*-space to trade off fidelity for diversity. Psi=1 means no truncation. Psi=0 gives the "average" scene learned by the generator. A Psi between 0 and 1, e.g. 0.7 is a compromise that reduces diversity somewhat but improves the overall consistency in quality. (See the Truncation Trick in StyleGAN for more info.)
-
----
-
-## Performance & capture
-
-### Render
-
-Displays the framerate of rendering. On an RTX 3090, with neural rendering resolution of 128, and with 48 uniform and 48 importance depth samples, we get 25-30 FPS.
-
-### Capture
-
-Save screenshots to the directory specified by the text field. Save image saves just the rendering; Save GUI saves the complete pane including the user interface.
-
----
-
-## Layers & channels
-
-### Cache backbone
-For rendering where the scene identity (the latent code *z* and conditioning pose) remain static, but rendering parameters (the camera pose, fov, render type, etc...) change, we can enable 'backbone caching' which will enable us to cache and reuse the existing triplanes computed by the convolutional backbone. Backbone caching slightly improves rendering speed.
-
-### Layer viewer
-View and analyze the intermediate weights and layers of the generator. Scroll through the network and select a layer using the checkbox. Use the "Channel" slider on the right to view different activations. Do note that when 'cache backbone' is enabled, you will be unable to view the intermediate weights of the convolutional backbone/triplanes.
diff --git a/spaces/gwang-kim/DATID-3D/eg3d/torch_utils/ops/bias_act.cpp b/spaces/gwang-kim/DATID-3D/eg3d/torch_utils/ops/bias_act.cpp
deleted file mode 100644
index ee6f6d0caaf4f84b94851d223e384344e1109cdc..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/eg3d/torch_utils/ops/bias_act.cpp
+++ /dev/null
@@ -1,103 +0,0 @@
-/*
- * SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
- * SPDX-License-Identifier: LicenseRef-NvidiaProprietary
- *
- * NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
- * property and proprietary rights in and to this material, related
- * documentation and any modifications thereto. Any use, reproduction,
- * disclosure or distribution of this material and related documentation
- * without an express license agreement from NVIDIA CORPORATION or
- * its affiliates is strictly prohibited.
- */
-
-#include
-#include
-#include
-#include "bias_act.h"
-
-//------------------------------------------------------------------------
-
-static bool has_same_layout(torch::Tensor x, torch::Tensor y)
-{
- if (x.dim() != y.dim())
- return false;
- for (int64_t i = 0; i < x.dim(); i++)
- {
- if (x.size(i) != y.size(i))
- return false;
- if (x.size(i) >= 2 && x.stride(i) != y.stride(i))
- return false;
- }
- return true;
-}
-
-//------------------------------------------------------------------------
-
-static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::Tensor xref, torch::Tensor yref, torch::Tensor dy, int grad, int dim, int act, float alpha, float gain, float clamp)
-{
- // Validate arguments.
- TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
- TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x");
- TORCH_CHECK(xref.numel() == 0 || (xref.sizes() == x.sizes() && xref.dtype() == x.dtype() && xref.device() == x.device()), "xref must have the same shape, dtype, and device as x");
- TORCH_CHECK(yref.numel() == 0 || (yref.sizes() == x.sizes() && yref.dtype() == x.dtype() && yref.device() == x.device()), "yref must have the same shape, dtype, and device as x");
- TORCH_CHECK(dy.numel() == 0 || (dy.sizes() == x.sizes() && dy.dtype() == x.dtype() && dy.device() == x.device()), "dy must have the same dtype and device as x");
- TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
- TORCH_CHECK(b.dim() == 1, "b must have rank 1");
- TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds");
- TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements");
- TORCH_CHECK(grad >= 0, "grad must be non-negative");
-
- // Validate layout.
- TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense");
- TORCH_CHECK(b.is_contiguous(), "b must be contiguous");
- TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x");
- TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x");
- TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x");
-
- // Create output tensor.
- const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
- torch::Tensor y = torch::empty_like(x);
- TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x");
-
- // Initialize CUDA kernel parameters.
- bias_act_kernel_params p;
- p.x = x.data_ptr();
- p.b = (b.numel()) ? b.data_ptr() : NULL;
- p.xref = (xref.numel()) ? xref.data_ptr() : NULL;
- p.yref = (yref.numel()) ? yref.data_ptr() : NULL;
- p.dy = (dy.numel()) ? dy.data_ptr() : NULL;
- p.y = y.data_ptr();
- p.grad = grad;
- p.act = act;
- p.alpha = alpha;
- p.gain = gain;
- p.clamp = clamp;
- p.sizeX = (int)x.numel();
- p.sizeB = (int)b.numel();
- p.stepB = (b.numel()) ? (int)x.stride(dim) : 1;
-
- // Choose CUDA kernel.
- void* kernel;
- AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
- {
- kernel = choose_bias_act_kernel(p);
- });
- TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func");
-
- // Launch CUDA kernel.
- p.loopX = 4;
- int blockSize = 4 * 32;
- int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1;
- void* args[] = {&p};
- AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
- return y;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
- m.def("bias_act", &bias_act);
-}
-
-//------------------------------------------------------------------------
diff --git a/spaces/gwang-kim/DATID-3D/eg3d/training/triplane.py b/spaces/gwang-kim/DATID-3D/eg3d/training/triplane.py
deleted file mode 100644
index 1b7d48dec681a7b3040c19317dbaf26f6ff7e3cf..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/eg3d/training/triplane.py
+++ /dev/null
@@ -1,135 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
-#
-# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
-# property and proprietary rights in and to this material, related
-# documentation and any modifications thereto. Any use, reproduction,
-# disclosure or distribution of this material and related documentation
-# without an express license agreement from NVIDIA CORPORATION or
-# its affiliates is strictly prohibited.
-
-import torch
-from torch_utils import persistence
-from training.networks_stylegan2 import Generator as StyleGAN2Backbone
-from training.volumetric_rendering.renderer import ImportanceRenderer
-from training.volumetric_rendering.ray_sampler import RaySampler
-import dnnlib
-
-@persistence.persistent_class
-class TriPlaneGenerator(torch.nn.Module):
- def __init__(self,
- z_dim, # Input latent (Z) dimensionality.
- c_dim, # Conditioning label (C) dimensionality.
- w_dim, # Intermediate latent (W) dimensionality.
- img_resolution, # Output resolution.
- img_channels, # Number of output color channels.
- sr_num_fp16_res = 0,
- mapping_kwargs = {}, # Arguments for MappingNetwork.
- rendering_kwargs = {},
- sr_kwargs = {},
- **synthesis_kwargs, # Arguments for SynthesisNetwork.
- ):
- super().__init__()
- self.z_dim=z_dim
- self.c_dim=c_dim
- self.w_dim=w_dim
- self.img_resolution=img_resolution
- self.img_channels=img_channels
- self.renderer = ImportanceRenderer()
- self.ray_sampler = RaySampler()
- self.backbone = StyleGAN2Backbone(z_dim, c_dim, w_dim, img_resolution=256, img_channels=32*3, mapping_kwargs=mapping_kwargs, **synthesis_kwargs)
- self.superresolution = dnnlib.util.construct_class_by_name(class_name=rendering_kwargs['superresolution_module'], channels=32, img_resolution=img_resolution, sr_num_fp16_res=sr_num_fp16_res, sr_antialias=rendering_kwargs['sr_antialias'], **sr_kwargs)
- self.decoder = OSGDecoder(32, {'decoder_lr_mul': rendering_kwargs.get('decoder_lr_mul', 1), 'decoder_output_dim': 32})
- self.neural_rendering_resolution = 64
- self.rendering_kwargs = rendering_kwargs
-
- self._last_planes = None
-
- def mapping(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False):
- if self.rendering_kwargs['c_gen_conditioning_zero']:
- c = torch.zeros_like(c)
- return self.backbone.mapping(z, c * self.rendering_kwargs.get('c_scale', 0), truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas)
-
- def synthesis(self, ws, c, neural_rendering_resolution=None, update_emas=False, cache_backbone=False, use_cached_backbone=False, **synthesis_kwargs):
- cam2world_matrix = c[:, :16].view(-1, 4, 4)
- intrinsics = c[:, 16:25].view(-1, 3, 3)
-
- if neural_rendering_resolution is None:
- neural_rendering_resolution = self.neural_rendering_resolution
- else:
- self.neural_rendering_resolution = neural_rendering_resolution
-
- # Create a batch of rays for volume rendering
- ray_origins, ray_directions = self.ray_sampler(cam2world_matrix, intrinsics, neural_rendering_resolution)
-
- # Create triplanes by running StyleGAN backbone
- N, M, _ = ray_origins.shape
- if use_cached_backbone and self._last_planes is not None:
- planes = self._last_planes
- else:
- planes = self.backbone.synthesis(ws, update_emas=update_emas, **synthesis_kwargs)
- if cache_backbone:
- self._last_planes = planes
-
- # Reshape output into three 32-channel planes
- planes = planes.view(len(planes), 3, 32, planes.shape[-2], planes.shape[-1])
-
- # Perform volume rendering
- feature_samples, depth_samples, weights_samples = self.renderer(planes, self.decoder, ray_origins, ray_directions, self.rendering_kwargs) # channels last
-
- # Reshape into 'raw' neural-rendered image
- H = W = self.neural_rendering_resolution
- feature_image = feature_samples.permute(0, 2, 1).reshape(N, feature_samples.shape[-1], H, W).contiguous()
- depth_image = depth_samples.permute(0, 2, 1).reshape(N, 1, H, W)
-
- # Run superresolution to get final image
- rgb_image = feature_image[:, :3]
- sr_image = self.superresolution(rgb_image, feature_image, ws, noise_mode=self.rendering_kwargs['superresolution_noise_mode'], **{k:synthesis_kwargs[k] for k in synthesis_kwargs.keys() if k != 'noise_mode'})
-
- return {'image': sr_image, 'image_raw': rgb_image, 'image_depth': depth_image}
-
- def sample(self, coordinates, directions, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs):
- # Compute RGB features, density for arbitrary 3D coordinates. Mostly used for extracting shapes.
- ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas)
- planes = self.backbone.synthesis(ws, update_emas=update_emas, **synthesis_kwargs)
- planes = planes.view(len(planes), 3, 32, planes.shape[-2], planes.shape[-1])
- return self.renderer.run_model(planes, self.decoder, coordinates, directions, self.rendering_kwargs)
-
- def sample_mixed(self, coordinates, directions, ws, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs):
- # Same as sample, but expects latent vectors 'ws' instead of Gaussian noise 'z'
- planes = self.backbone.synthesis(ws, update_emas = update_emas, **synthesis_kwargs)
- planes = planes.view(len(planes), 3, 32, planes.shape[-2], planes.shape[-1])
- return self.renderer.run_model(planes, self.decoder, coordinates, directions, self.rendering_kwargs)
-
- def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, neural_rendering_resolution=None, update_emas=False, cache_backbone=False, use_cached_backbone=False, **synthesis_kwargs):
- # Render a batch of generated images.
- ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas)
- return self.synthesis(ws, c, update_emas=update_emas, neural_rendering_resolution=neural_rendering_resolution, cache_backbone=cache_backbone, use_cached_backbone=use_cached_backbone, **synthesis_kwargs)
-
-
-from training.networks_stylegan2 import FullyConnectedLayer
-
-class OSGDecoder(torch.nn.Module):
- def __init__(self, n_features, options):
- super().__init__()
- self.hidden_dim = 64
-
- self.net = torch.nn.Sequential(
- FullyConnectedLayer(n_features, self.hidden_dim, lr_multiplier=options['decoder_lr_mul']),
- torch.nn.Softplus(),
- FullyConnectedLayer(self.hidden_dim, 1 + options['decoder_output_dim'], lr_multiplier=options['decoder_lr_mul'])
- )
-
- def forward(self, sampled_features, ray_directions):
- # Aggregate features
- sampled_features = sampled_features.mean(1)
- x = sampled_features
-
- N, M, C = x.shape
- x = x.view(N*M, C)
-
- x = self.net(x)
- x = x.view(N, M, -1)
- rgb = torch.sigmoid(x[..., 1:])*(1 + 2*0.001) - 0.001 # Uses sigmoid clamping from MipNeRF
- sigma = x[..., 0:1]
- return {'rgb': rgb, 'sigma': sigma}
diff --git a/spaces/haakohu/deep_privacy2_face/dp2/data/datasets/coco_cse.py b/spaces/haakohu/deep_privacy2_face/dp2/data/datasets/coco_cse.py
deleted file mode 100644
index b240932da41b2db03c3807830935a78b50f84c4f..0000000000000000000000000000000000000000
--- a/spaces/haakohu/deep_privacy2_face/dp2/data/datasets/coco_cse.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import pickle
-import torchvision
-import torch
-import pathlib
-import numpy as np
-from typing import Callable, Optional, Union
-from torch.hub import get_dir as get_hub_dir
-
-
-def cache_embed_stats(embed_map: torch.Tensor):
- mean = embed_map.mean(dim=0, keepdim=True)
- rstd = ((embed_map - mean).square().mean(dim=0, keepdim=True)+1e-8).rsqrt()
-
- cache = dict(mean=mean, rstd=rstd, embed_map=embed_map)
- path = pathlib.Path(get_hub_dir(), f"embed_map_stats.torch")
- path.parent.mkdir(exist_ok=True, parents=True)
- torch.save(cache, path)
-
-
-class CocoCSE(torch.utils.data.Dataset):
-
- def __init__(self,
- dirpath: Union[str, pathlib.Path],
- transform: Optional[Callable],
- normalize_E: bool,):
- dirpath = pathlib.Path(dirpath)
- self.dirpath = dirpath
-
- self.transform = transform
- assert self.dirpath.is_dir(),\
- f"Did not find dataset at: {dirpath}"
- self.image_paths, self.embedding_paths = self._load_impaths()
- self.embed_map = torch.from_numpy(np.load(self.dirpath.joinpath("embed_map.npy")))
- mean = self.embed_map.mean(dim=0, keepdim=True)
- rstd = ((self.embed_map - mean).square().mean(dim=0, keepdim=True)+1e-8).rsqrt()
- self.embed_map = (self.embed_map - mean) * rstd
- cache_embed_stats(self.embed_map)
-
- def _load_impaths(self):
- image_dir = self.dirpath.joinpath("images")
- image_paths = list(image_dir.glob("*.png"))
- image_paths.sort()
- embedding_paths = [
- self.dirpath.joinpath("embedding", x.stem + ".npy") for x in image_paths
- ]
- return image_paths, embedding_paths
-
- def __len__(self):
- return len(self.image_paths)
-
- def __getitem__(self, idx):
- im = torchvision.io.read_image(str(self.image_paths[idx]))
- vertices, mask, border = np.split(np.load(self.embedding_paths[idx]), 3, axis=-1)
- vertices = torch.from_numpy(vertices.squeeze()).long()
- mask = torch.from_numpy(mask.squeeze()).float()
- border = torch.from_numpy(border.squeeze()).float()
- E_mask = 1 - mask - border
- batch = {
- "img": im,
- "vertices": vertices[None],
- "mask": mask[None],
- "embed_map": self.embed_map,
- "border": border[None],
- "E_mask": E_mask[None]
- }
- if self.transform is None:
- return batch
- return self.transform(batch)
diff --git a/spaces/hamacojr/SAM-CAT-Seg/cat_seg/data/datasets/__init__.py b/spaces/hamacojr/SAM-CAT-Seg/cat_seg/data/datasets/__init__.py
deleted file mode 100644
index 90d0d07e352ea3952b34176383d89d02456f76d1..0000000000000000000000000000000000000000
--- a/spaces/hamacojr/SAM-CAT-Seg/cat_seg/data/datasets/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from . import (
- register_coco_stuff,
- register_ade20k_150,
- register_ade20k_847,
- register_pascal_20,
- register_pascal_59,
-)
diff --git a/spaces/hamel/hfspace_demo/update.sh b/spaces/hamel/hfspace_demo/update.sh
deleted file mode 100644
index dea2c58d8c10bc75713d6e7c4e56fb928130c2a1..0000000000000000000000000000000000000000
--- a/spaces/hamel/hfspace_demo/update.sh
+++ /dev/null
@@ -1,6 +0,0 @@
-#!/bin/bash
-
-# this is only for hamel, you can ignore this.
-jupyter nbconvert --to markdown app.ipynb
-cat yaml.md app.md > README.md
-git add -A; git commit -m "Add application files"; git push
\ No newline at end of file
diff --git a/spaces/hands012/gpt-academic/docs/waifu_plugin/waifu.css b/spaces/hands012/gpt-academic/docs/waifu_plugin/waifu.css
deleted file mode 100644
index 42639df0794e46fc58f66e2c772e2bf9ba605eed..0000000000000000000000000000000000000000
--- a/spaces/hands012/gpt-academic/docs/waifu_plugin/waifu.css
+++ /dev/null
@@ -1,290 +0,0 @@
-.waifu {
- position: fixed;
- bottom: 0;
- z-index: 1;
- font-size: 0;
- -webkit-transform: translateY(3px);
- transform: translateY(3px);
-}
-.waifu:hover {
- -webkit-transform: translateY(0);
- transform: translateY(0);
-}
-.waifu-tips {
- opacity: 0;
- margin: -20px 20px;
- padding: 5px 10px;
- border: 1px solid rgba(224, 186, 140, 0.62);
- border-radius: 12px;
- background-color: rgba(236, 217, 188, 0.5);
- box-shadow: 0 3px 15px 2px rgba(191, 158, 118, 0.2);
- text-overflow: ellipsis;
- overflow: hidden;
- position: absolute;
- animation-delay: 5s;
- animation-duration: 50s;
- animation-iteration-count: infinite;
- animation-name: shake;
- animation-timing-function: ease-in-out;
-}
-.waifu-tool {
- display: none;
- color: #aaa;
- top: 50px;
- right: 10px;
- position: absolute;
-}
-.waifu:hover .waifu-tool {
- display: block;
-}
-.waifu-tool span {
- display: block;
- cursor: pointer;
- color: #5b6c7d;
- transition: 0.2s;
-}
-.waifu-tool span:hover {
- color: #34495e;
-}
-.waifu #live2d{
- position: relative;
-}
-
-@keyframes shake {
- 2% {
- transform: translate(0.5px, -1.5px) rotate(-0.5deg);
- }
-
- 4% {
- transform: translate(0.5px, 1.5px) rotate(1.5deg);
- }
-
- 6% {
- transform: translate(1.5px, 1.5px) rotate(1.5deg);
- }
-
- 8% {
- transform: translate(2.5px, 1.5px) rotate(0.5deg);
- }
-
- 10% {
- transform: translate(0.5px, 2.5px) rotate(0.5deg);
- }
-
- 12% {
- transform: translate(1.5px, 1.5px) rotate(0.5deg);
- }
-
- 14% {
- transform: translate(0.5px, 0.5px) rotate(0.5deg);
- }
-
- 16% {
- transform: translate(-1.5px, -0.5px) rotate(1.5deg);
- }
-
- 18% {
- transform: translate(0.5px, 0.5px) rotate(1.5deg);
- }
-
- 20% {
- transform: translate(2.5px, 2.5px) rotate(1.5deg);
- }
-
- 22% {
- transform: translate(0.5px, -1.5px) rotate(1.5deg);
- }
-
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- transform: translate(-1.5px, 1.5px) rotate(-0.5deg);
- }
-
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- transform: translate(1.5px, 0.5px) rotate(1.5deg);
- }
-
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- transform: translate(-0.5px, -0.5px) rotate(-0.5deg);
- }
-
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- transform: translate(1.5px, -0.5px) rotate(-0.5deg);
- }
-
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- transform: translate(2.5px, -1.5px) rotate(1.5deg);
- }
-
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- transform: translate(2.5px, 2.5px) rotate(-0.5deg);
- }
-
- 36% {
- transform: translate(0.5px, -1.5px) rotate(0.5deg);
- }
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- transform: translate(2.5px, -0.5px) rotate(-0.5deg);
- }
-
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- transform: translate(-0.5px, 2.5px) rotate(0.5deg);
- }
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- transform: translate(-1.5px, 2.5px) rotate(0.5deg);
- }
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- transform: translate(-1.5px, 1.5px) rotate(0.5deg);
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- transform: translate(1.5px, -0.5px) rotate(-0.5deg);
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- transform: translate(2.5px, -0.5px) rotate(0.5deg);
- }
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- transform: translate(-1.5px, 1.5px) rotate(0.5deg);
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- transform: translate(-0.5px, 1.5px) rotate(0.5deg);
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- }
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- transform: translate(2.5px, 2.5px) rotate(0.5deg);
- }
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- transform: translate(2.5px, -1.5px) rotate(1.5deg);
- }
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- transform: translate(-1.5px, 0.5px) rotate(1.5deg);
- }
-
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- transform: translate(-1.5px, 1.5px) rotate(1.5deg);
- }
-
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- transform: translate(0.5px, 2.5px) rotate(1.5deg);
- }
-
- 68% {
- transform: translate(2.5px, -1.5px) rotate(1.5deg);
- }
-
- 70% {
- transform: translate(2.5px, 2.5px) rotate(0.5deg);
- }
-
- 72% {
- transform: translate(-0.5px, -1.5px) rotate(1.5deg);
- }
-
- 74% {
- transform: translate(-1.5px, 2.5px) rotate(1.5deg);
- }
-
- 76% {
- transform: translate(-1.5px, 2.5px) rotate(1.5deg);
- }
-
- 78% {
- transform: translate(-1.5px, 2.5px) rotate(0.5deg);
- }
-
- 80% {
- transform: translate(-1.5px, 0.5px) rotate(-0.5deg);
- }
-
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- transform: translate(-1.5px, 0.5px) rotate(-0.5deg);
- }
-
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- transform: translate(-0.5px, 0.5px) rotate(1.5deg);
- }
-
- 86% {
- transform: translate(2.5px, 1.5px) rotate(0.5deg);
- }
-
- 88% {
- transform: translate(-1.5px, 0.5px) rotate(1.5deg);
- }
-
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- transform: translate(-1.5px, -0.5px) rotate(-0.5deg);
- }
-
- 92% {
- transform: translate(-1.5px, -1.5px) rotate(1.5deg);
- }
-
- 94% {
- transform: translate(0.5px, 0.5px) rotate(-0.5deg);
- }
-
- 96% {
- transform: translate(2.5px, -0.5px) rotate(-0.5deg);
- }
-
- 98% {
- transform: translate(-1.5px, -1.5px) rotate(-0.5deg);
- }
-
- 0%, 100% {
- transform: translate(0, 0) rotate(0);
- }
-}
-@font-face {
- font-family: 'Flat-UI-Icons';
- src: url('flat-ui-icons-regular.eot');
- src: url('flat-ui-icons-regular.eot?#iefix') format('embedded-opentype'), url('flat-ui-icons-regular.woff') format('woff'), url('flat-ui-icons-regular.ttf') format('truetype'), url('flat-ui-icons-regular.svg#flat-ui-icons-regular') format('svg');
-}
-[class^="fui-"],
-[class*="fui-"] {
- font-family: 'Flat-UI-Icons';
- speak: none;
- font-style: normal;
- font-weight: normal;
- font-variant: normal;
- text-transform: none;
- -webkit-font-smoothing: antialiased;
- -moz-osx-font-smoothing: grayscale;
-}
-.fui-cross:before {
- content: "\e609";
-}
-.fui-info-circle:before {
- content: "\e60f";
-}
-.fui-photo:before {
- content: "\e62a";
-}
-.fui-eye:before {
- content: "\e62c";
-}
-.fui-chat:before {
- content: "\e62d";
-}
-.fui-home:before {
- content: "\e62e";
-}
-.fui-user:before {
- content: "\e631";
-}
\ No newline at end of file
diff --git a/spaces/hdhzk/bingo/src/components/ui/badge.tsx b/spaces/hdhzk/bingo/src/components/ui/badge.tsx
deleted file mode 100644
index d9a84b394090e5b4b3bd34f6135b9a2f2ead0aa2..0000000000000000000000000000000000000000
--- a/spaces/hdhzk/bingo/src/components/ui/badge.tsx
+++ /dev/null
@@ -1,36 +0,0 @@
-import * as React from 'react'
-import { cva, type VariantProps } from 'class-variance-authority'
-
-import { cn } from '@/lib/utils'
-
-const badgeVariants = cva(
- 'inline-flex items-center rounded-full border px-2.5 py-0.5 text-xs font-semibold transition-colors focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2',
- {
- variants: {
- variant: {
- default:
- 'border-transparent bg-primary text-primary-foreground hover:bg-primary/80',
- secondary:
- 'border-transparent bg-secondary text-secondary-foreground hover:bg-secondary/80',
- destructive:
- 'border-transparent bg-destructive text-destructive-foreground hover:bg-destructive/80',
- outline: 'text-foreground'
- }
- },
- defaultVariants: {
- variant: 'default'
- }
- }
-)
-
-export interface BadgeProps
- extends React.HTMLAttributes,
- VariantProps {}
-
-function Badge({ className, variant, ...props }: BadgeProps) {
- return (
-
- )
-}
-
-export { Badge, badgeVariants }
diff --git a/spaces/hf-task-exploration/ExploreACMnaacl/data_measurements_clusters/__init__.py b/spaces/hf-task-exploration/ExploreACMnaacl/data_measurements_clusters/__init__.py
deleted file mode 100644
index f0c22444d25da5eee6617272ba57c46773a5617c..0000000000000000000000000000000000000000
--- a/spaces/hf-task-exploration/ExploreACMnaacl/data_measurements_clusters/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .clustering import Clustering
diff --git a/spaces/huaiji3y/bingo-Public/src/components/ui/tooltip.tsx b/spaces/huaiji3y/bingo-Public/src/components/ui/tooltip.tsx
deleted file mode 100644
index af1d48beb90dd5ae311796539843700871052cae..0000000000000000000000000000000000000000
--- a/spaces/huaiji3y/bingo-Public/src/components/ui/tooltip.tsx
+++ /dev/null
@@ -1,30 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as TooltipPrimitive from '@radix-ui/react-tooltip'
-
-import { cn } from '@/lib/utils'
-
-const TooltipProvider = TooltipPrimitive.Provider
-
-const Tooltip = TooltipPrimitive.Root
-
-const TooltipTrigger = TooltipPrimitive.Trigger
-
-const TooltipContent = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef