Gothic 3: Forsaken Gods Enhanced Edition is a standalone game, which means you do not need to have Gothic 3 installed to play it. However, it is also an expansion for Gothic 3, which means it continues the story and gameplay of Gothic 3.
-Gothic 3: Forsaken Gods Enhanced Edition is a relatively short game compared to other open-world RPGs. It can take you around 10-15 hours to complete all the main quests and side quests in the game. However, you can spend more time exploring, fighting, looting, and crafting in the game if you want.
-Gothic 3: Forsaken Gods Enhanced Edition is not compatible with most mods and patches made for Gothic 3. have different files and scripts. However, you can use some mods and patches that are specifically made for Gothic 3: Forsaken Gods Enhanced Edition. You can find them on websites such as World of Gothic or Nexus Mods.
-Gothic 3: Forsaken Gods Enhanced Edition is a more stable and polished game than the original expansion, but it still has some bugs and issues that can affect your gameplay. Some of the common problems are crashes, freezes, stuttering, low FPS, graphical glitches, sound errors, and save game corruption. To fix these problems, you can try the following solutions:
--
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- Update your drivers and DirectX to the latest versions. -
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- Verify the integrity of your game files on Steam or reinstall the game if necessary. -
- Apply the latest official patch or community patch for the game. -
- Use a save game cleaner or editor to fix corrupted save files. -
- Check online forums or guides for specific solutions to specific problems. -
Gothic 3: Forsaken Gods Enhanced Edition is a challenging game that requires you to use your skills, strategy, and resources wisely. Here are some tips and tricks that can help you survive and thrive in the game:
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- Save your game often and in different slots, especially before entering a new area or starting a new quest. -
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Down Coreldraw X5 Full Crack: A Guide to Download and Install the Graphics Suite
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\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Clash Royale 3D A Fan-Made Game You Can Download and Play Now.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Clash Royale 3D A Fan-Made Game You Can Download and Play Now.md deleted file mode 100644 index b43905bf69fad3fc0e6e9f8a66010908269eb6db..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Clash Royale 3D A Fan-Made Game You Can Download and Play Now.md +++ /dev/null @@ -1,141 +0,0 @@ - -
Clash Royale 3D Download: How to Play Your Favorite Game in a New Dimension
-If you are a fan of Clash Royale, the popular mobile game that combines card collecting, tower defense, and real-time strategy, you might be wondering if there is a way to play it in a more immersive and realistic way. Well, you are in luck, because there is a fan-made project that allows you to play Clash Royale in 3D! In this article, we will tell you everything you need to know about Clash Royale 3D, how to download and install it, and how to enjoy it to the fullest. So, get ready to experience your favorite game in a new dimension!
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What is Clash Royale 3D?
-Clash Royale 3D is a fan-made project that brings Clash Royale to life in 3D. It is not an official game by Supercell, the developer of Clash Royale, but rather a tribute by some passionate fans who wanted to create something unique and amazing. Clash Royale 3D is not a simple port or remake of the original game, but rather a new way to play it with enhanced graphics, animations, sounds, and gameplay.
-A fan-made project that brings Clash Royale to life in 3D
-Clash Royale 3D is created by using Sketchfab, a platform that allows anyone to create, share, and discover 3D models online. Sketchfab has a large community of artists and enthusiasts who create and upload various 3D models, including those based on popular games, movies, characters, and more. Some of these models are inspired by Clash Royale, such as the characters, cards, towers, arenas, and effects. By using Sketchfab's viewer and API, the developers of Clash Royale 3D were able to combine these models into a playable game that runs on your browser.
-The features and benefits of playing Clash Royale 3D
-Clash Royale 3D has many features and benefits that make it worth playing. Here are some of them:
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- You can play Clash Royale in a more realistic and immersive way. You can see your cards come to life in 3D, watch them move and attack on the battlefield, and hear them make sounds and voices. You can also zoom in and out, rotate the camera, and change the perspective to get a better view of the action. -
- You can explore different arenas and environments in 3D. You can see the details and textures of each arena, such as the grass, rocks, trees, buildings, flags, and more. You can also see the weather effects, such as rain, snow, fog, and night. You can even interact with some elements of the arena, such as breaking barrels or opening chests. -
- You can enjoy the same gameplay and mechanics as the original game. You can still collect cards, build decks, join clans, chat with other players, participate in events, earn rewards, and more. You can also play against other players online or against bots offline. -
- You can customize your game settings and preferences. You can adjust the quality, resolution, sound, and performance of the game. You can also choose the language, theme, and mode of the game. You can even enable or disable some features, such as shadows, reflections, particles, and animations. -
How to download and install Clash Royale 3D?
-Clash Royale 3D is not available on the official app stores, such as Google Play or Apple Store, because it is not an official game by Supercell. However, you can still download and install it easily by following these steps:
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The requirements and steps for downloading Clash Royale 3D
-Before you download Clash Royale 3D, you need to make sure that your device meets the minimum requirements for running the game. These are:
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- A device that supports WebGL, which is a technology that enables 3D graphics on the web. Most modern browsers and devices support WebGL, but you can check if yours does by visiting this link: [WebGL Report]. -
- A stable internet connection, preferably with a high speed and low latency. This is because Clash Royale 3D is an online game that requires constant communication with the server and other players. -
- A sufficient amount of storage space on your device, depending on the size of the game files. The current version of Clash Royale 3D is about 300 MB, but it may vary depending on the updates and patches. -
Once you have verified that your device meets the requirements, you can proceed to download Clash Royale 3D by following these steps:
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- Visit the official website of Clash Royale 3D at [clashroyale3d.com]. -
- Click on the "Download" button and choose the version that matches your device (Android or iOS). -
- Wait for the download to finish and then open the downloaded file. -
- Follow the instructions on the screen to install Clash Royale 3D on your device. -
- Launch Clash Royale 3D and enjoy! -
The tips and tricks for playing Clash Royale 3D smoothly
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- Make sure that your device is fully charged or plugged in before playing Clash Royale 3D. The game can drain your battery quickly because of its high graphics and performance demands. -
- Close any other apps or programs that are running in the background while playing Clash Royale 3D. This can free up some memory and CPU resources for the game and prevent any lag or crashes. -
- Adjust your game settings according to your device's capabilities and preferences. You can lower the quality, resolution, sound, and performance of the game if you experience any slowness or stuttering. You can also enable or disable some features, such as shadows, reflections, particles, and animations, to improve the game's performance. -
- Use a reliable and secure network connection when playing Clash Royale 3D online. Avoid using public or unsecured Wi-Fi networks that may expose your data or interfere with your gameplay. You can also use a VPN service to protect your privacy and bypass any geo-restrictions or firewalls. -
- Update your game regularly to get the latest features, fixes, and improvements. You can check for updates by visiting the official website of Clash Royale 3D or by opening the game's settings menu. -
How to enjoy Clash Royale 3D to the fullest?
-Clash Royale 3D is not only a game, but also a way to express your creativity and passion for Clash Royale. There are many ways to enjoy Clash Royale 3D to the fullest, such as:
-The best 3D models and arenas to explore in Clash Royale 3D
-One of the main attractions of Clash Royale 3D is the variety and quality of the 3D models and arenas that you can explore. You can see your favorite characters, cards, towers, and effects in a new light, with more details, colors, and animations. You can also discover new and unique models and arenas that are not available in the original game, such as custom-made ones by other fans or artists. Here are some of the best 3D models and arenas that you can explore in Clash Royale 3D:
-| Model/Arena | -Description | -
|---|---|
| King Tower | -The King Tower is the main tower that you have to protect in Clash Royale. In Clash Royale 3D, you can see the King Tower in 3D, with its crown, cannons, flags, and windows. You can also see the King himself, sitting on his throne and cheering or taunting you. | -
| Princess | -The Princess is one of the most popular and iconic cards in Clash Royale. She is a legendary card that can shoot arrows from a long distance. In Clash Royale 3D, you can see the Princess in 3D, with her dress, hair, bow, and quiver. You can also see her facial expressions and hear her voice. | -
| Fireball | -The Fireball is one of the most powerful and versatile spells in Clash Royale. It is a rare card that can deal high damage to a large area. In Clash Royale 3D, you can see the Fireball in 3D, with its flames, sparks, and smoke. You can also feel its impact and hear its sound. | -
| Hog Mountain | -Hog Mountain is one of the most fun and colorful arenas in Clash Royale. It is the arena for players who have reached 3000 trophies. In Clash Royale 3D, you can see Hog Mountain in 3D, with its hills, bridges, balloons, hogs, and fireworks. You can also interact with some of the elements of the arena, such as popping balloons or riding hogs. | -
| Clashmas Village | -Clashmas Village is one of the most festive and special arenas in Clash Royale. It is a seasonal arena that appears during the Christmas period. In Clash Royale 3D, you can see Clashmas Village in 3D, with its snow, trees, lights, presents, and snowmen. You can also enjoy the Christmas music and atmosphere. | -
The challenges and rewards of playing Clash Royale 3D
-Clash Royale 3D is not only a game to admire, but also a game to challenge yourself and improve your skills. There are many challenges and rewards that you can face and earn while playing Clash Royale 3D, such as:
--
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- You can compete with other players online or offline in different modes and formats. You can play classic matches or tournaments with standard rules or custom rules. You can also play special events or modes that have different objectives or conditions. -
- You can collect cards and build decks that suit your style and strategy. You can unlock new cards by opening chests or buying them from the shop. You can also upgrade your cards by using gold or gems. You can create different decks for different situations or preferences. -
- You can join clans and chat with other players who share your passion for Clash Royale. You can exchange cards, tips, and ideas with your clanmates. You can also participate in clan wars or clan games to earn rewards and glory for your clan. -
- You can earn trophies and climb the ladder of rankings. You can gain trophies by winning matches or lose trophies by losing matches. You can also reach new arenas or leagues that have different rewards and challenges. -
- You can achieve goals and milestones that show your progress and achievements. You can complete quests or missions that have specific tasks or requirements. You can also earn badges or stars that indicate your level or skill. -
Conclusion
-Clash Royale 3D is a fan-made project that allows you to play Clash Royale in 3D. It is not an official game by Supercell, but rather a tribute by by visiting this link: [WebGL Report]. However, some devices and platforms may have better performance and compatibility than others, depending on their specifications and features. For example, Android devices may run Clash Royale 3D better than iOS devices, and Chrome browsers may run Clash Royale 3D better than Safari browsers.
-Q4: Is Clash Royale 3D updated regularly?
-A4: Clash Royale 3D is updated regularly. The developers of Clash Royale 3D are constantly working on improving the game and adding new features, fixes, and improvements. You can check for updates by visiting the official website of Clash Royale 3D or by opening the game's settings menu. You can also follow the developers on their social media accounts or join their Discord server to get the latest news and updates about Clash Royale 3D.
-Q5: How can I support the developers of Clash Royale 3D?
-A5: You can support the developers of Clash Royale 3D by doing the following things:
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- Share your feedback and suggestions with them. You can contact them via email, social media, or Discord. You can also rate and review the game on the website or app store. -
- Spread the word and invite your friends to play Clash Royale 3D. You can share the game's link or screenshots on your social media accounts or chat apps. You can also challenge your friends to play with you online or offline. -
- Donate or contribute to the game's development. You can donate money or resources to the developers via PayPal or Patreon. You can also contribute your skills or talents to the game, such as creating 3D models, graphics, sounds, or codes. -
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\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/CricHD App APK Live Cricket Streaming at Your Fingertips.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/CricHD App APK Live Cricket Streaming at Your Fingertips.md deleted file mode 100644 index d47b9f7506c5e3fbbdb10f2cf713ba89bfdc5d5d..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/CricHD App APK Live Cricket Streaming at Your Fingertips.md +++ /dev/null @@ -1,128 +0,0 @@ -
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-How to Download Live Cricket Streaming App APK
-Step 1: Find a reliable source for the APK file
-An APK file is an Android package file that contains all the necessary files and data for an application to run on your phone. You can find many sources for live cricket streaming app APK files online, but not all of them are safe and trustworthy. Some of them may contain viruses, malware, or spyware that can harm your phone or steal your personal information. Therefore, you need to be careful and choose a reliable source for the APK file.
-One way to find a reliable source is to check the reviews and ratings of other users who have downloaded and used the app. You can also look for official websites or social media pages of the app developers or publishers. You can also ask for recommendations from your friends or fellow cricket fans who have used live cricket streaming apps before.
-Once you have found a reliable source for the APK file, you need to download it to your phone. You can do this by clicking on the download link or scanning the QR code provided by the source. The download process may take a few minutes depending on the size of the file and the speed of your internet connection.
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Step 2: Enable unknown sources on your phone settings
-Before you can install the APK file on your phone, you need to enable unknown sources on your phone settings. This is because most live cricket streaming apps are not available on the official Google Play Store or Apple App Store, and your phone may block the installation of apps from unknown sources by default.
-To enable unknown sources on your phone settings, you need to follow these steps:
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- Go to your phone settings and look for security or privacy options. -
- Find the option that says unknown sources or allow installation of apps from unknown sources and toggle it on. -
- You may see a warning message that says installing apps from unknown sources may harm your device or data. Tap on OK or Continue to proceed. -
Once you have enabled unknown sources on your phone settings, you are ready to install the APK file on your phone.
-Step 3: Install the APK file on your phone
-To install the APK file on your phone, you need to follow these steps:
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- Locate the APK file on your phone storage. You can use a file manager app or search for it in your downloads folder. -
- Tap on the APK file and you will see a prompt that asks you to confirm the installation. Tap on Install or Next to start the installation process. -
- The installation process may take a few seconds or minutes depending on the size of the app and the performance of your phone. -
- Once the installation is complete, you will see a message that says App installed or Done. Tap on Open or Launch to open the app or tap on Done to exit the installer. -
Congratulations! You have successfully downloaded and installed live cricket streaming app APK on your phone. Now you can start using it to watch live cricket matches on your phone.
-How to Use Live Cricket Streaming App APK
-Step 1: Open the app and sign up or log in
-Once you have installed live cricket streaming app APK on your phone, you need to open it and sign up or log in. Some apps may require you to create an account or provide some personal information before you can use them. Others may allow you to use them without signing up or logging in. You can choose the option that suits you best.
-To sign up or log in, you need to follow these steps:
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- Open the app and look for the sign up or log in option. It may be on the home screen, the menu, or the settings of the app. -
- If you choose to sign up, you need to provide some basic information, such as your name, email address, password, country, etc. You may also need to verify your email address or phone number by entering a code sent to you by the app. -
- If you choose to log in, you need to enter your email address and password that you used to sign up. You may also need to enter a captcha code or a verification code sent to you by the app. -
- Once you have signed up or logged in, you will see your profile or dashboard where you can access different features and options of the app. -
Step 2: Choose a live match or a replay from the list
-Now that you have signed up or logged in, you can choose a live match or a replay from the list of available matches. You can find the list of matches on the home screen, the menu, or the categories of the app. You can also use the search function or filter function to find a specific match or league that you want to watch.
-To choose a live match or a replay from the list, you need to follow these steps:
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- Browse through the list of matches and look for the one that interests you. You can see some information about each match, such as the teams, the date, the time, the venue, etc. -
- Tap on the match that you want to watch and you will see more details about it, such as the score, the overs, the wickets, the run rate, etc. -
- If the match is live, you will see a button that says Watch Live or Live Stream. Tap on it and you will be directed to the live streaming page where you can watch the match in real time. -
- If the match is not live, you will see a button that says Watch Replay or Replay Stream. Tap on it and you will be directed to the replay streaming page where you can watch the match from the beginning or from any point that you want. -
Step 3: Enjoy watching cricket on your phone
-Now that you have chosen a live match or a replay from the list, you can enjoy watching cricket on your phone. You can adjust the video quality, the volume, the brightness, and the orientation of your phone according to your preference. You can also pause, resume, rewind, fast forward, or skip the match as you wish.
-While watching cricket on your phone, you can also access some interactive features that enhance your viewing experience. For example, you can:
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- Check the live scores, stats, highlights, replays, polls, chat, and more on the app. -
- Share your thoughts and opinions with other cricket fans on social media platforms, such as Facebook, Twitter, Instagram, etc. -
- Cast your phone screen to your TV or laptop for a bigger view using Chromecast, Airplay, Miracast, or other devices. -
- Use headphones or earphones for a better sound quality and avoid disturbing others. -
With live cricket streaming app download apk, you can watch cricket matches on your phone anytime and anywhere. You don't need to worry about missing any action or excitement. You can stay updated and entertained with live cricket streaming app download apk.
-Comparison of Different Live Cricket Streaming Apps APK
-There are many live cricket streaming apps APK available online, but not all of them are equally good. Some of them may have better features, quality, and performance than others. Some of them may also have more matches, leagues, and options than others. Therefore, it is important to compare different live cricket streaming apps APK before choosing one.
-Here are some of the popular and reliable live cricket streaming apps APK that you can try out:
-ICC.tv
-ICC.tv is the official app of the International Cricket Council (ICC), the governing body of world cricket. It offers live streaming of all ICC events and tournaments, such as the ICC World Cup, the ICC Champions Trophy, the ICC World Test Championship, the ICC Women's World Cup, and more. It also offers live streaming of other domestic and international matches from various countries and regions.
-Some of the features of ICC.tv are:
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- It has high-definition quality and smooth streaming without any buffering or lagging. -
- It has multiple languages and commentary options for different matches. -
- It has interactive features such as live scores, stats, highlights, replays, polls, chat, and more on the app. -
- It is free to download and use, but it may require registration or verification for some matches. -
You can download ICC.tv APK from its official website or from other sources online.
-CricHD
-CricHD is one of the most popular and widely used live cricket streaming apps APK. It offers live streaming of all kinds of cricket matches, such as Test, ODI, T20, IPL, BBL, PSL, CPL, and more. It also offers live streaming of other sports, such as football, basketball, tennis, hockey, rugby, and more.
-Some of the features of CricHD are:
--
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- It has high-definition quality and smooth streaming without any buffering or lagging. -
- It has multiple languages and commentary options for different matches. -
- It has interactive features such as live scores, stats, highlights, replays, polls, chat, and more on the app. -
- It is free to download and use, but it may show some ads or pop-ups during the streaming. -
You can download CricHD APK from its official website or from other sources online.
-Other options
-There are many other live cricket streaming apps APK that you can try out, such as:
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- Hotstar: It is a popular streaming platform that offers live cricket matches from India and other countries. It also offers other entertainment content, such as movies, TV shows, news, etc. It is free to download and use, but it may require a subscription for some content. -
- SonyLIV: It is another popular streaming platform that offers live cricket matches from India and other countries. It also offers other sports and entertainment content, such as football, WWE, comedy, etc. It is free to download and use, but it may require a subscription for some content. -
- ThopTV: It is a third-party streaming app that offers live cricket matches from various sources and channels. It also offers other content, such as movies, TV shows, music, etc. It is free to download and use, but it may not be safe or legal to use. -
You can compare these and other live cricket streaming apps APK based on your preferences and needs. You can also check the reviews and ratings of other users who have used them before. You can also try out different apps and see which one works best for you.
-Conclusion
-Live cricket streaming app download apk is a great way to watch live cricket matches on your phone. You don't need to have a TV or a cable subscription to enjoy watching cricket. You just need to have a stable internet connection and enough storage space on your phone. You can watch live cricket matches from different leagues, tournaments, countries, regions, languages, and commentary options. You can watch live cricket matches in high-definition quality and smooth streaming without any buffering or lagging. You can watch live cricket matches with interactive features, such as live scores, stats, highlights, replays, polls, chat, and more.
-To watch live cricket matches on your phone, you need to download and install live cricket streaming app APK on your phone. You need to find a reliable source for the APK file, enable unknown sources on your phone settings, and install the APK file on your phone. Then you need to open the app and sign up or log in, choose a live match or a replay from the list, and enjoy watching cricket on your phone.
-You can also compare different live cricket streaming apps APK based on their features, quality, performance, and options. You can try out some of the popular and reliable apps, such as ICC.tv, CricHD, Hotstar, SonyLIV, ThopTV, and more. You can also check the reviews and ratings of other users who have used them before. You can also try out different apps and see which one works best for you.
-Live cricket streaming app download apk is a must-have for any cricket fan who wants to watch live cricket matches on their phone. It is easy to use, convenient, and fun. It is the best way to stay updated and entertained with live cricket.
-So what are you waiting for? Download live cricket streaming app APK now and start watching live cricket matches on your phone!
-FAQs
-Here are some of the frequently asked questions about live cricket streaming app download apk:
-Q1: Is live cricket streaming app download apk safe and legal?
-A1: Live cricket streaming app download apk is safe and legal as long as you download it from a reliable source and use it for personal and non-commercial purposes. However, some apps may not have the rights or permissions to stream some matches or content, and they may violate the intellectual property or privacy rights of the owners or providers. Therefore, you should be careful and responsible when using live cricket streaming app download apk.
-Q2: How much data does live cricket streaming app download apk consume?
-A2: Live cricket streaming app download apk consumes data depending on the video quality, the duration, and the frequency of your streaming. Generally, the higher the video quality, the more data it consumes. For example, streaming a match in HD quality may consume about 1 GB of data per hour, while streaming it in SD quality may consume about 300 MB of data per hour. Therefore, you should monitor your data usage and choose a suitable video quality according to your data plan.
-Q3: What are the best live cricket streaming apps for Android and iOS?
-A3: There are many live cricket streaming apps for Android and iOS devices, but some of them may be better than others in terms of features, quality, performance, and options. Some of the best live cricket streaming apps for Android and iOS devices are: - ICC.tv: It is the official app of the International Cricket Council (ICC), the governing body of world cricket. It offers live streaming of all ICC events and tournaments, as well as other domestic and international matches from various countries and regions. It has high-definition quality and smooth streaming without any buffering or lagging. It has multiple languages and commentary options for different matches. It has interactive features such as live scores, stats, highlights, replays, polls, chat, and more on the app. It is free to download and use, but it may require registration or verification for some matches. - CricHD: It is one of the most popular and widely used live cricket streaming apps. It offers live streaming of all kinds of cricket matches, such as Test, ODI, T20, IPL, BBL, PSL, CPL, and more. It also offers live streaming of other sports, such as football, basketball, tennis, hockey, rugby, and more. It has high-definition quality and smooth streaming without any buffering or lagging. It has multiple languages and commentary options for different matches. It has interactive features such as live scores, stats, highlights, replays, polls, chat, and more on the app. It is free to download and use, but it may show some ads or pop-ups during the streaming. - Hotstar: It is a popular streaming platform that offers live cricket matches from India and other countries. It also offers other entertainment content, such as movies, TV shows, news, etc. It has high-definition quality and smooth streaming without any buffering or lagging. It has multiple languages and commentary options for different matches. It has interactive features such as live scores, stats, highlights, replays, polls, chat, and more on the app. It is free to download and use, but it may require a subscription for some content. - SonyLIV: It is another popular streaming platform that offers live cricket matches from India and other countries. It also offers other sports and entertainment content, such as football, WWE, comedy, etc. It has high-definition quality and smooth streaming without any buffering or lagging. It has multiple languages and commentary options for different matches. It has interactive features such as live scores, stats, highlights, replays, polls, chat, and more on the app. It is free to download and use, but it may require a subscription for some content. These are some of the best live cricket streaming apps for Android and iOS devices that you can try out. You can also check out other apps that may suit your preferences and needs.
-Q4: How can I watch live cricket streaming on my TV or laptop?
-A4: If you want to watch live cricket streaming on your TV or laptop instead of your phone, you have a few options to do so. For example, you can: - Use a device that supports casting or mirroring, such as Chromecast, Airplay, Miracast, or others. You can connect your phone to your TV or laptop using these devices and stream the live cricket match from your phone to your TV or laptop screen. You may need to download and install an app or a software that supports casting or mirroring on your phone and your TV or laptop. - Use a device that supports HDMI, such as a cable, an adapter, or a dongle. You can connect your phone to your TV or laptop using these devices and stream the live cricket match from your phone to your TV or laptop screen. You may need to adjust the settings on your phone and your TV or laptop to enable HDMI output and input. - Use a device that supports USB, such as a cable, an adapter, or a dongle. You can connect your phone to your TV or laptop using these devices and stream the live cricket match from your phone to your TV or laptop screen. You may need to enable USB debugging on your phone and install a driver or a software on your TV or laptop to recognize your phone. These are some of the ways you can watch live cricket streaming on your TV or laptop using live cricket streaming app download apk. You can also check out other methods that may work for you.
-Q5: How can I improve the quality and speed of live cricket streaming?
-A5: If you want to improve the quality and speed of live cricket streaming, you need to consider some factors that may affect them, such as: - Your internet connection: You need to have a stable and fast internet connection to stream live cricket matches without any buffering or lagging. You can check your internet speed and signal strength using an app or a website. You can also use a Wi-Fi network instead of a mobile data network if possible. You can also close any other apps or programs that may consume bandwidth on your phone or your router. - Your phone storage: You need to have enough storage space on your phone to download and install live cricket streaming app APK and store the data and cache of the app. You can check your phone storage using an app or a setting. You can also delete any unnecessary files or apps that may take up space on your phone. - Your phone performance: You need to have a good phone performance to run live cricket streaming app APK smoothly and efficiently. You can check your phone performance using an app or a setting. You can also clear any background processes or tasks that may slow down your phone. You can also update your phone software and firmware if available. These are some of the factors that may affect the quality and speed of live cricket streaming. You can try to optimize them as much as possible to improve your streaming experience.
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Free Download Video Game Player The Ultimate Guide to Playing Any Game on Any Device.md b/spaces/1phancelerku/anime-remove-background/Free Download Video Game Player The Ultimate Guide to Playing Any Game on Any Device.md deleted file mode 100644 index 042de4d5efed3691f5e5403c11fb827bf8d63087..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Free Download Video Game Player The Ultimate Guide to Playing Any Game on Any Device.md +++ /dev/null @@ -1,107 +0,0 @@ - -
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\ No newline at end of file diff --git a/spaces/1toTree/lora_test/ppdiffusers/schedulers/preconfig/preconfig_scheduling_lms_discrete.py b/spaces/1toTree/lora_test/ppdiffusers/schedulers/preconfig/preconfig_scheduling_lms_discrete.py deleted file mode 100644 index 13a567113246ecd0646d297c1bd9fd86dd7ee2bf..0000000000000000000000000000000000000000 --- a/spaces/1toTree/lora_test/ppdiffusers/schedulers/preconfig/preconfig_scheduling_lms_discrete.py +++ /dev/null @@ -1,299 +0,0 @@ -# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. -# Copyright 2022 Katherine Crowson and The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import warnings -from dataclasses import dataclass -from typing import List, Optional, Tuple, Union - -import numpy as np -import paddle -from scipy import integrate - -from ...configuration_utils import ConfigMixin, register_to_config -from ...utils import _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS, BaseOutput -from ..scheduling_utils import SchedulerMixin - - -@dataclass -# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete -class PreconfigLMSDiscreteSchedulerOutput(BaseOutput): - """ - Output class for the scheduler's step function output. - - Args: - prev_sample (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)` for images): - Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the - denoising loop. - pred_original_sample (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)` for images): - The predicted denoised sample (x_{0}) based on the model output from the current timestep. - `pred_original_sample` can be used to preview progress or for guidance. - """ - - prev_sample: paddle.Tensor - pred_original_sample: Optional[paddle.Tensor] = None - - -class PreconfigLMSDiscreteScheduler(SchedulerMixin, ConfigMixin): - """ - Linear Multistep Scheduler for discrete beta schedules. Based on the original k-diffusion implementation by - Katherine Crowson: - https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181 - - [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` - function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. - [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and - [`~SchedulerMixin.from_pretrained`] functions. - - Args: - num_train_timesteps (`int`): number of diffusion steps used to train the model. - beta_start (`float`): the starting `beta` value of inference. - beta_end (`float`): the final `beta` value. - beta_schedule (`str`): - the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from - `linear` or `scaled_linear`. - trained_betas (`np.ndarray`, optional): - option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. - prediction_type (`str`, default `epsilon`, optional): - prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion - process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 - https://imagen.research.google/video/paper.pdf) - """ - - _compatibles = _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS.copy() - order = 1 - - @register_to_config - def __init__( - self, - num_train_timesteps: int = 1000, - beta_start: float = 0.0001, - beta_end: float = 0.02, - beta_schedule: str = "linear", - trained_betas: Optional[Union[np.ndarray, List[float]]] = None, - prediction_type: str = "epsilon", - preconfig=True, - ): - if trained_betas is not None: - self.betas = paddle.to_tensor(trained_betas, dtype="float32") - elif beta_schedule == "linear": - self.betas = paddle.linspace(beta_start, beta_end, num_train_timesteps, dtype="float32") - elif beta_schedule == "scaled_linear": - # this schedule is very specific to the latent diffusion model. - self.betas = paddle.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype="float32") ** 2 - else: - raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") - - self.alphas = 1.0 - self.betas - self.alphas_cumprod = paddle.cumprod(self.alphas, 0) - - sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) - sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32) - self.sigmas = paddle.to_tensor(sigmas) - - # standard deviation of the initial noise distribution - self.init_noise_sigma = self.sigmas.max() - - # setable values - self.num_inference_steps = None - timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=float)[::-1].copy() - self.timesteps = paddle.to_tensor(timesteps, dtype="float32") - self.derivatives = [] - self.is_scale_input_called = False - self.preconfig = preconfig - - def scale_model_input( - self, sample: paddle.Tensor, timestep: Union[float, paddle.Tensor], **kwargs - ) -> paddle.Tensor: - """ - Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the K-LMS algorithm. - - Args: - sample (`paddle.Tensor`): input sample - timestep (`float` or `paddle.Tensor`): the current timestep in the diffusion chain - - Returns: - `paddle.Tensor`: scaled input sample - """ - if kwargs.get("step_index") is not None: - step_index = kwargs["step_index"] - else: - step_index = (self.timesteps == timestep).nonzero().item() - self.is_scale_input_called = True - if not self.preconfig: - sigma = self.sigmas[step_index] - sample = sample / ((sigma**2 + 1) ** 0.5) - return sample - else: - return sample * self.latent_scales[step_index] - - def get_lms_coefficient(self, order, t, current_order): - """ - Compute a linear multistep coefficient. - - Args: - order (TODO): - t (TODO): - current_order (TODO): - """ - - def lms_derivative(tau): - prod = 1.0 - for k in range(order): - if current_order == k: - continue - prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k]) - return prod - - integrated_coeff = integrate.quad(lms_derivative, self.sigmas[t], self.sigmas[t + 1], epsrel=1e-4)[0] - - return integrated_coeff - - def set_timesteps(self, num_inference_steps: int, preconfig_order: int = 4): - """ - Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. - - Args: - num_inference_steps (`int`): - the number of diffusion steps used when generating samples with a pre-trained model. - """ - self.num_inference_steps = num_inference_steps - - timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy() - sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) - sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas) - sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32) - self.sigmas = paddle.to_tensor(sigmas) - self.timesteps = paddle.to_tensor(timesteps, dtype="float32") - - self.derivatives = [] - if self.preconfig: - self.order = preconfig_order - self.lms_coeffs = [] - self.latent_scales = [1.0 / ((sigma**2 + 1) ** 0.5) for sigma in self.sigmas] - for step_index in range(self.num_inference_steps): - order = min(step_index + 1, preconfig_order) - self.lms_coeffs.append( - [self.get_lms_coefficient(order, step_index, curr_order) for curr_order in range(order)] - ) - - def step( - self, - model_output: paddle.Tensor, - timestep: Union[float, paddle.Tensor], - sample: paddle.Tensor, - order: int = 4, - return_dict: bool = True, - **kwargs - ) -> Union[PreconfigLMSDiscreteSchedulerOutput, Tuple]: - """ - Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion - process from the learned model outputs (most often the predicted noise). - - Args: - model_output (`paddle.Tensor`): direct output from learned diffusion model. - timestep (`float`): current timestep in the diffusion chain. - sample (`paddle.Tensor`): - current instance of sample being created by diffusion process. - order: coefficient for multi-step inference. - return_dict (`bool`): option for returning tuple rather than PreconfigLMSDiscreteSchedulerOutput class - Args in kwargs: - step_index (`int`): - return_pred_original_sample (`bool`): option for return pred_original_sample - - Returns: - [`~schedulers.scheduling_utils.PreconfigLMSDiscreteSchedulerOutput`] or `tuple`: - [`~schedulers.scheduling_utils.PreconfigLMSDiscreteSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. - When returning a tuple, the first element is the sample tensor. - - """ - if not self.is_scale_input_called: - warnings.warn( - "The `scale_model_input` function should be called before `step` to ensure correct denoising. " - "See `StableDiffusionPipeline` for a usage example." - ) - if kwargs.get("return_pred_original_sample") is not None: - return_pred_original_sample = kwargs["return_pred_original_sample"] - else: - return_pred_original_sample = True - if kwargs.get("step_index") is not None: - step_index = kwargs["step_index"] - else: - step_index = (self.timesteps == timestep).nonzero().item() - if self.config.prediction_type == "epsilon" and not return_pred_original_sample: - # if pred_original_sample is no need - self.derivatives.append(model_output) - pred_original_sample = None - else: - sigma = self.sigmas[step_index] - # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise - if self.config.prediction_type == "epsilon": - pred_original_sample = sample - sigma * model_output - elif self.config.prediction_type == "v_prediction": - # * c_out + input * c_skip - pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1)) - else: - raise ValueError( - f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" - ) - # 2. Convert to an ODE derivative - derivative = (sample - pred_original_sample) / sigma - self.derivatives.append(derivative) - - if len(self.derivatives) > order: - self.derivatives.pop(0) - - if not self.preconfig: - # 3. If not preconfiged, compute linear multistep coefficients. - order = min(step_index + 1, order) - lms_coeffs = [self.get_lms_coefficient(order, step_index, curr_order) for curr_order in range(order)] - # 4. Compute previous sample based on the derivatives path - prev_sample = sample + sum( - coeff * derivative for coeff, derivative in zip(lms_coeffs, reversed(self.derivatives)) - ) - else: - # 3. If preconfiged, direct compute previous sample based on the derivatives path - prev_sample = sample + sum( - coeff * derivative - for coeff, derivative in zip(self.lms_coeffs[step_index], reversed(self.derivatives)) - ) - - if not return_dict: - if not return_pred_original_sample: - return (prev_sample,) - else: - return (prev_sample, pred_original_sample) - - return PreconfigLMSDiscreteSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample) - - def add_noise( - self, - original_samples: paddle.Tensor, - noise: paddle.Tensor, - timesteps: paddle.Tensor, - ) -> paddle.Tensor: - # Make sure sigmas and timesteps have the same dtype as original_samples - sigmas = self.sigmas.cast(original_samples.dtype) - schedule_timesteps = self.timesteps - - step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps] - - sigma = sigmas[step_indices].flatten() - while len(sigma.shape) < len(original_samples.shape): - sigma = sigma.unsqueeze(-1) - - noisy_samples = original_samples + noise * sigma - return noisy_samples - - def __len__(self): - return self.config.num_train_timesteps diff --git a/spaces/ASJMO/freegpt/client/css/checkbox.css b/spaces/ASJMO/freegpt/client/css/checkbox.css deleted file mode 100644 index 94955b604ea3fab493a50d740fb29be1a8ef6cd3..0000000000000000000000000000000000000000 --- a/spaces/ASJMO/freegpt/client/css/checkbox.css +++ /dev/null @@ -1,55 +0,0 @@ -.checkbox input { - height: 0; - width: 0; - display: none; -} - -.checkbox span { - font-size: 0.875rem; - color: var(--colour-2); - margin-left: 4px; -} - -.checkbox label:after { - content: ""; - position: absolute; - top: 50%; - transform: translateY(-50%); - left: 5px; - width: 20px; - height: 20px; - background: var(--blur-border); - border-radius: 90px; - transition: 0.33s; -} - -.checkbox input + label:after, -.checkbox input:checked + label { - background: var(--colour-3); -} - -.checkbox input + label, -.checkbox input:checked + label:after { - background: var(--blur-border); -} - -.checkbox input:checked + label:after { - left: calc(100% - 5px - 20px); -} - -@media screen and (max-width: 990px) { - .checkbox label { - width: 25px; - height: 15px; - } - - .checkbox label:after { - left: 2px; - width: 10px; - height: 10px; - } - - .checkbox input:checked + label:after { - left: calc(100% - 2px - 10px); - } -} diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/admin/export/+server.ts b/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/admin/export/+server.ts deleted file mode 100644 index 2cdae1f2aa779316e5998907a2b0484be2847cfc..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/admin/export/+server.ts +++ /dev/null @@ -1,166 +0,0 @@ -import { - PARQUET_EXPORT_DATASET, - PARQUET_EXPORT_HF_TOKEN, - PARQUET_EXPORT_SECRET, -} from "$env/static/private"; -import { collections } from "$lib/server/database"; -import type { Message } from "$lib/types/Message"; -import { error } from "@sveltejs/kit"; -import { pathToFileURL } from "node:url"; -import { unlink } from "node:fs/promises"; -import { uploadFile } from "@huggingface/hub"; -import parquet from "parquetjs"; -import { z } from "zod"; - -// Triger like this: -// curl -X POST "http://localhost:5173/chat/admin/export" -H "Authorization: Bearer
:")
- environment.rule_params["code"] = cur_code
-
- from .code_api import execute_unit_tests
- feedback = execute_unit_tests(environment.rule_params["code"], eval(environment.rule_params["unit_tests"]))
-
- environment.rule_params["feedback"] = feedback
- selected[0].content = f":\n\n{cur_code}\n\n:\n{feedback}"
- f_dict = json.loads(feedback)
- if f_dict["is_passing"]:
- environment.rule_params["end_flag"] = True
-
- elif last_sender == "code_reviewer":
- code_review = selected[0].content
- cur_code = environment.rule_params["code"]
- selected[0].content = f":\n\n{cur_code}\n\n{code_review}"
- feedback = environment.rule_params["feedback"]
- f_dict = json.loads(feedback)
- if f_dict["is_passing"]:
- environment.rule_params["end_flag"] = True
-
- return selected
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/agentverse/memory_manipulator/plan.py b/spaces/AgentVerse/agentVerse/agentverse/memory_manipulator/plan.py
deleted file mode 100644
index 530190c0f9d233e4ceed3385fa5836b3214b8c4a..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse/memory_manipulator/plan.py
+++ /dev/null
@@ -1,79 +0,0 @@
-from __future__ import annotations
-
-from logging import getLogger
-from typing import List, TYPE_CHECKING
-
-from . import memory_manipulator_registry
-from .base import BaseMemoryManipulator
-from ..message import Message
-
-if TYPE_CHECKING:
- from agentverse.memory import VectorStoreMemory
- from agentverse.agents.reflection_agent import ReflectionAgent
-
-logger = getLogger(__file__)
-
-PLAN_PROMPT = """Now you are act for as an agent named ${agent_name} in a virtual world.
-You might need to performing reaction to the observation.
-Based on the following information:
-(1) The agent's description: ${role_description}
-(2) Current time is ${current_time}
-(3) Your history memory is ${chat_history}
-
-Now is ${current_time}. If all plans are expired, you have to plan for\
-the next time periods.
-Do you need to generate new plans?
-If yes, tell me the new plan, including the time period.
-If no, just tell me No."""
-
-
-@memory_manipulator_registry.register("plan")
-class Plan(BaseMemoryManipulator):
- """
- Memory manipulator for plan.
- """
- memory: VectorStoreMemory = None
- agent: ReflectionAgent = None # specify ReflectionAgent
- # later considering removing current_time to be more general
- # and then change to BaseAgent
- plan: List[str] = []
-
- def manipulate_memory(self) -> str:
- """
- Generate new plans
- """
- prompt = self._fill_prompt_template()
- result = self.agent.llm.generate_response(prompt).content
- result = result.strip('.')
- logger.info(f"{self.agent.name}'s new plan: {result}")
- if result == "No":
- return ""
- else:
- self.plan.append(result)
- plan_message = Message(
- content=result,
- sender=self.agent.name,
- receiver={self.agent.name})
- self.agent.memory.add_message([plan_message])
- return result
-
-
- def _fill_prompt_template(self) -> str:
- """Fill the placeholders in the prompt template
-
- In the conversation agent, three placeholders are supported:
- - ${agent_name}: the name of the agent
- - ${env_description}: the description of the environment
- - ${role_description}: the description of the role of the agent
- - ${chat_history}: the chat history of the agent
- """
- input_arguments = {
- "agent_name": self.agent.name,
- "role_description": self.agent.role_description,
- "chat_history": self.agent.memory.to_string(add_sender_prefix=True),
- "current_time": self.agent.current_time,
- }
- return PLAN_PROMPT.format(**input_arguments)
-
- def reset(self) -> None:
- pass
diff --git a/spaces/AhmedM20/Email_Marketing_Content_Generator/README.md b/spaces/AhmedM20/Email_Marketing_Content_Generator/README.md
deleted file mode 100644
index 040309416610a4f38056d6378ff0010cc565804c..0000000000000000000000000000000000000000
--- a/spaces/AhmedM20/Email_Marketing_Content_Generator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Email Marketing Geneartor
-emoji: 🏃
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.41.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AlexWang/lama/bin/paper_runfiles/blur_tests.sh b/spaces/AlexWang/lama/bin/paper_runfiles/blur_tests.sh
deleted file mode 100644
index 8f204a4c643d08935e5561ed27a286536643958d..0000000000000000000000000000000000000000
--- a/spaces/AlexWang/lama/bin/paper_runfiles/blur_tests.sh
+++ /dev/null
@@ -1,37 +0,0 @@
-##!/usr/bin/env bash
-#
-## !!! file set to make test_large_30k from the vanilla test_large: configs/test_large_30k.lst
-#
-## paths to data are valid for mml7
-#PLACES_ROOT="/data/inpainting/Places365"
-#OUT_DIR="/data/inpainting/paper_data/Places365_val_test"
-#
-#source "$(dirname $0)/env.sh"
-#
-#for datadir in test_large_30k # val_large
-#do
-# for conf in random_thin_256 random_medium_256 random_thick_256 random_thin_512 random_medium_512 random_thick_512
-# do
-# "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \
-# "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 8
-#
-# "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
-# done
-#
-# for conf in segm_256 segm_512
-# do
-# "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \
-# "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 2
-#
-# "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
-# done
-#done
-#
-#IN_DIR="/data/inpainting/paper_data/Places365_val_test/test_large_30k/random_medium_512"
-#PRED_DIR="/data/inpainting/predictions/final/images/r.suvorov_2021-03-05_17-08-35_train_ablv2_work_resume_epoch37/random_medium_512"
-#BLUR_OUT_DIR="/data/inpainting/predictions/final/blur/images"
-#
-#for b in 0.1
-#
-#"$BINDIR/blur_predicts.py" "$BASEDIR/../../configs/eval2.yaml" "$CUR_IN_DIR" "$CUR_OUT_DIR" "$CUR_EVAL_DIR"
-#
diff --git a/spaces/AlexZou/Deploy_Restoration/README.md b/spaces/AlexZou/Deploy_Restoration/README.md
deleted file mode 100644
index e1411f4e55c96cef4f5382d156f740954f4da43b..0000000000000000000000000000000000000000
--- a/spaces/AlexZou/Deploy_Restoration/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Deploy Restoration
-emoji: 👀
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.9.1
-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/AlignmentResearch/tuned-lens/README.md b/spaces/AlignmentResearch/tuned-lens/README.md
deleted file mode 100644
index 9e77df2f9974291988a66ba8982098cef3473962..0000000000000000000000000000000000000000
--- a/spaces/AlignmentResearch/tuned-lens/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: Tuned Lens
-emoji: 🔎
-colorFrom: pink
-colorTo: blue
-sdk: docker
-pinned: false
-license: mit
----
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/schedulers/multistep_dpm_solver_inverse.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/schedulers/multistep_dpm_solver_inverse.md
deleted file mode 100644
index 1b3348a5a3ea5c98f7d55639c2268693a639dc84..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/schedulers/multistep_dpm_solver_inverse.md
+++ /dev/null
@@ -1,22 +0,0 @@
-
-
-# Inverse Multistep DPM-Solver (DPMSolverMultistepInverse)
-
-## Overview
-
-This scheduler is the inverted scheduler of [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://arxiv.org/abs/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models
-](https://arxiv.org/abs/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.
-The implementation is mostly based on the DDIM inversion definition of [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://arxiv.org/pdf/2211.09794.pdf) and the ad-hoc notebook implementation for DiffEdit latent inversion [here](https://github.com/Xiang-cd/DiffEdit-stable-diffusion/blob/main/diffedit.ipynb).
-
-## DPMSolverMultistepInverseScheduler
-[[autodoc]] DPMSolverMultistepInverseScheduler
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/imagic_stable_diffusion.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/imagic_stable_diffusion.py
deleted file mode 100644
index 56bd381a9e65aa8edbe56cf7f22127c5c449b7ee..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/imagic_stable_diffusion.py
+++ /dev/null
@@ -1,496 +0,0 @@
-"""
- modeled after the textual_inversion.py / train_dreambooth.py and the work
- of justinpinkney here: https://github.com/justinpinkney/stable-diffusion/blob/main/notebooks/imagic.ipynb
-"""
-import inspect
-import warnings
-from typing import List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-import torch.nn.functional as F
-from accelerate import Accelerator
-
-# TODO: remove and import from diffusers.utils when the new version of diffusers is released
-from packaging import version
-from tqdm.auto import tqdm
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
-
-from diffusers import DiffusionPipeline
-from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from diffusers.utils import logging
-
-
-if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
- PIL_INTERPOLATION = {
- "linear": PIL.Image.Resampling.BILINEAR,
- "bilinear": PIL.Image.Resampling.BILINEAR,
- "bicubic": PIL.Image.Resampling.BICUBIC,
- "lanczos": PIL.Image.Resampling.LANCZOS,
- "nearest": PIL.Image.Resampling.NEAREST,
- }
-else:
- PIL_INTERPOLATION = {
- "linear": PIL.Image.LINEAR,
- "bilinear": PIL.Image.BILINEAR,
- "bicubic": PIL.Image.BICUBIC,
- "lanczos": PIL.Image.LANCZOS,
- "nearest": PIL.Image.NEAREST,
- }
-# ------------------------------------------------------------------------------
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-def preprocess(image):
- w, h = image.size
- w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
- image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
- image = np.array(image).astype(np.float32) / 255.0
- image = image[None].transpose(0, 3, 1, 2)
- image = torch.from_numpy(image)
- return 2.0 * image - 1.0
-
-
-class ImagicStableDiffusionPipeline(DiffusionPipeline):
- r"""
- Pipeline for imagic image editing.
- See paper here: https://arxiv.org/pdf/2210.09276.pdf
-
- This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
- library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
- Args:
- vae ([`AutoencoderKL`]):
- Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
- text_encoder ([`CLIPTextModel`]):
- Frozen text-encoder. Stable Diffusion uses the text portion of
- [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
- the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
- tokenizer (`CLIPTokenizer`):
- Tokenizer of class
- [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
- unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
- scheduler ([`SchedulerMixin`]):
- A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
- [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
- safety_checker ([`StableDiffusionSafetyChecker`]):
- Classification module that estimates whether generated images could be considered offsensive or harmful.
- Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
- feature_extractor ([`CLIPImageProcessor`]):
- Model that extracts features from generated images to be used as inputs for the `safety_checker`.
- """
-
- def __init__(
- self,
- vae: AutoencoderKL,
- text_encoder: CLIPTextModel,
- tokenizer: CLIPTokenizer,
- unet: UNet2DConditionModel,
- scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
- safety_checker: StableDiffusionSafetyChecker,
- feature_extractor: CLIPImageProcessor,
- ):
- super().__init__()
- self.register_modules(
- vae=vae,
- text_encoder=text_encoder,
- tokenizer=tokenizer,
- unet=unet,
- scheduler=scheduler,
- safety_checker=safety_checker,
- feature_extractor=feature_extractor,
- )
-
- def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
- r"""
- Enable sliced attention computation.
- When this option is enabled, the attention module will split the input tensor in slices, to compute attention
- in several steps. This is useful to save some memory in exchange for a small speed decrease.
- Args:
- slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
- When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
- a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
- `attention_head_dim` must be a multiple of `slice_size`.
- """
- if slice_size == "auto":
- # half the attention head size is usually a good trade-off between
- # speed and memory
- slice_size = self.unet.config.attention_head_dim // 2
- self.unet.set_attention_slice(slice_size)
-
- def disable_attention_slicing(self):
- r"""
- Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
- back to computing attention in one step.
- """
- # set slice_size = `None` to disable `attention slicing`
- self.enable_attention_slicing(None)
-
- def train(
- self,
- prompt: Union[str, List[str]],
- image: Union[torch.FloatTensor, PIL.Image.Image],
- height: Optional[int] = 512,
- width: Optional[int] = 512,
- generator: Optional[torch.Generator] = None,
- embedding_learning_rate: float = 0.001,
- diffusion_model_learning_rate: float = 2e-6,
- text_embedding_optimization_steps: int = 500,
- model_fine_tuning_optimization_steps: int = 1000,
- **kwargs,
- ):
- r"""
- Function invoked when calling the pipeline for generation.
- Args:
- prompt (`str` or `List[str]`):
- The prompt or prompts to guide the image generation.
- height (`int`, *optional*, defaults to 512):
- The height in pixels of the generated image.
- width (`int`, *optional*, defaults to 512):
- The width in pixels of the generated image.
- num_inference_steps (`int`, *optional*, defaults to 50):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference.
- guidance_scale (`float`, *optional*, defaults to 7.5):
- Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
- `guidance_scale` is defined as `w` of equation 2. of [Imagen
- Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
- 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
- usually at the expense of lower image quality.
- eta (`float`, *optional*, defaults to 0.0):
- Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
- [`schedulers.DDIMScheduler`], will be ignored for others.
- generator (`torch.Generator`, *optional*):
- A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
- deterministic.
- latents (`torch.FloatTensor`, *optional*):
- Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
- generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
- tensor will ge generated by sampling using the supplied random `generator`.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generate image. Choose between
- [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
- plain tuple.
- Returns:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
- When returning a tuple, the first element is a list with the generated images, and the second element is a
- list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
- (nsfw) content, according to the `safety_checker`.
- """
- accelerator = Accelerator(
- gradient_accumulation_steps=1,
- mixed_precision="fp16",
- )
-
- if "torch_device" in kwargs:
- device = kwargs.pop("torch_device")
- warnings.warn(
- "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
- " Consider using `pipe.to(torch_device)` instead."
- )
-
- if device is None:
- device = "cuda" if torch.cuda.is_available() else "cpu"
- self.to(device)
-
- if height % 8 != 0 or width % 8 != 0:
- raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
- # Freeze vae and unet
- self.vae.requires_grad_(False)
- self.unet.requires_grad_(False)
- self.text_encoder.requires_grad_(False)
- self.unet.eval()
- self.vae.eval()
- self.text_encoder.eval()
-
- if accelerator.is_main_process:
- accelerator.init_trackers(
- "imagic",
- config={
- "embedding_learning_rate": embedding_learning_rate,
- "text_embedding_optimization_steps": text_embedding_optimization_steps,
- },
- )
-
- # get text embeddings for prompt
- text_input = self.tokenizer(
- prompt,
- padding="max_length",
- max_length=self.tokenizer.model_max_length,
- truncation=True,
- return_tensors="pt",
- )
- text_embeddings = torch.nn.Parameter(
- self.text_encoder(text_input.input_ids.to(self.device))[0], requires_grad=True
- )
- text_embeddings = text_embeddings.detach()
- text_embeddings.requires_grad_()
- text_embeddings_orig = text_embeddings.clone()
-
- # Initialize the optimizer
- optimizer = torch.optim.Adam(
- [text_embeddings], # only optimize the embeddings
- lr=embedding_learning_rate,
- )
-
- if isinstance(image, PIL.Image.Image):
- image = preprocess(image)
-
- latents_dtype = text_embeddings.dtype
- image = image.to(device=self.device, dtype=latents_dtype)
- init_latent_image_dist = self.vae.encode(image).latent_dist
- image_latents = init_latent_image_dist.sample(generator=generator)
- image_latents = 0.18215 * image_latents
-
- progress_bar = tqdm(range(text_embedding_optimization_steps), disable=not accelerator.is_local_main_process)
- progress_bar.set_description("Steps")
-
- global_step = 0
-
- logger.info("First optimizing the text embedding to better reconstruct the init image")
- for _ in range(text_embedding_optimization_steps):
- with accelerator.accumulate(text_embeddings):
- # Sample noise that we'll add to the latents
- noise = torch.randn(image_latents.shape).to(image_latents.device)
- timesteps = torch.randint(1000, (1,), device=image_latents.device)
-
- # Add noise to the latents according to the noise magnitude at each timestep
- # (this is the forward diffusion process)
- noisy_latents = self.scheduler.add_noise(image_latents, noise, timesteps)
-
- # Predict the noise residual
- noise_pred = self.unet(noisy_latents, timesteps, text_embeddings).sample
-
- loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
- accelerator.backward(loss)
-
- optimizer.step()
- optimizer.zero_grad()
-
- # Checks if the accelerator has performed an optimization step behind the scenes
- if accelerator.sync_gradients:
- progress_bar.update(1)
- global_step += 1
-
- logs = {"loss": loss.detach().item()} # , "lr": lr_scheduler.get_last_lr()[0]}
- progress_bar.set_postfix(**logs)
- accelerator.log(logs, step=global_step)
-
- accelerator.wait_for_everyone()
-
- text_embeddings.requires_grad_(False)
-
- # Now we fine tune the unet to better reconstruct the image
- self.unet.requires_grad_(True)
- self.unet.train()
- optimizer = torch.optim.Adam(
- self.unet.parameters(), # only optimize unet
- lr=diffusion_model_learning_rate,
- )
- progress_bar = tqdm(range(model_fine_tuning_optimization_steps), disable=not accelerator.is_local_main_process)
-
- logger.info("Next fine tuning the entire model to better reconstruct the init image")
- for _ in range(model_fine_tuning_optimization_steps):
- with accelerator.accumulate(self.unet.parameters()):
- # Sample noise that we'll add to the latents
- noise = torch.randn(image_latents.shape).to(image_latents.device)
- timesteps = torch.randint(1000, (1,), device=image_latents.device)
-
- # Add noise to the latents according to the noise magnitude at each timestep
- # (this is the forward diffusion process)
- noisy_latents = self.scheduler.add_noise(image_latents, noise, timesteps)
-
- # Predict the noise residual
- noise_pred = self.unet(noisy_latents, timesteps, text_embeddings).sample
-
- loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
- accelerator.backward(loss)
-
- optimizer.step()
- optimizer.zero_grad()
-
- # Checks if the accelerator has performed an optimization step behind the scenes
- if accelerator.sync_gradients:
- progress_bar.update(1)
- global_step += 1
-
- logs = {"loss": loss.detach().item()} # , "lr": lr_scheduler.get_last_lr()[0]}
- progress_bar.set_postfix(**logs)
- accelerator.log(logs, step=global_step)
-
- accelerator.wait_for_everyone()
- self.text_embeddings_orig = text_embeddings_orig
- self.text_embeddings = text_embeddings
-
- @torch.no_grad()
- def __call__(
- self,
- alpha: float = 1.2,
- height: Optional[int] = 512,
- width: Optional[int] = 512,
- num_inference_steps: Optional[int] = 50,
- generator: Optional[torch.Generator] = None,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- guidance_scale: float = 7.5,
- eta: float = 0.0,
- ):
- r"""
- Function invoked when calling the pipeline for generation.
- Args:
- prompt (`str` or `List[str]`):
- The prompt or prompts to guide the image generation.
- height (`int`, *optional*, defaults to 512):
- The height in pixels of the generated image.
- width (`int`, *optional*, defaults to 512):
- The width in pixels of the generated image.
- num_inference_steps (`int`, *optional*, defaults to 50):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference.
- guidance_scale (`float`, *optional*, defaults to 7.5):
- Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
- `guidance_scale` is defined as `w` of equation 2. of [Imagen
- Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
- 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
- usually at the expense of lower image quality.
- eta (`float`, *optional*, defaults to 0.0):
- Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
- [`schedulers.DDIMScheduler`], will be ignored for others.
- generator (`torch.Generator`, *optional*):
- A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
- deterministic.
- latents (`torch.FloatTensor`, *optional*):
- Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
- generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
- tensor will ge generated by sampling using the supplied random `generator`.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generate image. Choose between
- [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
- plain tuple.
- Returns:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
- When returning a tuple, the first element is a list with the generated images, and the second element is a
- list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
- (nsfw) content, according to the `safety_checker`.
- """
- if height % 8 != 0 or width % 8 != 0:
- raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
- if self.text_embeddings is None:
- raise ValueError("Please run the pipe.train() before trying to generate an image.")
- if self.text_embeddings_orig is None:
- raise ValueError("Please run the pipe.train() before trying to generate an image.")
-
- text_embeddings = alpha * self.text_embeddings_orig + (1 - alpha) * self.text_embeddings
-
- # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
- # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
- # corresponds to doing no classifier free guidance.
- do_classifier_free_guidance = guidance_scale > 1.0
- # get unconditional embeddings for classifier free guidance
- if do_classifier_free_guidance:
- uncond_tokens = [""]
- max_length = self.tokenizer.model_max_length
- uncond_input = self.tokenizer(
- uncond_tokens,
- padding="max_length",
- max_length=max_length,
- truncation=True,
- return_tensors="pt",
- )
- uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
-
- # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
- seq_len = uncond_embeddings.shape[1]
- uncond_embeddings = uncond_embeddings.view(1, seq_len, -1)
-
- # For classifier free guidance, we need to do two forward passes.
- # Here we concatenate the unconditional and text embeddings into a single batch
- # to avoid doing two forward passes
- text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-
- # get the initial random noise unless the user supplied it
-
- # Unlike in other pipelines, latents need to be generated in the target device
- # for 1-to-1 results reproducibility with the CompVis implementation.
- # However this currently doesn't work in `mps`.
- latents_shape = (1, self.unet.config.in_channels, height // 8, width // 8)
- latents_dtype = text_embeddings.dtype
- if self.device.type == "mps":
- # randn does not exist on mps
- latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
- self.device
- )
- else:
- latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
-
- # set timesteps
- self.scheduler.set_timesteps(num_inference_steps)
-
- # Some schedulers like PNDM have timesteps as arrays
- # It's more optimized to move all timesteps to correct device beforehand
- timesteps_tensor = self.scheduler.timesteps.to(self.device)
-
- # scale the initial noise by the standard deviation required by the scheduler
- latents = latents * self.scheduler.init_noise_sigma
-
- # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
- # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
- # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
- # and should be between [0, 1]
- accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
- extra_step_kwargs = {}
- if accepts_eta:
- extra_step_kwargs["eta"] = eta
-
- for i, t in enumerate(self.progress_bar(timesteps_tensor)):
- # expand the latents if we are doing classifier free guidance
- latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
- # predict the noise residual
- noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-
- # perform guidance
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # compute the previous noisy sample x_t -> x_t-1
- latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
- latents = 1 / 0.18215 * latents
- image = self.vae.decode(latents).sample
-
- image = (image / 2 + 0.5).clamp(0, 1)
-
- # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
- image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
- if self.safety_checker is not None:
- safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
- self.device
- )
- image, has_nsfw_concept = self.safety_checker(
- images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
- )
- else:
- has_nsfw_concept = None
-
- if output_type == "pil":
- image = self.numpy_to_pil(image)
-
- if not return_dict:
- return (image, has_nsfw_concept)
-
- return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/README.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/README.md
deleted file mode 100644
index c28ecefc9a3002b2f6c6d3d97e53047e82ab2733..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/README.md
+++ /dev/null
@@ -1,50 +0,0 @@
-## Training examples
-
-Creating a training image set is [described in a different document](https://huggingface.co/docs/datasets/image_process#image-datasets).
-
-### Installing the dependencies
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-**Important**
-
-To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install .
-```
-
-Then cd in the example folder and run
-```bash
-pip install -r requirements.txt
-```
-
-
-And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-#### Use ONNXRuntime to accelerate training
-
-In order to leverage onnxruntime to accelerate training, please use train_unconditional_ort.py
-
-The command to train a DDPM UNet model on the Oxford Flowers dataset with onnxruntime:
-
-```bash
-accelerate launch train_unconditional.py \
- --dataset_name="huggan/flowers-102-categories" \
- --resolution=64 --center_crop --random_flip \
- --output_dir="ddpm-ema-flowers-64" \
- --use_ema \
- --train_batch_size=16 \
- --num_epochs=1 \
- --gradient_accumulation_steps=1 \
- --learning_rate=1e-4 \
- --lr_warmup_steps=500 \
- --mixed_precision=fp16
- ```
-
-Please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_repaint.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_repaint.py
deleted file mode 100644
index 41e7450d2df68c40c3b4f49669513832e443c5e3..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_repaint.py
+++ /dev/null
@@ -1,344 +0,0 @@
-# Copyright 2023 ETH Zurich Computer Vision Lab and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-from dataclasses import dataclass
-from typing import Optional, Tuple, Union
-
-import numpy as np
-import torch
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput, randn_tensor
-from .scheduling_utils import SchedulerMixin
-
-
-@dataclass
-class RePaintSchedulerOutput(BaseOutput):
- """
- Output class for the scheduler's step function output.
-
- Args:
- prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
- Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
- denoising loop.
- pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
- The predicted denoised sample (x_{0}) based on the model output from
- the current timestep. `pred_original_sample` can be used to preview progress or for guidance.
- """
-
- prev_sample: torch.FloatTensor
- pred_original_sample: torch.FloatTensor
-
-
-# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
-def betas_for_alpha_bar(
- num_diffusion_timesteps,
- max_beta=0.999,
- alpha_transform_type="cosine",
-):
- """
- Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
- (1-beta) over time from t = [0,1].
-
- Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
- to that part of the diffusion process.
-
-
- Args:
- num_diffusion_timesteps (`int`): the number of betas to produce.
- max_beta (`float`): the maximum beta to use; use values lower than 1 to
- prevent singularities.
- alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
- Choose from `cosine` or `exp`
-
- Returns:
- betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
- """
- if alpha_transform_type == "cosine":
-
- def alpha_bar_fn(t):
- return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
-
- elif alpha_transform_type == "exp":
-
- def alpha_bar_fn(t):
- return math.exp(t * -12.0)
-
- else:
- raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")
-
- betas = []
- for i in range(num_diffusion_timesteps):
- t1 = i / num_diffusion_timesteps
- t2 = (i + 1) / num_diffusion_timesteps
- betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
- return torch.tensor(betas, dtype=torch.float32)
-
-
-class RePaintScheduler(SchedulerMixin, ConfigMixin):
- """
- RePaint is a schedule for DDPM inpainting inside a given mask.
-
- [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
- function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
- [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
- [`~SchedulerMixin.from_pretrained`] functions.
-
- For more details, see the original paper: https://arxiv.org/pdf/2201.09865.pdf
-
- Args:
- num_train_timesteps (`int`): number of diffusion steps used to train the model.
- beta_start (`float`): the starting `beta` value of inference.
- beta_end (`float`): the final `beta` value.
- beta_schedule (`str`):
- the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
- `linear`, `scaled_linear`, `squaredcos_cap_v2` or `sigmoid`.
- eta (`float`):
- The weight of noise for added noise in a diffusion step. Its value is between 0.0 and 1.0 -0.0 is DDIM and
- 1.0 is DDPM scheduler respectively.
- trained_betas (`np.ndarray`, optional):
- option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
- variance_type (`str`):
- options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`,
- `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.
- clip_sample (`bool`, default `True`):
- option to clip predicted sample between -1 and 1 for numerical stability.
-
- """
-
- order = 1
-
- @register_to_config
- def __init__(
- self,
- num_train_timesteps: int = 1000,
- beta_start: float = 0.0001,
- beta_end: float = 0.02,
- beta_schedule: str = "linear",
- eta: float = 0.0,
- trained_betas: Optional[np.ndarray] = None,
- clip_sample: bool = True,
- ):
- if trained_betas is not None:
- self.betas = torch.from_numpy(trained_betas)
- elif beta_schedule == "linear":
- self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
- elif beta_schedule == "scaled_linear":
- # this schedule is very specific to the latent diffusion model.
- self.betas = (
- torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
- )
- elif beta_schedule == "squaredcos_cap_v2":
- # Glide cosine schedule
- self.betas = betas_for_alpha_bar(num_train_timesteps)
- elif beta_schedule == "sigmoid":
- # GeoDiff sigmoid schedule
- betas = torch.linspace(-6, 6, num_train_timesteps)
- self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start
- else:
- raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
-
- self.alphas = 1.0 - self.betas
- self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
- self.one = torch.tensor(1.0)
-
- self.final_alpha_cumprod = torch.tensor(1.0)
-
- # standard deviation of the initial noise distribution
- self.init_noise_sigma = 1.0
-
- # setable values
- self.num_inference_steps = None
- self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
-
- self.eta = eta
-
- def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
- """
- Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
- current timestep.
-
- Args:
- sample (`torch.FloatTensor`): input sample
- timestep (`int`, optional): current timestep
-
- Returns:
- `torch.FloatTensor`: scaled input sample
- """
- return sample
-
- def set_timesteps(
- self,
- num_inference_steps: int,
- jump_length: int = 10,
- jump_n_sample: int = 10,
- device: Union[str, torch.device] = None,
- ):
- num_inference_steps = min(self.config.num_train_timesteps, num_inference_steps)
- self.num_inference_steps = num_inference_steps
-
- timesteps = []
-
- jumps = {}
- for j in range(0, num_inference_steps - jump_length, jump_length):
- jumps[j] = jump_n_sample - 1
-
- t = num_inference_steps
- while t >= 1:
- t = t - 1
- timesteps.append(t)
-
- if jumps.get(t, 0) > 0:
- jumps[t] = jumps[t] - 1
- for _ in range(jump_length):
- t = t + 1
- timesteps.append(t)
-
- timesteps = np.array(timesteps) * (self.config.num_train_timesteps // self.num_inference_steps)
- self.timesteps = torch.from_numpy(timesteps).to(device)
-
- def _get_variance(self, t):
- prev_timestep = t - self.config.num_train_timesteps // self.num_inference_steps
-
- alpha_prod_t = self.alphas_cumprod[t]
- alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
- beta_prod_t = 1 - alpha_prod_t
- beta_prod_t_prev = 1 - alpha_prod_t_prev
-
- # For t > 0, compute predicted variance βt (see formula (6) and (7) from
- # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
- # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
- # variance to pred_sample
- # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
- # without eta.
- # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
- variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
-
- return variance
-
- def step(
- self,
- model_output: torch.FloatTensor,
- timestep: int,
- sample: torch.FloatTensor,
- original_image: torch.FloatTensor,
- mask: torch.FloatTensor,
- generator: Optional[torch.Generator] = None,
- return_dict: bool = True,
- ) -> Union[RePaintSchedulerOutput, Tuple]:
- """
- Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
- process from the learned model outputs (most often the predicted noise).
-
- Args:
- model_output (`torch.FloatTensor`): direct output from learned
- diffusion model.
- timestep (`int`): current discrete timestep in the diffusion chain.
- sample (`torch.FloatTensor`):
- current instance of sample being created by diffusion process.
- original_image (`torch.FloatTensor`):
- the original image to inpaint on.
- mask (`torch.FloatTensor`):
- the mask where 0.0 values define which part of the original image to inpaint (change).
- generator (`torch.Generator`, *optional*): random number generator.
- return_dict (`bool`): option for returning tuple rather than
- DDPMSchedulerOutput class
-
- Returns:
- [`~schedulers.scheduling_utils.RePaintSchedulerOutput`] or `tuple`:
- [`~schedulers.scheduling_utils.RePaintSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
- returning a tuple, the first element is the sample tensor.
-
- """
- t = timestep
- prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
-
- # 1. compute alphas, betas
- alpha_prod_t = self.alphas_cumprod[t]
- alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
- beta_prod_t = 1 - alpha_prod_t
-
- # 2. compute predicted original sample from predicted noise also called
- # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
- pred_original_sample = (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
-
- # 3. Clip "predicted x_0"
- if self.config.clip_sample:
- pred_original_sample = torch.clamp(pred_original_sample, -1, 1)
-
- # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
- # substitute formula (7) in the algorithm coming from DDPM paper
- # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
- # DDIM schedule gives the same results as DDPM with eta = 1.0
- # Noise is being reused in 7. and 8., but no impact on quality has
- # been observed.
-
- # 5. Add noise
- device = model_output.device
- noise = randn_tensor(model_output.shape, generator=generator, device=device, dtype=model_output.dtype)
- std_dev_t = self.eta * self._get_variance(timestep) ** 0.5
-
- variance = 0
- if t > 0 and self.eta > 0:
- variance = std_dev_t * noise
-
- # 6. compute "direction pointing to x_t" of formula (12)
- # from https://arxiv.org/pdf/2010.02502.pdf
- pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
-
- # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
- prev_unknown_part = alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance
-
- # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
- prev_known_part = (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise
-
- # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
- pred_prev_sample = mask * prev_known_part + (1.0 - mask) * prev_unknown_part
-
- if not return_dict:
- return (
- pred_prev_sample,
- pred_original_sample,
- )
-
- return RePaintSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)
-
- def undo_step(self, sample, timestep, generator=None):
- n = self.config.num_train_timesteps // self.num_inference_steps
-
- for i in range(n):
- beta = self.betas[timestep + i]
- if sample.device.type == "mps":
- # randn does not work reproducibly on mps
- noise = randn_tensor(sample.shape, dtype=sample.dtype, generator=generator)
- noise = noise.to(sample.device)
- else:
- noise = randn_tensor(sample.shape, generator=generator, device=sample.device, dtype=sample.dtype)
-
- # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
- sample = (1 - beta) ** 0.5 * sample + beta**0.5 * noise
-
- return sample
-
- def add_noise(
- self,
- original_samples: torch.FloatTensor,
- noise: torch.FloatTensor,
- timesteps: torch.IntTensor,
- ) -> torch.FloatTensor:
- raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.")
-
- def __len__(self):
- return self.config.num_train_timesteps
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py
deleted file mode 100644
index 4b28a59280e6701d31afeeaae7ae12cdbd4fb95e..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,86 +0,0 @@
-_base_ = [
- '../_base_/models/cascade_rcnn_r50_fpn.py',
- '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-# model settings
-model = dict(
- roi_head=dict(bbox_head=[
- dict(
- type='SABLHead',
- num_classes=80,
- cls_in_channels=256,
- reg_in_channels=256,
- roi_feat_size=7,
- reg_feat_up_ratio=2,
- reg_pre_kernel=3,
- reg_post_kernel=3,
- reg_pre_num=2,
- reg_post_num=1,
- cls_out_channels=1024,
- reg_offset_out_channels=256,
- reg_cls_out_channels=256,
- num_cls_fcs=1,
- num_reg_fcs=0,
- reg_class_agnostic=True,
- norm_cfg=None,
- bbox_coder=dict(
- type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
- loss_bbox_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
- loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
- loss_weight=1.0)),
- dict(
- type='SABLHead',
- num_classes=80,
- cls_in_channels=256,
- reg_in_channels=256,
- roi_feat_size=7,
- reg_feat_up_ratio=2,
- reg_pre_kernel=3,
- reg_post_kernel=3,
- reg_pre_num=2,
- reg_post_num=1,
- cls_out_channels=1024,
- reg_offset_out_channels=256,
- reg_cls_out_channels=256,
- num_cls_fcs=1,
- num_reg_fcs=0,
- reg_class_agnostic=True,
- norm_cfg=None,
- bbox_coder=dict(
- type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.5),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
- loss_bbox_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
- loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
- loss_weight=1.0)),
- dict(
- type='SABLHead',
- num_classes=80,
- cls_in_channels=256,
- reg_in_channels=256,
- roi_feat_size=7,
- reg_feat_up_ratio=2,
- reg_pre_kernel=3,
- reg_post_kernel=3,
- reg_pre_num=2,
- reg_post_num=1,
- cls_out_channels=1024,
- reg_offset_out_channels=256,
- reg_cls_out_channels=256,
- num_cls_fcs=1,
- num_reg_fcs=0,
- reg_class_agnostic=True,
- norm_cfg=None,
- bbox_coder=dict(
- type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.3),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
- loss_bbox_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
- loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, loss_weight=1.0))
- ]))
diff --git a/spaces/Andy1621/uniformer_image_detection/exp/cascade_mask_rcnn_3x_ms_hybrid_small/config.py b/spaces/Andy1621/uniformer_image_detection/exp/cascade_mask_rcnn_3x_ms_hybrid_small/config.py
deleted file mode 100644
index 1935f1914df202018438a21021ea1e7acf69e983..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/exp/cascade_mask_rcnn_3x_ms_hybrid_small/config.py
+++ /dev/null
@@ -1,142 +0,0 @@
-_base_ = [
- '../../configs/_base_/models/cascade_mask_rcnn_uniformer_fpn.py',
- '../../configs/_base_/datasets/coco_instance.py',
- '../../configs/_base_/schedules/schedule_1x.py',
- '../../configs/_base_/default_runtime.py'
-]
-
-model = dict(
- backbone=dict(
- embed_dim=[64, 128, 320, 512],
- layers=[3, 4, 8, 3],
- head_dim=64,
- drop_path_rate=0.2,
- use_checkpoint=True,
- checkpoint_num=[0, 0, 8, 0],
- windows=False,
- hybrid=True,
- window_size=14
- ),
- neck=dict(in_channels=[64, 128, 320, 512]),
- roi_head=dict(
- bbox_head=[
- dict(
- type='ConvFCBBoxHead',
- num_shared_convs=4,
- num_shared_fcs=1,
- in_channels=256,
- conv_out_channels=256,
- fc_out_channels=1024,
- roi_feat_size=7,
- num_classes=80,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0., 0., 0., 0.],
- target_stds=[0.1, 0.1, 0.2, 0.2]),
- reg_class_agnostic=False,
- reg_decoded_bbox=True,
- norm_cfg=dict(type='SyncBN', requires_grad=True),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
- loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
- dict(
- type='ConvFCBBoxHead',
- num_shared_convs=4,
- num_shared_fcs=1,
- in_channels=256,
- conv_out_channels=256,
- fc_out_channels=1024,
- roi_feat_size=7,
- num_classes=80,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0., 0., 0., 0.],
- target_stds=[0.05, 0.05, 0.1, 0.1]),
- reg_class_agnostic=False,
- reg_decoded_bbox=True,
- norm_cfg=dict(type='SyncBN', requires_grad=True),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
- loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
- dict(
- type='ConvFCBBoxHead',
- num_shared_convs=4,
- num_shared_fcs=1,
- in_channels=256,
- conv_out_channels=256,
- fc_out_channels=1024,
- roi_feat_size=7,
- num_classes=80,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0., 0., 0., 0.],
- target_stds=[0.033, 0.033, 0.067, 0.067]),
- reg_class_agnostic=False,
- reg_decoded_bbox=True,
- norm_cfg=dict(type='SyncBN', requires_grad=True),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
- loss_bbox=dict(type='GIoULoss', loss_weight=10.0))
- ]))
-
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-
-# augmentation strategy originates from DETR / Sparse RCNN
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='AutoAugment',
- policies=[
- [
- dict(type='Resize',
- img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
- (608, 1333), (640, 1333), (672, 1333), (704, 1333),
- (736, 1333), (768, 1333), (800, 1333)],
- multiscale_mode='value',
- keep_ratio=True)
- ],
- [
- dict(type='Resize',
- img_scale=[(400, 1333), (500, 1333), (600, 1333)],
- multiscale_mode='value',
- keep_ratio=True),
- dict(type='RandomCrop',
- crop_type='absolute_range',
- crop_size=(384, 600),
- allow_negative_crop=True),
- dict(type='Resize',
- img_scale=[(480, 1333), (512, 1333), (544, 1333),
- (576, 1333), (608, 1333), (640, 1333),
- (672, 1333), (704, 1333), (736, 1333),
- (768, 1333), (800, 1333)],
- multiscale_mode='value',
- override=True,
- keep_ratio=True)
- ]
- ]),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
-
-optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
- paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
- 'relative_position_bias_table': dict(decay_mult=0.),
- 'norm': dict(decay_mult=0.)}))
-lr_config = dict(step=[27, 33])
-runner = dict(type='EpochBasedRunnerAmp', max_epochs=36)
-
-# do not use mmdet version fp16
-fp16 = None
-optimizer_config = dict(
- type="DistOptimizerHook",
- update_interval=1,
- grad_clip=None,
- coalesce=True,
- bucket_size_mb=-1,
- use_fp16=True,
-)
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py
deleted file mode 100644
index 847932547c6c309ae38b45dc43ac0ef8ca66d347..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py
+++ /dev/null
@@ -1,83 +0,0 @@
-from abc import ABCMeta, abstractmethod
-
-import torch
-import torch.nn as nn
-from mmcv import ops
-
-
-class BaseRoIExtractor(nn.Module, metaclass=ABCMeta):
- """Base class for RoI extractor.
-
- Args:
- roi_layer (dict): Specify RoI layer type and arguments.
- out_channels (int): Output channels of RoI layers.
- featmap_strides (List[int]): Strides of input feature maps.
- """
-
- def __init__(self, roi_layer, out_channels, featmap_strides):
- super(BaseRoIExtractor, self).__init__()
- self.roi_layers = self.build_roi_layers(roi_layer, featmap_strides)
- self.out_channels = out_channels
- self.featmap_strides = featmap_strides
- self.fp16_enabled = False
-
- @property
- def num_inputs(self):
- """int: Number of input feature maps."""
- return len(self.featmap_strides)
-
- def init_weights(self):
- pass
-
- def build_roi_layers(self, layer_cfg, featmap_strides):
- """Build RoI operator to extract feature from each level feature map.
-
- Args:
- layer_cfg (dict): Dictionary to construct and config RoI layer
- operation. Options are modules under ``mmcv/ops`` such as
- ``RoIAlign``.
- featmap_strides (List[int]): The stride of input feature map w.r.t
- to the original image size, which would be used to scale RoI
- coordinate (original image coordinate system) to feature
- coordinate system.
-
- Returns:
- nn.ModuleList: The RoI extractor modules for each level feature
- map.
- """
-
- cfg = layer_cfg.copy()
- layer_type = cfg.pop('type')
- assert hasattr(ops, layer_type)
- layer_cls = getattr(ops, layer_type)
- roi_layers = nn.ModuleList(
- [layer_cls(spatial_scale=1 / s, **cfg) for s in featmap_strides])
- return roi_layers
-
- def roi_rescale(self, rois, scale_factor):
- """Scale RoI coordinates by scale factor.
-
- Args:
- rois (torch.Tensor): RoI (Region of Interest), shape (n, 5)
- scale_factor (float): Scale factor that RoI will be multiplied by.
-
- Returns:
- torch.Tensor: Scaled RoI.
- """
-
- cx = (rois[:, 1] + rois[:, 3]) * 0.5
- cy = (rois[:, 2] + rois[:, 4]) * 0.5
- w = rois[:, 3] - rois[:, 1]
- h = rois[:, 4] - rois[:, 2]
- new_w = w * scale_factor
- new_h = h * scale_factor
- x1 = cx - new_w * 0.5
- x2 = cx + new_w * 0.5
- y1 = cy - new_h * 0.5
- y2 = cy + new_h * 0.5
- new_rois = torch.stack((rois[:, 0], x1, y1, x2, y2), dim=-1)
- return new_rois
-
- @abstractmethod
- def forward(self, feats, rois, roi_scale_factor=None):
- pass
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/configs/_base_/default_runtime.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/configs/_base_/default_runtime.py
deleted file mode 100644
index b564cc4e7e7d9a67dacaaddecb100e4d8f5c005b..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/configs/_base_/default_runtime.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# yapf:disable
-log_config = dict(
- interval=50,
- hooks=[
- dict(type='TextLoggerHook', by_epoch=False),
- # dict(type='TensorboardLoggerHook')
- ])
-# yapf:enable
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-load_from = None
-resume_from = None
-workflow = [('train', 1)]
-cudnn_benchmark = True
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/iter_timer.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/iter_timer.py
deleted file mode 100644
index cfd5002fe85ffc6992155ac01003878064a1d9be..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/iter_timer.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import time
-
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class IterTimerHook(Hook):
-
- def before_epoch(self, runner):
- self.t = time.time()
-
- def before_iter(self, runner):
- runner.log_buffer.update({'data_time': time.time() - self.t})
-
- def after_iter(self, runner):
- runner.log_buffer.update({'time': time.time() - self.t})
- self.t = time.time()
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/gradio_annotator.py b/spaces/Anonymous-sub/Rerender/ControlNet/gradio_annotator.py
deleted file mode 100644
index 2b1a29ebbec24073a9e4357b700e0577a17a9379..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/gradio_annotator.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import gradio as gr
-
-from annotator.util import resize_image, HWC3
-
-
-model_canny = None
-
-
-def canny(img, res, l, h):
- img = resize_image(HWC3(img), res)
- global model_canny
- if model_canny is None:
- from annotator.canny import CannyDetector
- model_canny = CannyDetector()
- result = model_canny(img, l, h)
- return [result]
-
-
-model_hed = None
-
-
-def hed(img, res):
- img = resize_image(HWC3(img), res)
- global model_hed
- if model_hed is None:
- from annotator.hed import HEDdetector
- model_hed = HEDdetector()
- result = model_hed(img)
- return [result]
-
-
-model_mlsd = None
-
-
-def mlsd(img, res, thr_v, thr_d):
- img = resize_image(HWC3(img), res)
- global model_mlsd
- if model_mlsd is None:
- from annotator.mlsd import MLSDdetector
- model_mlsd = MLSDdetector()
- result = model_mlsd(img, thr_v, thr_d)
- return [result]
-
-
-model_midas = None
-
-
-def midas(img, res, a):
- img = resize_image(HWC3(img), res)
- global model_midas
- if model_midas is None:
- from annotator.midas import MidasDetector
- model_midas = MidasDetector()
- results = model_midas(img, a)
- return results
-
-
-model_openpose = None
-
-
-def openpose(img, res, has_hand):
- img = resize_image(HWC3(img), res)
- global model_openpose
- if model_openpose is None:
- from annotator.openpose import OpenposeDetector
- model_openpose = OpenposeDetector()
- result, _ = model_openpose(img, has_hand)
- return [result]
-
-
-model_uniformer = None
-
-
-def uniformer(img, res):
- img = resize_image(HWC3(img), res)
- global model_uniformer
- if model_uniformer is None:
- from annotator.uniformer import UniformerDetector
- model_uniformer = UniformerDetector()
- result = model_uniformer(img)
- return [result]
-
-
-block = gr.Blocks().queue()
-with block:
- with gr.Row():
- gr.Markdown("## Canny Edge")
- with gr.Row():
- with gr.Column():
- input_image = gr.Image(source='upload', type="numpy")
- low_threshold = gr.Slider(label="low_threshold", minimum=1, maximum=255, value=100, step=1)
- high_threshold = gr.Slider(label="high_threshold", minimum=1, maximum=255, value=200, step=1)
- resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
- run_button = gr.Button(label="Run")
- with gr.Column():
- gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
- run_button.click(fn=canny, inputs=[input_image, resolution, low_threshold, high_threshold], outputs=[gallery])
-
- with gr.Row():
- gr.Markdown("## HED Edge")
- with gr.Row():
- with gr.Column():
- input_image = gr.Image(source='upload', type="numpy")
- resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
- run_button = gr.Button(label="Run")
- with gr.Column():
- gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
- run_button.click(fn=hed, inputs=[input_image, resolution], outputs=[gallery])
-
- with gr.Row():
- gr.Markdown("## MLSD Edge")
- with gr.Row():
- with gr.Column():
- input_image = gr.Image(source='upload', type="numpy")
- value_threshold = gr.Slider(label="value_threshold", minimum=0.01, maximum=2.0, value=0.1, step=0.01)
- distance_threshold = gr.Slider(label="distance_threshold", minimum=0.01, maximum=20.0, value=0.1, step=0.01)
- resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=384, step=64)
- run_button = gr.Button(label="Run")
- with gr.Column():
- gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
- run_button.click(fn=mlsd, inputs=[input_image, resolution, value_threshold, distance_threshold], outputs=[gallery])
-
- with gr.Row():
- gr.Markdown("## MIDAS Depth and Normal")
- with gr.Row():
- with gr.Column():
- input_image = gr.Image(source='upload', type="numpy")
- alpha = gr.Slider(label="alpha", minimum=0.1, maximum=20.0, value=6.2, step=0.01)
- resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=384, step=64)
- run_button = gr.Button(label="Run")
- with gr.Column():
- gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
- run_button.click(fn=midas, inputs=[input_image, resolution, alpha], outputs=[gallery])
-
- with gr.Row():
- gr.Markdown("## Openpose")
- with gr.Row():
- with gr.Column():
- input_image = gr.Image(source='upload', type="numpy")
- hand = gr.Checkbox(label='detect hand', value=False)
- resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
- run_button = gr.Button(label="Run")
- with gr.Column():
- gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
- run_button.click(fn=openpose, inputs=[input_image, resolution, hand], outputs=[gallery])
-
-
- with gr.Row():
- gr.Markdown("## Uniformer Segmentation")
- with gr.Row():
- with gr.Column():
- input_image = gr.Image(source='upload', type="numpy")
- resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
- run_button = gr.Button(label="Run")
- with gr.Column():
- gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
- run_button.click(fn=uniformer, inputs=[input_image, resolution], outputs=[gallery])
-
-
-block.launch(server_name='0.0.0.0')
diff --git a/spaces/Armandoliv/document_parser/README.md b/spaces/Armandoliv/document_parser/README.md
deleted file mode 100644
index 77df6afdd5d1c6fc6ad33f1d51768d8df1a191f3..0000000000000000000000000000000000000000
--- a/spaces/Armandoliv/document_parser/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Document Parser
-emoji: 📈
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.2
-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/Arnx/MusicGenXvAKN/tests/utils/__init__.py b/spaces/Arnx/MusicGenXvAKN/tests/utils/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/Arnx/MusicGenXvAKN/tests/utils/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/Artrajz/vits-simple-api/vits/text/english.py b/spaces/Artrajz/vits-simple-api/vits/text/english.py
deleted file mode 100644
index 6817392ba8a9eb830351de89fb7afc5ad72f5e42..0000000000000000000000000000000000000000
--- a/spaces/Artrajz/vits-simple-api/vits/text/english.py
+++ /dev/null
@@ -1,188 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
- 1. "english_cleaners" for English text
- 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
- the Unidecode library (https://pypi.python.org/pypi/Unidecode)
- 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
- the symbols in symbols.py to match your data).
-'''
-
-
-# Regular expression matching whitespace:
-
-
-import re
-import inflect
-from unidecode import unidecode
-import eng_to_ipa as ipa
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
-_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
-_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
-_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
-_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
-_number_re = re.compile(r'[0-9]+')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
- ('mrs', 'misess'),
- ('mr', 'mister'),
- ('dr', 'doctor'),
- ('st', 'saint'),
- ('co', 'company'),
- ('jr', 'junior'),
- ('maj', 'major'),
- ('gen', 'general'),
- ('drs', 'doctors'),
- ('rev', 'reverend'),
- ('lt', 'lieutenant'),
- ('hon', 'honorable'),
- ('sgt', 'sergeant'),
- ('capt', 'captain'),
- ('esq', 'esquire'),
- ('ltd', 'limited'),
- ('col', 'colonel'),
- ('ft', 'fort'),
-]]
-
-
-# List of (ipa, lazy ipa) pairs:
-_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('æ', 'e'),
- ('ɑ', 'a'),
- ('ɔ', 'o'),
- ('ð', 'z'),
- ('θ', 's'),
- ('ɛ', 'e'),
- ('ɪ', 'i'),
- ('ʊ', 'u'),
- ('ʒ', 'ʥ'),
- ('ʤ', 'ʥ'),
- ('ˈ', '↓'),
-]]
-
-# List of (ipa, lazy ipa2) pairs:
-_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('ð', 'z'),
- ('θ', 's'),
- ('ʒ', 'ʑ'),
- ('ʤ', 'dʑ'),
- ('ˈ', '↓'),
-]]
-
-# List of (ipa, ipa2) pairs
-_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('ʤ', 'dʒ'),
- ('ʧ', 'tʃ')
-]]
-
-
-def expand_abbreviations(text):
- for regex, replacement in _abbreviations:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def collapse_whitespace(text):
- return re.sub(r'\s+', ' ', text)
-
-
-def _remove_commas(m):
- return m.group(1).replace(',', '')
-
-
-def _expand_decimal_point(m):
- return m.group(1).replace('.', ' point ')
-
-
-def _expand_dollars(m):
- match = m.group(1)
- parts = match.split('.')
- if len(parts) > 2:
- return match + ' dollars' # Unexpected format
- dollars = int(parts[0]) if parts[0] else 0
- cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
- if dollars and cents:
- dollar_unit = 'dollar' if dollars == 1 else 'dollars'
- cent_unit = 'cent' if cents == 1 else 'cents'
- return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
- elif dollars:
- dollar_unit = 'dollar' if dollars == 1 else 'dollars'
- return '%s %s' % (dollars, dollar_unit)
- elif cents:
- cent_unit = 'cent' if cents == 1 else 'cents'
- return '%s %s' % (cents, cent_unit)
- else:
- return 'zero dollars'
-
-
-def _expand_ordinal(m):
- return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
- num = int(m.group(0))
- if num > 1000 and num < 3000:
- if num == 2000:
- return 'two thousand'
- elif num > 2000 and num < 2010:
- return 'two thousand ' + _inflect.number_to_words(num % 100)
- elif num % 100 == 0:
- return _inflect.number_to_words(num // 100) + ' hundred'
- else:
- return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
- else:
- return _inflect.number_to_words(num, andword='')
-
-
-def normalize_numbers(text):
- text = re.sub(_comma_number_re, _remove_commas, text)
- text = re.sub(_pounds_re, r'\1 pounds', text)
- text = re.sub(_dollars_re, _expand_dollars, text)
- text = re.sub(_decimal_number_re, _expand_decimal_point, text)
- text = re.sub(_ordinal_re, _expand_ordinal, text)
- text = re.sub(_number_re, _expand_number, text)
- return text
-
-
-def mark_dark_l(text):
- return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
-
-
-def english_to_ipa(text):
- text = unidecode(text).lower()
- text = expand_abbreviations(text)
- text = normalize_numbers(text)
- phonemes = ipa.convert(text)
- phonemes = collapse_whitespace(phonemes)
- return phonemes
-
-
-def english_to_lazy_ipa(text):
- text = english_to_ipa(text)
- for regex, replacement in _lazy_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def english_to_ipa2(text):
- text = english_to_ipa(text)
- text = mark_dark_l(text)
- for regex, replacement in _ipa_to_ipa2:
- text = re.sub(regex, replacement, text)
- return text.replace('...', '…')
-
-
-def english_to_lazy_ipa2(text):
- text = english_to_ipa(text)
- for regex, replacement in _lazy_ipa2:
- text = re.sub(regex, replacement, text)
- return text
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/subprocess.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/subprocess.py
deleted file mode 100644
index 1e8ff50edfb8059799b334325e65eea9bb9b1ab3..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/subprocess.py
+++ /dev/null
@@ -1,260 +0,0 @@
-import logging
-import os
-import shlex
-import subprocess
-from typing import (
- TYPE_CHECKING,
- Any,
- Callable,
- Iterable,
- List,
- Mapping,
- Optional,
- Union,
-)
-
-from pip._vendor.rich.markup import escape
-
-from pip._internal.cli.spinners import SpinnerInterface, open_spinner
-from pip._internal.exceptions import InstallationSubprocessError
-from pip._internal.utils.logging import VERBOSE, subprocess_logger
-from pip._internal.utils.misc import HiddenText
-
-if TYPE_CHECKING:
- # Literal was introduced in Python 3.8.
- #
- # TODO: Remove `if TYPE_CHECKING` when dropping support for Python 3.7.
- from typing import Literal
-
-CommandArgs = List[Union[str, HiddenText]]
-
-
-def make_command(*args: Union[str, HiddenText, CommandArgs]) -> CommandArgs:
- """
- Create a CommandArgs object.
- """
- command_args: CommandArgs = []
- for arg in args:
- # Check for list instead of CommandArgs since CommandArgs is
- # only known during type-checking.
- if isinstance(arg, list):
- command_args.extend(arg)
- else:
- # Otherwise, arg is str or HiddenText.
- command_args.append(arg)
-
- return command_args
-
-
-def format_command_args(args: Union[List[str], CommandArgs]) -> str:
- """
- Format command arguments for display.
- """
- # For HiddenText arguments, display the redacted form by calling str().
- # Also, we don't apply str() to arguments that aren't HiddenText since
- # this can trigger a UnicodeDecodeError in Python 2 if the argument
- # has type unicode and includes a non-ascii character. (The type
- # checker doesn't ensure the annotations are correct in all cases.)
- return " ".join(
- shlex.quote(str(arg)) if isinstance(arg, HiddenText) else shlex.quote(arg)
- for arg in args
- )
-
-
-def reveal_command_args(args: Union[List[str], CommandArgs]) -> List[str]:
- """
- Return the arguments in their raw, unredacted form.
- """
- return [arg.secret if isinstance(arg, HiddenText) else arg for arg in args]
-
-
-def call_subprocess(
- cmd: Union[List[str], CommandArgs],
- show_stdout: bool = False,
- cwd: Optional[str] = None,
- on_returncode: 'Literal["raise", "warn", "ignore"]' = "raise",
- extra_ok_returncodes: Optional[Iterable[int]] = None,
- extra_environ: Optional[Mapping[str, Any]] = None,
- unset_environ: Optional[Iterable[str]] = None,
- spinner: Optional[SpinnerInterface] = None,
- log_failed_cmd: Optional[bool] = True,
- stdout_only: Optional[bool] = False,
- *,
- command_desc: str,
-) -> str:
- """
- Args:
- show_stdout: if true, use INFO to log the subprocess's stderr and
- stdout streams. Otherwise, use DEBUG. Defaults to False.
- extra_ok_returncodes: an iterable of integer return codes that are
- acceptable, in addition to 0. Defaults to None, which means [].
- unset_environ: an iterable of environment variable names to unset
- prior to calling subprocess.Popen().
- log_failed_cmd: if false, failed commands are not logged, only raised.
- stdout_only: if true, return only stdout, else return both. When true,
- logging of both stdout and stderr occurs when the subprocess has
- terminated, else logging occurs as subprocess output is produced.
- """
- if extra_ok_returncodes is None:
- extra_ok_returncodes = []
- if unset_environ is None:
- unset_environ = []
- # Most places in pip use show_stdout=False. What this means is--
- #
- # - We connect the child's output (combined stderr and stdout) to a
- # single pipe, which we read.
- # - We log this output to stderr at DEBUG level as it is received.
- # - If DEBUG logging isn't enabled (e.g. if --verbose logging wasn't
- # requested), then we show a spinner so the user can still see the
- # subprocess is in progress.
- # - If the subprocess exits with an error, we log the output to stderr
- # at ERROR level if it hasn't already been displayed to the console
- # (e.g. if --verbose logging wasn't enabled). This way we don't log
- # the output to the console twice.
- #
- # If show_stdout=True, then the above is still done, but with DEBUG
- # replaced by INFO.
- if show_stdout:
- # Then log the subprocess output at INFO level.
- log_subprocess: Callable[..., None] = subprocess_logger.info
- used_level = logging.INFO
- else:
- # Then log the subprocess output using VERBOSE. This also ensures
- # it will be logged to the log file (aka user_log), if enabled.
- log_subprocess = subprocess_logger.verbose
- used_level = VERBOSE
-
- # Whether the subprocess will be visible in the console.
- showing_subprocess = subprocess_logger.getEffectiveLevel() <= used_level
-
- # Only use the spinner if we're not showing the subprocess output
- # and we have a spinner.
- use_spinner = not showing_subprocess and spinner is not None
-
- log_subprocess("Running command %s", command_desc)
- env = os.environ.copy()
- if extra_environ:
- env.update(extra_environ)
- for name in unset_environ:
- env.pop(name, None)
- try:
- proc = subprocess.Popen(
- # Convert HiddenText objects to the underlying str.
- reveal_command_args(cmd),
- stdin=subprocess.PIPE,
- stdout=subprocess.PIPE,
- stderr=subprocess.STDOUT if not stdout_only else subprocess.PIPE,
- cwd=cwd,
- env=env,
- errors="backslashreplace",
- )
- except Exception as exc:
- if log_failed_cmd:
- subprocess_logger.critical(
- "Error %s while executing command %s",
- exc,
- command_desc,
- )
- raise
- all_output = []
- if not stdout_only:
- assert proc.stdout
- assert proc.stdin
- proc.stdin.close()
- # In this mode, stdout and stderr are in the same pipe.
- while True:
- line: str = proc.stdout.readline()
- if not line:
- break
- line = line.rstrip()
- all_output.append(line + "\n")
-
- # Show the line immediately.
- log_subprocess(line)
- # Update the spinner.
- if use_spinner:
- assert spinner
- spinner.spin()
- try:
- proc.wait()
- finally:
- if proc.stdout:
- proc.stdout.close()
- output = "".join(all_output)
- else:
- # In this mode, stdout and stderr are in different pipes.
- # We must use communicate() which is the only safe way to read both.
- out, err = proc.communicate()
- # log line by line to preserve pip log indenting
- for out_line in out.splitlines():
- log_subprocess(out_line)
- all_output.append(out)
- for err_line in err.splitlines():
- log_subprocess(err_line)
- all_output.append(err)
- output = out
-
- proc_had_error = proc.returncode and proc.returncode not in extra_ok_returncodes
- if use_spinner:
- assert spinner
- if proc_had_error:
- spinner.finish("error")
- else:
- spinner.finish("done")
- if proc_had_error:
- if on_returncode == "raise":
- error = InstallationSubprocessError(
- command_description=command_desc,
- exit_code=proc.returncode,
- output_lines=all_output if not showing_subprocess else None,
- )
- if log_failed_cmd:
- subprocess_logger.error("[present-rich] %s", error)
- subprocess_logger.verbose(
- "[bold magenta]full command[/]: [blue]%s[/]",
- escape(format_command_args(cmd)),
- extra={"markup": True},
- )
- subprocess_logger.verbose(
- "[bold magenta]cwd[/]: %s",
- escape(cwd or "[inherit]"),
- extra={"markup": True},
- )
-
- raise error
- elif on_returncode == "warn":
- subprocess_logger.warning(
- 'Command "%s" had error code %s in %s',
- command_desc,
- proc.returncode,
- cwd,
- )
- elif on_returncode == "ignore":
- pass
- else:
- raise ValueError(f"Invalid value: on_returncode={on_returncode!r}")
- return output
-
-
-def runner_with_spinner_message(message: str) -> Callable[..., None]:
- """Provide a subprocess_runner that shows a spinner message.
-
- Intended for use with for BuildBackendHookCaller. Thus, the runner has
- an API that matches what's expected by BuildBackendHookCaller.subprocess_runner.
- """
-
- def runner(
- cmd: List[str],
- cwd: Optional[str] = None,
- extra_environ: Optional[Mapping[str, Any]] = None,
- ) -> None:
- with open_spinner(message) as spinner:
- call_subprocess(
- cmd,
- command_desc=message,
- cwd=cwd,
- extra_environ=extra_environ,
- spinner=spinner,
- )
-
- return runner
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/vcs/versioncontrol.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/vcs/versioncontrol.py
deleted file mode 100644
index 02bbf68e7ad3ce14f191af24260312e817e12df7..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/vcs/versioncontrol.py
+++ /dev/null
@@ -1,705 +0,0 @@
-"""Handles all VCS (version control) support"""
-
-import logging
-import os
-import shutil
-import sys
-import urllib.parse
-from typing import (
- TYPE_CHECKING,
- Any,
- Dict,
- Iterable,
- Iterator,
- List,
- Mapping,
- Optional,
- Tuple,
- Type,
- Union,
-)
-
-from pip._internal.cli.spinners import SpinnerInterface
-from pip._internal.exceptions import BadCommand, InstallationError
-from pip._internal.utils.misc import (
- HiddenText,
- ask_path_exists,
- backup_dir,
- display_path,
- hide_url,
- hide_value,
- is_installable_dir,
- rmtree,
-)
-from pip._internal.utils.subprocess import (
- CommandArgs,
- call_subprocess,
- format_command_args,
- make_command,
-)
-from pip._internal.utils.urls import get_url_scheme
-
-if TYPE_CHECKING:
- # Literal was introduced in Python 3.8.
- #
- # TODO: Remove `if TYPE_CHECKING` when dropping support for Python 3.7.
- from typing import Literal
-
-
-__all__ = ["vcs"]
-
-
-logger = logging.getLogger(__name__)
-
-AuthInfo = Tuple[Optional[str], Optional[str]]
-
-
-def is_url(name: str) -> bool:
- """
- Return true if the name looks like a URL.
- """
- scheme = get_url_scheme(name)
- if scheme is None:
- return False
- return scheme in ["http", "https", "file", "ftp"] + vcs.all_schemes
-
-
-def make_vcs_requirement_url(
- repo_url: str, rev: str, project_name: str, subdir: Optional[str] = None
-) -> str:
- """
- Return the URL for a VCS requirement.
-
- Args:
- repo_url: the remote VCS url, with any needed VCS prefix (e.g. "git+").
- project_name: the (unescaped) project name.
- """
- egg_project_name = project_name.replace("-", "_")
- req = f"{repo_url}@{rev}#egg={egg_project_name}"
- if subdir:
- req += f"&subdirectory={subdir}"
-
- return req
-
-
-def find_path_to_project_root_from_repo_root(
- location: str, repo_root: str
-) -> Optional[str]:
- """
- Find the the Python project's root by searching up the filesystem from
- `location`. Return the path to project root relative to `repo_root`.
- Return None if the project root is `repo_root`, or cannot be found.
- """
- # find project root.
- orig_location = location
- while not is_installable_dir(location):
- last_location = location
- location = os.path.dirname(location)
- if location == last_location:
- # We've traversed up to the root of the filesystem without
- # finding a Python project.
- logger.warning(
- "Could not find a Python project for directory %s (tried all "
- "parent directories)",
- orig_location,
- )
- return None
-
- if os.path.samefile(repo_root, location):
- return None
-
- return os.path.relpath(location, repo_root)
-
-
-class RemoteNotFoundError(Exception):
- pass
-
-
-class RemoteNotValidError(Exception):
- def __init__(self, url: str):
- super().__init__(url)
- self.url = url
-
-
-class RevOptions:
-
- """
- Encapsulates a VCS-specific revision to install, along with any VCS
- install options.
-
- Instances of this class should be treated as if immutable.
- """
-
- def __init__(
- self,
- vc_class: Type["VersionControl"],
- rev: Optional[str] = None,
- extra_args: Optional[CommandArgs] = None,
- ) -> None:
- """
- Args:
- vc_class: a VersionControl subclass.
- rev: the name of the revision to install.
- extra_args: a list of extra options.
- """
- if extra_args is None:
- extra_args = []
-
- self.extra_args = extra_args
- self.rev = rev
- self.vc_class = vc_class
- self.branch_name: Optional[str] = None
-
- def __repr__(self) -> str:
- return f""
-
- @property
- def arg_rev(self) -> Optional[str]:
- if self.rev is None:
- return self.vc_class.default_arg_rev
-
- return self.rev
-
- def to_args(self) -> CommandArgs:
- """
- Return the VCS-specific command arguments.
- """
- args: CommandArgs = []
- rev = self.arg_rev
- if rev is not None:
- args += self.vc_class.get_base_rev_args(rev)
- args += self.extra_args
-
- return args
-
- def to_display(self) -> str:
- if not self.rev:
- return ""
-
- return f" (to revision {self.rev})"
-
- def make_new(self, rev: str) -> "RevOptions":
- """
- Make a copy of the current instance, but with a new rev.
-
- Args:
- rev: the name of the revision for the new object.
- """
- return self.vc_class.make_rev_options(rev, extra_args=self.extra_args)
-
-
-class VcsSupport:
- _registry: Dict[str, "VersionControl"] = {}
- schemes = ["ssh", "git", "hg", "bzr", "sftp", "svn"]
-
- def __init__(self) -> None:
- # Register more schemes with urlparse for various version control
- # systems
- urllib.parse.uses_netloc.extend(self.schemes)
- super().__init__()
-
- def __iter__(self) -> Iterator[str]:
- return self._registry.__iter__()
-
- @property
- def backends(self) -> List["VersionControl"]:
- return list(self._registry.values())
-
- @property
- def dirnames(self) -> List[str]:
- return [backend.dirname for backend in self.backends]
-
- @property
- def all_schemes(self) -> List[str]:
- schemes: List[str] = []
- for backend in self.backends:
- schemes.extend(backend.schemes)
- return schemes
-
- def register(self, cls: Type["VersionControl"]) -> None:
- if not hasattr(cls, "name"):
- logger.warning("Cannot register VCS %s", cls.__name__)
- return
- if cls.name not in self._registry:
- self._registry[cls.name] = cls()
- logger.debug("Registered VCS backend: %s", cls.name)
-
- def unregister(self, name: str) -> None:
- if name in self._registry:
- del self._registry[name]
-
- def get_backend_for_dir(self, location: str) -> Optional["VersionControl"]:
- """
- Return a VersionControl object if a repository of that type is found
- at the given directory.
- """
- vcs_backends = {}
- for vcs_backend in self._registry.values():
- repo_path = vcs_backend.get_repository_root(location)
- if not repo_path:
- continue
- logger.debug("Determine that %s uses VCS: %s", location, vcs_backend.name)
- vcs_backends[repo_path] = vcs_backend
-
- if not vcs_backends:
- return None
-
- # Choose the VCS in the inner-most directory. Since all repository
- # roots found here would be either `location` or one of its
- # parents, the longest path should have the most path components,
- # i.e. the backend representing the inner-most repository.
- inner_most_repo_path = max(vcs_backends, key=len)
- return vcs_backends[inner_most_repo_path]
-
- def get_backend_for_scheme(self, scheme: str) -> Optional["VersionControl"]:
- """
- Return a VersionControl object or None.
- """
- for vcs_backend in self._registry.values():
- if scheme in vcs_backend.schemes:
- return vcs_backend
- return None
-
- def get_backend(self, name: str) -> Optional["VersionControl"]:
- """
- Return a VersionControl object or None.
- """
- name = name.lower()
- return self._registry.get(name)
-
-
-vcs = VcsSupport()
-
-
-class VersionControl:
- name = ""
- dirname = ""
- repo_name = ""
- # List of supported schemes for this Version Control
- schemes: Tuple[str, ...] = ()
- # Iterable of environment variable names to pass to call_subprocess().
- unset_environ: Tuple[str, ...] = ()
- default_arg_rev: Optional[str] = None
-
- @classmethod
- def should_add_vcs_url_prefix(cls, remote_url: str) -> bool:
- """
- Return whether the vcs prefix (e.g. "git+") should be added to a
- repository's remote url when used in a requirement.
- """
- return not remote_url.lower().startswith(f"{cls.name}:")
-
- @classmethod
- def get_subdirectory(cls, location: str) -> Optional[str]:
- """
- Return the path to Python project root, relative to the repo root.
- Return None if the project root is in the repo root.
- """
- return None
-
- @classmethod
- def get_requirement_revision(cls, repo_dir: str) -> str:
- """
- Return the revision string that should be used in a requirement.
- """
- return cls.get_revision(repo_dir)
-
- @classmethod
- def get_src_requirement(cls, repo_dir: str, project_name: str) -> str:
- """
- Return the requirement string to use to redownload the files
- currently at the given repository directory.
-
- Args:
- project_name: the (unescaped) project name.
-
- The return value has a form similar to the following:
-
- {repository_url}@{revision}#egg={project_name}
- """
- repo_url = cls.get_remote_url(repo_dir)
-
- if cls.should_add_vcs_url_prefix(repo_url):
- repo_url = f"{cls.name}+{repo_url}"
-
- revision = cls.get_requirement_revision(repo_dir)
- subdir = cls.get_subdirectory(repo_dir)
- req = make_vcs_requirement_url(repo_url, revision, project_name, subdir=subdir)
-
- return req
-
- @staticmethod
- def get_base_rev_args(rev: str) -> List[str]:
- """
- Return the base revision arguments for a vcs command.
-
- Args:
- rev: the name of a revision to install. Cannot be None.
- """
- raise NotImplementedError
-
- def is_immutable_rev_checkout(self, url: str, dest: str) -> bool:
- """
- Return true if the commit hash checked out at dest matches
- the revision in url.
-
- Always return False, if the VCS does not support immutable commit
- hashes.
-
- This method does not check if there are local uncommitted changes
- in dest after checkout, as pip currently has no use case for that.
- """
- return False
-
- @classmethod
- def make_rev_options(
- cls, rev: Optional[str] = None, extra_args: Optional[CommandArgs] = None
- ) -> RevOptions:
- """
- Return a RevOptions object.
-
- Args:
- rev: the name of a revision to install.
- extra_args: a list of extra options.
- """
- return RevOptions(cls, rev, extra_args=extra_args)
-
- @classmethod
- def _is_local_repository(cls, repo: str) -> bool:
- """
- posix absolute paths start with os.path.sep,
- win32 ones start with drive (like c:\\folder)
- """
- drive, tail = os.path.splitdrive(repo)
- return repo.startswith(os.path.sep) or bool(drive)
-
- @classmethod
- def get_netloc_and_auth(
- cls, netloc: str, scheme: str
- ) -> Tuple[str, Tuple[Optional[str], Optional[str]]]:
- """
- Parse the repository URL's netloc, and return the new netloc to use
- along with auth information.
-
- Args:
- netloc: the original repository URL netloc.
- scheme: the repository URL's scheme without the vcs prefix.
-
- This is mainly for the Subversion class to override, so that auth
- information can be provided via the --username and --password options
- instead of through the URL. For other subclasses like Git without
- such an option, auth information must stay in the URL.
-
- Returns: (netloc, (username, password)).
- """
- return netloc, (None, None)
-
- @classmethod
- def get_url_rev_and_auth(cls, url: str) -> Tuple[str, Optional[str], AuthInfo]:
- """
- Parse the repository URL to use, and return the URL, revision,
- and auth info to use.
-
- Returns: (url, rev, (username, password)).
- """
- scheme, netloc, path, query, frag = urllib.parse.urlsplit(url)
- if "+" not in scheme:
- raise ValueError(
- "Sorry, {!r} is a malformed VCS url. "
- "The format is +://, "
- "e.g. svn+http://myrepo/svn/MyApp#egg=MyApp".format(url)
- )
- # Remove the vcs prefix.
- scheme = scheme.split("+", 1)[1]
- netloc, user_pass = cls.get_netloc_and_auth(netloc, scheme)
- rev = None
- if "@" in path:
- path, rev = path.rsplit("@", 1)
- if not rev:
- raise InstallationError(
- "The URL {!r} has an empty revision (after @) "
- "which is not supported. Include a revision after @ "
- "or remove @ from the URL.".format(url)
- )
- url = urllib.parse.urlunsplit((scheme, netloc, path, query, ""))
- return url, rev, user_pass
-
- @staticmethod
- def make_rev_args(
- username: Optional[str], password: Optional[HiddenText]
- ) -> CommandArgs:
- """
- Return the RevOptions "extra arguments" to use in obtain().
- """
- return []
-
- def get_url_rev_options(self, url: HiddenText) -> Tuple[HiddenText, RevOptions]:
- """
- Return the URL and RevOptions object to use in obtain(),
- as a tuple (url, rev_options).
- """
- secret_url, rev, user_pass = self.get_url_rev_and_auth(url.secret)
- username, secret_password = user_pass
- password: Optional[HiddenText] = None
- if secret_password is not None:
- password = hide_value(secret_password)
- extra_args = self.make_rev_args(username, password)
- rev_options = self.make_rev_options(rev, extra_args=extra_args)
-
- return hide_url(secret_url), rev_options
-
- @staticmethod
- def normalize_url(url: str) -> str:
- """
- Normalize a URL for comparison by unquoting it and removing any
- trailing slash.
- """
- return urllib.parse.unquote(url).rstrip("/")
-
- @classmethod
- def compare_urls(cls, url1: str, url2: str) -> bool:
- """
- Compare two repo URLs for identity, ignoring incidental differences.
- """
- return cls.normalize_url(url1) == cls.normalize_url(url2)
-
- def fetch_new(
- self, dest: str, url: HiddenText, rev_options: RevOptions, verbosity: int
- ) -> None:
- """
- Fetch a revision from a repository, in the case that this is the
- first fetch from the repository.
-
- Args:
- dest: the directory to fetch the repository to.
- rev_options: a RevOptions object.
- verbosity: verbosity level.
- """
- raise NotImplementedError
-
- def switch(self, dest: str, url: HiddenText, rev_options: RevOptions) -> None:
- """
- Switch the repo at ``dest`` to point to ``URL``.
-
- Args:
- rev_options: a RevOptions object.
- """
- raise NotImplementedError
-
- def update(self, dest: str, url: HiddenText, rev_options: RevOptions) -> None:
- """
- Update an already-existing repo to the given ``rev_options``.
-
- Args:
- rev_options: a RevOptions object.
- """
- raise NotImplementedError
-
- @classmethod
- def is_commit_id_equal(cls, dest: str, name: Optional[str]) -> bool:
- """
- Return whether the id of the current commit equals the given name.
-
- Args:
- dest: the repository directory.
- name: a string name.
- """
- raise NotImplementedError
-
- def obtain(self, dest: str, url: HiddenText, verbosity: int) -> None:
- """
- Install or update in editable mode the package represented by this
- VersionControl object.
-
- :param dest: the repository directory in which to install or update.
- :param url: the repository URL starting with a vcs prefix.
- :param verbosity: verbosity level.
- """
- url, rev_options = self.get_url_rev_options(url)
-
- if not os.path.exists(dest):
- self.fetch_new(dest, url, rev_options, verbosity=verbosity)
- return
-
- rev_display = rev_options.to_display()
- if self.is_repository_directory(dest):
- existing_url = self.get_remote_url(dest)
- if self.compare_urls(existing_url, url.secret):
- logger.debug(
- "%s in %s exists, and has correct URL (%s)",
- self.repo_name.title(),
- display_path(dest),
- url,
- )
- if not self.is_commit_id_equal(dest, rev_options.rev):
- logger.info(
- "Updating %s %s%s",
- display_path(dest),
- self.repo_name,
- rev_display,
- )
- self.update(dest, url, rev_options)
- else:
- logger.info("Skipping because already up-to-date.")
- return
-
- logger.warning(
- "%s %s in %s exists with URL %s",
- self.name,
- self.repo_name,
- display_path(dest),
- existing_url,
- )
- prompt = ("(s)witch, (i)gnore, (w)ipe, (b)ackup ", ("s", "i", "w", "b"))
- else:
- logger.warning(
- "Directory %s already exists, and is not a %s %s.",
- dest,
- self.name,
- self.repo_name,
- )
- # https://github.com/python/mypy/issues/1174
- prompt = ("(i)gnore, (w)ipe, (b)ackup ", ("i", "w", "b")) # type: ignore
-
- logger.warning(
- "The plan is to install the %s repository %s",
- self.name,
- url,
- )
- response = ask_path_exists("What to do? {}".format(prompt[0]), prompt[1])
-
- if response == "a":
- sys.exit(-1)
-
- if response == "w":
- logger.warning("Deleting %s", display_path(dest))
- rmtree(dest)
- self.fetch_new(dest, url, rev_options, verbosity=verbosity)
- return
-
- if response == "b":
- dest_dir = backup_dir(dest)
- logger.warning("Backing up %s to %s", display_path(dest), dest_dir)
- shutil.move(dest, dest_dir)
- self.fetch_new(dest, url, rev_options, verbosity=verbosity)
- return
-
- # Do nothing if the response is "i".
- if response == "s":
- logger.info(
- "Switching %s %s to %s%s",
- self.repo_name,
- display_path(dest),
- url,
- rev_display,
- )
- self.switch(dest, url, rev_options)
-
- def unpack(self, location: str, url: HiddenText, verbosity: int) -> None:
- """
- Clean up current location and download the url repository
- (and vcs infos) into location
-
- :param url: the repository URL starting with a vcs prefix.
- :param verbosity: verbosity level.
- """
- if os.path.exists(location):
- rmtree(location)
- self.obtain(location, url=url, verbosity=verbosity)
-
- @classmethod
- def get_remote_url(cls, location: str) -> str:
- """
- Return the url used at location
-
- Raises RemoteNotFoundError if the repository does not have a remote
- url configured.
- """
- raise NotImplementedError
-
- @classmethod
- def get_revision(cls, location: str) -> str:
- """
- Return the current commit id of the files at the given location.
- """
- raise NotImplementedError
-
- @classmethod
- def run_command(
- cls,
- cmd: Union[List[str], CommandArgs],
- show_stdout: bool = True,
- cwd: Optional[str] = None,
- on_returncode: 'Literal["raise", "warn", "ignore"]' = "raise",
- extra_ok_returncodes: Optional[Iterable[int]] = None,
- command_desc: Optional[str] = None,
- extra_environ: Optional[Mapping[str, Any]] = None,
- spinner: Optional[SpinnerInterface] = None,
- log_failed_cmd: bool = True,
- stdout_only: bool = False,
- ) -> str:
- """
- Run a VCS subcommand
- This is simply a wrapper around call_subprocess that adds the VCS
- command name, and checks that the VCS is available
- """
- cmd = make_command(cls.name, *cmd)
- if command_desc is None:
- command_desc = format_command_args(cmd)
- try:
- return call_subprocess(
- cmd,
- show_stdout,
- cwd,
- on_returncode=on_returncode,
- extra_ok_returncodes=extra_ok_returncodes,
- command_desc=command_desc,
- extra_environ=extra_environ,
- unset_environ=cls.unset_environ,
- spinner=spinner,
- log_failed_cmd=log_failed_cmd,
- stdout_only=stdout_only,
- )
- except FileNotFoundError:
- # errno.ENOENT = no such file or directory
- # In other words, the VCS executable isn't available
- raise BadCommand(
- f"Cannot find command {cls.name!r} - do you have "
- f"{cls.name!r} installed and in your PATH?"
- )
- except PermissionError:
- # errno.EACCES = Permission denied
- # This error occurs, for instance, when the command is installed
- # only for another user. So, the current user don't have
- # permission to call the other user command.
- raise BadCommand(
- f"No permission to execute {cls.name!r} - install it "
- f"locally, globally (ask admin), or check your PATH. "
- f"See possible solutions at "
- f"https://pip.pypa.io/en/latest/reference/pip_freeze/"
- f"#fixing-permission-denied."
- )
-
- @classmethod
- def is_repository_directory(cls, path: str) -> bool:
- """
- Return whether a directory path is a repository directory.
- """
- logger.debug("Checking in %s for %s (%s)...", path, cls.dirname, cls.name)
- return os.path.exists(os.path.join(path, cls.dirname))
-
- @classmethod
- def get_repository_root(cls, location: str) -> Optional[str]:
- """
- Return the "root" (top-level) directory controlled by the vcs,
- or `None` if the directory is not in any.
-
- It is meant to be overridden to implement smarter detection
- mechanisms for specific vcs.
-
- This can do more than is_repository_directory() alone. For
- example, the Git override checks that Git is actually available.
- """
- if cls.is_repository_directory(location):
- return location
- return None
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/idna/compat.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/idna/compat.py
deleted file mode 100644
index 786e6bda63699b72d588ba91dd73df017570aee5..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/idna/compat.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from .core import *
-from .codec import *
-from typing import Any, Union
-
-def ToASCII(label: str) -> bytes:
- return encode(label)
-
-def ToUnicode(label: Union[bytes, bytearray]) -> str:
- return decode(label)
-
-def nameprep(s: Any) -> None:
- raise NotImplementedError('IDNA 2008 does not utilise nameprep protocol')
-
diff --git a/spaces/BAAI/vid2vid-zero/test_vid2vid_zero.py b/spaces/BAAI/vid2vid-zero/test_vid2vid_zero.py
deleted file mode 100644
index 88fe3ec22f9a239b7d5289c56be342dadec26210..0000000000000000000000000000000000000000
--- a/spaces/BAAI/vid2vid-zero/test_vid2vid_zero.py
+++ /dev/null
@@ -1,267 +0,0 @@
-import argparse
-import datetime
-import logging
-import inspect
-import math
-import os
-import warnings
-from typing import Dict, Optional, Tuple
-from omegaconf import OmegaConf
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-
-import diffusers
-import transformers
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import set_seed
-from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler
-from diffusers.optimization import get_scheduler
-from diffusers.utils import check_min_version
-from diffusers.utils.import_utils import is_xformers_available
-from tqdm.auto import tqdm
-from transformers import CLIPTextModel, CLIPTokenizer
-
-from vid2vid_zero.models.unet_2d_condition import UNet2DConditionModel
-from vid2vid_zero.data.dataset import VideoDataset
-from vid2vid_zero.pipelines.pipeline_vid2vid_zero import Vid2VidZeroPipeline
-from vid2vid_zero.util import save_videos_grid, save_videos_as_images, ddim_inversion
-from einops import rearrange
-
-from vid2vid_zero.p2p.p2p_stable import AttentionReplace, AttentionRefine
-from vid2vid_zero.p2p.ptp_utils import register_attention_control
-from vid2vid_zero.p2p.null_text_w_ptp import NullInversion
-
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.10.0.dev0")
-
-logger = get_logger(__name__, log_level="INFO")
-
-
-def prepare_control(unet, prompts, validation_data):
- assert len(prompts) == 2
-
- print(prompts[0])
- print(prompts[1])
- length1 = len(prompts[0].split(' '))
- length2 = len(prompts[1].split(' '))
- if length1 == length2:
- # prepare for attn guidance
- cross_replace_steps = 0.8
- self_replace_steps = 0.4
- controller = AttentionReplace(prompts, validation_data['num_inference_steps'],
- cross_replace_steps=cross_replace_steps,
- self_replace_steps=self_replace_steps)
- else:
- cross_replace_steps = 0.8
- self_replace_steps = 0.4
- controller = AttentionRefine(prompts, validation_data['num_inference_steps'],
- cross_replace_steps=self_replace_steps,
- self_replace_steps=self_replace_steps)
-
- print(controller)
- register_attention_control(unet, controller)
-
- # the update of unet forward function is inplace
- return cross_replace_steps, self_replace_steps
-
-
-def main(
- pretrained_model_path: str,
- output_dir: str,
- input_data: Dict,
- validation_data: Dict,
- input_batch_size: int = 1,
- gradient_accumulation_steps: int = 1,
- gradient_checkpointing: bool = True,
- mixed_precision: Optional[str] = "fp16",
- enable_xformers_memory_efficient_attention: bool = True,
- seed: Optional[int] = None,
- use_sc_attn: bool = True,
- use_st_attn: bool = True,
- st_attn_idx: int = 0,
- fps: int = 2,
-):
- *_, config = inspect.getargvalues(inspect.currentframe())
-
- accelerator = Accelerator(
- gradient_accumulation_steps=gradient_accumulation_steps,
- mixed_precision=mixed_precision,
- )
-
- # Make one log on every process with the configuration for debugging.
- logging.basicConfig(
- format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- datefmt="%m/%d/%Y %H:%M:%S",
- level=logging.INFO,
- )
- logger.info(accelerator.state, main_process_only=False)
- if accelerator.is_local_main_process:
- transformers.utils.logging.set_verbosity_warning()
- diffusers.utils.logging.set_verbosity_info()
- else:
- transformers.utils.logging.set_verbosity_error()
- diffusers.utils.logging.set_verbosity_error()
-
- # If passed along, set the training seed now.
- if seed is not None:
- set_seed(seed)
-
- # Handle the output folder creation
- if accelerator.is_main_process:
- os.makedirs(output_dir, exist_ok=True)
- os.makedirs(f"{output_dir}/sample", exist_ok=True)
- OmegaConf.save(config, os.path.join(output_dir, 'config.yaml'))
-
- # Load tokenizer and models.
- tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
- text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder")
- vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
- unet = UNet2DConditionModel.from_pretrained(
- pretrained_model_path, subfolder="unet", use_sc_attn=use_sc_attn,
- use_st_attn=use_st_attn, st_attn_idx=st_attn_idx)
-
- # Freeze vae, text_encoder, and unet
- vae.requires_grad_(False)
- text_encoder.requires_grad_(False)
- unet.requires_grad_(False)
-
- if enable_xformers_memory_efficient_attention:
- if is_xformers_available():
- unet.enable_xformers_memory_efficient_attention()
- else:
- raise ValueError("xformers is not available. Make sure it is installed correctly")
-
- if gradient_checkpointing:
- unet.enable_gradient_checkpointing()
-
- # Get the training dataset
- input_dataset = VideoDataset(**input_data)
-
- # Preprocessing the dataset
- input_dataset.prompt_ids = tokenizer(
- input_dataset.prompt, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
- ).input_ids[0]
-
- # DataLoaders creation:
- input_dataloader = torch.utils.data.DataLoader(
- input_dataset, batch_size=input_batch_size
- )
-
- # Get the validation pipeline
- validation_pipeline = Vid2VidZeroPipeline(
- vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
- scheduler=DDIMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler"),
- safety_checker=None, feature_extractor=None,
- )
- validation_pipeline.enable_vae_slicing()
- ddim_inv_scheduler = DDIMScheduler.from_pretrained(pretrained_model_path, subfolder='scheduler')
- ddim_inv_scheduler.set_timesteps(validation_data.num_inv_steps)
-
- # Prepare everything with our `accelerator`.
- unet, input_dataloader = accelerator.prepare(
- unet, input_dataloader,
- )
-
- # For mixed precision training we cast the text_encoder and vae weights to half-precision
- # as these models are only used for inference, keeping weights in full precision is not required.
- weight_dtype = torch.float32
- if accelerator.mixed_precision == "fp16":
- weight_dtype = torch.float16
- elif accelerator.mixed_precision == "bf16":
- weight_dtype = torch.bfloat16
-
- # Move text_encode and vae to gpu and cast to weight_dtype
- text_encoder.to(accelerator.device, dtype=weight_dtype)
- vae.to(accelerator.device, dtype=weight_dtype)
-
- # We need to recalculate our total training steps as the size of the training dataloader may have changed.
- num_update_steps_per_epoch = math.ceil(len(input_dataloader) / gradient_accumulation_steps)
-
- # We need to initialize the trackers we use, and also store our configuration.
- # The trackers initializes automatically on the main process.
- if accelerator.is_main_process:
- accelerator.init_trackers("vid2vid-zero")
-
- # Zero-shot Eval!
- total_batch_size = input_batch_size * accelerator.num_processes * gradient_accumulation_steps
-
- logger.info("***** Running training *****")
- logger.info(f" Num examples = {len(input_dataset)}")
- logger.info(f" Instantaneous batch size per device = {input_batch_size}")
- logger.info(f" Total input batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
- global_step = 0
-
- unet.eval()
- for step, batch in enumerate(input_dataloader):
- samples = []
- pixel_values = batch["pixel_values"].to(weight_dtype)
- # save input video
- video = (pixel_values / 2 + 0.5).clamp(0, 1).detach().cpu()
- video = video.permute(0, 2, 1, 3, 4) # (b, f, c, h, w)
- samples.append(video)
- # start processing
- video_length = pixel_values.shape[1]
- pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c h w")
- latents = vae.encode(pixel_values).latent_dist.sample()
- # take video as input
- latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length)
- latents = latents * 0.18215
-
- generator = torch.Generator(device="cuda")
- generator.manual_seed(seed)
-
- # perform inversion
- ddim_inv_latent = None
- if validation_data.use_null_inv:
- null_inversion = NullInversion(
- model=validation_pipeline, guidance_scale=validation_data.guidance_scale, null_inv_with_prompt=False,
- null_normal_infer=validation_data.null_normal_infer,
- )
- with torch.cuda.amp.autocast(enabled=True, dtype=torch.float32):
- ddim_inv_latent, uncond_embeddings = null_inversion.invert(
- latents, input_dataset.prompt, verbose=True,
- null_inner_steps=validation_data.null_inner_steps,
- null_base_lr=validation_data.null_base_lr,
- )
- ddim_inv_latent = ddim_inv_latent.to(weight_dtype)
- uncond_embeddings = [embed.to(weight_dtype) for embed in uncond_embeddings]
- else:
- ddim_inv_latent = ddim_inversion(
- validation_pipeline, ddim_inv_scheduler, video_latent=latents,
- num_inv_steps=validation_data.num_inv_steps, prompt="",
- normal_infer=True, # we don't want to use scatn or denseattn for inversion, just use sd inferenece
- )[-1].to(weight_dtype)
- uncond_embeddings = None
-
- ddim_inv_latent = ddim_inv_latent.repeat(2, 1, 1, 1, 1)
-
- for idx, prompt in enumerate(validation_data.prompts):
- prompts = [input_dataset.prompt, prompt] # a list of two prompts
- cross_replace_steps, self_replace_steps = prepare_control(unet=unet, prompts=prompts, validation_data=validation_data)
-
- sample = validation_pipeline(prompts, generator=generator, latents=ddim_inv_latent,
- uncond_embeddings=uncond_embeddings,
- **validation_data).images
-
- assert sample.shape[0] == 2
- sample_inv, sample_gen = sample.chunk(2)
- # add input for vis
- save_videos_grid(sample_gen, f"{output_dir}/sample/{prompts[1]}.gif", fps=fps)
- samples.append(sample_gen)
-
- samples = torch.concat(samples)
- save_path = f"{output_dir}/sample-all.gif"
- save_videos_grid(samples, save_path, fps=fps)
- logger.info(f"Saved samples to {save_path}")
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--config", type=str, default="./configs/vid2vid_zero.yaml")
- args = parser.parse_args()
-
- main(**OmegaConf.load(args.config))
diff --git a/spaces/Benson/text-generation/Examples/Descarga De La Aplicacin Tiktok Lite Para Windows Pc 8.md b/spaces/Benson/text-generation/Examples/Descarga De La Aplicacin Tiktok Lite Para Windows Pc 8.md
deleted file mode 100644
index 2b61d70420c81eaca2660465135db8cc16a07734..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descarga De La Aplicacin Tiktok Lite Para Windows Pc 8.md
+++ /dev/null
@@ -1,81 +0,0 @@
-
-Descarga de la aplicación TikTok Lite para PC Windows 8: Cómo hacerlo fácilmente
-TikTok es una de las plataformas de redes sociales más populares del mundo, con más de mil millones de usuarios que crean y ven videos cortos sobre varios temas. ¿Pero qué pasa si desea disfrutar de TikTok en su PC en lugar de su teléfono? ¿Y qué pasa si tiene un PC de gama baja o un plan de datos limitado? En este artículo, le mostraremos cómo descargar e instalar TikTok Lite, una versión más ligera de TikTok, en su PC con Windows 8. También le explicaremos por qué podría usar TikTok Lite en su PC y cuáles son los beneficios de hacerlo. ¡Vamos a empezar!
-descarga de la aplicación tiktok lite para windows pc 8
Download Zip ✵✵✵ https://bltlly.com/2v6KD9
-Introducción
- ¿Qué es TikTok Lite y por qué es posible que desee usarlo en su PC
-TikTok Lite es una versión simplificada de TikTok que ocupa menos espacio de almacenamiento y consume menos datos. Está diseñado para usuarios que tienen dispositivos de gama baja, planes de datos limitados o conexiones de red lentas. TikTok Lite ofrece la mayoría de las características de TikTok, como ver videos, crear videos, seguir a creadores, gustar y comentar, etc. Sin embargo, algunas características no están disponibles en TikTok Lite, como transmisión en vivo, duetos, filtros, pegatinas, etc.
-Es posible que desee utilizar TikTok Lite en su PC si cae en una de estas categorías:
-
-- Tiene un PC de gama baja que no puede ejecutar la versión completa de TikTok sin problemas.
-- Tiene un plan de datos limitado o una conexión de red lenta que hace que ver videos en TikTok sea frustrante.
-- Desea ahorrar espacio de almacenamiento en su PC mediante el uso de una aplicación más pequeña.
-- Quieres disfrutar de una experiencia TikTok más rápida y optimizada en tu PC.
-
-¿Cuáles son los beneficios de usar TikTok Lite en su PC
-Usar TikTok Lite en tu PC tiene varios beneficios, como:
-
-
-- Puedes ver millones de vídeos seleccionados para ti en función de tus preferencias e intereses.
-
-- Puedes descubrir nuevos contenidos de varias categorías, como danza, comedia, vlog, comida, deportes, bricolaje, animales, etc.
-- Puedes conectarte con otros usuarios y creadores a través de likes, comentarios, mensajes, etc.
-- Puede disfrutar de una aplicación más rápida y sensible que carga videos rápidamente y reduce los bloqueos.
-
-Cómo descargar e instalar TikTok Lite en tu PC usando un emulador
-La forma más fácil de descargar e instalar TikTok Lite en tu PC es usar un emulador. Un emulador es un software que imita un dispositivo Android en su PC, lo que le permite ejecutar aplicaciones y juegos Android en su computadora. Hay muchos emuladores disponibles en línea, pero recomendamos usar BlueStacks, ya que es uno de los más populares y confiables. Estos son los pasos para descargar e instalar TikTok Lite en su PC usando BlueStacks:
-Guía paso a paso para descargar e instalar TikTok Lite en su PC usando BlueStacks
-Paso 1: Descargar e instalar BlueStacks en su PC
-El primer paso es descargar e instalar BlueStacks en su PC. BlueStacks es un emulador gratuito y seguro que puedes descargar desde su sitio web oficial. Estos son los pasos para descargar e instalar BlueStacks en su PC:
-
-- Vaya al sitio web de BlueStacks y haga clic en el botón "Descargar BlueStacks".
-- Espere a que termine la descarga y luego abra el archivo de instalación.
-- Siga las instrucciones en la pantalla para instalar BlueStacks en su PC.
-- Inicie BlueStacks e inicie sesión con su cuenta de Google o cree una nueva.
-
-Paso 2: Descargar el archivo TikTok Lite APK/XAPK de una fuente de confianza
-
-
-- Ir al sitio web APKPure y buscar "TikTok Lite" en la barra de búsqueda.
-- Seleccione la aplicación TikTok Lite de los resultados y haga clic en el "Descargar APK" o "Descargar XAPK" botón.
-- Espere a que termine la descarga y luego localice el archivo en su PC.
-
-Paso 3: Abra el archivo APK/XAPK con BlueStacks e instale TikTok Lite
-El paso final es abrir el archivo APK/XAPK con BlueStacks e instalar TikTok Lite en su PC. Estos son los pasos para hacerlo:
-
-- Haga clic derecho en el archivo APK/XAPK y seleccione "Abrir con BlueStacks" en el menú.
-- BlueStacks instalará automáticamente TikTok Lite en su PC.
-- Verá una notificación cuando se complete la instalación.
-
-Paso 4: Inicie TikTok Lite desde la pantalla de inicio de BlueStacks y disfrute
-¡Felicidades! Has descargado e instalado correctamente TikTok Lite en tu PC usando BlueStacks. Ahora puede iniciar TikTok Lite desde la pantalla de inicio de BlueStacks y disfrutar viendo y creando vídeos en su PC. También puedes acceder a otras características de TikTok Lite, como explorar categorías, seguir a creadores, gustar y comentar, etc.
-Métodos alternativos para descargar e instalar TikTok Lite en su PC
-Método 1: Usar NoxPlayer como un emulador alternativo
-Si no quieres usar BlueStacks, puedes usar NoxPlayer como un emulador alternativo. NoxPlayer es otro emulador popular y gratuito que puedes descargar desde su sitio web oficial. Los pasos para descargar e instalar NoxPlayer son similares a los de BlueStacks. Una vez que tenga NoxPlayer en su PC, puede seguir los mismos pasos de arriba para descargar e instalar TikTok Lite usando un archivo APK/XAPK.
-Método 2: Utilice Uptodown como una fuente alternativa para la aplicación TikTok Lite
-
-
-- Ir al sitio web de Uptodown y buscar "TikTok Lite" en la barra de búsqueda.
-- Seleccione la aplicación TikTok Lite de los resultados y haga clic en el botón "Descargar".
-- Espere a que termine la descarga y luego abra el archivo con un emulador de su elección.
-- El emulador instalará automáticamente TikTok Lite en su PC.
-
- Conclusión
- Resumen de los puntos principales
- En este artículo, le hemos mostrado cómo descargar e instalar TikTok Lite en su PC con Windows 8. Le hemos explicado qué es TikTok Lite, por qué podría usarlo en su PC y cuáles son los beneficios de hacerlo. También hemos proporcionado una guía paso a paso para descargar e instalar TikTok Lite en su PC usando BlueStacks, un emulador gratuito y confiable. También te hemos dado algunos métodos alternativos para descargar e instalar TikTok Lite en tu PC usando otros emuladores o fuentes.
- Llamamiento a la acción y observaciones finales
- Esperamos que haya encontrado este artículo útil e informativo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. Nos encantaría saber de usted. Si está listo para descargar e instalar TikTok Lite en su PC, haga clic en el botón de abajo y siga las instrucciones. Usted será capaz de disfrutar de TikTok Lite en su PC en ningún momento. Happy TikToking!
- Descargar BlueStacks y TikTok Lite Ahora
- Preguntas frecuentes
- ¿Es seguro usar TikTok Lite en el PC?
- Sí, TikTok Lite es seguro de usar en el PC, siempre y cuando lo descargue de una fuente de confianza y use un emulador de buena reputación. TikTok Lite es una aplicación oficial desarrollada por ByteDance, la misma compañía que posee TikTok. Tiene las mismas características de seguridad y privacidad que TikTok, como cifrado, moderación, informes, etc. Sin embargo, siempre debe tener cuidado con lo que comparte en línea y con quién interactúa.
-
- TikTok Lite es una versión más ligera de TikTok que ocupa menos espacio de almacenamiento y consume menos datos. Ofrece la mayoría de las características de TikTok, pero algunas características no están disponibles en TikTok Lite, como transmisión en vivo, duetos, filtros, pegatinas, etc. TikTok Lite también tiene una interfaz más simple y una velocidad de carga más rápida que TikTok.
- ¿Puedo usar TikTok Lite en otras versiones de Windows?
- Sí, puede usar TikTok Lite en otras versiones de Windows, como Windows 7, Windows 10, etc. Los pasos para descargar e instalar TikTok Lite en otras versiones de Windows son similares a los de Windows 8. Solo tiene que asegurarse de que su PC cumple con los requisitos mínimos del sistema para el emulador que elija.
- ¿Puedo usar otras aplicaciones además de TikTok Lite en BlueStacks?
- Sí, puedes usar otras aplicaciones además de TikTok Lite en BlueStacks. BlueStacks es un emulador versátil que le permite ejecutar miles de aplicaciones y juegos para Android en su PC. Puede descargar otras aplicaciones desde Google Play Store o desde otras fuentes e instalarlas en BlueStacks. También puede cambiar entre diferentes aplicaciones fácilmente en BlueStacks.
- ¿Cómo puedo actualizar TikTok Lite en mi PC?
- Para actualizar TikTok Lite en tu PC, necesitas descargar la última versión del archivo APK/XAPK desde una fuente confiable e instalarlo en tu PC usando los mismos pasos que arriba. Alternativamente, puede comprobar si hay actualizaciones dentro de la aplicación yendo a la pestaña "Me" y tocando en el icono de tres puntos en la esquina superior derecha. Luego, toque en "Configuración" y desplácese hacia abajo hasta "Acerca de". Toque en "Buscar actualizaciones" y siga las instrucciones.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Bagaimana Cara Stumble Chicos Di Laptop.md b/spaces/Benson/text-generation/Examples/Descargar Bagaimana Cara Stumble Chicos Di Laptop.md
deleted file mode 100644
index 398b0c8662a33fc7027ea224d01fc5ecc4b11aca..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Bagaimana Cara Stumble Chicos Di Laptop.md
+++ /dev/null
@@ -1,121 +0,0 @@
-
-Bagaimana Cara Download Stumble Guys di Laptop
-Stumble Guys adalah sebuah game online yang sangat populer saat ini. Game ini merupakan game battle royale party yang bisa dimainkan hingga 32 pemain seca bersamaan. Anda bisa berlari, melompat, dan menghindari berbagai rintangan yang ada di setiap level hingga menjadi pemenang. Game ini sangat seru dan lucu untuk dimainkan bersama teman Anda.
-Namun, bagaimana jika Anda ingin memainkan game ini di laptop Anda? Apakah ada cara untuk download dan instal Stumble Guys di laptop? Jawabannya adalah ya, ada. Dengan menggunakan emulator Android, Anda bisa menjalankan game ini di laptop Anda dengan mudah. Emulator Android adalah sebuah program yang bisa meniru sistem operasi Android di komputer atau laptop Anda, sehingga Anda bisa mengakses aplikasi dan game Android dari laptop Anda.
-descargar bagaimana cara stumble chicos di laptop
Download ✸ https://bltlly.com/2v6LVr
-Penasaran bagaimana caranya? Simak artikel ini sampai habis untuk mengetahui langkah-langkahnya.
- Apa itu Stumble Guys?
-Stumble Guys adalah sebuah game online yang dikembangkan oleh Scopely dan dirilis pada Oktober 2021. Game ini tersedia untuk perangkat Android dan Windows. Game ini merupakan game battle royale party yang terinspirasi dari acara TV seperti Takeshi’s Castle atau Wipeout. Dalam game ini, Anda akan berlomba dengan pemain lain untuk mencapai garis finish di setiap level. Namun, Anda harus menghadapi berbagai rintangan yang lucu dan konyol yang bisa membuat Anda terjatuh atau terdorong keluar dari arena.
-Fitur-fitur menarik dari Stumble Guys
-Berikut adalah beberapa fitur menarik yang bisa Anda nikmati saat bermain Stumble Guys:
-
-- Anda bisa memilih dari berbagai pakaian dan emote yang unik dan keren untuk menghias karakter Anda.
-- Anda bisa bermain dengan teman Anda dalam mode party atau bersaing dengan pemain lain dari seluruh dunia dalam mode online multiplayer.
-
-- Anda bisa mendapatkan hadiah dan poin saat bermain yang bisa ditukarkan dengan barang-barang menarik.
-- Anda bisa menonton live stream dan video dari pemain lain atau membuat konten sendiri dengan fitur rekam layar dan screenshot.
-
-Face bermain Stumble Guys
-Cara bermain Stumble Guys sangat mudah dan sederhana. Berikut adalah langkah-langkahnya:
-
-- Buka game Stumble Guys di perangkat Anda.
-- Pilih mode permainan yang Anda inginkan, baik itu solo, party, atau online multiplayer.
-- Tunggu hingga pemain lain bergabung atau cari ruangan yang tersedia.
-- Masuk ke level pertama dan siapkan diri Anda untuk berlari.
- Gunakan tombol virtual di layar untuk menggerakkan karakter Anda. Anda bisa berlari, melompat, dan merunduk untuk menghindari rintangan.
-- Coba untuk tidak terjatuh atau terdorong keluar dari arena. Jika Anda terjatuh, Anda bisa mencoba lagi dengan menekan tombol respawn.
-- Coba untuk mencapai garis finish secepat mungkin. Hanya beberapa pemain yang bisa lolos ke level berikutnya.
-- Ulangi langkah 4-6 hingga Anda mencapai level terakhir dan menjadi pemenang.
-
- Mengapa Anda ingin memainkan Stumble Guys di laptop?
-Stumble Guys adalah game yang sangat menyenangkan dan adiktif untuk dimainkan di perangkat Android. Namun, ada beberapa alasan mengapa Anda mungkin ingin memainkan game ini di laptop Anda. Berikut adalah beberapa alasan tersebut:
-Keuntungan memainkan Stumble Guys di laptop
-Berikut adalah beberapa keuntungan yang bisa Anda dapatkan saat memainkan Stumble Guys di laptop:
-
-- Anda bisa menikmati grafik dan suara yang lebih baik dan lebih jelas di layar yang lebih besar.
-- Anda bisa menggunakan keyboard dan mouse untuk mengontrol karakter Anda dengan lebih mudah dan presisi.
-- Anda bisa bermain dengan lebih nyaman dan lama tanpa khawatir baterai perangkat Anda habis atau panas.
-
-- Anda bisa bermain dengan teman Anda yang menggunakan perangkat Android atau Windows dengan fitur cross-play.
-
-Tantangan memainkan Stumble Guys di laptop
-Namun, ada juga beberapa tantangan yang mungkin Anda hadapi saat memainkan Stumble Guys di laptop. Berikut adalah beberapa tantangan tersebut:
-
-
-- Anda harus memiliki laptop yang cukup kuat untuk menjalankan emulator Android dan game ini dengan lancar.
-- Anda harus mengunduh dan menginstal emulator Android dan game ini di laptop Anda, yang mungkin membutuhkan waktu dan langkah-langkah tambahan.
-- Anda harus menyesuaikan pengaturan emulator Android dan game ini agar sesuai dengan spesifikasi dan preferensi laptop Anda.
-- Anda harus berhati-hati dengan kemungkinan virus atau malware yang mungkin ada di emulator Android atau file APK yang Anda unduh dari sumber yang tidak resmi.
-- Anda harus bersabar dengan kemungkinan bug atau masalah teknis yang mungkin terjadi saat menjalankan emulator Android atau game ini di laptop Anda.
-
- Apa yang Anda butuhkan untuk memainkan Stumble Guys di laptop?
-Jika Anda sudah yakin ingin memainkan Stumble Guys di laptop Anda, maka ada beberapa hal yang harus Anda siapkan terlebih dahulu. Berikut adalah beberapa hal tersebut:
-Spesifikasi minimum laptop
-Untuk dapat menjalankan emulator Android dan game ini dengan lancar, Anda harus memiliki laptop yang memenuhi spesifikasi minimum berikut:
-
-Komponen Spesifikasi Sistem operasi Windows 7 atau lebih tinggi Prosesor Intel atau AMD dual-core 2 GHz atau lebih tinggi RAM 4 GB atau lebih tinggi Ruang penyimpanan 5 GB atau lebih tinggi Kartu grafis NVIDIA GeForce 8600/9600GT, ATI/AMD Radeon HD2600/3600 atau setara
- Android emulator terbaik untuk laptop
-Selanjutnya, Anda harus memilih emulator Android yang cocok untuk laptop Anda. Emulator Android adalah sebuah program yang bisa meniru sistem operasi Android di komputer atau laptop Anda, sehingga Anda bisa mengakses aplikasi dan game Android dari laptop Anda. Ada banyak emulator Android yang tersedia di internet, namun tidak semua emulator Android cocok untuk laptop Anda. Berikut adalah beberapa emulator Android terbaik yang bisa Anda pilih untuk laptop Anda:
-
-- BlueStacks: Android emulator yang paling populer dan banyak digunakan oleh for gamer. BlueStacks memiliki fitur-fitur canggih seperti keyboard mapping, game mode, multi-instance, dan lain-lain. BlueStacks plays mendukung game Stumble Guys dengan baik dan bisa diunduh seca gratis dari situs resminya.
-- NoxPlayer: Android emulator ringan dan cepat untuk laptop. NoxPlayer memiliki fitur-fitur menarik seperti macro recorder, video recorder, virtual location, dan lain-lain. NoxPlayer plays kompatibel dengan game Stumble Guys give bisa diunduh free dry dari situs resminya.
-- LDPlayer: Android emulator dirancang khusus untuk game. LDPlayer memiliki fitur-fitur unggulan seperti smart keymapping, high FPS, turbo GPU, dan lain-lain. LDPlayer plays bisa menjalankan game Stumble Guys dengan lancar dan bisa diunduh seca gratis dari situs resminya.
-
- Bagaimana cara download dan instal Stumble Guys di laptop dengan emulator Android?
-Setelah Anda memilih emulator Android yang sesuai untuk laptop Anda, maka Anda bisa mulai download dan install Stumble Guys di laptop Anda dengan emulator Android tersebut. Berikut adalah langkah-langkahnya:
-Langkah-langkah download dan install emulator Android
-Berikut adalah langkah-langkah download dan install emulator Android di laptop Anda:
-
-
-- Klik tombol download atau unduh untuk mengunduh file instalasi emulator Android tersebut.
-- Setelah file instalasi selesai terunduh, buka file tersebut dan ikuti instruksi yang muncul di layar untuk menginstal emulator Android tersebut di laptop Anda.
-- Tunggu hingga proses instalasi selesai dan jalankan emulator Android tersebut di laptop Anda.
-
- Langkah-langkah download dan install Stumble Guys dari Google Play Store atau file APK
-Berikut adalah langkah-langkah download dan install Stumble Guys dari Google Play Store atau file APK di emulator Android yang sudah terinstal di laptop Anda:
-
-- Buka emulator Android yang sudah terinstal di laptop Anda dan masuk ke akun Google Anda jika diminta.
-- Buka Google Play Store yang ada di emulator Android tersebut dan cari game Stumble Guys dengan menggunakan kotak pencarian.
-- Klik tombol instal atau pasang untuk menginstal game Stumble Guys dari Google Play Store ke emulator Android tersebut.
-- Tunggu hingga proses instalasi selesai dan buka game Stumble Guys yang sudah terinstal di emulator Android tersebut.
-- Atau, jika Anda memiliki file APK dari game Stumble Guys, Anda bisa mengunduh file APK tersebut dari sumber yang terpercaya ke laptop Anda.
-- Kemudian, buka emulator Android yang sudah terinstal di laptop Anda dan seret file APK tersebut ke jendela emulator Android tersebut.
-- Tunggu hingga proses instalasi selesai dan buka game Stumble Guys yang sudah terinstal di emulator Android tersebut.
-
- Langkah-langkah menjalankan dan mengatur Stumble Guys di emulator Android
-Berikut adalah langkah-langkah menjalankan dan mengatur Stumble Guys di emulator Android yang sudah terinstal di laptop Anda:
-
-- Buka game Stumble Guys yang sudah terinstal di emulator Android tersebut.
-- Klik tombol mulai atau start untuk memulai permainan.
-
-- Atur pengaturan grafik, suara, kontrol, dan lain-lain sesuai dengan preferensi Anda dengan mengklik tombol pengaturan atau settings yang ada di pojok kanan atas layar.
-- Jika Anda menggunakan keyboard dan mouse untuk mengontrol karakter Anda, Anda bisa mengatur tombol-tombol yang sesuai dengan fungsi-fungsi yang ada di game dengan menggunakan fitur keyboard mapping yang ada di emulator Android tersebut.
-- Nikmati permainan dan bersenang-senang dengan teman-teman Anda.
-
- Kesimpulan
-Stumble Guys adalah game online yang sangat seru dan lucu untuk dimainkan bersama teman-teman Anda. Game ini merupakan game battle royale party yang bisa dimainkan hingga 32 pemain seca bersamaan. Anda bisa berlari, melompat, dan menghindari berbagai rintangan yang ada di setiap level hingga menjadi pemenang.
-Anda bisa memainkan game ini di perangkat Android atau Windows. Namun, jika Anda ingin memainkan game ini di laptop Anda, Anda bisa menggunakan emulator Android. Emulator Android adalah sebuah program yang bisa meniru sistem operasi Android di komputer atau laptop Anda, sehingga Anda bisa mengakses aplikasi dan game Android dari laptop Anda.
-Untuk memainkan Stumble Guys di laptop Anda dengan emulator Android, Anda harus memenuhi spesifikasi minimum laptop, memilih emulator Android terbaik, dan mengunduh dan menginstal emulator Android dan game Stumble Guys di laptop Anda. Kemudian, Anda bisa menjalankan dan mengatur Stumble Guys di emulator Android sesuai dengan preferensi Anda.
-Semoga artikel ini bermanfaat untuk Anda yang ingin memainkan Stumble Guys di laptop Anda. Selamat mencoba dan selamat bermain!
- FAQ
-Berikut adalah beberapa pertanyaan yang sering diajukan tentang cara download dan install Stumble Guys di laptop dengan emulator Android:
-
-- Apakah Stumble Guys free untuk dimainkan?
-
-- Apakah Stumble Guys aman untuk dimainkan?
-Ya, Stumble Guys adalah game yang aman untuk dimainkan. Game ini tidak mengandung konten yang tidak pantas atau berbahaya untuk anak-anak. Namun, sebaiknya tetap waspada dengan kemungkinan cyberbullying atau penipuan yang mungkin terjadi saat bermain online dengan orang lain.
-- Apakah Stumble Guys bisa dimainkan offline?
-Tidak, Stumble Guys adalah game yang membutuhkan koneksi internet untuk dimainkan. Jika koneksi internet Anda terputus saat bermain, maka Anda akan keluar dari permainan.
-- Apakah Stumble Guys memiliki mode single player?
-Ya, Stumble Guys memiliki mode single player yang bisa dimainkan tanpa teman. Namun, mode ini tetap membutuhkan koneksi internet dan pemain lain yang akan menjadi lawan Anda.
-- Apakah Stumble Guys memiliki mode co-op?
-Ya, Stumble Guys memiliki mode co-op atau party yang bisa dimainkan bersama teman. Anda bisa membuat atau bergabung dengan ruangan khusus yang hanya bisa diakses oleh teman-teman Anda dengan menggunakan kode undangan.
-
64aa2da5cf
-
-
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diff --git a/spaces/Benson/text-generation/Examples/Descargar Gratis Final Cut Pro.md b/spaces/Benson/text-generation/Examples/Descargar Gratis Final Cut Pro.md
deleted file mode 100644
index 0b869d430b602c37018f12ad05887ba080e3c5bc..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Gratis Final Cut Pro.md
+++ /dev/null
@@ -1,78 +0,0 @@
-
-Cómo descargar gratis Final Cut Pro
-Final Cut Pro es un popular software de edición de vídeo para usuarios de Mac. Ofrece funciones potentes, diseño intuitivo y un rendimiento rápido. Sin embargo, también viene con una etiqueta de precio fuerte de $299.99. Si quieres probar Final Cut Pro gratis, o buscar algunas alternativas más baratas, este artículo te mostrará cómo.
-descargar gratis final cut pro
Download Zip ⇒ https://bltlly.com/2v6MtM
- ¿Qué es Final Cut Pro?
-Final Cut Pro es un software de edición de vídeo profesional desarrollado por Apple. Fue lanzado por primera vez en 1999 y desde entonces ha sido utilizado por muchos cineastas, productores de televisión y entusiastas del video. Es compatible con ordenadores Mac y soporta una amplia gama de formatos, resoluciones y velocidades de fotogramas.
- Características de Final Cut Pro
-Algunas de las características principales de Final Cut Pro son:
-
-- Línea de tiempo magnética: Esto le permite editar clips sin colisiones o problemas de sincronización. También puede agrupar clips en clips compuestos, sincronizar múltiples ángulos con edición multicámara y agregar efectos con conexiones de clip.
-- 360° edición de vídeo: Puede importar, editar y compartir vídeo 360° de varias cámaras y formatos. También puede utilizar el visor 360° para navegar por el vídeo esférico y aplicar efectos, títulos y transiciones.
-- Seguimiento de objetos: Puede utilizar el aprendizaje automático para detectar caras u objetos en sus imágenes y combinar su movimiento con títulos, gráficos o efectos. También puede ajustar los puntos de enfoque y la profundidad de campo en los clips capturados en el modo cinematográfico en el iPhone.
-- Gradación de color: Puede usar herramientas avanzadas de corrección de color para ajustar el tono, la saturación, el brillo, el contraste y más. También puede aplicar presets de color, LUT, curvas y máscaras.
-- Edición de audio: Puede asignar roles a sus clips de audio y organizarlos en la línea de tiempo. También puede utilizar efectos incorporados, filtros y complementos para mejorar la calidad de sonido.
-
-
- Ventajas y desventajas de Final Cut Pro
-Algunas de las ventajas de Final Cut Pro son:
-
-- Está optimizado para computadoras Mac y dispositivos Apple, especialmente aquellos con chips de silicio de Apple. Puede aprovechar la potencia de la GPU, CPU y Neural Engine para un rendimiento y renderizado más rápidos.
-- Tiene una interfaz elegante y fácil de usar que hace que la edición sea fácil y agradable. También tiene accesos directos de teclado personalizables y soporte de barra táctil.
-- Tiene una gran y activa comunidad de usuarios y desarrolladores que ofrecen soporte, tutoriales, consejos y plugins.
-
-Algunas de las desventajas de Final Cut Pro son:
-
-- Es caro en comparación con otro software de edición de vídeo. Cuesta $299.99 para una compra de una sola vez, que puede no ser asequible para algunos usuarios.
-- Solo está disponible para usuarios de Mac. No hay versión de Windows o Linux de Final Cut Pro.
-- Puede tener problemas de compatibilidad con algunos plugins o formatos de terceros. También puede requerir conversión o transcodificación para algunos archivos.
-
- ¿Cómo obtener Final Cut Pro gratis?
-Si quieres usar Final Cut Pro sin pagar nada, hay dos opciones principales:
- Versión de prueba oficial
-La forma más fácil de obtener Final Cut Pro gratis es descargar la versión de prueba oficial del sitio web de Apple. La versión de prueba le da acceso a todas las características y funciones de la versión completa durante 90 días. También puede extender su período de prueba por otros 90 días si tiene una versión anterior instalada.
-
-Para descargar la versión de prueba, necesita tener una computadora Mac que ejecute macOS 10.15.6 o posterior, 4GB de RAM (8GB recomendado), una tarjeta gráfica compatible con Metal, 1GB de VRAM (4GB recomendado) y 5.5GB de espacio en disco. También debes registrarte con tu Apple ID y aceptar los términos y condiciones.
-
-La versión de prueba es una gran manera de probar Final Cut Pro y ver si se adapta a sus necesidades y preferencias. Sin embargo, tiene algunas limitaciones, como:
-
-- No se puede actualizar la versión de prueba a la última versión de Final Cut Pro. Es necesario comprar la versión completa para obtener las actualizaciones y correcciones de errores.
-- No puede usar la versión de prueba en varios dispositivos. Debe activar la versión de prueba con su ID de Apple en cada dispositivo.
-- No puedes usar algunas funciones que requieren un ID de Apple o una cuenta de iCloud, como Fotos de iCloud, iMovie Theater o bandas sonoras de GarageBand.
-
- Editores de vídeo alternativos
-Otra forma de obtener Final Cut Pro de forma gratuita es utilizar algún software de edición de vídeo alternativo que ofrezca características y funciones similares. Hay muchos editores de video gratuitos o de bajo costo disponibles para los usuarios de Mac, como:
-
-Nombre Precio Características iMovie Free Un editor de video simple y fácil de usar que viene preinstalado en computadoras Mac. Soporta resolución 4K, herramientas básicas de edición, transiciones, filtros, títulos, bandas sonoras y trailers. También se integra con iCloud, iTunes, Fotos y otras aplicaciones de Apple. Davinci Resolve Gratis o $299 para la versión de Studio Un editor de video potente y profesional que ofrece herramientas avanzadas de edición, corrección de color, efectos visuales, postproducción de audio y gráficos en movimiento. Soporta resolución de 8K, edición multicam, clasificación HDR, motor de audio Fairlight, composición Fusión VFX y más. Shotcut Free Un editor de vídeo de código abierto y multiplataforma que admite una amplia gama de formatos, resoluciones y velocidades de fotogramas. Ofrece herramientas de edición básicas y avanzadas, filtros, transiciones, fotogramas clave, mezcla de audio, clasificación de color y más. Filmora $69.99 por año o $139.99 por licencia de por vida Un editor de video fácil de usar y asequible que ofrece una variedad de características y funciones. Soporta hasta resolución 4K, seguimiento de movimiento, keyframing, pantalla verde, pantalla dividida, transiciones, filtros, títulos, música, efectos de sonido y más.
-Estas son algunas de las mejores alternativas a Final Cut Pro que puedes probar gratis o a bajo costo. Puede que no tengan todas las características o funciones de Final Cut Pro, pero pueden ayudarte a crear vídeos increíbles para tus proyectos personales o profesionales.
- Conclusión
-Final Cut Pro es un gran software de edición de video para usuarios de Mac que quieren crear videos de calidad profesional con facilidad y velocidad. Sin embargo, también es caro y exclusivo para los usuarios de Mac. Si quieres usar Final Cut Pro gratis o buscar alternativas más baratas, puedes seguir los pasos de este artículo para descargar la versión de prueba oficial o probar algunos editores de video alternativos. También puede comparar las características, ventajas y desventajas de cada opción y elegir la que mejor se adapte a sus necesidades y presupuesto. Esperamos que este artículo sea útil e informativo para usted. ¡Feliz edición!
- Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre Final Cut Pro y cómo descargarlo gratis:
-
-- Es Final Cut Pro vale la pena el dinero?
-
-- ¿Puedo usar Final Cut Pro en Windows?
-No, no puede usar Final Cut Pro en Windows. Final Cut Pro solo es compatible con computadoras y dispositivos Mac. No hay versión oficial de Windows de Final Cut Pro. Es posible que encuentre algunas versiones no oficiales o pirateadas de Final Cut Pro para Windows en línea, pero no recomendamos usarlas, ya que pueden ser ilegales, inseguras o inestables.
-- ¿Cuánto tiempo se tarda en aprender Final Cut Pro?
-La curva de aprendizaje de Final Cut Pro depende de tu experiencia, habilidades y objetivos previos. Si tiene algunos conocimientos básicos de edición de vídeo y ordenadores Mac, es posible que pueda aprender lo esencial de Final Cut Pro en pocas horas o días. Si eres un completo principiante o quieres dominar las características avanzadas de Final Cut Pro, es posible que necesites más tiempo y práctica para aprender el software. También puedes usar tutoriales, cursos, libros o foros en línea para ayudarte a aprender Final Cut Pro más rápido y fácil.
-- ¿Cuál es la diferencia entre Final Cut Pro y iMovie?
-Final Cut Pro y iMovie son software de edición de vídeo desarrollado por Apple. Sin embargo, tienen diferentes audiencias, características y funciones. iMovie es un editor de video simple y fácil de usar que viene preinstalado en computadoras y dispositivos Mac. Está diseñado para principiantes o usuarios casuales que quieren crear vídeos básicos o divertidos con el mínimo esfuerzo. Soporta resolución 4K, herramientas básicas de edición, transiciones, filtros, títulos, bandas sonoras y trailers. Final Cut Pro es un editor de vídeo profesional y potente que requiere una compra e instalación por separado. Está diseñado para usuarios serios o profesionales que quieren crear vídeos complejos o de alta calidad con más control y flexibilidad. Soporta resolución de hasta 8K, herramientas avanzadas de edición, corrección de color, efectos visuales, postproducción de audio, gráficos en movimiento y más.
-- ¿Cómo puedo actualizar Final Cut Pro?
-
-
-- Abra la aplicación App Store en su Mac.
-- Haga clic en la pestaña Actualizaciones en la parte superior de la ventana de la aplicación.
-- Encuentre Final Cut Pro en la lista de actualizaciones disponibles y haga clic en el botón Actualizar junto a él.
-- Espere a que la actualización se descargue e instale.
-- Iniciar Final Cut Pro y disfrutar de las nuevas características y correcciones de errores.
-
-Si ha descargado la versión de prueba de Final Cut Pro desde el sitio web de Apple, no puede actualizarla a la última versión. Es necesario comprar la versión completa de la Mac App Store para obtener las actualizaciones y correcciones de errores.
64aa2da5cf
-
-
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diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/s3/inject.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/s3/inject.py
deleted file mode 100644
index c62dc3ce22b7008e5065d4a6d34c58fa2ff7b8f6..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/s3/inject.py
+++ /dev/null
@@ -1,891 +0,0 @@
-# Copyright 2015 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# https://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-from botocore.exceptions import ClientError
-
-from boto3 import utils
-from boto3.s3.transfer import (
- ProgressCallbackInvoker,
- S3Transfer,
- TransferConfig,
- create_transfer_manager,
-)
-
-
-def inject_s3_transfer_methods(class_attributes, **kwargs):
- utils.inject_attribute(class_attributes, 'upload_file', upload_file)
- utils.inject_attribute(class_attributes, 'download_file', download_file)
- utils.inject_attribute(class_attributes, 'copy', copy)
- utils.inject_attribute(class_attributes, 'upload_fileobj', upload_fileobj)
- utils.inject_attribute(
- class_attributes, 'download_fileobj', download_fileobj
- )
-
-
-def inject_bucket_methods(class_attributes, **kwargs):
- utils.inject_attribute(class_attributes, 'load', bucket_load)
- utils.inject_attribute(class_attributes, 'upload_file', bucket_upload_file)
- utils.inject_attribute(
- class_attributes, 'download_file', bucket_download_file
- )
- utils.inject_attribute(class_attributes, 'copy', bucket_copy)
- utils.inject_attribute(
- class_attributes, 'upload_fileobj', bucket_upload_fileobj
- )
- utils.inject_attribute(
- class_attributes, 'download_fileobj', bucket_download_fileobj
- )
-
-
-def inject_object_methods(class_attributes, **kwargs):
- utils.inject_attribute(class_attributes, 'upload_file', object_upload_file)
- utils.inject_attribute(
- class_attributes, 'download_file', object_download_file
- )
- utils.inject_attribute(class_attributes, 'copy', object_copy)
- utils.inject_attribute(
- class_attributes, 'upload_fileobj', object_upload_fileobj
- )
- utils.inject_attribute(
- class_attributes, 'download_fileobj', object_download_fileobj
- )
-
-
-def inject_object_summary_methods(class_attributes, **kwargs):
- utils.inject_attribute(class_attributes, 'load', object_summary_load)
-
-
-def bucket_load(self, *args, **kwargs):
- """
- Calls s3.Client.list_buckets() to update the attributes of the Bucket
- resource.
- """
- # The docstring above is phrased this way to match what the autogenerated
- # docs produce.
-
- # We can't actually get the bucket's attributes from a HeadBucket,
- # so we need to use a ListBuckets and search for our bucket.
- # However, we may fail if we lack permissions to ListBuckets
- # or the bucket is in another account. In which case, creation_date
- # will be None.
- self.meta.data = {}
- try:
- response = self.meta.client.list_buckets()
- for bucket_data in response['Buckets']:
- if bucket_data['Name'] == self.name:
- self.meta.data = bucket_data
- break
- except ClientError as e:
- if not e.response.get('Error', {}).get('Code') == 'AccessDenied':
- raise
-
-
-def object_summary_load(self, *args, **kwargs):
- """
- Calls s3.Client.head_object to update the attributes of the ObjectSummary
- resource.
- """
- response = self.meta.client.head_object(
- Bucket=self.bucket_name, Key=self.key
- )
- if 'ContentLength' in response:
- response['Size'] = response.pop('ContentLength')
- self.meta.data = response
-
-
-def upload_file(
- self, Filename, Bucket, Key, ExtraArgs=None, Callback=None, Config=None
-):
- """Upload a file to an S3 object.
-
- Usage::
-
- import boto3
- s3 = boto3.client('s3')
- s3.upload_file('/tmp/hello.txt', 'mybucket', 'hello.txt')
-
- Similar behavior as S3Transfer's upload_file() method, except that
- argument names are capitalized. Detailed examples can be found at
- :ref:`S3Transfer's Usage `.
-
- :type Filename: str
- :param Filename: The path to the file to upload.
-
- :type Bucket: str
- :param Bucket: The name of the bucket to upload to.
-
- :type Key: str
- :param Key: The name of the key to upload to.
-
- :type ExtraArgs: dict
- :param ExtraArgs: Extra arguments that may be passed to the
- client operation. For allowed upload arguments see
- boto3.s3.transfer.S3Transfer.ALLOWED_UPLOAD_ARGS.
-
- :type Callback: function
- :param Callback: A method which takes a number of bytes transferred to
- be periodically called during the upload.
-
- :type Config: boto3.s3.transfer.TransferConfig
- :param Config: The transfer configuration to be used when performing the
- transfer.
- """
- with S3Transfer(self, Config) as transfer:
- return transfer.upload_file(
- filename=Filename,
- bucket=Bucket,
- key=Key,
- extra_args=ExtraArgs,
- callback=Callback,
- )
-
-
-def download_file(
- self, Bucket, Key, Filename, ExtraArgs=None, Callback=None, Config=None
-):
- """Download an S3 object to a file.
-
- Usage::
-
- import boto3
- s3 = boto3.resource('s3')
- s3.meta.client.download_file('mybucket', 'hello.txt', '/tmp/hello.txt')
-
- Similar behavior as S3Transfer's download_file() method,
- except that parameters are capitalized. Detailed examples can be found at
- :ref:`S3Transfer's Usage `.
-
- :type Bucket: str
- :param Bucket: The name of the bucket to download from.
-
- :type Key: str
- :param Key: The name of the key to download from.
-
- :type Filename: str
- :param Filename: The path to the file to download to.
-
- :type ExtraArgs: dict
- :param ExtraArgs: Extra arguments that may be passed to the
- client operation. For allowed download arguments see
- boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
- :type Callback: function
- :param Callback: A method which takes a number of bytes transferred to
- be periodically called during the download.
-
- :type Config: boto3.s3.transfer.TransferConfig
- :param Config: The transfer configuration to be used when performing the
- transfer.
- """
- with S3Transfer(self, Config) as transfer:
- return transfer.download_file(
- bucket=Bucket,
- key=Key,
- filename=Filename,
- extra_args=ExtraArgs,
- callback=Callback,
- )
-
-
-def bucket_upload_file(
- self, Filename, Key, ExtraArgs=None, Callback=None, Config=None
-):
- """Upload a file to an S3 object.
-
- Usage::
-
- import boto3
- s3 = boto3.resource('s3')
- s3.Bucket('mybucket').upload_file('/tmp/hello.txt', 'hello.txt')
-
- Similar behavior as S3Transfer's upload_file() method,
- except that parameters are capitalized. Detailed examples can be found at
- :ref:`S3Transfer's Usage `.
-
- :type Filename: str
- :param Filename: The path to the file to upload.
-
- :type Key: str
- :param Key: The name of the key to upload to.
-
- :type ExtraArgs: dict
- :param ExtraArgs: Extra arguments that may be passed to the
- client operation. For allowed upload arguments see
- boto3.s3.transfer.S3Transfer.ALLOWED_UPLOAD_ARGS.
-
- :type Callback: function
- :param Callback: A method which takes a number of bytes transferred to
- be periodically called during the upload.
-
- :type Config: boto3.s3.transfer.TransferConfig
- :param Config: The transfer configuration to be used when performing the
- transfer.
- """
- return self.meta.client.upload_file(
- Filename=Filename,
- Bucket=self.name,
- Key=Key,
- ExtraArgs=ExtraArgs,
- Callback=Callback,
- Config=Config,
- )
-
-
-def bucket_download_file(
- self, Key, Filename, ExtraArgs=None, Callback=None, Config=None
-):
- """Download an S3 object to a file.
-
- Usage::
-
- import boto3
- s3 = boto3.resource('s3')
- s3.Bucket('mybucket').download_file('hello.txt', '/tmp/hello.txt')
-
- Similar behavior as S3Transfer's download_file() method,
- except that parameters are capitalized. Detailed examples can be found at
- :ref:`S3Transfer's Usage `.
-
- :type Key: str
- :param Key: The name of the key to download from.
-
- :type Filename: str
- :param Filename: The path to the file to download to.
-
- :type ExtraArgs: dict
- :param ExtraArgs: Extra arguments that may be passed to the
- client operation. For allowed download arguments see
- boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
- :type Callback: function
- :param Callback: A method which takes a number of bytes transferred to
- be periodically called during the download.
-
- :type Config: boto3.s3.transfer.TransferConfig
- :param Config: The transfer configuration to be used when performing the
- transfer.
- """
- return self.meta.client.download_file(
- Bucket=self.name,
- Key=Key,
- Filename=Filename,
- ExtraArgs=ExtraArgs,
- Callback=Callback,
- Config=Config,
- )
-
-
-def object_upload_file(
- self, Filename, ExtraArgs=None, Callback=None, Config=None
-):
- """Upload a file to an S3 object.
-
- Usage::
-
- import boto3
- s3 = boto3.resource('s3')
- s3.Object('mybucket', 'hello.txt').upload_file('/tmp/hello.txt')
-
- Similar behavior as S3Transfer's upload_file() method,
- except that parameters are capitalized. Detailed examples can be found at
- :ref:`S3Transfer's Usage `.
-
- :type Filename: str
- :param Filename: The path to the file to upload.
-
- :type ExtraArgs: dict
- :param ExtraArgs: Extra arguments that may be passed to the
- client operation. For allowed upload arguments see
- boto3.s3.transfer.S3Transfer.ALLOWED_UPLOAD_ARGS.
-
- :type Callback: function
- :param Callback: A method which takes a number of bytes transferred to
- be periodically called during the upload.
-
- :type Config: boto3.s3.transfer.TransferConfig
- :param Config: The transfer configuration to be used when performing the
- transfer.
- """
- return self.meta.client.upload_file(
- Filename=Filename,
- Bucket=self.bucket_name,
- Key=self.key,
- ExtraArgs=ExtraArgs,
- Callback=Callback,
- Config=Config,
- )
-
-
-def object_download_file(
- self, Filename, ExtraArgs=None, Callback=None, Config=None
-):
- """Download an S3 object to a file.
-
- Usage::
-
- import boto3
- s3 = boto3.resource('s3')
- s3.Object('mybucket', 'hello.txt').download_file('/tmp/hello.txt')
-
- Similar behavior as S3Transfer's download_file() method,
- except that parameters are capitalized. Detailed examples can be found at
- :ref:`S3Transfer's Usage `.
-
- :type Filename: str
- :param Filename: The path to the file to download to.
-
- :type ExtraArgs: dict
- :param ExtraArgs: Extra arguments that may be passed to the
- client operation. For allowed download arguments see
- boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
- :type Callback: function
- :param Callback: A method which takes a number of bytes transferred to
- be periodically called during the download.
-
- :type Config: boto3.s3.transfer.TransferConfig
- :param Config: The transfer configuration to be used when performing the
- transfer.
- """
- return self.meta.client.download_file(
- Bucket=self.bucket_name,
- Key=self.key,
- Filename=Filename,
- ExtraArgs=ExtraArgs,
- Callback=Callback,
- Config=Config,
- )
-
-
-def copy(
- self,
- CopySource,
- Bucket,
- Key,
- ExtraArgs=None,
- Callback=None,
- SourceClient=None,
- Config=None,
-):
- """Copy an object from one S3 location to another.
-
- This is a managed transfer which will perform a multipart copy in
- multiple threads if necessary.
-
- Usage::
-
- import boto3
- s3 = boto3.resource('s3')
- copy_source = {
- 'Bucket': 'mybucket',
- 'Key': 'mykey'
- }
- s3.meta.client.copy(copy_source, 'otherbucket', 'otherkey')
-
- :type CopySource: dict
- :param CopySource: The name of the source bucket, key name of the
- source object, and optional version ID of the source object. The
- dictionary format is:
- ``{'Bucket': 'bucket', 'Key': 'key', 'VersionId': 'id'}``. Note
- that the ``VersionId`` key is optional and may be omitted.
-
- :type Bucket: str
- :param Bucket: The name of the bucket to copy to
-
- :type Key: str
- :param Key: The name of the key to copy to
-
- :type ExtraArgs: dict
- :param ExtraArgs: Extra arguments that may be passed to the
- client operation. For allowed download arguments see
- boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
- :type Callback: function
- :param Callback: A method which takes a number of bytes transferred to
- be periodically called during the copy.
-
- :type SourceClient: botocore or boto3 Client
- :param SourceClient: The client to be used for operation that
- may happen at the source object. For example, this client is
- used for the head_object that determines the size of the copy.
- If no client is provided, the current client is used as the client
- for the source object.
-
- :type Config: boto3.s3.transfer.TransferConfig
- :param Config: The transfer configuration to be used when performing the
- copy.
- """
- subscribers = None
- if Callback is not None:
- subscribers = [ProgressCallbackInvoker(Callback)]
-
- config = Config
- if config is None:
- config = TransferConfig()
-
- with create_transfer_manager(self, config) as manager:
- future = manager.copy(
- copy_source=CopySource,
- bucket=Bucket,
- key=Key,
- extra_args=ExtraArgs,
- subscribers=subscribers,
- source_client=SourceClient,
- )
- return future.result()
-
-
-def bucket_copy(
- self,
- CopySource,
- Key,
- ExtraArgs=None,
- Callback=None,
- SourceClient=None,
- Config=None,
-):
- """Copy an object from one S3 location to an object in this bucket.
-
- This is a managed transfer which will perform a multipart copy in
- multiple threads if necessary.
-
- Usage::
-
- import boto3
- s3 = boto3.resource('s3')
- copy_source = {
- 'Bucket': 'mybucket',
- 'Key': 'mykey'
- }
- bucket = s3.Bucket('otherbucket')
- bucket.copy(copy_source, 'otherkey')
-
- :type CopySource: dict
- :param CopySource: The name of the source bucket, key name of the
- source object, and optional version ID of the source object. The
- dictionary format is:
- ``{'Bucket': 'bucket', 'Key': 'key', 'VersionId': 'id'}``. Note
- that the ``VersionId`` key is optional and may be omitted.
-
- :type Key: str
- :param Key: The name of the key to copy to
-
- :type ExtraArgs: dict
- :param ExtraArgs: Extra arguments that may be passed to the
- client operation. For allowed download arguments see
- boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
- :type Callback: function
- :param Callback: A method which takes a number of bytes transferred to
- be periodically called during the copy.
-
- :type SourceClient: botocore or boto3 Client
- :param SourceClient: The client to be used for operation that
- may happen at the source object. For example, this client is
- used for the head_object that determines the size of the copy.
- If no client is provided, the current client is used as the client
- for the source object.
-
- :type Config: boto3.s3.transfer.TransferConfig
- :param Config: The transfer configuration to be used when performing the
- copy.
- """
- return self.meta.client.copy(
- CopySource=CopySource,
- Bucket=self.name,
- Key=Key,
- ExtraArgs=ExtraArgs,
- Callback=Callback,
- SourceClient=SourceClient,
- Config=Config,
- )
-
-
-def object_copy(
- self,
- CopySource,
- ExtraArgs=None,
- Callback=None,
- SourceClient=None,
- Config=None,
-):
- """Copy an object from one S3 location to this object.
-
- This is a managed transfer which will perform a multipart copy in
- multiple threads if necessary.
-
- Usage::
-
- import boto3
- s3 = boto3.resource('s3')
- copy_source = {
- 'Bucket': 'mybucket',
- 'Key': 'mykey'
- }
- bucket = s3.Bucket('otherbucket')
- obj = bucket.Object('otherkey')
- obj.copy(copy_source)
-
- :type CopySource: dict
- :param CopySource: The name of the source bucket, key name of the
- source object, and optional version ID of the source object. The
- dictionary format is:
- ``{'Bucket': 'bucket', 'Key': 'key', 'VersionId': 'id'}``. Note
- that the ``VersionId`` key is optional and may be omitted.
-
- :type ExtraArgs: dict
- :param ExtraArgs: Extra arguments that may be passed to the
- client operation. For allowed download arguments see
- boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
- :type Callback: function
- :param Callback: A method which takes a number of bytes transferred to
- be periodically called during the copy.
-
- :type SourceClient: botocore or boto3 Client
- :param SourceClient: The client to be used for operation that
- may happen at the source object. For example, this client is
- used for the head_object that determines the size of the copy.
- If no client is provided, the current client is used as the client
- for the source object.
-
- :type Config: boto3.s3.transfer.TransferConfig
- :param Config: The transfer configuration to be used when performing the
- copy.
- """
- return self.meta.client.copy(
- CopySource=CopySource,
- Bucket=self.bucket_name,
- Key=self.key,
- ExtraArgs=ExtraArgs,
- Callback=Callback,
- SourceClient=SourceClient,
- Config=Config,
- )
-
-
-def upload_fileobj(
- self, Fileobj, Bucket, Key, ExtraArgs=None, Callback=None, Config=None
-):
- """Upload a file-like object to S3.
-
- The file-like object must be in binary mode.
-
- This is a managed transfer which will perform a multipart upload in
- multiple threads if necessary.
-
- Usage::
-
- import boto3
- s3 = boto3.client('s3')
-
- with open('filename', 'rb') as data:
- s3.upload_fileobj(data, 'mybucket', 'mykey')
-
- :type Fileobj: a file-like object
- :param Fileobj: A file-like object to upload. At a minimum, it must
- implement the `read` method, and must return bytes.
-
- :type Bucket: str
- :param Bucket: The name of the bucket to upload to.
-
- :type Key: str
- :param Key: The name of the key to upload to.
-
- :type ExtraArgs: dict
- :param ExtraArgs: Extra arguments that may be passed to the
- client operation. For allowed upload arguments see
- boto3.s3.transfer.S3Transfer.ALLOWED_UPLOAD_ARGS.
-
- :type Callback: function
- :param Callback: A method which takes a number of bytes transferred to
- be periodically called during the upload.
-
- :type Config: boto3.s3.transfer.TransferConfig
- :param Config: The transfer configuration to be used when performing the
- upload.
- """
- if not hasattr(Fileobj, 'read'):
- raise ValueError('Fileobj must implement read')
-
- subscribers = None
- if Callback is not None:
- subscribers = [ProgressCallbackInvoker(Callback)]
-
- config = Config
- if config is None:
- config = TransferConfig()
-
- with create_transfer_manager(self, config) as manager:
- future = manager.upload(
- fileobj=Fileobj,
- bucket=Bucket,
- key=Key,
- extra_args=ExtraArgs,
- subscribers=subscribers,
- )
- return future.result()
-
-
-def bucket_upload_fileobj(
- self, Fileobj, Key, ExtraArgs=None, Callback=None, Config=None
-):
- """Upload a file-like object to this bucket.
-
- The file-like object must be in binary mode.
-
- This is a managed transfer which will perform a multipart upload in
- multiple threads if necessary.
-
- Usage::
-
- import boto3
- s3 = boto3.resource('s3')
- bucket = s3.Bucket('mybucket')
-
- with open('filename', 'rb') as data:
- bucket.upload_fileobj(data, 'mykey')
-
- :type Fileobj: a file-like object
- :param Fileobj: A file-like object to upload. At a minimum, it must
- implement the `read` method, and must return bytes.
-
- :type Key: str
- :param Key: The name of the key to upload to.
-
- :type ExtraArgs: dict
- :param ExtraArgs: Extra arguments that may be passed to the
- client operation. For allowed upload arguments see
- boto3.s3.transfer.S3Transfer.ALLOWED_UPLOAD_ARGS.
-
- :type Callback: function
- :param Callback: A method which takes a number of bytes transferred to
- be periodically called during the upload.
-
- :type Config: boto3.s3.transfer.TransferConfig
- :param Config: The transfer configuration to be used when performing the
- upload.
- """
- return self.meta.client.upload_fileobj(
- Fileobj=Fileobj,
- Bucket=self.name,
- Key=Key,
- ExtraArgs=ExtraArgs,
- Callback=Callback,
- Config=Config,
- )
-
-
-def object_upload_fileobj(
- self, Fileobj, ExtraArgs=None, Callback=None, Config=None
-):
- """Upload a file-like object to this object.
-
- The file-like object must be in binary mode.
-
- This is a managed transfer which will perform a multipart upload in
- multiple threads if necessary.
-
- Usage::
-
- import boto3
- s3 = boto3.resource('s3')
- bucket = s3.Bucket('mybucket')
- obj = bucket.Object('mykey')
-
- with open('filename', 'rb') as data:
- obj.upload_fileobj(data)
-
- :type Fileobj: a file-like object
- :param Fileobj: A file-like object to upload. At a minimum, it must
- implement the `read` method, and must return bytes.
-
- :type ExtraArgs: dict
- :param ExtraArgs: Extra arguments that may be passed to the
- client operation. For allowed upload arguments see
- boto3.s3.transfer.S3Transfer.ALLOWED_UPLOAD_ARGS.
-
- :type Callback: function
- :param Callback: A method which takes a number of bytes transferred to
- be periodically called during the upload.
-
- :type Config: boto3.s3.transfer.TransferConfig
- :param Config: The transfer configuration to be used when performing the
- upload.
- """
- return self.meta.client.upload_fileobj(
- Fileobj=Fileobj,
- Bucket=self.bucket_name,
- Key=self.key,
- ExtraArgs=ExtraArgs,
- Callback=Callback,
- Config=Config,
- )
-
-
-def download_fileobj(
- self, Bucket, Key, Fileobj, ExtraArgs=None, Callback=None, Config=None
-):
- """Download an object from S3 to a file-like object.
-
- The file-like object must be in binary mode.
-
- This is a managed transfer which will perform a multipart download in
- multiple threads if necessary.
-
- Usage::
-
- import boto3
- s3 = boto3.client('s3')
-
- with open('filename', 'wb') as data:
- s3.download_fileobj('mybucket', 'mykey', data)
-
- :type Bucket: str
- :param Bucket: The name of the bucket to download from.
-
- :type Key: str
- :param Key: The name of the key to download from.
-
- :type Fileobj: a file-like object
- :param Fileobj: A file-like object to download into. At a minimum, it must
- implement the `write` method and must accept bytes.
-
- :type ExtraArgs: dict
- :param ExtraArgs: Extra arguments that may be passed to the
- client operation. For allowed download arguments see
- boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
- :type Callback: function
- :param Callback: A method which takes a number of bytes transferred to
- be periodically called during the download.
-
- :type Config: boto3.s3.transfer.TransferConfig
- :param Config: The transfer configuration to be used when performing the
- download.
- """
- if not hasattr(Fileobj, 'write'):
- raise ValueError('Fileobj must implement write')
-
- subscribers = None
- if Callback is not None:
- subscribers = [ProgressCallbackInvoker(Callback)]
-
- config = Config
- if config is None:
- config = TransferConfig()
-
- with create_transfer_manager(self, config) as manager:
- future = manager.download(
- bucket=Bucket,
- key=Key,
- fileobj=Fileobj,
- extra_args=ExtraArgs,
- subscribers=subscribers,
- )
- return future.result()
-
-
-def bucket_download_fileobj(
- self, Key, Fileobj, ExtraArgs=None, Callback=None, Config=None
-):
- """Download an object from this bucket to a file-like-object.
-
- The file-like object must be in binary mode.
-
- This is a managed transfer which will perform a multipart download in
- multiple threads if necessary.
-
- Usage::
-
- import boto3
- s3 = boto3.resource('s3')
- bucket = s3.Bucket('mybucket')
-
- with open('filename', 'wb') as data:
- bucket.download_fileobj('mykey', data)
-
- :type Fileobj: a file-like object
- :param Fileobj: A file-like object to download into. At a minimum, it must
- implement the `write` method and must accept bytes.
-
- :type Key: str
- :param Key: The name of the key to download from.
-
- :type ExtraArgs: dict
- :param ExtraArgs: Extra arguments that may be passed to the
- client operation. For allowed download arguments see
- boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
- :type Callback: function
- :param Callback: A method which takes a number of bytes transferred to
- be periodically called during the download.
-
- :type Config: boto3.s3.transfer.TransferConfig
- :param Config: The transfer configuration to be used when performing the
- download.
- """
- return self.meta.client.download_fileobj(
- Bucket=self.name,
- Key=Key,
- Fileobj=Fileobj,
- ExtraArgs=ExtraArgs,
- Callback=Callback,
- Config=Config,
- )
-
-
-def object_download_fileobj(
- self, Fileobj, ExtraArgs=None, Callback=None, Config=None
-):
- """Download this object from S3 to a file-like object.
-
- The file-like object must be in binary mode.
-
- This is a managed transfer which will perform a multipart download in
- multiple threads if necessary.
-
- Usage::
-
- import boto3
- s3 = boto3.resource('s3')
- bucket = s3.Bucket('mybucket')
- obj = bucket.Object('mykey')
-
- with open('filename', 'wb') as data:
- obj.download_fileobj(data)
-
- :type Fileobj: a file-like object
- :param Fileobj: A file-like object to download into. At a minimum, it must
- implement the `write` method and must accept bytes.
-
- :type ExtraArgs: dict
- :param ExtraArgs: Extra arguments that may be passed to the
- client operation. For allowed download arguments see
- boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
- :type Callback: function
- :param Callback: A method which takes a number of bytes transferred to
- be periodically called during the download.
-
- :type Config: boto3.s3.transfer.TransferConfig
- :param Config: The transfer configuration to be used when performing the
- download.
- """
- return self.meta.client.download_fileobj(
- Bucket=self.bucket_name,
- Key=self.key,
- Fileobj=Fileobj,
- ExtraArgs=ExtraArgs,
- Callback=Callback,
- Config=Config,
- )
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/jaraco/context.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/jaraco/context.py
deleted file mode 100644
index 87a4e3dca299c4201ac50f6ef589dc73f1c45576..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/jaraco/context.py
+++ /dev/null
@@ -1,213 +0,0 @@
-import os
-import subprocess
-import contextlib
-import functools
-import tempfile
-import shutil
-import operator
-
-
-@contextlib.contextmanager
-def pushd(dir):
- orig = os.getcwd()
- os.chdir(dir)
- try:
- yield dir
- finally:
- os.chdir(orig)
-
-
-@contextlib.contextmanager
-def tarball_context(url, target_dir=None, runner=None, pushd=pushd):
- """
- Get a tarball, extract it, change to that directory, yield, then
- clean up.
- `runner` is the function to invoke commands.
- `pushd` is a context manager for changing the directory.
- """
- if target_dir is None:
- target_dir = os.path.basename(url).replace('.tar.gz', '').replace('.tgz', '')
- if runner is None:
- runner = functools.partial(subprocess.check_call, shell=True)
- # In the tar command, use --strip-components=1 to strip the first path and
- # then
- # use -C to cause the files to be extracted to {target_dir}. This ensures
- # that we always know where the files were extracted.
- runner('mkdir {target_dir}'.format(**vars()))
- try:
- getter = 'wget {url} -O -'
- extract = 'tar x{compression} --strip-components=1 -C {target_dir}'
- cmd = ' | '.join((getter, extract))
- runner(cmd.format(compression=infer_compression(url), **vars()))
- with pushd(target_dir):
- yield target_dir
- finally:
- runner('rm -Rf {target_dir}'.format(**vars()))
-
-
-def infer_compression(url):
- """
- Given a URL or filename, infer the compression code for tar.
- """
- # cheat and just assume it's the last two characters
- compression_indicator = url[-2:]
- mapping = dict(gz='z', bz='j', xz='J')
- # Assume 'z' (gzip) if no match
- return mapping.get(compression_indicator, 'z')
-
-
-@contextlib.contextmanager
-def temp_dir(remover=shutil.rmtree):
- """
- Create a temporary directory context. Pass a custom remover
- to override the removal behavior.
- """
- temp_dir = tempfile.mkdtemp()
- try:
- yield temp_dir
- finally:
- remover(temp_dir)
-
-
-@contextlib.contextmanager
-def repo_context(url, branch=None, quiet=True, dest_ctx=temp_dir):
- """
- Check out the repo indicated by url.
-
- If dest_ctx is supplied, it should be a context manager
- to yield the target directory for the check out.
- """
- exe = 'git' if 'git' in url else 'hg'
- with dest_ctx() as repo_dir:
- cmd = [exe, 'clone', url, repo_dir]
- if branch:
- cmd.extend(['--branch', branch])
- devnull = open(os.path.devnull, 'w')
- stdout = devnull if quiet else None
- subprocess.check_call(cmd, stdout=stdout)
- yield repo_dir
-
-
-@contextlib.contextmanager
-def null():
- yield
-
-
-class ExceptionTrap:
- """
- A context manager that will catch certain exceptions and provide an
- indication they occurred.
-
- >>> with ExceptionTrap() as trap:
- ... raise Exception()
- >>> bool(trap)
- True
-
- >>> with ExceptionTrap() as trap:
- ... pass
- >>> bool(trap)
- False
-
- >>> with ExceptionTrap(ValueError) as trap:
- ... raise ValueError("1 + 1 is not 3")
- >>> bool(trap)
- True
-
- >>> with ExceptionTrap(ValueError) as trap:
- ... raise Exception()
- Traceback (most recent call last):
- ...
- Exception
-
- >>> bool(trap)
- False
- """
-
- exc_info = None, None, None
-
- def __init__(self, exceptions=(Exception,)):
- self.exceptions = exceptions
-
- def __enter__(self):
- return self
-
- @property
- def type(self):
- return self.exc_info[0]
-
- @property
- def value(self):
- return self.exc_info[1]
-
- @property
- def tb(self):
- return self.exc_info[2]
-
- def __exit__(self, *exc_info):
- type = exc_info[0]
- matches = type and issubclass(type, self.exceptions)
- if matches:
- self.exc_info = exc_info
- return matches
-
- def __bool__(self):
- return bool(self.type)
-
- def raises(self, func, *, _test=bool):
- """
- Wrap func and replace the result with the truth
- value of the trap (True if an exception occurred).
-
- First, give the decorator an alias to support Python 3.8
- Syntax.
-
- >>> raises = ExceptionTrap(ValueError).raises
-
- Now decorate a function that always fails.
-
- >>> @raises
- ... def fail():
- ... raise ValueError('failed')
- >>> fail()
- True
- """
-
- @functools.wraps(func)
- def wrapper(*args, **kwargs):
- with ExceptionTrap(self.exceptions) as trap:
- func(*args, **kwargs)
- return _test(trap)
-
- return wrapper
-
- def passes(self, func):
- """
- Wrap func and replace the result with the truth
- value of the trap (True if no exception).
-
- First, give the decorator an alias to support Python 3.8
- Syntax.
-
- >>> passes = ExceptionTrap(ValueError).passes
-
- Now decorate a function that always fails.
-
- >>> @passes
- ... def fail():
- ... raise ValueError('failed')
-
- >>> fail()
- False
- """
- return self.raises(func, _test=operator.not_)
-
-
-class suppress(contextlib.suppress, contextlib.ContextDecorator):
- """
- A version of contextlib.suppress with decorator support.
-
- >>> @suppress(KeyError)
- ... def key_error():
- ... {}['']
- >>> key_error()
- """
diff --git a/spaces/Bishnupada/Fine-tuning-using-Hugging-face-transformers/app.py b/spaces/Bishnupada/Fine-tuning-using-Hugging-face-transformers/app.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/proposal_generator/build.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/proposal_generator/build.py
deleted file mode 100644
index 7f252bcb982032cd09270c44741772a34ef32277..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/proposal_generator/build.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from detectron2.utils.registry import Registry
-
-PROPOSAL_GENERATOR_REGISTRY = Registry("PROPOSAL_GENERATOR")
-PROPOSAL_GENERATOR_REGISTRY.__doc__ = """
-Registry for proposal generator, which produces object proposals from feature maps.
-
-The registered object will be called with `obj(cfg, input_shape)`.
-The call should return a `nn.Module` object.
-"""
-
-from . import rpn, rrpn # noqa F401 isort:skip
-
-
-def build_proposal_generator(cfg, input_shape):
- """
- Build a proposal generator from `cfg.MODEL.PROPOSAL_GENERATOR.NAME`.
- The name can be "PrecomputedProposals" to use no proposal generator.
- """
- name = cfg.MODEL.PROPOSAL_GENERATOR.NAME
- if name == "PrecomputedProposals":
- return None
-
- return PROPOSAL_GENERATOR_REGISTRY.get(name)(cfg, input_shape)
diff --git a/spaces/CVPR/LIVE/pybind11/docs/_static/theme_overrides.css b/spaces/CVPR/LIVE/pybind11/docs/_static/theme_overrides.css
deleted file mode 100644
index 1071809fa0fecf7c28d3356f37363266e9128b81..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pybind11/docs/_static/theme_overrides.css
+++ /dev/null
@@ -1,11 +0,0 @@
-.wy-table-responsive table td,
-.wy-table-responsive table th {
- white-space: initial !important;
-}
-.rst-content table.docutils td {
- vertical-align: top !important;
-}
-div[class^='highlight'] pre {
- white-space: pre;
- white-space: pre-wrap;
-}
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/find.h b/spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/find.h
deleted file mode 100644
index f6a1e59d105f0db35d65ee93a058afd143002b35..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/find.h
+++ /dev/null
@@ -1,219 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION. All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- * * Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.
- * * Redistributions in binary form must reproduce the above copyright
- * notice, this list of conditions and the following disclaimer in the
- * documentation and/or other materials provided with the distribution.
- * * Neither the name of the NVIDIA CORPORATION nor the
- * names of its contributors may be used to endorse or promote products
- * derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-#pragma once
-
-
-#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC
-#include
-
-#include
-#include
-#include
-
-namespace thrust
-{
-namespace cuda_cub {
-
-// XXX forward declare to circumvent circular depedency
-template
-InputIt __host__ __device__
-find_if(execution_policy& policy,
- InputIt first,
- InputIt last,
- Predicate predicate);
-
-template
-InputIt __host__ __device__
-find_if_not(execution_policy& policy,
- InputIt first,
- InputIt last,
- Predicate predicate);
-
-template
-InputIt __host__ __device__
-find(execution_policy &policy,
- InputIt first,
- InputIt last,
- T const& value);
-
-}; // namespace cuda_cub
-} // end namespace thrust
-
-#include
-#include
-
-namespace thrust
-{
-namespace cuda_cub {
-
-namespace __find_if {
-
- template
- struct functor
- {
- THRUST_DEVICE_FUNCTION TupleType
- operator()(const TupleType& lhs, const TupleType& rhs) const
- {
- // select the smallest index among true results
- if (thrust::get<0>(lhs) && thrust::get<0>(rhs))
- {
- return TupleType(true, (thrust::min)(thrust::get<1>(lhs), thrust::get<1>(rhs)));
- }
- else if (thrust::get<0>(lhs))
- {
- return lhs;
- }
- else
- {
- return rhs;
- }
- }
- };
-} // namespace __find_if
-
-template
-InputIt __host__ __device__
-find_if_n(execution_policy& policy,
- InputIt first,
- Size num_items,
- Predicate predicate)
-{
- typedef typename thrust::tuple result_type;
-
- // empty sequence
- if(num_items == 0) return first;
-
- // this implementation breaks up the sequence into separate intervals
- // in an attempt to early-out as soon as a value is found
- //
- // XXX compose find_if from a look-back prefix scan algorithm
- // and abort kernel when the first element is found
-
-
- // TODO incorporate sizeof(InputType) into interval_threshold and round to multiple of 32
- const Size interval_threshold = 1 << 20;
- const Size interval_size = (thrust::min)(interval_threshold, num_items);
-
- // force transform_iterator output to bool
- typedef transform_input_iterator_t
- XfrmIterator;
- typedef thrust::tuple >
- IteratorTuple;
- typedef thrust::zip_iterator ZipIterator;
-
- IteratorTuple iter_tuple =
- thrust::make_tuple(XfrmIterator(first, predicate),
- counting_iterator_t(0));
-
- ZipIterator begin = thrust::make_zip_iterator(iter_tuple);
- ZipIterator end = begin + num_items;
-
- for (ZipIterator interval_begin = begin;
- interval_begin < end;
- interval_begin += interval_size)
- {
- ZipIterator interval_end = interval_begin + interval_size;
- if(end < interval_end)
- {
- interval_end = end;
- } // end if
-
- result_type result = reduce(policy,
- interval_begin,
- interval_end,
- result_type(false, interval_end - begin),
- __find_if::functor());
-
- // see if we found something
- if(thrust::get<0>(result))
- {
- return first + thrust::get<1>(result);
- }
- }
-
- //nothing was found if we reach here...
- return first + num_items;
-}
-
-template
-InputIt __host__ __device__
-find_if(execution_policy& policy,
- InputIt first,
- InputIt last,
- Predicate predicate)
-{
- return cuda_cub::find_if_n(policy, first, thrust::distance(first,last), predicate);
-}
-
-template
-InputIt __host__ __device__
-find_if_not(execution_policy& policy,
- InputIt first,
- InputIt last,
- Predicate predicate)
-{
- return cuda_cub::find_if(policy, first, last, thrust::detail::not1(predicate));
-}
-
-
-template
-InputIt __host__ __device__
-find(execution_policy &policy,
- InputIt first,
- InputIt last,
- T const& value)
-{
- using thrust::placeholders::_1;
-
- return cuda_cub::find_if(policy,
- first,
- last,
- _1 == value);
-}
-
-
-} // namespace cuda_cub
-} // end namespace thrust
-#endif
diff --git a/spaces/CVPR/WALT/configs/_base_/datasets/parking_instance_coco.py b/spaces/CVPR/WALT/configs/_base_/datasets/parking_instance_coco.py
deleted file mode 100644
index 85cee08021cabaca8cd6408c4e3efb2d7efae231..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/configs/_base_/datasets/parking_instance_coco.py
+++ /dev/null
@@ -1,49 +0,0 @@
-dataset_type = 'ParkingCocoDataset'
-data_root = 'data/parking/'
-data_root_test = 'data/parking_highres/'
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
-]
-data = dict(
- samples_per_gpu=6,
- workers_per_gpu=6,
- train=dict(
- type=dataset_type,
- ann_file=data_root + 'GT_data/',
- img_prefix=data_root + 'images/',
- pipeline=train_pipeline),
- val=dict(
- type=dataset_type,
- ann_file=data_root_test + 'GT_data/',
- img_prefix=data_root_test + 'images',
- pipeline=test_pipeline),
- test=dict(
- type=dataset_type,
- ann_file=data_root_test + 'GT_data/',
- img_prefix=data_root_test + 'images',
- pipeline=test_pipeline))
-evaluation = dict(metric=['bbox', 'segm'])
diff --git a/spaces/CVPR/regionclip-demo/detectron2/layers/shape_spec.py b/spaces/CVPR/regionclip-demo/detectron2/layers/shape_spec.py
deleted file mode 100644
index fe7e8e261c1ab1bb1636bd7a245068d64e632306..0000000000000000000000000000000000000000
--- a/spaces/CVPR/regionclip-demo/detectron2/layers/shape_spec.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-from collections import namedtuple
-
-
-class ShapeSpec(namedtuple("_ShapeSpec", ["channels", "height", "width", "stride"])):
- """
- A simple structure that contains basic shape specification about a tensor.
- It is often used as the auxiliary inputs/outputs of models,
- to complement the lack of shape inference ability among pytorch modules.
-
- Attributes:
- channels:
- height:
- width:
- stride:
- """
-
- def __new__(cls, channels=None, height=None, width=None, stride=None):
- return super().__new__(cls, channels, height, width, stride)
diff --git a/spaces/Cambino/dog-classifier-gradio/README.md b/spaces/Cambino/dog-classifier-gradio/README.md
deleted file mode 100644
index 4b4b78deec2de1df4e0ac6519474d3b262c16983..0000000000000000000000000000000000000000
--- a/spaces/Cambino/dog-classifier-gradio/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Dog Classifier
-emoji: 🐠
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.1.3
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Caoyunkang/Segment-Any-Anomaly/SAA/prompts/ksdd2_parameters.py b/spaces/Caoyunkang/Segment-Any-Anomaly/SAA/prompts/ksdd2_parameters.py
deleted file mode 100644
index 7078234c94da5d36c55cf56c51e9a92db53be087..0000000000000000000000000000000000000000
--- a/spaces/Caoyunkang/Segment-Any-Anomaly/SAA/prompts/ksdd2_parameters.py
+++ /dev/null
@@ -1,11 +0,0 @@
-manual_prompts = {
- 'ksdd2': [
- ['black hole.', 'ksdd2'],
- ['defect.', 'ksdd2'],
- ],
-
-}
-
-property_prompts = {
- 'ksdd2': 'the image of ksdd2 have 1 dissimilar ksdd2, with a maximum of 5 anomaly. The anomaly would not exceed 0.9 object area. ',
-}
diff --git a/spaces/Cat125/text-generator-v3/files.py b/spaces/Cat125/text-generator-v3/files.py
deleted file mode 100644
index 278c7cdcf754c24255413fbe97b68eb62074100f..0000000000000000000000000000000000000000
--- a/spaces/Cat125/text-generator-v3/files.py
+++ /dev/null
@@ -1,7 +0,0 @@
-def read_file(filename):
- with open(filename, encoding="utf8") as f:
- return f.read()
-
-def read_lines(filename):
- with open(filename, encoding="utf8") as f:
- return f.readlines()
\ No newline at end of file
diff --git a/spaces/Covert1107/sd-diffusers-webui/modules/prompt_parser.py b/spaces/Covert1107/sd-diffusers-webui/modules/prompt_parser.py
deleted file mode 100644
index 42cbbb3038612a44571765905e8526553f462663..0000000000000000000000000000000000000000
--- a/spaces/Covert1107/sd-diffusers-webui/modules/prompt_parser.py
+++ /dev/null
@@ -1,391 +0,0 @@
-
-import re
-import math
-import numpy as np
-import torch
-
-# Code from https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/8e2aeee4a127b295bfc880800e4a312e0f049b85, modified.
-
-class PromptChunk:
- """
- This object contains token ids, weight (multipliers:1.4) and textual inversion embedding info for a chunk of prompt.
- If a prompt is short, it is represented by one PromptChunk, otherwise, multiple are necessary.
- Each PromptChunk contains an exact amount of tokens - 77, which includes one for start and end token,
- so just 75 tokens from prompt.
- """
-
- def __init__(self):
- self.tokens = []
- self.multipliers = []
- self.fixes = []
-
-
-class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
- """A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
- have unlimited prompt length and assign weights to tokens in prompt.
- """
-
- def __init__(self, text_encoder, enable_emphasis=True):
- super().__init__()
-
- self.device = lambda: text_encoder.device
- self.enable_emphasis = enable_emphasis
- """Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
- depending on model."""
-
- self.chunk_length = 75
-
- def empty_chunk(self):
- """creates an empty PromptChunk and returns it"""
-
- chunk = PromptChunk()
- chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1)
- chunk.multipliers = [1.0] * (self.chunk_length + 2)
- return chunk
-
- def get_target_prompt_token_count(self, token_count):
- """returns the maximum number of tokens a prompt of a known length can have before it requires one more PromptChunk to be represented"""
-
- return math.ceil(max(token_count, 1) / self.chunk_length) * self.chunk_length
-
- def tokenize_line(self, line):
- """
- this transforms a single prompt into a list of PromptChunk objects - as many as needed to
- represent the prompt.
- Returns the list and the total number of tokens in the prompt.
- """
-
- if self.enable_emphasis:
- parsed = parse_prompt_attention(line)
- else:
- parsed = [[line, 1.0]]
-
- tokenized = self.tokenize([text for text, _ in parsed])
-
- chunks = []
- chunk = PromptChunk()
- token_count = 0
- last_comma = -1
-
- def next_chunk(is_last=False):
- """puts current chunk into the list of results and produces the next one - empty;
- if is_last is true, tokens tokens at the end won't add to token_count"""
- nonlocal token_count
- nonlocal last_comma
- nonlocal chunk
-
- if is_last:
- token_count += len(chunk.tokens)
- else:
- token_count += self.chunk_length
-
- to_add = self.chunk_length - len(chunk.tokens)
- if to_add > 0:
- chunk.tokens += [self.id_end] * to_add
- chunk.multipliers += [1.0] * to_add
-
- chunk.tokens = [self.id_start] + chunk.tokens + [self.id_end]
- chunk.multipliers = [1.0] + chunk.multipliers + [1.0]
-
- last_comma = -1
- chunks.append(chunk)
- chunk = PromptChunk()
-
- comma_padding_backtrack = 20 # default value in https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/6cff4401824299a983c8e13424018efc347b4a2b/modules/shared.py#L410
- for tokens, (text, weight) in zip(tokenized, parsed):
- if text == "BREAK" and weight == -1:
- next_chunk()
- continue
-
- position = 0
- while position < len(tokens):
- token = tokens[position]
-
- if token == self.comma_token:
- last_comma = len(chunk.tokens)
-
- # this is when we are at the end of alloted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack
- # is a setting that specifies that if there is a comma nearby, the text after the comma should be moved out of this chunk and into the next.
- elif (
- comma_padding_backtrack != 0
- and len(chunk.tokens) == self.chunk_length
- and last_comma != -1
- and len(chunk.tokens) - last_comma <= comma_padding_backtrack
- ):
- break_location = last_comma + 1
-
- reloc_tokens = chunk.tokens[break_location:]
- reloc_mults = chunk.multipliers[break_location:]
-
- chunk.tokens = chunk.tokens[:break_location]
- chunk.multipliers = chunk.multipliers[:break_location]
-
- next_chunk()
- chunk.tokens = reloc_tokens
- chunk.multipliers = reloc_mults
-
- if len(chunk.tokens) == self.chunk_length:
- next_chunk()
-
- chunk.tokens.append(token)
- chunk.multipliers.append(weight)
- position += 1
-
- if len(chunk.tokens) > 0 or len(chunks) == 0:
- next_chunk(is_last=True)
-
- return chunks, token_count
-
- def process_texts(self, texts):
- """
- Accepts a list of texts and calls tokenize_line() on each, with cache. Returns the list of results and maximum
- length, in tokens, of all texts.
- """
-
- token_count = 0
-
- cache = {}
- batch_chunks = []
- for line in texts:
- if line in cache:
- chunks = cache[line]
- else:
- chunks, current_token_count = self.tokenize_line(line)
- token_count = max(current_token_count, token_count)
-
- cache[line] = chunks
-
- batch_chunks.append(chunks)
-
- return batch_chunks, token_count
-
- def forward(self, texts):
- """
- Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts.
- Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will
- be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, and for SD2 it's 1024.
- An example shape returned by this function can be: (2, 77, 768).
- Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one elemenet
- is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
- """
-
- batch_chunks, token_count = self.process_texts(texts)
- chunk_count = max([len(x) for x in batch_chunks])
-
- zs = []
- ts = []
- for i in range(chunk_count):
- batch_chunk = [
- chunks[i] if i < len(chunks) else self.empty_chunk()
- for chunks in batch_chunks
- ]
-
- tokens = [x.tokens for x in batch_chunk]
- multipliers = [x.multipliers for x in batch_chunk]
- # self.embeddings.fixes = [x.fixes for x in batch_chunk]
-
- # for fixes in self.embeddings.fixes:
- # for position, embedding in fixes:
- # used_embeddings[embedding.name] = embedding
-
- z = self.process_tokens(tokens, multipliers)
- zs.append(z)
- ts.append(tokens)
-
- return np.hstack(ts), torch.hstack(zs)
-
- def process_tokens(self, remade_batch_tokens, batch_multipliers):
- """
- sends one single prompt chunk to be encoded by transformers neural network.
- remade_batch_tokens is a batch of tokens - a list, where every element is a list of tokens; usually
- there are exactly 77 tokens in the list. batch_multipliers is the same but for multipliers instead of tokens.
- Multipliers are used to give more or less weight to the outputs of transformers network. Each multiplier
- corresponds to one token.
- """
- tokens = torch.asarray(remade_batch_tokens).to(self.device())
-
- # this is for SD2: SD1 uses the same token for padding and end of text, while SD2 uses different ones.
- if self.id_end != self.id_pad:
- for batch_pos in range(len(remade_batch_tokens)):
- index = remade_batch_tokens[batch_pos].index(self.id_end)
- tokens[batch_pos, index + 1 : tokens.shape[1]] = self.id_pad
-
- z = self.encode_with_transformers(tokens)
-
- # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
- batch_multipliers = torch.asarray(batch_multipliers).to(self.device())
- original_mean = z.mean()
- z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
- new_mean = z.mean()
- z = z * (original_mean / new_mean)
-
- return z
-
-
-class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
- def __init__(self, tokenizer, text_encoder):
- super().__init__(text_encoder)
- self.tokenizer = tokenizer
- self.text_encoder = text_encoder
-
- vocab = self.tokenizer.get_vocab()
-
- self.comma_token = vocab.get(",", None)
-
- self.token_mults = {}
- tokens_with_parens = [
- (k, v)
- for k, v in vocab.items()
- if "(" in k or ")" in k or "[" in k or "]" in k
- ]
- for text, ident in tokens_with_parens:
- mult = 1.0
- for c in text:
- if c == "[":
- mult /= 1.1
- if c == "]":
- mult *= 1.1
- if c == "(":
- mult *= 1.1
- if c == ")":
- mult /= 1.1
-
- if mult != 1.0:
- self.token_mults[ident] = mult
-
- self.id_start = self.tokenizer.bos_token_id
- self.id_end = self.tokenizer.eos_token_id
- self.id_pad = self.id_end
-
- def tokenize(self, texts):
- tokenized = self.tokenizer(
- texts, truncation=False, add_special_tokens=False
- )["input_ids"]
-
- return tokenized
-
- def encode_with_transformers(self, tokens):
- CLIP_stop_at_last_layers = 1
- tokens = tokens.to(self.text_encoder.device)
- outputs = self.text_encoder(tokens, output_hidden_states=True)
-
- if CLIP_stop_at_last_layers > 1:
- z = outputs.hidden_states[-CLIP_stop_at_last_layers]
- z = self.text_encoder.text_model.final_layer_norm(z)
- else:
- z = outputs.last_hidden_state
-
- return z
-
-
-re_attention = re.compile(
- r"""
-\\\(|
-\\\)|
-\\\[|
-\\]|
-\\\\|
-\\|
-\(|
-\[|
-:([+-]?[.\d]+)\)|
-\)|
-]|
-[^\\()\[\]:]+|
-:
-""",
- re.X,
-)
-
-re_break = re.compile(r"\s*\bBREAK\b\s*", re.S)
-
-
-def parse_prompt_attention(text):
- """
- Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
- Accepted tokens are:
- (abc) - increases attention to abc by a multiplier of 1.1
- (abc:3.12) - increases attention to abc by a multiplier of 3.12
- [abc] - decreases attention to abc by a multiplier of 1.1
- \( - literal character '('
- \[ - literal character '['
- \) - literal character ')'
- \] - literal character ']'
- \\ - literal character '\'
- anything else - just text
-
- >>> parse_prompt_attention('normal text')
- [['normal text', 1.0]]
- >>> parse_prompt_attention('an (important) word')
- [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
- >>> parse_prompt_attention('(unbalanced')
- [['unbalanced', 1.1]]
- >>> parse_prompt_attention('\(literal\]')
- [['(literal]', 1.0]]
- >>> parse_prompt_attention('(unnecessary)(parens)')
- [['unnecessaryparens', 1.1]]
- >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
- [['a ', 1.0],
- ['house', 1.5730000000000004],
- [' ', 1.1],
- ['on', 1.0],
- [' a ', 1.1],
- ['hill', 0.55],
- [', sun, ', 1.1],
- ['sky', 1.4641000000000006],
- ['.', 1.1]]
- """
-
- res = []
- round_brackets = []
- square_brackets = []
-
- round_bracket_multiplier = 1.1
- square_bracket_multiplier = 1 / 1.1
-
- def multiply_range(start_position, multiplier):
- for p in range(start_position, len(res)):
- res[p][1] *= multiplier
-
- for m in re_attention.finditer(text):
- text = m.group(0)
- weight = m.group(1)
-
- if text.startswith("\\"):
- res.append([text[1:], 1.0])
- elif text == "(":
- round_brackets.append(len(res))
- elif text == "[":
- square_brackets.append(len(res))
- elif weight is not None and len(round_brackets) > 0:
- multiply_range(round_brackets.pop(), float(weight))
- elif text == ")" and len(round_brackets) > 0:
- multiply_range(round_brackets.pop(), round_bracket_multiplier)
- elif text == "]" and len(square_brackets) > 0:
- multiply_range(square_brackets.pop(), square_bracket_multiplier)
- else:
- parts = re.split(re_break, text)
- for i, part in enumerate(parts):
- if i > 0:
- res.append(["BREAK", -1])
- res.append([part, 1.0])
-
- for pos in round_brackets:
- multiply_range(pos, round_bracket_multiplier)
-
- for pos in square_brackets:
- multiply_range(pos, square_bracket_multiplier)
-
- if len(res) == 0:
- res = [["", 1.0]]
-
- # merge runs of identical weights
- i = 0
- while i + 1 < len(res):
- if res[i][1] == res[i + 1][1]:
- res[i][0] += res[i + 1][0]
- res.pop(i + 1)
- else:
- i += 1
-
- return res
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/abc/_subprocesses.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/abc/_subprocesses.py
deleted file mode 100644
index 704b44a2dda9e21997acf52c268e414d01bd2eb5..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/abc/_subprocesses.py
+++ /dev/null
@@ -1,79 +0,0 @@
-from __future__ import annotations
-
-from abc import abstractmethod
-from signal import Signals
-
-from ._resources import AsyncResource
-from ._streams import ByteReceiveStream, ByteSendStream
-
-
-class Process(AsyncResource):
- """An asynchronous version of :class:`subprocess.Popen`."""
-
- @abstractmethod
- async def wait(self) -> int:
- """
- Wait until the process exits.
-
- :return: the exit code of the process
- """
-
- @abstractmethod
- def terminate(self) -> None:
- """
- Terminates the process, gracefully if possible.
-
- On Windows, this calls ``TerminateProcess()``.
- On POSIX systems, this sends ``SIGTERM`` to the process.
-
- .. seealso:: :meth:`subprocess.Popen.terminate`
- """
-
- @abstractmethod
- def kill(self) -> None:
- """
- Kills the process.
-
- On Windows, this calls ``TerminateProcess()``.
- On POSIX systems, this sends ``SIGKILL`` to the process.
-
- .. seealso:: :meth:`subprocess.Popen.kill`
- """
-
- @abstractmethod
- def send_signal(self, signal: Signals) -> None:
- """
- Send a signal to the subprocess.
-
- .. seealso:: :meth:`subprocess.Popen.send_signal`
-
- :param signal: the signal number (e.g. :data:`signal.SIGHUP`)
- """
-
- @property
- @abstractmethod
- def pid(self) -> int:
- """The process ID of the process."""
-
- @property
- @abstractmethod
- def returncode(self) -> int | None:
- """
- The return code of the process. If the process has not yet terminated, this will be
- ``None``.
- """
-
- @property
- @abstractmethod
- def stdin(self) -> ByteSendStream | None:
- """The stream for the standard input of the process."""
-
- @property
- @abstractmethod
- def stdout(self) -> ByteReceiveStream | None:
- """The stream for the standard output of the process."""
-
- @property
- @abstractmethod
- def stderr(self) -> ByteReceiveStream | None:
- """The stream for the standard error output of the process."""
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/lowlevel.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/lowlevel.py
deleted file mode 100644
index 0e908c65474402fa89fe933d65205378c543e3bf..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/lowlevel.py
+++ /dev/null
@@ -1,174 +0,0 @@
-from __future__ import annotations
-
-import enum
-import sys
-from dataclasses import dataclass
-from typing import Any, Generic, TypeVar, overload
-from weakref import WeakKeyDictionary
-
-from ._core._eventloop import get_asynclib
-
-if sys.version_info >= (3, 8):
- from typing import Literal
-else:
- from typing_extensions import Literal
-
-T = TypeVar("T")
-D = TypeVar("D")
-
-
-async def checkpoint() -> None:
- """
- Check for cancellation and allow the scheduler to switch to another task.
-
- Equivalent to (but more efficient than)::
-
- await checkpoint_if_cancelled()
- await cancel_shielded_checkpoint()
-
-
- .. versionadded:: 3.0
-
- """
- await get_asynclib().checkpoint()
-
-
-async def checkpoint_if_cancelled() -> None:
- """
- Enter a checkpoint if the enclosing cancel scope has been cancelled.
-
- This does not allow the scheduler to switch to a different task.
-
- .. versionadded:: 3.0
-
- """
- await get_asynclib().checkpoint_if_cancelled()
-
-
-async def cancel_shielded_checkpoint() -> None:
- """
- Allow the scheduler to switch to another task but without checking for cancellation.
-
- Equivalent to (but potentially more efficient than)::
-
- with CancelScope(shield=True):
- await checkpoint()
-
-
- .. versionadded:: 3.0
-
- """
- await get_asynclib().cancel_shielded_checkpoint()
-
-
-def current_token() -> object:
- """Return a backend specific token object that can be used to get back to the event loop."""
- return get_asynclib().current_token()
-
-
-_run_vars: WeakKeyDictionary[Any, dict[str, Any]] = WeakKeyDictionary()
-_token_wrappers: dict[Any, _TokenWrapper] = {}
-
-
-@dataclass(frozen=True)
-class _TokenWrapper:
- __slots__ = "_token", "__weakref__"
- _token: object
-
-
-class _NoValueSet(enum.Enum):
- NO_VALUE_SET = enum.auto()
-
-
-class RunvarToken(Generic[T]):
- __slots__ = "_var", "_value", "_redeemed"
-
- def __init__(self, var: RunVar[T], value: T | Literal[_NoValueSet.NO_VALUE_SET]):
- self._var = var
- self._value: T | Literal[_NoValueSet.NO_VALUE_SET] = value
- self._redeemed = False
-
-
-class RunVar(Generic[T]):
- """
- Like a :class:`~contextvars.ContextVar`, except scoped to the running event loop.
- """
-
- __slots__ = "_name", "_default"
-
- NO_VALUE_SET: Literal[_NoValueSet.NO_VALUE_SET] = _NoValueSet.NO_VALUE_SET
-
- _token_wrappers: set[_TokenWrapper] = set()
-
- def __init__(
- self,
- name: str,
- default: T | Literal[_NoValueSet.NO_VALUE_SET] = NO_VALUE_SET,
- ):
- self._name = name
- self._default = default
-
- @property
- def _current_vars(self) -> dict[str, T]:
- token = current_token()
- while True:
- try:
- return _run_vars[token]
- except TypeError:
- # Happens when token isn't weak referable (TrioToken).
- # This workaround does mean that some memory will leak on Trio until the problem
- # is fixed on their end.
- token = _TokenWrapper(token)
- self._token_wrappers.add(token)
- except KeyError:
- run_vars = _run_vars[token] = {}
- return run_vars
-
- @overload
- def get(self, default: D) -> T | D:
- ...
-
- @overload
- def get(self) -> T:
- ...
-
- def get(
- self, default: D | Literal[_NoValueSet.NO_VALUE_SET] = NO_VALUE_SET
- ) -> T | D:
- try:
- return self._current_vars[self._name]
- except KeyError:
- if default is not RunVar.NO_VALUE_SET:
- return default
- elif self._default is not RunVar.NO_VALUE_SET:
- return self._default
-
- raise LookupError(
- f'Run variable "{self._name}" has no value and no default set'
- )
-
- def set(self, value: T) -> RunvarToken[T]:
- current_vars = self._current_vars
- token = RunvarToken(self, current_vars.get(self._name, RunVar.NO_VALUE_SET))
- current_vars[self._name] = value
- return token
-
- def reset(self, token: RunvarToken[T]) -> None:
- if token._var is not self:
- raise ValueError("This token does not belong to this RunVar")
-
- if token._redeemed:
- raise ValueError("This token has already been used")
-
- if token._value is _NoValueSet.NO_VALUE_SET:
- try:
- del self._current_vars[self._name]
- except KeyError:
- pass
- else:
- self._current_vars[self._name] = token._value
-
- token._redeemed = True
-
- def __repr__(self) -> str:
- return f""
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-1d5a6686.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-1d5a6686.js
deleted file mode 100644
index 36567a7772cbe31ec8ae2b3342e9f6135db903ce..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-1d5a6686.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as h,e as p,s as v,h as T,j as k,k as C,o as S,t as j,z as m,v as d,x as q,B as w,a9 as z,ab as B,ac as D,ad as E,F as u}from"./index-3370be2a.js";import{T as F}from"./TabItem.svelte_svelte_type_style_lang-ffbad424.js";/* empty css */function A(s){let e;const i=s[4].default,t=z(i,s,s[8],null);return{c(){t&&t.c()},m(n,o){t&&t.m(n,o),e=!0},p(n,o){t&&t.p&&(!e||o&256)&&B(t,i,n,n[8],e?E(i,n[8],o,null):D(n[8]),null)},i(n){e||(m(t,n),e=!0)},o(n){d(t,n),e=!1},d(n){t&&t.d(n)}}}function G(s){let e,i,t;function n(l){s[5](l)}let o={visible:s[1],elem_id:s[2],elem_classes:s[3],$$slots:{default:[A]},$$scope:{ctx:s}};return s[0]!==void 0&&(o.selected=s[0]),e=new F({props:o}),T.push(()=>k(e,"selected",n)),e.$on("change",s[6]),e.$on("select",s[7]),{c(){C(e.$$.fragment)},m(l,c){S(e,l,c),t=!0},p(l,[c]){const _={};c&2&&(_.visible=l[1]),c&4&&(_.elem_id=l[2]),c&8&&(_.elem_classes=l[3]),c&256&&(_.$$scope={dirty:c,ctx:l}),!i&&c&1&&(i=!0,_.selected=l[0],j(()=>i=!1)),e.$set(_)},i(l){t||(m(e.$$.fragment,l),t=!0)},o(l){d(e.$$.fragment,l),t=!1},d(l){q(e,l)}}}function H(s,e,i){let{$$slots:t={},$$scope:n}=e;const o=w();let{visible:l=!0}=e,{elem_id:c=""}=e,{elem_classes:_=[]}=e,{selected:f}=e;function r(a){f=a,i(0,f)}function b(a){u.call(this,s,a)}function g(a){u.call(this,s,a)}return s.$$set=a=>{"visible"in a&&i(1,l=a.visible),"elem_id"in a&&i(2,c=a.elem_id),"elem_classes"in a&&i(3,_=a.elem_classes),"selected"in a&&i(0,f=a.selected),"$$scope"in a&&i(8,n=a.$$scope)},s.$$.update=()=>{s.$$.dirty&1&&o("prop_change",{selected:f})},[f,l,c,_,t,r,b,g,n]}class I extends h{constructor(e){super(),p(this,e,H,G,v,{visible:1,elem_id:2,elem_classes:3,selected:0})}}const M=I,N=["static"];export{M as Component,N as modes};
-//# sourceMappingURL=index-1d5a6686.js.map
diff --git a/spaces/DShrimp/PoseMaker/static/poseEditor.js b/spaces/DShrimp/PoseMaker/static/poseEditor.js
deleted file mode 100644
index 8bd3d5e81bc92b88c5b07abdf2133bd2cffb8329..0000000000000000000000000000000000000000
--- a/spaces/DShrimp/PoseMaker/static/poseEditor.js
+++ /dev/null
@@ -1,238 +0,0 @@
-console.log("hello from poseEditor.js")
-var canvas = null;
-var ctx = null;
-
-// candidateの形式：[[x1, y1, score1], [x2, y2, score2], ...]
-let candidateSource = [
- [235, 158, 0.93167633],
- [234, 220, 0.97106987],
- [193, 222, 0.93366587],
- [138, 263, 0.87655306],
- [89, 308, 0.8868227],
- [276, 220, 0.9038924],
- [325, 264, 0.92930061],
- [375, 309, 0.9217211],
- [207, 347, 0.86410147],
- [203, 433, 0.86538297],
- [199, 523, 0.95236528],
- [261, 347, 0.88489777],
- [262, 430, 0.90848708],
- [261, 522, 0.94749999],
- [227, 148, 0.94189668],
- [245, 148, 0.93967074],
- [208, 158, 0.92053992],
- [258, 154, 0.73533273]
-];
-
-// subsetの形式：[[index1, index2, ..., -1], [index1, index2, ..., -1], ...]
-let subset = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 33.81122635, 18]];
-
-// const candidateSource = [[618.00, 0.00], [618.00, 44.00], [304.00, 81.00], [482.00, 96.00], [66.00, 270.00], [171.00, 280.00], [618.00, 82.00], [307.00, 112.00], [460.00, 143.00], [0.00, 301.00], [65.00, 301.00], [172.00, 303.00], [584.00, 86.00], [275.00, 119.00], [420.00, 139.00], [0.00, 301.00], [41.00, 301.00], [144.00, 303.00], [544.00, 131.00], [348.00, 139.00], [262.00, 160.00], [0.00, 337.00], [52.00, 339.00], [130.00, 348.00], [570.00, 175.00], [283.00, 177.00], [78.00, 338.00], [172.00, 380.00], [651.00, 78.00], [338.00, 111.00], [505.00, 144.00], [92.00, 301.00], [198.00, 305.00], [661.00, 132.00], [349.00, 156.00], [541.00, 179.00], [106.00, 336.00], [203.00, 348.00], [305.00, 159.00], [665.00, 160.00], [563.00, 192.00], [80.00, 343.00], [181.00, 385.00], [614.00, 205.00], [291.00, 220.00], [432.00, 320.00], [152.00, 372.00], [43.00, 380.00], [0.00, 386.00], [623.00, 281.00], [306.00, 290.00], [92.00, 357.00], [509.00, 434.00], [304.00, 357.00], [622.00, 368.00], [47.00, 394.00], [0.00, 395.00], [142.00, 405.00], [535.00, 565.00], [655.00, 200.00], [337.00, 217.00], [467.00, 322.00], [191.00, 372.00], [83.00, 375.00], [344.00, 282.00], [655.00, 282.00], [103.00, 343.00], [237.00, 368.00], [22.00, 377.00], [0.00, 379.00], [460.00, 459.00], [305.00, 352.00], [638.00, 355.00], [0.00, 401.00], [110.00, 412.00], [411.00, 570.00], [608.00, 0.00], [608.00, 40.00], [297.00, 75.00], [469.00, 84.00], [0.00, 261.00], [58.00, 263.00], [165.00, 275.00], [625.00, 0.00], [625.00, 39.00], [309.00, 74.00], [486.00, 83.00], [71.00, 264.00], [180.00, 276.00], [599.00, 0.00], [599.00, 44.00], [284.00, 80.00], [440.00, 93.00], [48.00, 271.00], [0.00, 272.00], [157.00, 277.00], [634.00, 0.00], [633.00, 41.00], [319.00, 77.00], [79.00, 269.00], [190.00, 277.00]];
-// const subset = [[1.00,6.00,12.00,18.00,24.00,28.00,33.00,39.00,43.00,49.00,54.00,59.00,65.00,72.00,77.00,84.00,90.00,97.00,32.98,18.00],[5.00,11.00,17.00,23.00,27.00,32.00,37.00,42.00,46.00,-1.00,-1.00,62.00,67.00,-1.00,82.00,88.00,95.00,100.00,25.45,15.00],[4.00,10.00,16.00,22.00,26.00,31.00,36.00,41.00,47.00,51.00,57.00,63.00,66.00,74.00,81.00,87.00,93.00,99.00,26.97,18.00],[3.00,8.00,14.00,19.00,25.00,30.00,35.00,40.00,45.00,52.00,58.00,61.00,70.00,75.00,79.00,86.00,92.00,-1.00,30.45,17.00],[2.00,7.00,13.00,20.00,-1.00,29.00,34.00,38.00,44.00,50.00,53.00,60.00,64.00,71.00,78.00,85.00,91.00,98.00,27.89,17.00],[0.00,-1.00,-1.00,-1.00,-1.00,-1.00,-1.00,-1.00,-1.00,-1.00,-1.00,-1.00,-1.00,-1.00,76.00,83.00,-1.00,96.00,3.33,4.00]];
-
-let candidate = candidateSource.map(point => [point[0], point[1] - 70]);
-
-
-function clearCanvas() {
- var w = canvas.width;
- var h = canvas.height;
- ctx.fillStyle = 'black';
- ctx.fillRect(0, 0, w, h);
-}
-
-function resizeCanvas(width, height) {
- canvas.width = width ? width : canvas.width;
- canvas.height = height ? height : canvas.height;
- clearCanvas();
- drawBodyPose(candidate, subset);
-}
-
-function drawBodyPose(candidate, subset) {
- const stickwidth = 4;
- const limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10],
- [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17],
- [1, 16], [16, 18], [3, 17], [6, 18]];
-
- const colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0],
- [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255],
- [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]];
-
- for (let i = 0; i < 17; i++) {
- for (let n = 0; n < subset.length; n++) {
- const index0 = subset[n][limbSeq[i][0]-1];
- const index1 = subset[n][limbSeq[i][1]-1];
- if (index0 === -1 || index1 === -1) {
- continue;
- }
- const [X0, Y0] = candidate[index0].slice(0, 2);
- const [X1, Y1] = candidate[index1].slice(0, 2);
- ctx.beginPath();
- ctx.lineWidth=stickwidth;
- ctx.strokeStyle = `rgb(${colors[i].join(',')})`;
- ctx.moveTo(X0, Y0);
- ctx.lineTo(X1, Y1);
- ctx.stroke();
- }
- }
-
- ctx.font = '12px serif';
- for (let i = 0; i < 18; i++) {
- for (let n = 0; n < subset.length; n++) {
- const index = subset[n][i];
- if (index === -1) {
- continue;
- }
- const [x, y] = candidate[index].slice(0, 2);
- ctx.beginPath();
- ctx.arc(x, y, 4, 0, 2 * Math.PI);
- ctx.fillStyle = `rgb(${colors[i].join(',')})`;
- ctx.fill();
- ctx.fillStyle = 'rgb(255,255,255)'
- // ctx.fillText(index, x-3, y+4);
- }
- }
-}
-
-function getNearestCandidate(x, y) {
- let minDist = Infinity;
- let minIndex = -1;
- for (let i = 0; i < candidate.length; i++) {
- const dist = Math.sqrt((x - candidate[i][0]) ** 2 + (y - candidate[i][1]) ** 2);
- if (dist < minDist) {
- minDist = dist;
- minIndex = i;
- }
- }
- return [minIndex,minDist];
-}
-
-// ドラッグ中に座標を保持するための変数
-let isDragging = false;
-let dragIndex = -1;
-let dragStartX = 0;
-let dragStartY = 0;
-let draggingCandidate = null;
-let dragPersonIndex = -1;
-
-function getCanvasPosition(event) {
- const rect = canvas.getBoundingClientRect();
- const x = event.clientX - rect.left;
- const y = event.clientY - rect.top;
- return [x, y];
-}
-
-// Canvas要素上でマウスが押された場合に呼び出される関数
-function handleMouseDown(event) {
- const [x, y] = getCanvasPosition(event);
- const [index, minDist] = getNearestCandidate(x, y);
-
- // ドラッグ処理の開始
- if (event.altKey || event.ctrlKey || event.shiftKey || minDist < 16) {
- isDragging = true;
- dragIndex = index;
- dragStartX = x;
- dragStartY = y;
- draggingCandidate = JSON.parse(JSON.stringify(candidate))
-
- // indexが含まれる人間を探す
- for (let i = 0; i < subset.length; i++) {
- var found = subset[i].indexOf(index);
- if (found != -1 && found < 18) {
- dragPersonIndex = i;
- break;
- }
- }
- }
-}
-
-function forEachCandidateOfPerson(personIndex, callback) {
- if (personIndex === -1) { return; }
-
- for (let i = 0; i < 18; i++) {
- const index = subset[personIndex][i];
- if (index === -1) {
- continue;
- }
- callback(index);
- }
-}
-
-// Canvas要素上でマウスが動いた場合に呼び出される関数
-function handleMouseMove(event) {
- if (!isDragging) {
- return;
- }
-
- const [x, y] = getCanvasPosition(event);
-
- const dragOffsetX = x - dragStartX;
- const dragOffsetY = y - dragStartY;
-
- if (event.ctrlKey) {
- // 拡大縮小（人間ごと）
- let xScale = 1 + dragOffsetX / canvas.width;
- let yScale = 1 + dragOffsetY / canvas.height;
- forEachCandidateOfPerson(dragPersonIndex, (index) => {
- candidate[index][0] = (draggingCandidate[index][0] - dragStartX) * xScale + dragStartX;
- candidate[index][1] = (draggingCandidate[index][1] - dragStartY) * yScale + dragStartY;
- });
- } else if (event.shiftKey) {
- // 回転（人間ごと）
- let angle = Math.atan2(dragOffsetY, dragOffsetX);
- forEachCandidateOfPerson(dragPersonIndex, (index) => {
- let x = draggingCandidate[index][0] - dragStartX;
- let y = draggingCandidate[index][1] - dragStartY;
- candidate[index][0] = x * Math.cos(angle) - y * Math.sin(angle) + dragStartX;
- candidate[index][1] = x * Math.sin(angle) + y * Math.cos(angle) + dragStartY;
- });
- } else if (event.altKey) {
- // 全体移動(人間ごと
- forEachCandidateOfPerson(dragPersonIndex, (index) => {
- candidate[index][0] = draggingCandidate[index][0] + dragOffsetX;
- candidate[index][1] = draggingCandidate[index][1] + dragOffsetY;
- });
- } else {
- // 個別移動
- candidate[dragIndex][0] = draggingCandidate[dragIndex][0] + dragOffsetX;
- candidate[dragIndex][1] = draggingCandidate[dragIndex][1] + dragOffsetY;
- }
-
- clearCanvas();
- drawBodyPose(candidate, subset);
-}
-
-// Canvas要素上でマウスが離された場合に呼び出される関数
-function handleMouseUp(event) {
- isDragging = false;
-}
-
-function initializePose(jsonData,w,h) {
- console.log("initializePose");
- if (jsonData != null) {
- candidate = jsonData.candidate;
- subset = jsonData.subset;
- }
-
- canvas = document.getElementById('canvas');
- ctx = canvas.getContext('2d');
-
- canvas.addEventListener('mousedown', handleMouseDown);
- canvas.addEventListener('mousemove', handleMouseMove);
- canvas.addEventListener('mouseup', handleMouseUp);
-
- resizeCanvas(w, h);
-}
-
-function savePose() {
- const canvasUrl = canvas.toDataURL();
-
- const createEl = document.createElement('a');
- createEl.href = canvasUrl;
-
- // This is the name of our downloaded file
- createEl.download = "pose.png";
-
- createEl.click();
- createEl.remove();
- return { "candidate": candidate, "subset": subset };
-}
diff --git a/spaces/Datasculptor/DescriptionGPT/detic/data/transforms/custom_augmentation_impl.py b/spaces/Datasculptor/DescriptionGPT/detic/data/transforms/custom_augmentation_impl.py
deleted file mode 100644
index 47bef39566ed741287ceb55fb98ec9b03ee30b63..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/DescriptionGPT/detic/data/transforms/custom_augmentation_impl.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-# Part of the code is from https://github.com/rwightman/efficientdet-pytorch/blob/master/effdet/data/transforms.py
-# Modified by Xingyi Zhou
-# The original code is under Apache-2.0 License
-import numpy as np
-import sys
-from fvcore.transforms.transform import (
- BlendTransform,
- CropTransform,
- HFlipTransform,
- NoOpTransform,
- Transform,
- VFlipTransform,
-)
-from PIL import Image
-
-from detectron2.data.transforms.augmentation import Augmentation
-from .custom_transform import EfficientDetResizeCropTransform
-
-__all__ = [
- "EfficientDetResizeCrop",
-]
-
-class EfficientDetResizeCrop(Augmentation):
- """
- Scale the shorter edge to the given size, with a limit of `max_size` on the longer edge.
- If `max_size` is reached, then downscale so that the longer edge does not exceed max_size.
- """
-
- def __init__(
- self, size, scale, interp=Image.BILINEAR
- ):
- """
- """
- super().__init__()
- self.target_size = (size, size)
- self.scale = scale
- self.interp = interp
-
- def get_transform(self, img):
- # Select a random scale factor.
- scale_factor = np.random.uniform(*self.scale)
- scaled_target_height = scale_factor * self.target_size[0]
- scaled_target_width = scale_factor * self.target_size[1]
- # Recompute the accurate scale_factor using rounded scaled image size.
- width, height = img.shape[1], img.shape[0]
- img_scale_y = scaled_target_height / height
- img_scale_x = scaled_target_width / width
- img_scale = min(img_scale_y, img_scale_x)
-
- # Select non-zero random offset (x, y) if scaled image is larger than target size
- scaled_h = int(height * img_scale)
- scaled_w = int(width * img_scale)
- offset_y = scaled_h - self.target_size[0]
- offset_x = scaled_w - self.target_size[1]
- offset_y = int(max(0.0, float(offset_y)) * np.random.uniform(0, 1))
- offset_x = int(max(0.0, float(offset_x)) * np.random.uniform(0, 1))
- return EfficientDetResizeCropTransform(
- scaled_h, scaled_w, offset_y, offset_x, img_scale, self.target_size, self.interp)
diff --git a/spaces/EuroPython2022/Sketch2ColourDemo/README.md b/spaces/EuroPython2022/Sketch2ColourDemo/README.md
deleted file mode 100644
index ef966aa2bde7e9f9cf5e54e11ba69aefc83870fb..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/Sketch2ColourDemo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Sketch2ColourDemo
-emoji: 📈
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.0.24
-app_file: app.py
-pinned: false
-license: eupl-1.1
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/EyanAn/vits-uma-genshin-honkai/mel_processing.py b/spaces/EyanAn/vits-uma-genshin-honkai/mel_processing.py
deleted file mode 100644
index 3e252e76320522a8a4195a60665168f22769aec2..0000000000000000000000000000000000000000
--- a/spaces/EyanAn/vits-uma-genshin-honkai/mel_processing.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- """
- PARAMS
- ------
- C: compression factor
- """
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
- """
- PARAMS
- ------
- C: compression factor used to compress
- """
- return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
- output = dynamic_range_compression_torch(magnitudes)
- return output
-
-
-def spectral_de_normalize_torch(magnitudes):
- output = dynamic_range_decompression_torch(magnitudes)
- return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
- return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
- global mel_basis
- dtype_device = str(spec.dtype) + '_' + str(spec.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
- return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global mel_basis, hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
-
- return spec
diff --git a/spaces/FantasticGNU/AnomalyGPT/utils/io.py b/spaces/FantasticGNU/AnomalyGPT/utils/io.py
deleted file mode 100644
index d0edd1dd450d18981c545a9cb7460184186d6708..0000000000000000000000000000000000000000
--- a/spaces/FantasticGNU/AnomalyGPT/utils/io.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import h5py
-import numpy as np
-import open3d
-import os
-
-class IO:
- @classmethod
- def get(cls, file_path):
- _, file_extension = os.path.splitext(file_path)
-
- if file_extension in ['.npy']:
- return cls._read_npy(file_path)
- elif file_extension in ['.pcd']:
- return cls._read_pcd(file_path)
- elif file_extension in ['.h5']:
- return cls._read_h5(file_path)
- elif file_extension in ['.txt']:
- return cls._read_txt(file_path)
- else:
- raise Exception('Unsupported file extension: %s' % file_extension)
-
- # References: https://github.com/numpy/numpy/blob/master/numpy/lib/format.py
- @classmethod
- def _read_npy(cls, file_path):
- return np.load(file_path)
-
- # References: https://github.com/dimatura/pypcd/blob/master/pypcd/pypcd.py#L275
- # Support PCD files without compression ONLY!
- @classmethod
- def _read_pcd(cls, file_path):
- pc = open3d.io.read_point_cloud(file_path)
- ptcloud = np.array(pc.points)
- return ptcloud
-
- @classmethod
- def _read_txt(cls, file_path):
- return np.loadtxt(file_path)
-
- @classmethod
- def _read_h5(cls, file_path):
- f = h5py.File(file_path, 'r')
- return f['data'][()]
\ No newline at end of file
diff --git a/spaces/GT4SD/moler/utils.py b/spaces/GT4SD/moler/utils.py
deleted file mode 100644
index 2ddf89fb4439d5e64cb4174413dbdd8210521fb2..0000000000000000000000000000000000000000
--- a/spaces/GT4SD/moler/utils.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import json
-import logging
-import os
-from collections import defaultdict
-from typing import Dict, List, Tuple
-
-import mols2grid
-import pandas as pd
-from rdkit import Chem
-from terminator.selfies import decoder
-
-logger = logging.getLogger(__name__)
-logger.addHandler(logging.NullHandler())
-
-
-def draw_grid_generate(
- seeds: List[str],
- scaffolds: List[str],
- samples: List[str],
- n_cols: int = 5,
- size=(140, 200),
-) -> str:
- """
- Uses mols2grid to draw a HTML grid for the generated molecules
-
- Args:
- samples: The generated samples.
- n_cols: Number of columns in grid. Defaults to 5.
- size: Size of molecule in grid. Defaults to (140, 200).
-
- Returns:
- HTML to display
- """
-
- result = defaultdict(list)
- result.update(
- {
- "SMILES": seeds + scaffolds + samples,
- "Name": [f"Seed_{i}" for i in range(len(seeds))]
- + [f"Scaffold_{i}" for i in range(len(scaffolds))]
- + [f"Generated_{i}" for i in range(len(samples))],
- },
- )
-
- result_df = pd.DataFrame(result)
- obj = mols2grid.display(
- result_df,
- tooltip=list(result.keys()),
- height=1100,
- n_cols=n_cols,
- name="Results",
- size=size,
- )
- return obj.data
diff --git a/spaces/GaenKoki/voicevox/build_util/check_release_build.py b/spaces/GaenKoki/voicevox/build_util/check_release_build.py
deleted file mode 100644
index 71bf49c080f4fc39d1e08ccaa9cd6b1c35731ce8..0000000000000000000000000000000000000000
--- a/spaces/GaenKoki/voicevox/build_util/check_release_build.py
+++ /dev/null
@@ -1,70 +0,0 @@
-"""
-ビルド結果をテストする
-"""
-import argparse
-import json
-import time
-from io import BytesIO
-from pathlib import Path
-from subprocess import Popen
-from urllib.parse import urlencode
-from urllib.request import Request, urlopen
-
-import soundfile
-
-base_url = "http://127.0.0.1:50021/"
-
-
-def test_release_build(dist_dir: Path, skip_run_process: bool) -> None:
- run_file = dist_dir / "run"
- if not run_file.exists():
- run_file = dist_dir / "run.exe"
-
- # 起動
- process = None
- if not skip_run_process:
- process = Popen([run_file.absolute()], cwd=dist_dir)
- time.sleep(60) # 待機
-
- # バージョン取得テスト
- req = Request(base_url + "version")
- with urlopen(req) as res:
- assert len(res.read()) > 0
-
- # テキスト -> クエリ
- text = "こんにちは、音声合成の世界へようこそ"
- req = Request(
- base_url + "audio_query?" + urlencode({"speaker": "1", "text": text}),
- method="POST",
- )
- with urlopen(req) as res:
- query = json.loads(res.read().decode("utf-8"))
-
- # クエリ -> 音声
- req = Request(base_url + "synthesis?speaker=1", method="POST")
- req.add_header("Content-Type", "application/json")
- req.data = json.dumps(query).encode("utf-8")
- with urlopen(req) as res:
- wave = res.read()
- soundfile.read(BytesIO(wave))
-
- # エンジンマニフェスト
- req = Request(base_url + "engine_manifest", method="GET")
- with urlopen(req) as res:
- manifest = json.loads(res.read().decode("utf-8"))
- assert "uuid" in manifest
-
- if not skip_run_process:
- # プロセスが稼働中であることを確認
- assert process.poll() is None
-
- # 停止
- process.terminate()
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--dist_dir", type=Path, default=Path("dist/"))
- parser.add_argument("--skip_run_process", action="store_true")
- args = parser.parse_args()
- test_release_build(dist_dir=args.dist_dir, skip_run_process=args.skip_run_process)
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py
deleted file mode 100644
index 0fcc558018b69beedbd05781163c8043d93f7277..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py
+++ /dev/null
@@ -1,3 +0,0 @@
-_base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py'
-model = dict(
- pretrained='open-mmlab://detectron/resnet101_gn', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py
deleted file mode 100644
index 9d729cea699e1c845549c74b52703c9ee3273662..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
- '../_base_/models/pspnet_unet_s5-d16.py', '../_base_/datasets/stare.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
-]
-model = dict(test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
-evaluation = dict(metric='mDice')
diff --git a/spaces/HOLYBOY/Customer_Churn_App/README.md b/spaces/HOLYBOY/Customer_Churn_App/README.md
deleted file mode 100644
index c94382d5c4f9be190cfac63807056ba5bd688fa3..0000000000000000000000000000000000000000
--- a/spaces/HOLYBOY/Customer_Churn_App/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Customer Churn App
-emoji: ⚡
-colorFrom: indigo
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.26.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/amp_optimizer.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/amp_optimizer.py
deleted file mode 100644
index 3b7958e50ce444474c48d1f5aeff05d66c19e5b6..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/amp_optimizer.py
+++ /dev/null
@@ -1,105 +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
-
-import torch
-from fairseq import optim
-from omegaconf import DictConfig
-
-logger = logging.getLogger(__name__)
-
-
-class AMPOptimizer(optim.FairseqOptimizer):
- """
- Wrap an *optimizer* to support AMP (automatic mixed precision) training.
- """
-
- def __init__(self, cfg: DictConfig, params, fp32_optimizer, **kwargs):
- super().__init__(cfg.optimizer)
- self.fp32_optimizer = fp32_optimizer
- amp_kwargs = {"init_scale": cfg.common.fp16_init_scale}
- if getattr(cfg.common, "amp_scale_window", None) is not None:
- amp_kwargs["growth_interval"] = cfg.common.amp_init_scale
- self._grad_scaler = torch.cuda.amp.GradScaler(**amp_kwargs)
- self.min_loss_scale = cfg.common.min_loss_scale
-
- @classmethod
- def build_optimizer(cls, cfg: DictConfig, params, **kwargs):
- """
- Args:
- cfg (omegaconf.DictConfig): fairseq args
- params (iterable): iterable of parameters to optimize
- """
- fp32_optimizer = optim.build_optimizer(cfg.optimizer, params)
- return cls(cfg, params, fp32_optimizer, **kwargs)
-
- def backward(self, loss):
- """Computes the sum of gradients of the given tensor w.r.t. graph leaves.
-
- Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this
- function additionally dynamically scales the loss to avoid gradient
- underflow.
- """
- self._grad_scaler.scale(loss).backward()
-
- def step(self):
- self.scaler.step(self.fp32_optimizer)
- self.scaler.update()
-
- def clip_grad_norm(self, max_norm, aggregate_norm_fn=None):
- """Clips gradient norm."""
- self.scaler.unscale_(self.optimizer)
- grad_norm = self.fp32_optimizer.clip_grad_norm(max_norm, aggregate_norm_fn)
- if not torch.isfinite(grad_norm).all():
- new_loss_scale = self.next_loss_scale
- if new_loss_scale <= self.min_loss_scale:
- raise FloatingPointError(
- (
- "AMP: Minimum loss scale reached ({}). Your loss is probably exploding. "
- "Try restarting training or use fp32. {}"
- ).format(self.min_loss_scale, new_loss_scale)
- )
- else:
- logger.info("AMP: overflow detected, setting scale to "
- f"to {new_loss_scale}")
- return grad_norm
-
- @property
- def scaler(self):
- return self._grad_scaler
-
- @property
- def next_loss_scale(self):
- return self.scaler.get_scale() * self.scaler.get_backoff_factor()
-
- @property
- def optimizer(self):
- return self.fp32_optimizer.optimizer
-
- @optimizer.setter
- def optimizer(self, optimizer):
- self.fp32_optimizer.optimizer = optimizer
-
- @property
- def lr_scheduler(self):
- return getattr(self.fp32_optimizer, "lr_scheduler", None)
-
- @property
- def optimizer_config(self):
- return self.fp32_optimizer.optimizer_config
-
- def get_lr(self):
- return self.fp32_optimizer.get_lr()
-
- def set_lr(self, lr):
- self.fp32_optimizer.set_lr(lr)
-
- def all_reduce_grads(self, module):
- self.fp32_optimizer.all_reduce_grads(module)
-
- @property
- def supports_flat_params(self):
- return self.fp32_optimizer.supports_flat_params
diff --git a/spaces/Harveenchadha/en_to_indic_translation/apply_bpe_traindevtest_notag.sh b/spaces/Harveenchadha/en_to_indic_translation/apply_bpe_traindevtest_notag.sh
deleted file mode 100644
index a3bd22677f2d9082f82052a1831139ea3d855cd5..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/en_to_indic_translation/apply_bpe_traindevtest_notag.sh
+++ /dev/null
@@ -1,41 +0,0 @@
-#!/bin/bash
-
-expdir=$1 # EXPDIR
-
-SUBWORD_NMT_DIR="subword-nmt"
-
-data_dir="$expdir/data"
-mkdir -p $expdir/bpe
-
-for dset in `echo train dev test`
-do
- echo $dset
- in_dset_dir="$data_dir/$dset"
- out_dset_dir="$expdir/bpe/$dset"
- # out_dset_dir="$expdir/final/$dset"
- echo "Apply joint vocab to SRC corpus"
- # for very large datasets, use gnu-parallel to speed up applying bpe
- # uncomment the below line if the apply bpe is slow
-
- # parallel --pipe --keep-order \
- python $SUBWORD_NMT_DIR/subword_nmt/apply_bpe.py \
- -c $expdir/vocab/bpe_codes.32k.SRC_TGT \
- --vocabulary $expdir/vocab/vocab.SRC \
- --vocabulary-threshold 5 \
- --num-workers "-1" \
- < $in_dset_dir.SRC \
- > $out_dset_dir.SRC
- echo "Apply joint vocab to TGT corpus"
-
- # for very large datasets, use gnu-parallel to speed up applying bpe
- # uncomment the below line if the apply bpe is slow
-
- # parallel --pipe --keep-order \
- python $SUBWORD_NMT_DIR/subword_nmt/apply_bpe.py \
- -c $expdir/vocab/bpe_codes.32k.SRC_TGT \
- --vocabulary $expdir/vocab/vocab.TGT \
- --vocabulary-threshold 5 \
- --num-workers "-1" \
- < $in_dset_dir.TGT \
- > $out_dset_dir.TGT
-done
diff --git a/spaces/Heckeroo/Cyberpunk-Anime-Diffusion/app.py b/spaces/Heckeroo/Cyberpunk-Anime-Diffusion/app.py
deleted file mode 100644
index c7cca2477c1da5a9e1e93ea27b5e00aa2158ca96..0000000000000000000000000000000000000000
--- a/spaces/Heckeroo/Cyberpunk-Anime-Diffusion/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/DGSpitzer/Cyberpunk-Anime-Diffusion").launch()
\ No newline at end of file
diff --git a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/index.9578e2e6.js b/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/index.9578e2e6.js
deleted file mode 100644
index acd5d5d4e98304997f17adb5c58649d72b523cd2..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/index.9578e2e6.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as r,i as h,s as g,p as c,e as d,b as o,d as f,f as q,u as v,q as b,r as w,j as C,k as R,n as S}from"./index.396f4a72.js";function j(s){let e,a;const u=s[5].default,t=c(u,s,s[4],null);return{c(){e=d("div"),t&&t.c(),o(e,"class","flex row w-full flex-wrap gap-4"),o(e,"id",s[1]),f(e,"gr-compact",s[3]==="compact"),f(e,"gr-panel",s[3]==="panel"),f(e,"unequal-height",s[0].equal_height===!1),f(e,"items-stretch",s[0].equal_height),f(e,"!hidden",!s[2])},m(l,i){q(l,e,i),t&&t.m(e,null),a=!0},p(l,[i]){t&&t.p&&(!a||i&16)&&v(t,u,l,l[4],a?w(u,l[4],i,null):b(l[4]),null),(!a||i&2)&&o(e,"id",l[1]),i&8&&f(e,"gr-compact",l[3]==="compact"),i&8&&f(e,"gr-panel",l[3]==="panel"),i&1&&f(e,"unequal-height",l[0].equal_height===!1),i&1&&f(e,"items-stretch",l[0].equal_height),i&4&&f(e,"!hidden",!l[2])},i(l){a||(C(t,l),a=!0)},o(l){R(t,l),a=!1},d(l){l&&S(e),t&&t.d(l)}}}function k(s,e,a){let{$$slots:u={},$$scope:t}=e,{style:l={}}=e,{elem_id:i}=e,{visible:_=!0}=e,{variant:m="default"}=e;return s.$$set=n=>{"style"in n&&a(0,l=n.style),"elem_id"in n&&a(1,i=n.elem_id),"visible"in n&&a(2,_=n.visible),"variant"in n&&a(3,m=n.variant),"$$scope"in n&&a(4,t=n.$$scope)},[l,i,_,m,t,u]}class z extends r{constructor(e){super(),h(this,e,k,j,g,{style:0,elem_id:1,visible:2,variant:3})}}var B=z;const D=["static"];export{B as Component,D as modes};
-//# sourceMappingURL=index.9578e2e6.js.map
diff --git a/spaces/HoangHa/IELTS_Speaking_GPT/app.py b/spaces/HoangHa/IELTS_Speaking_GPT/app.py
deleted file mode 100644
index ee7cb52a8182ea09335ecb132adc6a3690e6a11a..0000000000000000000000000000000000000000
--- a/spaces/HoangHa/IELTS_Speaking_GPT/app.py
+++ /dev/null
@@ -1,238 +0,0 @@
-# Library
-import openai
-import streamlit as st
-import pandas as pd
-from datetime import datetime
-from TTS.api import TTS
-import whisper
-from audio_recorder import record
-
-# Custom Streamlit app title and icon
-st.set_page_config(
- page_title="IELTS Speaking",
- page_icon=":robot_face:",
-)
-
-# Set the title
-st.title("Part 1 Speaking")
-
-# Sidebar Configuration
-st.sidebar.title(":gear: Model Configuration")
-
-# Toggle for API activation
-api_toggle = st.sidebar.toggle("Activate free API")
-
-# Define an empty API key
-openai_key = ""
-
-# Check if the toggle is on
-if api_toggle:
- # If the toggle is on, access the API key from secrets
- openai_key = st.secrets["OPENAI_API_KEY"]
- openai.api_key = openai_key
-else:
- # If the toggle is off, allow the user to input the API key
- openai_key = st.sidebar.text_input('Your OpenAI API key here:', value="")
- openai.api_key = openai_key
-
-# User Input and AI Response
-user_input_type = st.sidebar.selectbox("Choose input type:", ["Chat", "Record Audio"])
-
-# Model Name Selector
-model_name = st.sidebar.selectbox(
- "Select a Model",
- ["gpt-3.5-turbo", "gpt-4"], # Add more model names as needed
- key="model_name",
-)
-
-# Temperature Slider
-temperature = st.sidebar.slider(
- ":thermometer: Temperature",
- min_value=0.2,
- max_value=2.0,
- value=1.0,
- step=0.1,
- key="temperature",
-)
-
-# Max tokens Slider
-max_tokens = st.sidebar.slider(
- ":straight_ruler: Max Tokens",
- min_value=1,
- max_value=4095,
- value=256,
- step=1,
- key="max_tokens",
-)
-
-# Top p Slider
-# top_p = st.sidebar.slider(
-# "🎯 Top P",
-# min_value=0.00,
-# max_value=1.00,
-# value=1.00,
-# step=0.01,
-# key="top_p",
-# )
-
-# Presence penalty Slider
-# presence_penalty = st.sidebar.slider(
-# "🚫 Presence penalty",
-# min_value=0.00,
-# max_value=2.00,
-# value=0.00,
-# step=0.01,
-# key="presence_penalty",
-# )
-
-# Frequency penalty Slider
-# frequency_penalty = st.sidebar.slider(
-# "🤐 Frequency penalty",
-# min_value=0.00,
-# max_value=2.00,
-# value=0.00,
-# step=0.01,
-# key="frequency_penalty",
-# )
-
-# TEXT2SPEECH MODEL
-# Instantiate the TTS class
-tts = TTS(TTS().list_models()[13])
-def convert_2_speech(given_text):
- tts.tts_to_file(text=given_text, file_path="response.wav")
- return("response.wav")
-
-# SPEECH2TEXT MODEL
-model_whisper = whisper.load_model("tiny.en")
-def convert_2_text(speech):
- user_message = model_whisper.transcribe(speech)["text"]
- return user_message
-
-# CHAT MODEL
-# Initialize DataFrame to store chat history
-chat_history_df = pd.DataFrame(columns=["Timestamp", "Chat"])
-
-# Reset Button
-if st.sidebar.button(":arrows_counterclockwise: Reset Chat"):
- # Save the chat history to the DataFrame before clearing it
- if st.session_state.messages:
- timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
- chat_history = "\n".join([f"{m['role']}: {m['content']}" for m in st.session_state.messages])
- new_entry = pd.DataFrame({"Timestamp": [timestamp], "Chat": [chat_history]})
- chat_history_df = pd.concat([chat_history_df, new_entry], ignore_index=True)
-
- # Save the DataFrame to a CSV file
- chat_history_df.to_csv("chat_history.csv", index=False)
-
- # Clear the chat messages and reset the full response
- st.session_state.messages = []
- full_response = ""
-
-# Initialize Chat Messages
-if "messages" not in st.session_state:
- st.session_state.messages = []
-
-# Initialize full_response outside the user input check
-full_response = ""
-
-# Display Chat History
-for message in st.session_state.messages:
- if message["role"] != "system":
- with st.chat_message(message["role"]):
- st.markdown(message["content"])
-
-system_text="""As a helpful, thoughtful, and wise IELTS instructor responsible for testing Speaking Part 1. The users will provide the {subject} they want to talk about.
-It's important to follow these guidelines:
-- Give only original question for provided {subject}.
-- Give one question at a time.
-For example:
-{subject}: Work
-What is your job?
-Where do you work?
-{subject}: Study
-What do you study?
-Where do you study that?
-{subject}: Hometown
-Do you live in a house or a flat?
-How are the walls decorated?
-Let's start the test."""
-
-# User Input and AI Response
-# For "Chat mode"
-# Use st.toggle to allow users to choose input type
-# record_audio_input = st.toggle("Record Audio Input", value=False) # for toggle only
-
-if user_input_type == "Chat":
-# if not record_audio_input: # for toggle only
- if prompt := st.chat_input("What is up?"):
- # System
- st.session_state.messages.append({"role": "system", "content": system_text})
-
- # User
- st.session_state.messages.append({"role": "user", "content": prompt})
- with st.chat_message("user"):
- st.markdown(prompt)
-
- # Assistant
- with st.chat_message("assistant"):
- with st.status("Generating response..."):
- message_placeholder = st.empty()
- for response in openai.ChatCompletion.create(
- model=model_name, # Use the selected model name
- messages=[
- {"role": m["role"], "content": m["content"]}
- for m in st.session_state.messages
- ],
- temperature=temperature, # Set temperature
- max_tokens=max_tokens, # Set max tokens
- # top_p=top_p, # Set top p
- # frequency_penalty=frequency_penalty, # Set frequency penalty
- # presence_penalty=presence_penalty, # Set presence penalty
- stream=True,
- ):
- full_response += response.choices[0].delta.get("content", "")
- message_placeholder.markdown(full_response + "▌")
- message_placeholder.markdown(full_response)
-
- st.session_state.messages.append({"role": "assistant", "content": full_response})
- st.audio(convert_2_speech(full_response))
-
-elif user_input_type == "Record Audio":
-# else: # for toggle only
- # Record audio when the "Record Audio" button is clicked
- if st.button("Record Audio"):
- st.write("Recording... Please speak for 10 seconds.")
- output = record(seconds=10, filename='my_recording.wav')
- st.write("Recording complete!")
-
- # Convert the recorded audio to text using the Whisper model
- user_message = convert_2_text(output)
-
- # Display the transcribed text as user input
- st.session_state.messages.append({"role": "user", "content": user_message})
- with st.chat_message("user"):
- st.markdown(user_message)
-
- # Assistant
- with st.chat_message("assistant"):
- with st.status("Generating response..."):
- message_placeholder = st.empty()
- for response in openai.ChatCompletion.create(
- model=model_name, # Use the selected model name
- messages=[
- {"role": m["role"], "content": m["content"]}
- for m in st.session_state.messages
- ],
- temperature=temperature, # Set temperature
- max_tokens=max_tokens, # Set max tokens
- # top_p=top_p, # Set top p
- # frequency_penalty=frequency_penalty, # Set frequency penalty
- # presence_penalty=presence_penalty, # Set presence penalty
- stream=True,
- ):
- full_response += response.choices[0].delta.get("content", "")
- message_placeholder.markdown(full_response + "▌")
- message_placeholder.markdown(full_response)
-
- st.session_state.messages.append({"role": "assistant", "content": full_response})
- st.audio(convert_2_speech(full_response))
\ No newline at end of file
diff --git a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/vq-wav2vec_featurize.py b/spaces/ICML2022/OFA/fairseq/examples/wav2vec/vq-wav2vec_featurize.py
deleted file mode 100644
index 627072ee174c22831209e00984b945eb9dc2c279..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/vq-wav2vec_featurize.py
+++ /dev/null
@@ -1,250 +0,0 @@
-#!/usr/bin/env python3
-# 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.
-
-"""
-Helper script to pre-compute embeddings for a flashlight (previously called wav2letter++) dataset
-"""
-
-import argparse
-import glob
-import os
-import os.path as osp
-import pprint
-
-import soundfile as sf
-import torch
-import fairseq
-from torch import nn
-from torch.utils.data import DataLoader
-
-
-try:
- import tqdm
-except:
- print("Install tqdm to use --log-format=tqdm")
-
-
-class FilesDataset:
- def __init__(self, files, labels):
- self.files = files
- if labels and osp.exists(labels):
- with open(labels, "r") as lbl_f:
- self.labels = [line.rstrip() for line in lbl_f]
- else:
- self.labels = labels
-
- def __len__(self):
- return len(self.files)
-
- def __getitem__(self, index):
- fname = self.files[index]
-
- wav, sr = sf.read(fname)
- assert sr == 16000
-
- wav = torch.from_numpy(wav).float()
- lbls = None
- if self.labels:
- if isinstance(self.labels, str):
- lbl_file = osp.splitext(fname)[0] + "." + self.labels
- with open(lbl_file, "r") as lblf:
- lbls = lblf.readline()
- assert lbls is not None
- else:
- lbls = self.labels[index]
- return wav, lbls
-
- def collate(self, batch):
- return batch
-
-
-class ArgTypes:
- @staticmethod
- def existing_path(arg):
- arg = str(arg)
- assert osp.exists(arg), f"File {arg} does not exist"
- return arg
-
- @staticmethod
- def mkdir(arg):
- arg = str(arg)
- os.makedirs(arg, exist_ok=True)
- return arg
-
-
-class DatasetWriter:
- def __init__(self):
-
- self.args = self.load_config()
- pprint.pprint(self.args.__dict__)
-
- self.model = self.load_model()
-
- def __getattr__(self, attr):
- return getattr(self.args, attr)
-
- def read_manifest(self, fname):
-
- with open(fname, "r") as fp:
- lines = fp.read().split("\n")
- root = lines.pop(0).strip()
- fnames = [
- osp.join(root, line.split("\t")[0]) for line in lines if len(line) > 0
- ]
-
- return fnames
-
- def process_splits(self):
-
- if self.args.shard is not None or self.args.num_shards is not None:
- assert self.args.shard is not None and self.args.num_shards is not None
-
- for split in self.splits:
- print(split)
-
- if self.extension == "tsv":
- datadir = osp.join(self.data_dir, f"{split}.{self.extension}")
- print("Reading manifest file: ", datadir)
- files = self.read_manifest(datadir)
- else:
- datadir = osp.join(self.data_dir, split, f"**/*.{self.extension}")
- files = glob.glob(datadir, recursive=True)
-
- assert len(files) > 0
-
- if self.args.shard is not None:
- files = files[self.args.shard :: self.args.num_shards]
-
- lbls = []
- with open(self.data_file(split), "w") as srcf:
- for line, lbl in self.iterate(files):
- print(line, file=srcf)
- if self.args.labels:
- lbls.append(lbl + "\n")
-
- if self.args.labels:
- assert all(a is not None for a in lbls)
- with open(self.lbl_file(split), "w") as lblf:
- lblf.writelines(lbls)
-
- def iterate(self, files):
-
- data = self.load_data(files)
- for samples in tqdm.tqdm(data, total=len(files) // 32):
-
- for wav, lbl in samples:
- x = wav.unsqueeze(0).float().cuda()
-
- div = 1
- while x.size(-1) // div > self.args.max_size:
- div += 1
-
- xs = x.chunk(div, dim=-1)
-
- result = []
- for x in xs:
- torch.cuda.empty_cache()
- x = self.model.feature_extractor(x)
- if self.quantize_location == "encoder":
- with torch.no_grad():
- _, idx = self.model.vector_quantizer.forward_idx(x)
- idx = idx.squeeze(0).cpu()
- else:
- with torch.no_grad():
- z = self.model.feature_aggregator(x)
- _, idx = self.model.vector_quantizer.forward_idx(z)
- idx = idx.squeeze(0).cpu()
- result.append(idx)
-
- idx = torch.cat(result, dim=0)
- yield " ".join("-".join(map(str, a.tolist())) for a in idx), lbl
-
- def lbl_file(self, name):
- shard_part = "" if self.args.shard is None else f".{self.args.shard}"
- return osp.join(self.output_dir, f"{name}.lbl{shard_part}")
-
- def data_file(self, name):
- shard_part = "" if self.args.shard is None else f".{self.args.shard}"
- return osp.join(self.output_dir, f"{name}.src{shard_part}")
-
- def var_file(self):
- return osp.join(self.output_dir, f"vars.pt")
-
- def load_config(self):
-
- parser = argparse.ArgumentParser("Vector Quantized wav2vec features")
-
- # Model Arguments
- parser.add_argument("--checkpoint", type=ArgTypes.existing_path, required=True)
- parser.add_argument("--data-parallel", action="store_true")
-
- # Output Arguments
- parser.add_argument("--output-dir", type=ArgTypes.mkdir, required=True)
-
- # Data Arguments
- parser.add_argument("--data-dir", type=ArgTypes.existing_path, required=True)
- parser.add_argument("--splits", type=str, nargs="+", required=True)
- parser.add_argument("--extension", type=str, required=True)
- parser.add_argument("--labels", type=str, required=False)
-
- parser.add_argument("--shard", type=int, default=None)
- parser.add_argument("--num-shards", type=int, default=None)
- parser.add_argument("--max-size", type=int, default=1300000)
-
- # Logger Arguments
- parser.add_argument(
- "--log-format", type=str, choices=["none", "simple", "tqdm"]
- )
-
- return parser.parse_args()
-
- def load_data(self, fnames):
-
- dataset = FilesDataset(fnames, self.args.labels)
- loader = DataLoader(
- dataset, batch_size=32, collate_fn=dataset.collate, num_workers=8
- )
- return loader
-
- def load_model(self):
- model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([self.checkpoint])
- model = model[0]
-
- self.quantize_location = getattr(cfg.model, "vq", "encoder")
-
- model.eval().float()
- model.cuda()
-
- if self.data_parallel:
- model = nn.DataParallel(model)
-
- return model
-
- def __call__(self):
-
- self.process_splits()
-
- if hasattr(self.model.feature_extractor, "vars") and (
- self.args.shard is None or self.args.shard == 0
- ):
- vars = (
- self.model.feature_extractor.vars.view(
- self.model.feature_extractor.banks,
- self.model.feature_extractor.num_vars,
- -1,
- )
- .cpu()
- .detach()
- )
- print("writing learned latent variable embeddings: ", vars.shape)
- torch.save(vars, self.var_file())
-
-
-if __name__ == "__main__":
- write_data = DatasetWriter()
-
- write_data()
- print("Done.")
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/dataclass/configs.py b/spaces/ICML2022/OFA/fairseq/fairseq/dataclass/configs.py
deleted file mode 100644
index 8e8cec92814f55a504d36f80fb79c3e0f8280eee..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/dataclass/configs.py
+++ /dev/null
@@ -1,1058 +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 sys
-from dataclasses import _MISSING_TYPE, dataclass, field
-from typing import Any, List, Optional
-
-import torch
-
-from fairseq.dataclass.constants import (
- DATASET_IMPL_CHOICES,
- DDP_BACKEND_CHOICES,
- DDP_COMM_HOOK_CHOICES,
- GENERATION_CONSTRAINTS_CHOICES,
- GENERATION_DECODING_FORMAT_CHOICES,
- LOG_FORMAT_CHOICES,
- PIPELINE_CHECKPOINT_CHOICES,
- PRINT_ALIGNMENT_CHOICES,
- ZERO_SHARDING_CHOICES,
-)
-
-from omegaconf import II, MISSING
-
-
-@dataclass
-class FairseqDataclass:
- """fairseq base dataclass that supported fetching attributes and metas"""
-
- _name: Optional[str] = None
-
- @staticmethod
- def name():
- return None
-
- def _get_all_attributes(self) -> List[str]:
- return [k for k in self.__dataclass_fields__.keys()]
-
- def _get_meta(
- self, attribute_name: str, meta: str, default: Optional[Any] = None
- ) -> Any:
- return self.__dataclass_fields__[attribute_name].metadata.get(meta, default)
-
- def _get_name(self, attribute_name: str) -> str:
- return self.__dataclass_fields__[attribute_name].name
-
- def _get_default(self, attribute_name: str) -> Any:
- if hasattr(self, attribute_name):
- if str(getattr(self, attribute_name)).startswith("${"):
- return str(getattr(self, attribute_name))
- elif str(self.__dataclass_fields__[attribute_name].default).startswith(
- "${"
- ):
- return str(self.__dataclass_fields__[attribute_name].default)
- elif (
- getattr(self, attribute_name)
- != self.__dataclass_fields__[attribute_name].default
- ):
- return getattr(self, attribute_name)
-
- f = self.__dataclass_fields__[attribute_name]
- if not isinstance(f.default_factory, _MISSING_TYPE):
- return f.default_factory()
- return f.default
-
- def _get_type(self, attribute_name: str) -> Any:
- return self.__dataclass_fields__[attribute_name].type
-
- def _get_help(self, attribute_name: str) -> Any:
- return self._get_meta(attribute_name, "help")
-
- def _get_argparse_const(self, attribute_name: str) -> Any:
- return self._get_meta(attribute_name, "argparse_const")
-
- def _get_argparse_alias(self, attribute_name: str) -> Any:
- return self._get_meta(attribute_name, "argparse_alias")
-
- def _get_choices(self, attribute_name: str) -> Any:
- return self._get_meta(attribute_name, "choices")
-
- @classmethod
- def from_namespace(cls, args):
- if isinstance(args, cls):
- return args
- else:
- config = cls()
- for k in config.__dataclass_fields__.keys():
- if k.startswith("_"):
- # private member, skip
- continue
- if hasattr(args, k):
- setattr(config, k, getattr(args, k))
-
- return config
-
-
-
-@dataclass
-class CommonConfig(FairseqDataclass):
- # This is the core dataclass including common parameters shared by all different jobs. Please append your params to other dataclasses if they were
- # used for a particular purpose or task, such as those dedicated for `distributed training`, `optimization`, etc.
- no_progress_bar: bool = field(
- default=False, metadata={"help": "disable progress bar"}
- )
- log_interval: int = field(
- default=100,
- metadata={
- "help": "log progress every N batches (when progress bar is disabled)"
- },
- )
- log_format: Optional[LOG_FORMAT_CHOICES] = field(
- default=None, metadata={"help": "log format to use"}
- )
- log_file: Optional[str] = field(
- default=None, metadata={"help": "log file to copy metrics to."}
- )
- tensorboard_logdir: Optional[str] = field(
- default=None,
- metadata={
- "help": "path to save logs for tensorboard, should match --logdir "
- "of running tensorboard (default: no tensorboard logging)"
- },
- )
- wandb_project: Optional[str] = field(
- default=None,
- metadata={"help": "Weights and Biases project name to use for logging"},
- )
- azureml_logging: Optional[bool] = field(
- default=False, metadata={"help": "Log scalars to AzureML context"},
- )
- seed: int = field(
- default=1, metadata={"help": "pseudo random number generator seed"}
- )
- cpu: bool = field(default=False, metadata={"help": "use CPU instead of CUDA"})
- tpu: bool = field(default=False, metadata={"help": "use TPU instead of CUDA"})
- bf16: bool = field(default=False, metadata={"help": "use bfloat16; implies --tpu"})
- memory_efficient_bf16: bool = field(
- default=False,
- metadata={
- "help": "use a memory-efficient version of BF16 training; implies --bf16"
- },
- )
- fp16: bool = field(default=False, metadata={"help": "use FP16"})
- memory_efficient_fp16: bool = field(
- default=False,
- metadata={
- "help": "use a memory-efficient version of FP16 training; implies --fp16"
- },
- )
- fp16_no_flatten_grads: bool = field(
- default=False, metadata={"help": "don't flatten FP16 grads tensor"}
- )
- fp16_init_scale: int = field(
- default=2 ** 7, metadata={"help": "default FP16 loss scale"}
- )
- fp16_scale_window: Optional[int] = field(
- default=None,
- metadata={"help": "number of updates before increasing loss scale"},
- )
- fp16_scale_tolerance: float = field(
- default=0.0,
- metadata={
- "help": "pct of updates that can overflow before decreasing the loss scale"
- },
- )
- on_cpu_convert_precision: bool = field(
- default=False,
- metadata={
- "help": "if set, the floating point conversion to fp16/bf16 runs on CPU. "
- "This reduces bus transfer time and GPU memory usage."
- }
- )
- min_loss_scale: float = field(
- default=1e-4,
- metadata={"help": "minimum FP16/AMP loss scale, after which training is stopped"},
- )
- threshold_loss_scale: Optional[float] = field(
- default=None, metadata={"help": "threshold FP16 loss scale from below"}
- )
- amp: bool = field(default=False, metadata={"help": "use automatic mixed precision"})
- amp_batch_retries: int = field(
- default=2,
- metadata={"help": "number of retries of same batch after reducing loss scale with AMP"},
- )
- amp_init_scale: int = field(
- default=2 ** 7, metadata={"help": "default AMP loss scale"}
- )
- amp_scale_window: Optional[int] = field(
- default=None,
- metadata={"help": "number of updates before increasing AMP loss scale"},
- )
- user_dir: Optional[str] = field(
- default=None,
- metadata={
- "help": "path to a python module containing custom extensions (tasks and/or architectures)"
- },
- )
- empty_cache_freq: int = field(
- default=0,
- metadata={"help": "how often to clear the PyTorch CUDA cache (0 to disable)"},
- )
- all_gather_list_size: int = field(
- default=16384,
- metadata={"help": "number of bytes reserved for gathering stats from workers"},
- )
- model_parallel_size: int = field(
- default=1, metadata={"help": "total number of GPUs to parallelize model over"}
- )
- quantization_config_path: Optional[str] = field(
- default=None, metadata={"help": "path to quantization config file"}
- )
- profile: bool = field(
- default=False, metadata={"help": "enable autograd profiler emit_nvtx"}
- )
- reset_logging: bool = field(
- default=False,
- metadata={
- "help": "when using Hydra, reset the logging at the beginning of training"
- },
- )
- suppress_crashes: bool = field(
- default=False,
- metadata={
- "help": "suppress crashes when training with the hydra_train entry point so that the "
- "main method can return a value (useful for sweeps)"
- },
- )
- use_plasma_view: bool = field(
- default=False, metadata={"help": "Store indices and sizes in shared memory"}
- )
- plasma_path: Optional[str] = field(
- default="/tmp/plasma",
- metadata={
- "help": "path to run plasma_store, defaults to /tmp/plasma. Paths outside /tmp tend to fail."
- },
- )
-
-
-@dataclass
-class DistributedTrainingConfig(FairseqDataclass):
- distributed_world_size: int = field(
- default=max(1, torch.cuda.device_count()),
- metadata={
- "help": "total number of GPUs across all nodes (default: all visible GPUs)"
- },
- )
- distributed_num_procs: Optional[int] = field(
- default=max(1, torch.cuda.device_count()),
- metadata={
- "help": "total number of processes to fork (default: all visible GPUs)"
- },
- )
- distributed_rank: Optional[int] = field(
- default=0, metadata={"help": "rank of the current worker"}
- )
- distributed_backend: str = field(
- default="nccl", metadata={"help": "distributed backend"}
- )
- distributed_init_method: Optional[str] = field(
- default=None,
- metadata={
- "help": "typically tcp://hostname:port that will be used to "
- "establish initial connetion"
- },
- )
- distributed_port: int = field(
- default=-1,
- metadata={
- "help": "port number (not required if using --distributed-init-method)"
- },
- )
- device_id: int = field(
- default=0,
- metadata={
- "help": "which GPU to use (usually configured automatically)",
- "argparse_alias": "--local_rank",
- },
- )
- distributed_no_spawn: bool = field(
- default=False,
- metadata={
- "help": "do not spawn multiple processes even if multiple GPUs are visible"
- },
- )
- ddp_backend: DDP_BACKEND_CHOICES = field(
- default="pytorch_ddp", metadata={"help": "DistributedDataParallel backend"}
- )
- ddp_comm_hook: DDP_COMM_HOOK_CHOICES = field(
- default="none", metadata={"help": "communication hook"}
- )
- bucket_cap_mb: int = field(
- default=25, metadata={"help": "bucket size for reduction"}
- )
- fix_batches_to_gpus: bool = field(
- default=False,
- metadata={
- "help": "don't shuffle batches between GPUs; this reduces overall "
- "randomness and may affect precision but avoids the cost of re-reading the data"
- },
- )
- find_unused_parameters: bool = field(
- default=False,
- metadata={
- "help": "disable unused parameter detection (not applicable to "
- "--ddp-backend=legacy_ddp)"
- },
- )
- gradient_as_bucket_view: bool = field(
- default=False,
- metadata={
- "help": "when set to True, gradients will be views pointing to different offsets of allreduce communication buckets. This can reduce peak memory usage, where the saved memory size will be equal to the total gradients size. "
- "--gradient-as-bucket-view=gradient_as_bucket_view)"
- },
- )
- fast_stat_sync: bool = field(
- default=False,
- metadata={"help": "[deprecated] this is now defined per Criterion"},
- )
- heartbeat_timeout: int = field(
- default=-1,
- metadata={
- "help": "kill the job if no progress is made in N seconds; "
- "set to -1 to disable"
- },
- )
- broadcast_buffers: bool = field(
- default=False,
- metadata={
- "help": "Copy non-trainable parameters between GPUs, such as "
- "batchnorm population statistics"
- },
- )
- slowmo_momentum: Optional[float] = field(
- default=None,
- metadata={
- "help": "SlowMo momentum term; by default use 0.0 for 16 GPUs, "
- "0.2 for 32 GPUs; 0.5 for 64 GPUs, 0.6 for > 64 GPUs"
- },
- )
- slowmo_algorithm: str = field(
- default="LocalSGD", metadata={"help": "whether to use LocalSGD or SGP"}
- )
- localsgd_frequency: int = field(
- default=3, metadata={"help": "Local SGD allreduce frequency"}
- )
- nprocs_per_node: int = field(
- default=max(1, torch.cuda.device_count()),
- metadata={
- "help": "number of GPUs in each node. An allreduce operation across GPUs in "
- "a node is very fast. Hence, we do allreduce across GPUs in a node, "
- "and gossip across different nodes"
- },
- )
- pipeline_model_parallel: bool = field(
- default=False,
- metadata={"help": "if set, use pipeline model parallelism across GPUs"},
- )
- pipeline_balance: Optional[str] = field(
- default=None,
- metadata={
- "help": "partition the model into N_K pieces, where each piece "
- "contains N_i layers. The sum(args.pipeline_balance) "
- "should equal the total number of layers in the model"
- },
- )
- pipeline_devices: Optional[str] = field(
- default=None,
- metadata={
- "help": "a list of device indices indicating which device to place "
- "each of the N_K partitions. The length of this list should "
- "equal the length of the --pipeline-balance argument"
- },
- )
- pipeline_chunks: Optional[int] = field(
- default=0, metadata={"help": "microbatch count for pipeline model parallelism"}
- )
- pipeline_encoder_balance: Optional[str] = field(
- default=None,
- metadata={
- "help": "partition the pipeline parallel encoder into N_K pieces, where each piece "
- "contains N_i layers. The sum(args.pipeline_encoder_balance) "
- "should equal the total number of encoder layers in the model"
- },
- )
- pipeline_encoder_devices: Optional[str] = field(
- default=None,
- metadata={
- "help": "a list of device indices indicating which device to place "
- "each of the N_K partitions. The length of this list should "
- "equal the length of the --pipeline-encoder-balance argument"
- },
- )
- pipeline_decoder_balance: Optional[str] = field(
- default=None,
- metadata={
- "help": "partition the pipeline parallel decoder into N_K pieces, where each piece "
- "contains N_i layers. The sum(args.pipeline_decoder_balance) "
- "should equal the total number of decoder layers in the model"
- },
- )
- pipeline_decoder_devices: Optional[str] = field(
- default=None,
- metadata={
- "help": "a list of device indices indicating which device to place "
- "each of the N_K partitions. The length of this list should "
- "equal the length of the --pipeline-decoder-balance argument"
- },
- )
- pipeline_checkpoint: PIPELINE_CHECKPOINT_CHOICES = field(
- default="never",
- metadata={"help": "checkpointing mode for pipeline model parallelism"},
- )
- zero_sharding: ZERO_SHARDING_CHOICES = field(
- default="none", metadata={"help": "ZeRO sharding"}
- )
- fp16: bool = II("common.fp16")
- memory_efficient_fp16: bool = II("common.memory_efficient_fp16")
- tpu: bool = II("common.tpu")
- # configuration for --ddp-backend=fully_sharded
- no_reshard_after_forward: bool = field(
- default=False, metadata={"help": "don't reshard parameters after forward pass"},
- )
- fp32_reduce_scatter: bool = field(
- default=False, metadata={"help": "reduce-scatter grads in FP32"},
- )
- cpu_offload: bool = field(
- default=False, metadata={"help": "offload FP32 params to CPU"}
- )
- use_sharded_state: bool = field(
- default=False, metadata={"help": "use sharded checkpoint files"},
- )
-
-
-@dataclass
-class DatasetConfig(FairseqDataclass):
- num_workers: int = field(
- default=1, metadata={"help": "how many subprocesses to use for data loading"}
- )
- skip_invalid_size_inputs_valid_test: bool = field(
- default=False,
- metadata={"help": "ignore too long or too short lines in valid and test set"},
- )
- max_tokens: Optional[int] = field(
- default=None, metadata={"help": "maximum number of tokens in a batch"}
- )
- batch_size: Optional[int] = field(
- default=None,
- metadata={
- "help": "number of examples in a batch",
- "argparse_alias": "--max-sentences",
- },
- )
- required_batch_size_multiple: int = field(
- default=8, metadata={"help": "batch size will be a multiplier of this value"}
- )
- required_seq_len_multiple: int = field(
- default=1,
- metadata={
- "help": "maximum sequence length in batch will be a multiplier of this value"
- },
- )
- dataset_impl: Optional[DATASET_IMPL_CHOICES] = field(
- default=None, metadata={"help": "output dataset implementation"}
- )
- data_buffer_size: int = field(
- default=10, metadata={"help": "Number of batches to preload"}
- )
- train_subset: str = field(
- default="train",
- metadata={"help": "data subset to use for training (e.g. train, valid, test)"},
- )
- valid_subset: str = field(
- default="valid",
- metadata={
- "help": "comma separated list of data subsets to use for validation"
- " (e.g. train, valid, test)"
- },
- )
- combine_valid_subsets: Optional[bool] = field(
- default=None,
- metadata={
- "help": "comma separated list of data subsets to use for validation"
- " (e.g. train, valid, test)",
- "argparse_alias": "--combine-val",
- },
- )
- ignore_unused_valid_subsets: Optional[bool] = field(
- default=False,
- metadata={"help": "do not raise error if valid subsets are ignored"},
- )
-
- validate_interval: int = field(
- default=1, metadata={"help": "validate every N epochs"}
- )
- validate_interval_updates: int = field(
- default=0, metadata={"help": "validate every N updates"}
- )
- validate_after_updates: int = field(
- default=0, metadata={"help": "dont validate until reaching this many updates"}
- )
- fixed_validation_seed: Optional[int] = field(
- default=None, metadata={"help": "specified random seed for validation"}
- )
- disable_validation: bool = field(
- default=False, metadata={"help": "disable validation"}
- )
- max_tokens_valid: Optional[int] = field(
- default=II("dataset.max_tokens"),
- metadata={
- "help": "maximum number of tokens in a validation batch"
- " (defaults to --max-tokens)"
- },
- )
- batch_size_valid: Optional[int] = field(
- default=II("dataset.batch_size"),
- metadata={
- "help": "batch size of the validation batch (defaults to --batch-size)",
- "argparse_alias": "--max-sentences-valid",
- },
- )
- max_valid_steps: Optional[int] = field(default=None, metadata={'help': 'How many batches to evaluate',
- "argparse_alias": "--nval"})
- curriculum: int = field(
- default=0, metadata={"help": "don't shuffle batches for first N epochs"}
- )
- gen_subset: str = field(
- default="test",
- metadata={"help": "data subset to generate (train, valid, test)"},
- )
- num_shards: int = field(
- default=1, metadata={"help": "shard generation over N shards"}
- )
- shard_id: int = field(
- default=0, metadata={"help": "id of the shard to generate (id < num_shards)"}
- )
-
-
-@dataclass
-class OptimizationConfig(FairseqDataclass):
- max_epoch: int = field(
- default=0, metadata={"help": "force stop training at specified epoch"}
- )
- max_update: int = field(
- default=0, metadata={"help": "force stop training at specified update"}
- )
- stop_time_hours: float = field(
- default=0,
- metadata={
- "help": "force stop training after specified cumulative time (if >0)"
- },
- )
- clip_norm: float = field(
- default=0.0, metadata={"help": "clip threshold of gradients"}
- )
- sentence_avg: bool = field(
- default=False,
- metadata={
- "help": "normalize gradients by the number of sentences in a batch"
- " (default is to normalize by number of tokens)"
- },
- )
- update_freq: List[int] = field(
- default_factory=lambda: [1],
- metadata={"help": "update parameters every N_i batches, when in epoch i"},
- )
- lr: List[float] = field(
- default_factory=lambda: [0.25],
- metadata={
- "help": "learning rate for the first N epochs; all epochs >N using LR_N"
- " (note: this may be interpreted differently depending on --lr-scheduler)"
- },
- )
- stop_min_lr: float = field(
- default=-1.0,
- metadata={"help": "stop training when the learning rate reaches this minimum"},
- )
- use_bmuf: bool = field(
- default=False,
- metadata={
- "help": "specify global optimizer for syncing models on different GPUs/shards"
- },
- )
-
-
-@dataclass
-class CheckpointConfig(FairseqDataclass):
- save_dir: str = field(
- default="checkpoints", metadata={"help": "path to save checkpoints"}
- )
- restore_file: str = field(
- default="checkpoint_last.pt",
- metadata={
- "help": "filename from which to load checkpoint "
- "(default: /checkpoint_last.pt"
- },
- )
- finetune_from_model: Optional[str] = field(
- default=None,
- metadata={
- "help": "finetune from a pretrained model; note that meters and lr scheduler will be reset"
- },
- )
- reset_dataloader: bool = field(
- default=False,
- metadata={
- "help": "if set, does not reload dataloader state from the checkpoint"
- },
- )
- reset_lr_scheduler: bool = field(
- default=False,
- metadata={
- "help": "if set, does not load lr scheduler state from the checkpoint"
- },
- )
- reset_meters: bool = field(
- default=False,
- metadata={"help": "if set, does not load meters from the checkpoint"},
- )
- reset_optimizer: bool = field(
- default=False,
- metadata={"help": "if set, does not load optimizer state from the checkpoint"},
- )
- optimizer_overrides: str = field(
- default="{}",
- metadata={
- "help": "a dictionary used to override optimizer args when loading a checkpoint"
- },
- )
- save_interval: int = field(
- default=1, metadata={"help": "save a checkpoint every N epochs"}
- )
- save_interval_updates: int = field(
- default=0, metadata={"help": "save a checkpoint (and validate) every N updates"}
- )
- keep_interval_updates: int = field(
- default=-1,
- metadata={
- "help": "keep the last N checkpoints saved with --save-interval-updates"
- },
- )
- keep_interval_updates_pattern: int = field(
- default=-1,
- metadata={
- "help": "when used with --keep-interval-updates, skips deleting "
- "any checkpoints with update X where "
- "X %% keep_interval_updates_pattern == 0"
- },
- )
- keep_last_epochs: int = field(
- default=-1, metadata={"help": "keep last N epoch checkpoints"}
- )
- keep_best_checkpoints: int = field(
- default=-1, metadata={"help": "keep best N checkpoints based on scores"}
- )
- no_save: bool = field(
- default=False, metadata={"help": "don't save models or checkpoints"}
- )
- no_epoch_checkpoints: bool = field(
- default=False, metadata={"help": "only store last and best checkpoints"}
- )
- no_last_checkpoints: bool = field(
- default=False, metadata={"help": "don't store last checkpoints"}
- )
- no_save_optimizer_state: bool = field(
- default=False,
- metadata={"help": "don't save optimizer-state as part of checkpoint"},
- )
- best_checkpoint_metric: str = field(
- default="loss", metadata={"help": 'metric to use for saving "best" checkpoints'}
- )
- maximize_best_checkpoint_metric: bool = field(
- default=False,
- metadata={
- "help": 'select the largest metric value for saving "best" checkpoints'
- },
- )
- patience: int = field(
- default=-1,
- metadata={
- "help": (
- "early stop training if valid performance doesn't "
- "improve for N consecutive validation runs; note "
- "that this is influenced by --validate-interval"
- )
- },
- )
- checkpoint_suffix: str = field(
- default="", metadata={"help": "suffix to add to the checkpoint file name"}
- )
- checkpoint_shard_count: int = field(
- default=1,
- metadata={
- "help": "Number of shards containing the checkpoint - "
- "if the checkpoint is over 300GB, it is preferable "
- "to split it into shards to prevent OOM on CPU while loading "
- "the checkpoint"
- },
- )
- load_checkpoint_on_all_dp_ranks: bool = field(
- default=False,
- metadata={
- "help": "load checkpoints on all data parallel devices "
- "(default: only load on rank 0 and broadcast to other devices)"
- },
- )
- write_checkpoints_asynchronously: bool = field(
- default=False,
- metadata={
- "help": (
- "Write checkpoints asynchronously in a separate "
- "thread. NOTE: This feature is currently being tested."
- ),
- "argparse_alias": "--save-async",
- },
- )
- model_parallel_size: int = II("common.model_parallel_size")
- use_ema_weights_to_init_param: bool = field(
- default=False,
- metadata={
- "help": "if the checkpoint has ema weights, then use it to init the model param"
- "(default: false, use noema weights to init the model param)"
- },
- )
- use_latest_weights_to_init_ema: bool = field(
- default=False,
- metadata={
- "help": "if the model has ema params, then force to use the latest weights in the ckpt to init the ema param, even ema weights exist in the ckpt"
- "(default: false, use ema weights (if exist) to init the ema param)"
- },
- )
-
-
-@dataclass
-class FairseqBMUFConfig(FairseqDataclass):
- block_lr: float = field(
- default=1, metadata={"help": "block learning rate for bmuf"}
- )
- block_momentum: float = field(
- default=0.875, metadata={"help": "block momentum for bmuf"}
- )
- global_sync_iter: int = field(
- default=50, metadata={"help": "Iteration for syncing global model"}
- )
- warmup_iterations: int = field(
- default=500, metadata={"help": "warmup iterations for model to broadcast"}
- )
- use_nbm: bool = field(
- default=False,
- metadata={"help": "Specify whether you want to use classical BM / Nesterov BM"},
- )
- average_sync: bool = field(
- default=False,
- metadata={
- "help": "Specify whether you want to average the local momentum after each sync"
- },
- )
- distributed_world_size: int = II("distributed_training.distributed_world_size")
-
-
-@dataclass
-class GenerationConfig(FairseqDataclass):
- beam: int = field(
- default=5, metadata={"help": "beam size"},
- )
- nbest: int = field(
- default=1, metadata={"help": "number of hypotheses to output"},
- )
- max_len_a: float = field(
- default=0,
- metadata={
- "help": "generate sequences of maximum length ax + b, where x is the source length"
- },
- )
- max_len_b: int = field(
- default=200,
- metadata={
- "help": "generate sequences of maximum length ax + b, where x is the source length"
- },
- )
- min_len: int = field(
- default=1, metadata={"help": "minimum generation length"},
- )
- match_source_len: bool = field(
- default=False, metadata={"help": "generations should match the source length"},
- )
- unnormalized: bool = field(
- default=False, metadata={"help": "compare unnormalized hypothesis scores"},
- )
- no_early_stop: bool = field(
- default=False, metadata={"help": "deprecated"},
- )
- no_beamable_mm: bool = field(
- default=False, metadata={"help": "don't use BeamableMM in attention layers"},
- )
- lenpen: float = field(
- default=1,
- metadata={
- "help": "length penalty: <1.0 favors shorter, >1.0 favors longer sentences"
- },
- )
- unkpen: float = field(
- default=0,
- metadata={
- "help": "unknown word penalty: <0 produces more unks, >0 produces fewer"
- },
- )
- replace_unk: Optional[str] = field(
- default=None,
- metadata={
- "help": "perform unknown replacement (optionally with alignment dictionary)",
- "argparse_const": "@@ ",
- },
- )
- sacrebleu: bool = field(
- default=False, metadata={"help": "score with sacrebleu"},
- )
- score_reference: bool = field(
- default=False, metadata={"help": "just score the reference translation"},
- )
- prefix_size: int = field(
- default=0,
- metadata={"help": "initialize generation by target prefix of given length"},
- )
- no_repeat_ngram_size: int = field(
- default=0,
- metadata={
- "help": "ngram blocking such that this size ngram cannot be repeated in the generation"
- },
- )
- sampling: bool = field(
- default=False,
- metadata={"help": "sample hypotheses instead of using beam search"},
- )
- sampling_topk: int = field(
- default=-1,
- metadata={"help": "sample from top K likely next words instead of all words"},
- )
- sampling_topp: float = field(
- default=-1.0,
- metadata={
- "help": "sample from the smallest set whose cumulative probability mass exceeds p for next words"
- },
- )
- constraints: Optional[GENERATION_CONSTRAINTS_CHOICES] = field(
- default=None,
- metadata={
- "help": "enables lexically constrained decoding",
- "argparse_const": "ordered",
- },
- )
- temperature: float = field(
- default=1.0, metadata={"help": "temperature for generation"},
- )
- diverse_beam_groups: int = field(
- default=-1, metadata={"help": "number of groups for Diverse Beam Search"},
- )
- diverse_beam_strength: float = field(
- default=0.5,
- metadata={"help": "strength of diversity penalty for Diverse Beam Search"},
- )
- diversity_rate: float = field(
- default=-1.0,
- metadata={"help": "strength of diversity penalty for Diverse Siblings Search"},
- )
- print_alignment: Optional[PRINT_ALIGNMENT_CHOICES] = field(
- default=None,
- metadata={
- "help": "if set, uses attention feedback to compute and print alignment to source tokens "
- "(valid options are: hard, soft, otherwise treated as hard alignment)",
- "argparse_const": "hard",
- },
- )
- print_step: bool = field(
- default=False, metadata={"help": "print steps"},
- )
- lm_path: Optional[str] = field(
- default=None, metadata={"help": "path to lm checkpoint for lm fusion"},
- )
- lm_weight: float = field(
- default=0.0, metadata={"help": "weight for lm probs for lm fusion"},
- )
-
- # arguments for iterative refinement generator
- iter_decode_eos_penalty: float = field(
- default=0.0,
- metadata={"help": "if > 0.0, it penalized early-stopping in decoding."},
- )
- iter_decode_max_iter: int = field(
- default=10, metadata={"help": "maximum iterations for iterative refinement."},
- )
- iter_decode_force_max_iter: bool = field(
- default=False,
- metadata={
- "help": "if set, run exact the maximum number of iterations without early stop"
- },
- )
- iter_decode_with_beam: int = field(
- default=1,
- metadata={
- "help": "if > 1, model will generate translations varying by the lengths."
- },
- )
- iter_decode_with_external_reranker: bool = field(
- default=False,
- metadata={
- "help": "if set, the last checkpoint are assumed to be a reranker to rescore the translations"
- },
- )
- retain_iter_history: bool = field(
- default=False,
- metadata={
- "help": "if set, decoding returns the whole history of iterative refinement"
- },
- )
- retain_dropout: bool = field(
- default=False, metadata={"help": "Use dropout at inference time"},
- )
- # temporarily set to Any until https://github.com/facebookresearch/hydra/issues/1117 is fixed
- # retain_dropout_modules: Optional[List[str]] = field(
- retain_dropout_modules: Any = field(
- default=None,
- metadata={
- "help": "if set, only retain dropout for the specified modules; "
- "if not set, then dropout will be retained for all modules"
- },
- )
- # special decoding format for advanced decoding.
- decoding_format: Optional[GENERATION_DECODING_FORMAT_CHOICES] = field(
- default=None,
- metadata={"help": "special decoding format for advanced decoding."},
- )
- no_seed_provided: bool = field(
- default=False,
- metadata={"help": "if set, dont use seed for initializing random generators"},
- )
-
-
-@dataclass
-class CommonEvalConfig(FairseqDataclass):
- path: Optional[str] = field(
- default=None, metadata={"help": "path(s) to model file(s), colon separated"},
- )
- post_process: Optional[str] = field(
- default=None,
- metadata={
- "help": (
- "post-process text by removing BPE, letter segmentation, etc. "
- "Valid options can be found in fairseq.data.utils.post_process."
- ),
- "argparse_const": "subword_nmt",
- "argparse_alias": "--remove-bpe",
- },
- )
- quiet: bool = field(default=False, metadata={"help": "only print final scores"})
- model_overrides: str = field(
- default="{}",
- metadata={
- "help": "a dictionary used to override model args at generation that were used during model training"
- },
- )
- results_path: Optional[str] = field(
- default=None, metadata={"help": "path to save eval results (optional)"}
- )
-
-
-@dataclass
-class EvalLMConfig(FairseqDataclass):
- output_word_probs: bool = field(
- default=False,
- metadata={
- "help": "if set, outputs words and their predicted log probabilities to standard output"
- },
- )
- output_word_stats: bool = field(
- default=False,
- metadata={
- "help": "if set, outputs word statistics such as word count, average probability, etc"
- },
- )
- context_window: int = field(
- default=0,
- metadata={
- "help": "ensures that every evaluated token has access to a context of at least this size, if possible"
- },
- )
- softmax_batch: int = field(
- default=sys.maxsize,
- metadata={
- "help": "if BxT is more than this, will batch the softmax over vocab to this amount of tokens, in order to fit into GPU memory"
- },
- )
-
-
-@dataclass
-class InteractiveConfig(FairseqDataclass):
- buffer_size: int = field(
- default=0,
- metadata={
- "help": "read this many sentences into a buffer before processing them"
- },
- )
- input: str = field(
- default="-", metadata={"help": "file to read from; use - for stdin"},
- )
-
-
-@dataclass
-class EMAConfig(FairseqDataclass):
- store_ema: bool = field(
- default=False, metadata={
- help: "store exponential moving average shadow model"
- }
- )
- ema_decay: float = field(
- default=0.9999, metadata={
- "help": 'decay for exponential moving average model'
- }
- )
- ema_start_update : int = field(
- default=0, metadata={"help": "start EMA update after this many model updates"}
- )
- ema_seed_model : Optional[str] = field(
- default=None, metadata={
- "help": "Seed to load EMA model from. "
- "Used to load EMA model separately from the actual model."
- }
- )
- ema_update_freq : int = field(
- default=1, metadata={"help": "Do EMA update every this many model updates"}
- )
- ema_fp32: bool = field(
- default=False,
- metadata={"help": "If true, store EMA model in fp32 even if model is in fp16"},
- )
-
-
-@dataclass
-class FairseqConfig(FairseqDataclass):
- common: CommonConfig = CommonConfig()
- common_eval: CommonEvalConfig = CommonEvalConfig()
- distributed_training: DistributedTrainingConfig = DistributedTrainingConfig()
- dataset: DatasetConfig = DatasetConfig()
- optimization: OptimizationConfig = OptimizationConfig()
- checkpoint: CheckpointConfig = CheckpointConfig()
- bmuf: FairseqBMUFConfig = FairseqBMUFConfig()
- generation: GenerationConfig = GenerationConfig()
- eval_lm: EvalLMConfig = EvalLMConfig()
- interactive: InteractiveConfig = InteractiveConfig()
- model: Any = MISSING
- task: Any = None
- criterion: Any = None
- optimizer: Any = None
- lr_scheduler: Any = None
- scoring: Any = None
- bpe: Any = None
- tokenizer: Any = None
- ema: EMAConfig = EMAConfig()
diff --git a/spaces/IDEA-CCNL/ziya2-13B-base/README.md b/spaces/IDEA-CCNL/ziya2-13B-base/README.md
deleted file mode 100644
index 8e99a95563c3e2c32e72ad210eb8b421f91d2034..0000000000000000000000000000000000000000
--- a/spaces/IDEA-CCNL/ziya2-13B-base/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Ziya LLaMA2 CPT
-emoji: 🏆
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.38.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/util/box_ops.py b/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/util/box_ops.py
deleted file mode 100644
index 781068d294e576954edb4bd07b6e0f30e4e1bcd9..0000000000000000000000000000000000000000
--- a/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/util/box_ops.py
+++ /dev/null
@@ -1,140 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Utilities for bounding box manipulation and GIoU.
-"""
-import torch
-from torchvision.ops.boxes import box_area
-
-
-def box_cxcywh_to_xyxy(x):
- x_c, y_c, w, h = x.unbind(-1)
- b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
- return torch.stack(b, dim=-1)
-
-
-def box_xyxy_to_cxcywh(x):
- x0, y0, x1, y1 = x.unbind(-1)
- b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
- return torch.stack(b, dim=-1)
-
-
-# modified from torchvision to also return the union
-def box_iou(boxes1, boxes2):
- area1 = box_area(boxes1)
- area2 = box_area(boxes2)
-
- # import ipdb; ipdb.set_trace()
- lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
- rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
-
- wh = (rb - lt).clamp(min=0) # [N,M,2]
- inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]
-
- union = area1[:, None] + area2 - inter
-
- iou = inter / (union + 1e-6)
- return iou, union
-
-
-def generalized_box_iou(boxes1, boxes2):
- """
- Generalized IoU from https://giou.stanford.edu/
-
- The boxes should be in [x0, y0, x1, y1] format
-
- Returns a [N, M] pairwise matrix, where N = len(boxes1)
- and M = len(boxes2)
- """
- # degenerate boxes gives inf / nan results
- # so do an early check
- assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
- assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
- # except:
- # import ipdb; ipdb.set_trace()
- iou, union = box_iou(boxes1, boxes2)
-
- lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
- rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
-
- wh = (rb - lt).clamp(min=0) # [N,M,2]
- area = wh[:, :, 0] * wh[:, :, 1]
-
- return iou - (area - union) / (area + 1e-6)
-
-
-# modified from torchvision to also return the union
-def box_iou_pairwise(boxes1, boxes2):
- area1 = box_area(boxes1)
- area2 = box_area(boxes2)
-
- lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # [N,2]
- rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # [N,2]
-
- wh = (rb - lt).clamp(min=0) # [N,2]
- inter = wh[:, 0] * wh[:, 1] # [N]
-
- union = area1 + area2 - inter
-
- iou = inter / union
- return iou, union
-
-
-def generalized_box_iou_pairwise(boxes1, boxes2):
- """
- Generalized IoU from https://giou.stanford.edu/
-
- Input:
- - boxes1, boxes2: N,4
- Output:
- - giou: N, 4
- """
- # degenerate boxes gives inf / nan results
- # so do an early check
- assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
- assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
- assert boxes1.shape == boxes2.shape
- iou, union = box_iou_pairwise(boxes1, boxes2) # N, 4
-
- lt = torch.min(boxes1[:, :2], boxes2[:, :2])
- rb = torch.max(boxes1[:, 2:], boxes2[:, 2:])
-
- wh = (rb - lt).clamp(min=0) # [N,2]
- area = wh[:, 0] * wh[:, 1]
-
- return iou - (area - union) / area
-
-
-def masks_to_boxes(masks):
- """Compute the bounding boxes around the provided masks
-
- The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
-
- Returns a [N, 4] tensors, with the boxes in xyxy format
- """
- if masks.numel() == 0:
- return torch.zeros((0, 4), device=masks.device)
-
- h, w = masks.shape[-2:]
-
- y = torch.arange(0, h, dtype=torch.float)
- x = torch.arange(0, w, dtype=torch.float)
- y, x = torch.meshgrid(y, x)
-
- x_mask = masks * x.unsqueeze(0)
- x_max = x_mask.flatten(1).max(-1)[0]
- x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
-
- y_mask = masks * y.unsqueeze(0)
- y_max = y_mask.flatten(1).max(-1)[0]
- y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
-
- return torch.stack([x_min, y_min, x_max, y_max], 1)
-
-
-if __name__ == "__main__":
- x = torch.rand(5, 4)
- y = torch.rand(3, 4)
- iou, union = box_iou(x, y)
- import ipdb
-
- ipdb.set_trace()
diff --git a/spaces/IgorSense/Diffusion_Space2/utils.py b/spaces/IgorSense/Diffusion_Space2/utils.py
deleted file mode 100644
index ff1c065d186347ca51b47d010a697dbe1814695c..0000000000000000000000000000000000000000
--- a/spaces/IgorSense/Diffusion_Space2/utils.py
+++ /dev/null
@@ -1,6 +0,0 @@
-def is_google_colab():
- try:
- import google.colab
- return True
- except:
- return False
\ No newline at end of file
diff --git a/spaces/Ikaros521/so-vits-svc-4.0-ikaros/modules/modules.py b/spaces/Ikaros521/so-vits-svc-4.0-ikaros/modules/modules.py
deleted file mode 100644
index 54290fd207b25e93831bd21005990ea137e6b50e..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/so-vits-svc-4.0-ikaros/modules/modules.py
+++ /dev/null
@@ -1,342 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import modules.commons as commons
-from modules.commons import init_weights, get_padding
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(
- nn.ReLU(),
- nn.Dropout(p_dropout))
- for _ in range(n_layers-1):
- self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size ** i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
- groups=channels, dilation=dilation, padding=padding
- ))
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
- super(WN, self).__init__()
- assert(kernel_size % 2 == 1)
- self.hidden_channels =hidden_channels
- self.kernel_size = kernel_size,
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
- for i in range(n_layers):
- dilation = dilation_rate ** i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
- dilation=dilation, padding=padding)
- in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(
- x_in,
- g_l,
- n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:,:self.hidden_channels,:]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:,self.hidden_channels:,:]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels,1))
- self.logs = nn.Parameter(torch.zeros(channels,1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1,2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels]*2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1,2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
diff --git a/spaces/Intae/deepfake/training/transforms/__init__.py b/spaces/Intae/deepfake/training/transforms/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/JUNGU/Image-to-Story-Ko-multiplot/share_btn.py b/spaces/JUNGU/Image-to-Story-Ko-multiplot/share_btn.py
deleted file mode 100644
index a14082bc8a4902e010d8a98431adc3e7ffecf5a3..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/Image-to-Story-Ko-multiplot/share_btn.py
+++ /dev/null
@@ -1,75 +0,0 @@
-community_icon_html = """"""
-
-loading_icon_html = """"""
-
-share_js = """async () => {
- async function uploadFile(file){
- const UPLOAD_URL = 'https://huggingface.co/uploads';
- const response = await fetch(UPLOAD_URL, {
- method: 'POST',
- headers: {
- 'Content-Type': file.type,
- 'X-Requested-With': 'XMLHttpRequest',
- },
- body: file, /// <- File inherits from Blob
- });
- const url = await response.text();
- return url;
- }
-
- async function getInputImgFile(imgEl){
- const res = await fetch(imgEl.src);
- const blob = await res.blob();
- const imgId = Date.now() % 200;
- const isPng = imgEl.src.startsWith(`data:image/png`);
- if(isPng){
- const fileName = `sd-perception-${{imgId}}.png`;
- return new File([blob], fileName, { type: 'image/png' });
- }else{
- const fileName = `sd-perception-${{imgId}}.jpg`;
- return new File([blob], fileName, { type: 'image/jpeg' });
- }
- }
-
- const gradioEl = document.querySelector("gradio-app").shadowRoot || document.querySelector('body > gradio-app');
- const inputImgEl = gradioEl.querySelector('#image-in img');
- const outputTxt = gradioEl.querySelector('#story textarea').value;
- const wordsArray = outputTxt.split(" ");
- const firstWords = wordsArray.slice(0, 12).join(" ");
-
- const shareBtnEl = gradioEl.querySelector('#share-btn');
- const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
- const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-
- shareBtnEl.style.pointerEvents = 'none';
- shareIconEl.style.display = 'none';
- loadingIconEl.style.removeProperty('display');
-
-
- const inputFile = await getInputImgFile(inputImgEl);
- const urlInputImg = await uploadFile(inputFile);
-
- const descriptionMd = `
-
-#### Image input:
-
-
-#### Generated Story:
-${outputTxt}
-`;
- const params = new URLSearchParams({
- title: firstWords,
- description: descriptionMd,
- });
- const paramsStr = params.toString();
- window.open(`https://huggingface.co/spaces/fffiloni/Image-to-Story/discussions/new?${paramsStr}`, '_blank');
- shareBtnEl.style.removeProperty('pointer-events');
- shareIconEl.style.removeProperty('display');
- loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/JackBAI/MassageMateNLP/models/bert/position_y/README.md b/spaces/JackBAI/MassageMateNLP/models/bert/position_y/README.md
deleted file mode 100644
index 16b794008dfcf6e8150518dabfcabecfce6f0d2d..0000000000000000000000000000000000000000
--- a/spaces/JackBAI/MassageMateNLP/models/bert/position_y/README.md
+++ /dev/null
@@ -1,56 +0,0 @@
----
-license: apache-2.0
-tags:
-- generated_from_trainer
-metrics:
-- accuracy
-model-index:
-- name: position_y
- results: []
----
-
-
-
-# position_y
-
-This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.4375
-- Accuracy: 0.8227
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 128
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 12.0
-
-### Training results
-
-
-
-### Framework versions
-
-- Transformers 4.25.1
-- Pytorch 1.11.0+cu113
-- Datasets 2.8.0
-- Tokenizers 0.13.2
diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/models/unet_2d_blocks_flax.py b/spaces/Jackflack09/diffuse-custom/diffusers/models/unet_2d_blocks_flax.py
deleted file mode 100644
index 96e76cb06a59a31beebf4449786b72a7c838a298..0000000000000000000000000000000000000000
--- a/spaces/Jackflack09/diffuse-custom/diffusers/models/unet_2d_blocks_flax.py
+++ /dev/null
@@ -1,365 +0,0 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import flax.linen as nn
-import jax.numpy as jnp
-
-from .attention_flax import FlaxTransformer2DModel
-from .resnet_flax import FlaxDownsample2D, FlaxResnetBlock2D, FlaxUpsample2D
-
-
-class FlaxCrossAttnDownBlock2D(nn.Module):
- r"""
- Cross Attention 2D Downsizing block - original architecture from Unet transformers:
- https://arxiv.org/abs/2103.06104
-
- Parameters:
- in_channels (:obj:`int`):
- Input channels
- out_channels (:obj:`int`):
- Output channels
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- num_layers (:obj:`int`, *optional*, defaults to 1):
- Number of attention blocks layers
- attn_num_head_channels (:obj:`int`, *optional*, defaults to 1):
- Number of attention heads of each spatial transformer block
- add_downsample (:obj:`bool`, *optional*, defaults to `True`):
- Whether to add downsampling layer before each final output
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- """
- in_channels: int
- out_channels: int
- dropout: float = 0.0
- num_layers: int = 1
- attn_num_head_channels: int = 1
- add_downsample: bool = True
- use_linear_projection: bool = False
- only_cross_attention: bool = False
- dtype: jnp.dtype = jnp.float32
-
- def setup(self):
- resnets = []
- attentions = []
-
- for i in range(self.num_layers):
- in_channels = self.in_channels if i == 0 else self.out_channels
-
- res_block = FlaxResnetBlock2D(
- in_channels=in_channels,
- out_channels=self.out_channels,
- dropout_prob=self.dropout,
- dtype=self.dtype,
- )
- resnets.append(res_block)
-
- attn_block = FlaxTransformer2DModel(
- in_channels=self.out_channels,
- n_heads=self.attn_num_head_channels,
- d_head=self.out_channels // self.attn_num_head_channels,
- depth=1,
- use_linear_projection=self.use_linear_projection,
- only_cross_attention=self.only_cross_attention,
- dtype=self.dtype,
- )
- attentions.append(attn_block)
-
- self.resnets = resnets
- self.attentions = attentions
-
- if self.add_downsample:
- self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)
-
- def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True):
- output_states = ()
-
- for resnet, attn in zip(self.resnets, self.attentions):
- hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
- hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)
- output_states += (hidden_states,)
-
- if self.add_downsample:
- hidden_states = self.downsamplers_0(hidden_states)
- output_states += (hidden_states,)
-
- return hidden_states, output_states
-
-
-class FlaxDownBlock2D(nn.Module):
- r"""
- Flax 2D downsizing block
-
- Parameters:
- in_channels (:obj:`int`):
- Input channels
- out_channels (:obj:`int`):
- Output channels
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- num_layers (:obj:`int`, *optional*, defaults to 1):
- Number of attention blocks layers
- add_downsample (:obj:`bool`, *optional*, defaults to `True`):
- Whether to add downsampling layer before each final output
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- """
- in_channels: int
- out_channels: int
- dropout: float = 0.0
- num_layers: int = 1
- add_downsample: bool = True
- dtype: jnp.dtype = jnp.float32
-
- def setup(self):
- resnets = []
-
- for i in range(self.num_layers):
- in_channels = self.in_channels if i == 0 else self.out_channels
-
- res_block = FlaxResnetBlock2D(
- in_channels=in_channels,
- out_channels=self.out_channels,
- dropout_prob=self.dropout,
- dtype=self.dtype,
- )
- resnets.append(res_block)
- self.resnets = resnets
-
- if self.add_downsample:
- self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)
-
- def __call__(self, hidden_states, temb, deterministic=True):
- output_states = ()
-
- for resnet in self.resnets:
- hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
- output_states += (hidden_states,)
-
- if self.add_downsample:
- hidden_states = self.downsamplers_0(hidden_states)
- output_states += (hidden_states,)
-
- return hidden_states, output_states
-
-
-class FlaxCrossAttnUpBlock2D(nn.Module):
- r"""
- Cross Attention 2D Upsampling block - original architecture from Unet transformers:
- https://arxiv.org/abs/2103.06104
-
- Parameters:
- in_channels (:obj:`int`):
- Input channels
- out_channels (:obj:`int`):
- Output channels
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- num_layers (:obj:`int`, *optional*, defaults to 1):
- Number of attention blocks layers
- attn_num_head_channels (:obj:`int`, *optional*, defaults to 1):
- Number of attention heads of each spatial transformer block
- add_upsample (:obj:`bool`, *optional*, defaults to `True`):
- Whether to add upsampling layer before each final output
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- """
- in_channels: int
- out_channels: int
- prev_output_channel: int
- dropout: float = 0.0
- num_layers: int = 1
- attn_num_head_channels: int = 1
- add_upsample: bool = True
- use_linear_projection: bool = False
- only_cross_attention: bool = False
- dtype: jnp.dtype = jnp.float32
-
- def setup(self):
- resnets = []
- attentions = []
-
- for i in range(self.num_layers):
- res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels
- resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels
-
- res_block = FlaxResnetBlock2D(
- in_channels=resnet_in_channels + res_skip_channels,
- out_channels=self.out_channels,
- dropout_prob=self.dropout,
- dtype=self.dtype,
- )
- resnets.append(res_block)
-
- attn_block = FlaxTransformer2DModel(
- in_channels=self.out_channels,
- n_heads=self.attn_num_head_channels,
- d_head=self.out_channels // self.attn_num_head_channels,
- depth=1,
- use_linear_projection=self.use_linear_projection,
- only_cross_attention=self.only_cross_attention,
- dtype=self.dtype,
- )
- attentions.append(attn_block)
-
- self.resnets = resnets
- self.attentions = attentions
-
- if self.add_upsample:
- self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)
-
- def __call__(self, hidden_states, res_hidden_states_tuple, temb, encoder_hidden_states, deterministic=True):
- for resnet, attn in zip(self.resnets, self.attentions):
- # pop res hidden states
- res_hidden_states = res_hidden_states_tuple[-1]
- res_hidden_states_tuple = res_hidden_states_tuple[:-1]
- hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1)
-
- hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
- hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)
-
- if self.add_upsample:
- hidden_states = self.upsamplers_0(hidden_states)
-
- return hidden_states
-
-
-class FlaxUpBlock2D(nn.Module):
- r"""
- Flax 2D upsampling block
-
- Parameters:
- in_channels (:obj:`int`):
- Input channels
- out_channels (:obj:`int`):
- Output channels
- prev_output_channel (:obj:`int`):
- Output channels from the previous block
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- num_layers (:obj:`int`, *optional*, defaults to 1):
- Number of attention blocks layers
- add_downsample (:obj:`bool`, *optional*, defaults to `True`):
- Whether to add downsampling layer before each final output
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- """
- in_channels: int
- out_channels: int
- prev_output_channel: int
- dropout: float = 0.0
- num_layers: int = 1
- add_upsample: bool = True
- dtype: jnp.dtype = jnp.float32
-
- def setup(self):
- resnets = []
-
- for i in range(self.num_layers):
- res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels
- resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels
-
- res_block = FlaxResnetBlock2D(
- in_channels=resnet_in_channels + res_skip_channels,
- out_channels=self.out_channels,
- dropout_prob=self.dropout,
- dtype=self.dtype,
- )
- resnets.append(res_block)
-
- self.resnets = resnets
-
- if self.add_upsample:
- self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)
-
- def __call__(self, hidden_states, res_hidden_states_tuple, temb, deterministic=True):
- for resnet in self.resnets:
- # pop res hidden states
- res_hidden_states = res_hidden_states_tuple[-1]
- res_hidden_states_tuple = res_hidden_states_tuple[:-1]
- hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1)
-
- hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
-
- if self.add_upsample:
- hidden_states = self.upsamplers_0(hidden_states)
-
- return hidden_states
-
-
-class FlaxUNetMidBlock2DCrossAttn(nn.Module):
- r"""
- Cross Attention 2D Mid-level block - original architecture from Unet transformers: https://arxiv.org/abs/2103.06104
-
- Parameters:
- in_channels (:obj:`int`):
- Input channels
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- num_layers (:obj:`int`, *optional*, defaults to 1):
- Number of attention blocks layers
- attn_num_head_channels (:obj:`int`, *optional*, defaults to 1):
- Number of attention heads of each spatial transformer block
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- """
- in_channels: int
- dropout: float = 0.0
- num_layers: int = 1
- attn_num_head_channels: int = 1
- use_linear_projection: bool = False
- dtype: jnp.dtype = jnp.float32
-
- def setup(self):
- # there is always at least one resnet
- resnets = [
- FlaxResnetBlock2D(
- in_channels=self.in_channels,
- out_channels=self.in_channels,
- dropout_prob=self.dropout,
- dtype=self.dtype,
- )
- ]
-
- attentions = []
-
- for _ in range(self.num_layers):
- attn_block = FlaxTransformer2DModel(
- in_channels=self.in_channels,
- n_heads=self.attn_num_head_channels,
- d_head=self.in_channels // self.attn_num_head_channels,
- depth=1,
- use_linear_projection=self.use_linear_projection,
- dtype=self.dtype,
- )
- attentions.append(attn_block)
-
- res_block = FlaxResnetBlock2D(
- in_channels=self.in_channels,
- out_channels=self.in_channels,
- dropout_prob=self.dropout,
- dtype=self.dtype,
- )
- resnets.append(res_block)
-
- self.resnets = resnets
- self.attentions = attentions
-
- def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True):
- hidden_states = self.resnets[0](hidden_states, temb)
- for attn, resnet in zip(self.attentions, self.resnets[1:]):
- hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)
- hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
-
- return hidden_states
diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/html/update.html b/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/html/update.html
deleted file mode 100644
index 6f005e11a0a11f441ff2c56d05b98402c640a53f..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/html/update.html
+++ /dev/null
@@ -1,29 +0,0 @@
-
-
-
-
- Latest Version: getting latest version...
-
-
- Getting update...
-
-
-
-
- Getting Release Note...
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/JosephTK/object-detection-count/README.md b/spaces/JosephTK/object-detection-count/README.md
deleted file mode 100644
index 4b79812bb9f6b491e5e9be71bfedbb7df874a443..0000000000000000000000000000000000000000
--- a/spaces/JosephTK/object-detection-count/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Object Detection Count
-emoji: 📈
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 3.32.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/ppg_extractor/encoder/multi_layer_conv.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/ppg_extractor/encoder/multi_layer_conv.py
deleted file mode 100644
index fdb7fe70810eda54c727367efc986ce02ce581cc..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/ppg_extractor/encoder/multi_layer_conv.py
+++ /dev/null
@@ -1,105 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Tomoki Hayashi
-# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Layer modules for FFT block in FastSpeech (Feed-forward Transformer)."""
-
-import torch
-
-
-class MultiLayeredConv1d(torch.nn.Module):
- """Multi-layered conv1d for Transformer block.
-
- This is a module of multi-leyered conv1d designed
- to replace positionwise feed-forward network
- in Transforner block, which is introduced in
- `FastSpeech: Fast, Robust and Controllable Text to Speech`_.
-
- .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
- https://arxiv.org/pdf/1905.09263.pdf
-
- """
-
- def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
- """Initialize MultiLayeredConv1d module.
-
- Args:
- in_chans (int): Number of input channels.
- hidden_chans (int): Number of hidden channels.
- kernel_size (int): Kernel size of conv1d.
- dropout_rate (float): Dropout rate.
-
- """
- super(MultiLayeredConv1d, self).__init__()
- self.w_1 = torch.nn.Conv1d(
- in_chans,
- hidden_chans,
- kernel_size,
- stride=1,
- padding=(kernel_size - 1) // 2,
- )
- self.w_2 = torch.nn.Conv1d(
- hidden_chans,
- in_chans,
- kernel_size,
- stride=1,
- padding=(kernel_size - 1) // 2,
- )
- self.dropout = torch.nn.Dropout(dropout_rate)
-
- def forward(self, x):
- """Calculate forward propagation.
-
- Args:
- x (Tensor): Batch of input tensors (B, ..., in_chans).
-
- Returns:
- Tensor: Batch of output tensors (B, ..., hidden_chans).
-
- """
- x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
- return self.w_2(self.dropout(x).transpose(-1, 1)).transpose(-1, 1)
-
-
-class Conv1dLinear(torch.nn.Module):
- """Conv1D + Linear for Transformer block.
-
- A variant of MultiLayeredConv1d, which replaces second conv-layer to linear.
-
- """
-
- def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
- """Initialize Conv1dLinear module.
-
- Args:
- in_chans (int): Number of input channels.
- hidden_chans (int): Number of hidden channels.
- kernel_size (int): Kernel size of conv1d.
- dropout_rate (float): Dropout rate.
-
- """
- super(Conv1dLinear, self).__init__()
- self.w_1 = torch.nn.Conv1d(
- in_chans,
- hidden_chans,
- kernel_size,
- stride=1,
- padding=(kernel_size - 1) // 2,
- )
- self.w_2 = torch.nn.Linear(hidden_chans, in_chans)
- self.dropout = torch.nn.Dropout(dropout_rate)
-
- def forward(self, x):
- """Calculate forward propagation.
-
- Args:
- x (Tensor): Batch of input tensors (B, ..., in_chans).
-
- Returns:
- Tensor: Batch of output tensors (B, ..., hidden_chans).
-
- """
- x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
- return self.w_2(self.dropout(x))
diff --git a/spaces/Kevin676/TalktoAI/app.py b/spaces/Kevin676/TalktoAI/app.py
deleted file mode 100644
index 9d4c4bbdeb7812696a6a297fa89d3d40e1bf05d6..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/TalktoAI/app.py
+++ /dev/null
@@ -1,251 +0,0 @@
-import os
-os.system("pip install git+https://github.com/openai/whisper.git")
-import gradio as gr
-import whisper
-import openai
-
-from share_btn import community_icon_html, loading_icon_html, share_js
-
-model = whisper.load_model("base")
-
-mes1 = [
- {"role": "system", "content": "You are a TOEFL examiner who will ask me questions in a TOEFL test."}
-]
-
-mes2 = [
- {"role": "system", "content": "You are a mental health therapist. Your name is Tina."}
-]
-
-mes3 = [
- {"role": "system", "content": "You are my personal assistant. Your name is Alice."}
-]
-
-res = []
-
-def inference(apikey, audio, choice1):
-
- #global messages
-
- openai.api_key = apikey
-
- # time.sleep(3)
- # load audio and pad/trim it to fit 30 seconds
- audio = whisper.load_audio(audio)
- audio = whisper.pad_or_trim(audio)
-
- # make log-Mel spectrogram and move to the same device as the model
- mel = whisper.log_mel_spectrogram(audio).to(model.device)
-
- # detect the spoken language
- _, probs = model.detect_language(mel)
- print(f"Detected language: {max(probs, key=probs.get)}")
-
- # decode the audio
- options = whisper.DecodingOptions()
- result = whisper.decode(model, mel, options)
- res.append(result.text)
-
- if choice1 == "TOEFL":
- messages = mes1
- elif choice1 == "Therapist":
- messages = mes2
- elif choice1 == "Alice":
- messages = mes3
-
- # chatgpt
- n = len(res)
- content = res[n-1]
- messages.append({"role": "user", "content": content})
-
- completion = openai.ChatCompletion.create(
- model = "gpt-3.5-turbo",
- messages = messages
- )
-
- chat_response = completion.choices[0].message.content
-
- messages.append({"role": "assistant", "content": chat_response})
-
- return [result.text, chat_response, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)]
-
-
-css = """
- .gradio-container {
- font-family: 'IBM Plex Sans', sans-serif;
- }
- .gr-button {
- color: white;
- border-color: black;
- background: black;
- }
- input[type='range'] {
- accent-color: black;
- }
- .dark input[type='range'] {
- accent-color: #dfdfdf;
- }
- .container {
- max-width: 730px;
- margin: auto;
- padding-top: 1.5rem;
- }
-
- .details:hover {
- text-decoration: underline;
- }
- .gr-button {
- white-space: nowrap;
- }
- .gr-button:focus {
- border-color: rgb(147 197 253 / var(--tw-border-opacity));
- outline: none;
- box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
- --tw-border-opacity: 1;
- --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
- --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
- --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
- --tw-ring-opacity: .5;
- }
- .footer {
- margin-bottom: 45px;
- margin-top: 35px;
- text-align: center;
- border-bottom: 1px solid #e5e5e5;
- }
- .footer>p {
- font-size: .8rem;
- display: inline-block;
- padding: 0 10px;
- transform: translateY(10px);
- background: white;
- }
- .dark .footer {
- border-color: #303030;
- }
- .dark .footer>p {
- background: #0b0f19;
- }
- .prompt h4{
- margin: 1.25em 0 .25em 0;
- font-weight: bold;
- font-size: 115%;
- }
- .animate-spin {
- animation: spin 1s linear infinite;
- }
- @keyframes spin {
- from {
- transform: rotate(0deg);
- }
- to {
- transform: rotate(360deg);
- }
- }
- #share-btn-container {
- display: flex; margin-top: 1.5rem !important; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
- }
- #share-btn {
- all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
- }
- #share-btn * {
- all: unset;
- }
-"""
-
-block = gr.Blocks(css=css)
-
-
-
-with block:
- gr.HTML(
- """
-
-
-
-
- TalktoAI，随时随地，谈天说地！
-
-
-
- 让有人文关怀的AI造福每一个人！AI向善，文明璀璨！
-
-
- """
- )
-
- with gr.Group():
- with gr.Box():
- with gr.Row().style(mobile_collapse=False, equal_height=True):
- apikey = gr.Textbox(lines=3, label = "请填写您的OpenAI_API_key")
- audio = gr.Audio(
- label="Input Audio",
- show_label=False,
- source="microphone",
- type="filepath"
- )
- choice1 = gr.Radio(["TOEFL", "Therapist", "Alice"], label="TOEFL Examiner, Therapist Tina, or Assistant Alice?")
-
- btn = gr.Button("提交")
-
- text = gr.Textbox(label="Speech to Text", elem_id="result-textarea")
-
- response = gr.Textbox(label="ChatGPT Output")
-
- with gr.Group(elem_id="share-btn-container"):
- community_icon = gr.HTML(community_icon_html, visible=False)
- loading_icon = gr.HTML(loading_icon_html, visible=False)
- share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
-
-
-
-
- btn.click(inference, inputs=[apikey, audio, choice1], outputs=[text, response, community_icon, loading_icon, share_button])
- share_button.click(None, [], [], _js=share_js)
-
- gr.HTML('''
-
- ''')
-
-block.launch()
\ No newline at end of file
diff --git a/spaces/Lbin123/Lbingo/src/components/chat.tsx b/spaces/Lbin123/Lbingo/src/components/chat.tsx
deleted file mode 100644
index a37ab1cc96ca2e6bfd9acbe313a8d946bfd5c3d4..0000000000000000000000000000000000000000
--- a/spaces/Lbin123/Lbingo/src/components/chat.tsx
+++ /dev/null
@@ -1,93 +0,0 @@
-'use client'
-
-import { useCallback, useEffect, useMemo, useState } from 'react'
-import { useAtom } from 'jotai'
-import Image from 'next/image'
-import { cn } from '@/lib/utils'
-import { ChatList } from '@/components/chat-list'
-import { ChatPanel } from '@/components/chat-panel'
-import { WelcomeScreen } from '@/components/welcome-screen'
-import { ChatScrollAnchor } from '@/components/chat-scroll-anchor'
-import { ToneSelector } from './tone-selector'
-import { ChatHeader } from './chat-header'
-import { ChatSuggestions } from './chat-suggestions'
-import { bingConversationStyleAtom } from '@/state'
-import { ButtonScrollToBottom } from '@/components/button-scroll-to-bottom'
-import StopIcon from '@/assets/images/stop.svg'
-import { useBing } from '@/lib/hooks/use-bing'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-import { ChatNotification } from './chat-notification'
-import { Settings } from './settings'
-import { ChatHistory } from './chat-history'
-
-export type ChatProps = React.ComponentProps<'div'> & { initialMessages?: ChatMessageModel[] }
-
-export default function Chat({ className }: ChatProps) {
-
- const [bingStyle, setBingStyle] = useAtom(bingConversationStyleAtom)
- const {
- messages,
- sendMessage,
- resetConversation,
- stopGenerating,
- setInput,
- bot,
- input,
- generating,
- isSpeaking,
- uploadImage,
- attachmentList,
- setAttachmentList,
- } = useBing()
-
- useEffect(() => {
- window.scrollTo({
- top: document.body.offsetHeight,
- behavior: 'smooth'
- })
- }, [])
-
- return (
-
-
- 1) Select the possible model variations and tasks;
- 2) Change the inputs and Click the buttons to produce results;
- 3) See attention visualisations, by choosing a specific layer and head;
- """)
- with gr.Row():
- context_input = gr.Textbox(lines=2, value="What's the meaning of life?", label='Input:')
- model_result_output = gr.Textbox(lines=2, label='Model result:')
- with gr.Column():
- task_type_choice = gr.Radio(
- ["eli5", "commongen"], value="eli5", label="What task do you want to try?"
- )
- decoding_type_choice = gr.Radio(
- ["default", "constraint"], value="default", label="What decoding strategy do you want to use?"
- )
- with gr.Row():
- model_btn = gr.Button(value="See Model Results")
- gr.Markdown(
- """
- ---
- Observe Attention
- """
- )
- with gr.Row():
- with gr.Column():
- layer = gr.Slider(0, 11, 0, step=1, label="Layer")
- head = gr.Slider(0, 15, 0, step=1, label="Head")
- with gr.Column():
- plot_output = gr.Plot()
- with gr.Row():
- vis_btn = gr.Button(value="See Attention Scores")
- model_btn.click(fn=infer_bart, inputs=[context_input, task_type_choice, decoding_type_choice],
- outputs=[model_result_output])
- vis_btn.click(fn=plot_attention, inputs=[context_input, task_type_choice, layer, head], outputs=[plot_output])
-
-if __name__ == '__main__':
- app.launch()
\ No newline at end of file
diff --git a/spaces/NATSpeech/PortaSpeech/utils/metrics/laplace_var.py b/spaces/NATSpeech/PortaSpeech/utils/metrics/laplace_var.py
deleted file mode 100644
index ec6f5f8d877195e7ee512d7e9f6f8a879d3ef32c..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/PortaSpeech/utils/metrics/laplace_var.py
+++ /dev/null
@@ -1,4 +0,0 @@
-import scipy.ndimage
-
-def laplace_var(x):
- return scipy.ndimage.laplace(x).var()
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/xlnet/xlnet_modeling_test.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/xlnet/xlnet_modeling_test.py
deleted file mode 100644
index dce887aebd77c75999091af9ec112f8d0d336eee..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/xlnet/xlnet_modeling_test.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from absl import logging
-import numpy as np
-import tensorflow as tf
-
-from official.nlp.xlnet import xlnet_modeling
-
-
-class PositionalEmbeddingLayerTest(tf.test.TestCase):
-
- def test_positional_embedding(self):
- """A low-dimensional example is tested.
-
- With len(pos_seq)=2 and d_model=4:
-
- pos_seq = [[1.], [0.]]
- inv_freq = [1., 0.01]
- pos_seq x inv_freq = [[1, 0.01], [0., 0.]]
- pos_emb = [[sin(1.), sin(0.01), cos(1.), cos(0.01)],
- [sin(0.), sin(0.), cos(0.), cos(0.)]]
- = [[0.84147096, 0.00999983, 0.54030228, 0.99994999],
- [0., 0., 1., 1.]]
- """
- target = np.array([[[0.84147096, 0.00999983, 0.54030228, 0.99994999]],
- [[0., 0., 1., 1.]]])
- d_model = 4
- pos_seq = tf.range(1, -1, -1.0) # [1., 0.]
- pos_emb_layer = xlnet_modeling.PositionalEmbedding(d_model)
- pos_emb = pos_emb_layer(pos_seq, batch_size=None).numpy().astype(float)
-
- logging.info(pos_emb)
- self.assertAllClose(pos_emb, target)
-
-if __name__ == "__main__":
- tf.test.main()
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/detection/dataloader/retinanet_parser.py b/spaces/NCTCMumbai/NCTC/models/official/vision/detection/dataloader/retinanet_parser.py
deleted file mode 100644
index d226a6da7e2fc2e650ad6ecdfb5a431d13df97a3..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/detection/dataloader/retinanet_parser.py
+++ /dev/null
@@ -1,422 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Data parser and processing.
-
-Parse image and ground truths in a dataset to training targets and package them
-into (image, labels) tuple for RetinaNet.
-
-T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar
-Focal Loss for Dense Object Detection. arXiv:1708.02002
-"""
-
-import tensorflow as tf
-
-from official.vision.detection.dataloader import anchor
-from official.vision.detection.dataloader import mode_keys as ModeKeys
-from official.vision.detection.dataloader import tf_example_decoder
-from official.vision.detection.utils import box_utils
-from official.vision.detection.utils import input_utils
-
-
-def process_source_id(source_id):
- """Processes source_id to the right format."""
- if source_id.dtype == tf.string:
- source_id = tf.cast(tf.strings.to_number(source_id), tf.int32)
- with tf.control_dependencies([source_id]):
- source_id = tf.cond(
- pred=tf.equal(tf.size(input=source_id), 0),
- true_fn=lambda: tf.cast(tf.constant(-1), tf.int32),
- false_fn=lambda: tf.identity(source_id))
- return source_id
-
-
-def pad_groundtruths_to_fixed_size(gt, n):
- """Pads the first dimension of groundtruths labels to the fixed size."""
- gt['boxes'] = input_utils.pad_to_fixed_size(gt['boxes'], n, -1)
- gt['is_crowds'] = input_utils.pad_to_fixed_size(gt['is_crowds'], n, 0)
- gt['areas'] = input_utils.pad_to_fixed_size(gt['areas'], n, -1)
- gt['classes'] = input_utils.pad_to_fixed_size(gt['classes'], n, -1)
- return gt
-
-
-class Parser(object):
- """Parser to parse an image and its annotations into a dictionary of tensors."""
-
- def __init__(self,
- output_size,
- min_level,
- max_level,
- num_scales,
- aspect_ratios,
- anchor_size,
- match_threshold=0.5,
- unmatched_threshold=0.5,
- aug_rand_hflip=False,
- aug_scale_min=1.0,
- aug_scale_max=1.0,
- use_autoaugment=False,
- autoaugment_policy_name='v0',
- skip_crowd_during_training=True,
- max_num_instances=100,
- use_bfloat16=True,
- mode=None):
- """Initializes parameters for parsing annotations in the dataset.
-
- Args:
- output_size: `Tensor` or `list` for [height, width] of output image. The
- output_size should be divided by the largest feature stride 2^max_level.
- min_level: `int` number of minimum level of the output feature pyramid.
- max_level: `int` number of maximum level of the output feature pyramid.
- num_scales: `int` number representing intermediate scales added
- on each level. For instances, num_scales=2 adds one additional
- intermediate anchor scales [2^0, 2^0.5] on each level.
- aspect_ratios: `list` of float numbers representing the aspect raito
- anchors added on each level. The number indicates the ratio of width to
- height. For instances, aspect_ratios=[1.0, 2.0, 0.5] adds three anchors
- on each scale level.
- anchor_size: `float` number representing the scale of size of the base
- anchor to the feature stride 2^level.
- match_threshold: `float` number between 0 and 1 representing the
- lower-bound threshold to assign positive labels for anchors. An anchor
- with a score over the threshold is labeled positive.
- unmatched_threshold: `float` number between 0 and 1 representing the
- upper-bound threshold to assign negative labels for anchors. An anchor
- with a score below the threshold is labeled negative.
- aug_rand_hflip: `bool`, if True, augment training with random
- horizontal flip.
- aug_scale_min: `float`, the minimum scale applied to `output_size` for
- data augmentation during training.
- aug_scale_max: `float`, the maximum scale applied to `output_size` for
- data augmentation during training.
- use_autoaugment: `bool`, if True, use the AutoAugment augmentation policy
- during training.
- autoaugment_policy_name: `string` that specifies the name of the
- AutoAugment policy that will be used during training.
- skip_crowd_during_training: `bool`, if True, skip annotations labeled with
- `is_crowd` equals to 1.
- max_num_instances: `int` number of maximum number of instances in an
- image. The groundtruth data will be padded to `max_num_instances`.
- use_bfloat16: `bool`, if True, cast output image to tf.bfloat16.
- mode: a ModeKeys. Specifies if this is training, evaluation, prediction
- or prediction with groundtruths in the outputs.
- """
- self._mode = mode
- self._max_num_instances = max_num_instances
- self._skip_crowd_during_training = skip_crowd_during_training
- self._is_training = (mode == ModeKeys.TRAIN)
-
- self._example_decoder = tf_example_decoder.TfExampleDecoder(
- include_mask=False)
-
- # Anchor.
- self._output_size = output_size
- self._min_level = min_level
- self._max_level = max_level
- self._num_scales = num_scales
- self._aspect_ratios = aspect_ratios
- self._anchor_size = anchor_size
- self._match_threshold = match_threshold
- self._unmatched_threshold = unmatched_threshold
-
- # Data augmentation.
- self._aug_rand_hflip = aug_rand_hflip
- self._aug_scale_min = aug_scale_min
- self._aug_scale_max = aug_scale_max
-
- # Data Augmentation with AutoAugment.
- self._use_autoaugment = use_autoaugment
- self._autoaugment_policy_name = autoaugment_policy_name
-
- # Device.
- self._use_bfloat16 = use_bfloat16
-
- # Data is parsed depending on the model Modekey.
- if mode == ModeKeys.TRAIN:
- self._parse_fn = self._parse_train_data
- elif mode == ModeKeys.EVAL:
- self._parse_fn = self._parse_eval_data
- elif mode == ModeKeys.PREDICT or mode == ModeKeys.PREDICT_WITH_GT:
- self._parse_fn = self._parse_predict_data
- else:
- raise ValueError('mode is not defined.')
-
- def __call__(self, value):
- """Parses data to an image and associated training labels.
-
- Args:
- value: a string tensor holding a serialized tf.Example proto.
-
- Returns:
- image: image tensor that is preproessed to have normalized value and
- dimension [output_size[0], output_size[1], 3]
- labels:
- cls_targets: ordered dictionary with keys
- [min_level, min_level+1, ..., max_level]. The values are tensor with
- shape [height_l, width_l, anchors_per_location]. The height_l and
- width_l represent the dimension of class logits at l-th level.
- box_targets: ordered dictionary with keys
- [min_level, min_level+1, ..., max_level]. The values are tensor with
- shape [height_l, width_l, anchors_per_location * 4]. The height_l and
- width_l represent the dimension of bounding box regression output at
- l-th level.
- num_positives: number of positive anchors in the image.
- anchor_boxes: ordered dictionary with keys
- [min_level, min_level+1, ..., max_level]. The values are tensor with
- shape [height_l, width_l, 4] representing anchor boxes at each level.
- image_info: a 2D `Tensor` that encodes the information of the image and
- the applied preprocessing. It is in the format of
- [[original_height, original_width], [scaled_height, scaled_width],
- [y_scale, x_scale], [y_offset, x_offset]].
- groundtruths:
- source_id: source image id. Default value -1 if the source id is empty
- in the groundtruth annotation.
- boxes: groundtruth bounding box annotations. The box is represented in
- [y1, x1, y2, x2] format. The tennsor is padded with -1 to the fixed
- dimension [self._max_num_instances, 4].
- classes: groundtruth classes annotations. The tennsor is padded with
- -1 to the fixed dimension [self._max_num_instances].
- areas: groundtruth areas annotations. The tennsor is padded with -1
- to the fixed dimension [self._max_num_instances].
- is_crowds: groundtruth annotations to indicate if an annotation
- represents a group of instances by value {0, 1}. The tennsor is
- padded with 0 to the fixed dimension [self._max_num_instances].
- """
- with tf.name_scope('parser'):
- data = self._example_decoder.decode(value)
- return self._parse_fn(data)
-
- def _parse_train_data(self, data):
- """Parses data for training and evaluation."""
- classes = data['groundtruth_classes']
- boxes = data['groundtruth_boxes']
- is_crowds = data['groundtruth_is_crowd']
- # Skips annotations with `is_crowd` = True.
- if self._skip_crowd_during_training and self._is_training:
- num_groundtrtuhs = tf.shape(input=classes)[0]
- with tf.control_dependencies([num_groundtrtuhs, is_crowds]):
- indices = tf.cond(
- pred=tf.greater(tf.size(input=is_crowds), 0),
- true_fn=lambda: tf.where(tf.logical_not(is_crowds))[:, 0],
- false_fn=lambda: tf.cast(tf.range(num_groundtrtuhs), tf.int64))
- classes = tf.gather(classes, indices)
- boxes = tf.gather(boxes, indices)
-
- # Gets original image and its size.
- image = data['image']
-
- image_shape = tf.shape(input=image)[0:2]
-
- # Normalizes image with mean and std pixel values.
- image = input_utils.normalize_image(image)
-
- # Flips image randomly during training.
- if self._aug_rand_hflip:
- image, boxes = input_utils.random_horizontal_flip(image, boxes)
-
- # Converts boxes from normalized coordinates to pixel coordinates.
- boxes = box_utils.denormalize_boxes(boxes, image_shape)
-
- # Resizes and crops image.
- image, image_info = input_utils.resize_and_crop_image(
- image,
- self._output_size,
- padded_size=input_utils.compute_padded_size(
- self._output_size, 2 ** self._max_level),
- aug_scale_min=self._aug_scale_min,
- aug_scale_max=self._aug_scale_max)
- image_height, image_width, _ = image.get_shape().as_list()
-
- # Resizes and crops boxes.
- image_scale = image_info[2, :]
- offset = image_info[3, :]
- boxes = input_utils.resize_and_crop_boxes(
- boxes, image_scale, image_info[1, :], offset)
- # Filters out ground truth boxes that are all zeros.
- indices = box_utils.get_non_empty_box_indices(boxes)
- boxes = tf.gather(boxes, indices)
- classes = tf.gather(classes, indices)
-
- # Assigns anchors.
- input_anchor = anchor.Anchor(
- self._min_level, self._max_level, self._num_scales,
- self._aspect_ratios, self._anchor_size, (image_height, image_width))
- anchor_labeler = anchor.AnchorLabeler(
- input_anchor, self._match_threshold, self._unmatched_threshold)
- (cls_targets, box_targets, num_positives) = anchor_labeler.label_anchors(
- boxes,
- tf.cast(tf.expand_dims(classes, axis=1), tf.float32))
-
- # If bfloat16 is used, casts input image to tf.bfloat16.
- if self._use_bfloat16:
- image = tf.cast(image, dtype=tf.bfloat16)
-
- # Packs labels for model_fn outputs.
- labels = {
- 'cls_targets': cls_targets,
- 'box_targets': box_targets,
- 'anchor_boxes': input_anchor.multilevel_boxes,
- 'num_positives': num_positives,
- 'image_info': image_info,
- }
- return image, labels
-
- def _parse_eval_data(self, data):
- """Parses data for training and evaluation."""
- groundtruths = {}
- classes = data['groundtruth_classes']
- boxes = data['groundtruth_boxes']
-
- # Gets original image and its size.
- image = data['image']
- image_shape = tf.shape(input=image)[0:2]
-
- # Normalizes image with mean and std pixel values.
- image = input_utils.normalize_image(image)
-
- # Converts boxes from normalized coordinates to pixel coordinates.
- boxes = box_utils.denormalize_boxes(boxes, image_shape)
-
- # Resizes and crops image.
- image, image_info = input_utils.resize_and_crop_image(
- image,
- self._output_size,
- padded_size=input_utils.compute_padded_size(
- self._output_size, 2 ** self._max_level),
- aug_scale_min=1.0,
- aug_scale_max=1.0)
- image_height, image_width, _ = image.get_shape().as_list()
-
- # Resizes and crops boxes.
- image_scale = image_info[2, :]
- offset = image_info[3, :]
- boxes = input_utils.resize_and_crop_boxes(
- boxes, image_scale, image_info[1, :], offset)
- # Filters out ground truth boxes that are all zeros.
- indices = box_utils.get_non_empty_box_indices(boxes)
- boxes = tf.gather(boxes, indices)
- classes = tf.gather(classes, indices)
-
- # Assigns anchors.
- input_anchor = anchor.Anchor(
- self._min_level, self._max_level, self._num_scales,
- self._aspect_ratios, self._anchor_size, (image_height, image_width))
- anchor_labeler = anchor.AnchorLabeler(
- input_anchor, self._match_threshold, self._unmatched_threshold)
- (cls_targets, box_targets, num_positives) = anchor_labeler.label_anchors(
- boxes,
- tf.cast(tf.expand_dims(classes, axis=1), tf.float32))
-
- # If bfloat16 is used, casts input image to tf.bfloat16.
- if self._use_bfloat16:
- image = tf.cast(image, dtype=tf.bfloat16)
-
- # Sets up groundtruth data for evaluation.
- groundtruths = {
- 'source_id': data['source_id'],
- 'num_groundtrtuhs': tf.shape(data['groundtruth_classes']),
- 'image_info': image_info,
- 'boxes': box_utils.denormalize_boxes(
- data['groundtruth_boxes'], image_shape),
- 'classes': data['groundtruth_classes'],
- 'areas': data['groundtruth_area'],
- 'is_crowds': tf.cast(data['groundtruth_is_crowd'], tf.int32),
- }
- groundtruths['source_id'] = process_source_id(groundtruths['source_id'])
- groundtruths = pad_groundtruths_to_fixed_size(
- groundtruths, self._max_num_instances)
-
- # Packs labels for model_fn outputs.
- labels = {
- 'cls_targets': cls_targets,
- 'box_targets': box_targets,
- 'anchor_boxes': input_anchor.multilevel_boxes,
- 'num_positives': num_positives,
- 'image_info': image_info,
- 'groundtruths': groundtruths,
- }
- return image, labels
-
- def _parse_predict_data(self, data):
- """Parses data for prediction."""
- # Gets original image and its size.
- image = data['image']
- image_shape = tf.shape(input=image)[0:2]
-
- # Normalizes image with mean and std pixel values.
- image = input_utils.normalize_image(image)
-
- # Resizes and crops image.
- image, image_info = input_utils.resize_and_crop_image(
- image,
- self._output_size,
- padded_size=input_utils.compute_padded_size(
- self._output_size, 2 ** self._max_level),
- aug_scale_min=1.0,
- aug_scale_max=1.0)
- image_height, image_width, _ = image.get_shape().as_list()
-
- # If bfloat16 is used, casts input image to tf.bfloat16.
- if self._use_bfloat16:
- image = tf.cast(image, dtype=tf.bfloat16)
-
- # Compute Anchor boxes.
- input_anchor = anchor.Anchor(
- self._min_level, self._max_level, self._num_scales,
- self._aspect_ratios, self._anchor_size, (image_height, image_width))
-
- labels = {
- 'anchor_boxes': input_anchor.multilevel_boxes,
- 'image_info': image_info,
- }
- # If mode is PREDICT_WITH_GT, returns groundtruths and training targets
- # in labels.
- if self._mode == ModeKeys.PREDICT_WITH_GT:
- # Converts boxes from normalized coordinates to pixel coordinates.
- boxes = box_utils.denormalize_boxes(
- data['groundtruth_boxes'], image_shape)
- groundtruths = {
- 'source_id': data['source_id'],
- 'num_detections': tf.shape(data['groundtruth_classes']),
- 'boxes': boxes,
- 'classes': data['groundtruth_classes'],
- 'areas': data['groundtruth_area'],
- 'is_crowds': tf.cast(data['groundtruth_is_crowd'], tf.int32),
- }
- groundtruths['source_id'] = process_source_id(groundtruths['source_id'])
- groundtruths = pad_groundtruths_to_fixed_size(
- groundtruths, self._max_num_instances)
- labels['groundtruths'] = groundtruths
-
- # Computes training objective for evaluation loss.
- classes = data['groundtruth_classes']
-
- image_scale = image_info[2, :]
- offset = image_info[3, :]
- boxes = input_utils.resize_and_crop_boxes(
- boxes, image_scale, image_info[1, :], offset)
- # Filters out ground truth boxes that are all zeros.
- indices = box_utils.get_non_empty_box_indices(boxes)
- boxes = tf.gather(boxes, indices)
-
- # Assigns anchors.
- anchor_labeler = anchor.AnchorLabeler(
- input_anchor, self._match_threshold, self._unmatched_threshold)
- (cls_targets, box_targets, num_positives) = anchor_labeler.label_anchors(
- boxes,
- tf.cast(tf.expand_dims(classes, axis=1), tf.float32))
- labels['cls_targets'] = cls_targets
- labels['box_targets'] = box_targets
- labels['num_positives'] = num_positives
- return image, labels
diff --git a/spaces/NSect/VALL-E-X/modules/embedding.py b/spaces/NSect/VALL-E-X/modules/embedding.py
deleted file mode 100644
index 17f6c316da3de6a432f4d43f9563800fdb6d58c4..0000000000000000000000000000000000000000
--- a/spaces/NSect/VALL-E-X/modules/embedding.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# Copyright 2023 (authors: Feiteng Li)
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-
-import torch
-import torch.nn as nn
-
-
-class TokenEmbedding(nn.Module):
- def __init__(
- self,
- dim_model: int,
- vocab_size: int,
- dropout: float = 0.0,
- ):
- super().__init__()
-
- self.vocab_size = vocab_size
- self.dim_model = dim_model
-
- self.dropout = torch.nn.Dropout(p=dropout)
- self.word_embeddings = nn.Embedding(self.vocab_size, self.dim_model)
-
- @property
- def weight(self) -> torch.Tensor:
- return self.word_embeddings.weight
-
- def embedding(self, index: int) -> torch.Tensor:
- return self.word_embeddings.weight[index : index + 1]
-
- def forward(self, x: torch.Tensor):
- X = self.word_embeddings(x)
- X = self.dropout(X)
-
- return X
-
-
-class SinePositionalEmbedding(nn.Module):
- def __init__(
- self,
- dim_model: int,
- dropout: float = 0.0,
- scale: bool = False,
- alpha: bool = False,
- ):
- super().__init__()
- self.dim_model = dim_model
- self.x_scale = math.sqrt(dim_model) if scale else 1.0
- self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha)
- self.dropout = torch.nn.Dropout(p=dropout)
-
- self.reverse = False
- self.pe = None
- self.extend_pe(torch.tensor(0.0).expand(1, 4000))
-
- def extend_pe(self, x):
- """Reset the positional encodings."""
- if self.pe is not None:
- if self.pe.size(1) >= x.size(1):
- if self.pe.dtype != x.dtype or self.pe.device != x.device:
- self.pe = self.pe.to(dtype=x.dtype, device=x.device)
- return
- pe = torch.zeros(x.size(1), self.dim_model)
- if self.reverse:
- position = torch.arange(
- x.size(1) - 1, -1, -1.0, dtype=torch.float32
- ).unsqueeze(1)
- else:
- position = torch.arange(
- 0, x.size(1), dtype=torch.float32
- ).unsqueeze(1)
- div_term = torch.exp(
- torch.arange(0, self.dim_model, 2, dtype=torch.float32)
- * -(math.log(10000.0) / self.dim_model)
- )
- pe[:, 0::2] = torch.sin(position * div_term)
- pe[:, 1::2] = torch.cos(position * div_term)
- pe = pe.unsqueeze(0)
- self.pe = pe.to(device=x.device, dtype=x.dtype).detach()
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- self.extend_pe(x)
- output = x.unsqueeze(-1) if x.ndim == 2 else x
- output = output * self.x_scale + self.alpha * self.pe[:, : x.size(1)]
- return self.dropout(output)
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/multilingual/data_scripts/download_iwslt_and_extract.sh b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/multilingual/data_scripts/download_iwslt_and_extract.sh
deleted file mode 100644
index ca3591b3db1715f136773d62e4b9b9ede97d436c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/multilingual/data_scripts/download_iwslt_and_extract.sh
+++ /dev/null
@@ -1,225 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-#echo 'Cloning Moses github repository (for tokenization scripts)...'
-#git clone https://github.com/moses-smt/mosesdecoder.git
-
-if [ -z $WORKDIR_ROOT ] ;
-then
- echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..."
- exit
-fi
-
-
-
-data_root=${WORKDIR_ROOT}/iwsltv2
-DESTDIR=${WORKDIR_ROOT}/ML50/raw
-
-
-langs="ar_AR it_IT nl_XX ko_KR vi_VN"
-echo "data_root: $data_root"
-
-download_path=${data_root}/downloads
-raw=${DESTDIR}
-tmp=${data_root}/tmp
-orig=${data_root}/orig
-
-mkdir -p $download_path $orig $raw $tmp
-#######################
-download_iwslt(){
- iwslt_key=$1
- src=$2
- tgt=$3
- save_prefix=$4
- pushd ${download_path}
- if [[ ! -f ${save_prefix}$src-$tgt.tgz ]]; then
- wget https://wit3.fbk.eu/archive/${iwslt_key}/texts/$src/$tgt/$src-$tgt.tgz -O ${save_prefix}$src-$tgt.tgz
- [ $? -eq 0 ] && return 0
- fi
- popd
-}
-
-extract_iwslt(){
- src=$1
- tgt=$2
- prefix=$3
- pushd $orig
- tar zxvf ${download_path}/${prefix}$src-${tgt}.tgz
- popd
-}
-
-generate_train(){
- lsrc=$1
- ltgt=$2
- src=${lsrc:0:2}
- tgt=${ltgt:0:2}
- for ll in $lsrc $ltgt; do
- l=${ll:0:2}
- f="$orig/*/train.tags.$src-$tgt.$l"
- f_raw=$raw/train.$lsrc-$ltgt.$ll
- cat $f \
- | grep -v '' \
- | grep -v '' \
- | grep -v '' \
- | grep -v '' \
- | grep -v '' \
- | sed -e 's///g' \
- | sed -e 's/<\/title>//g' \
- | sed -e 's/<description>//g' \
- | sed -e 's/<\/description>//g' \
- | sed 's/^\s*//g' \
- | sed 's/\s*$//g' \
- > $f_raw
- [ $? -eq 0 ] && echo "extracted $f to $f_raw"
- done
- return 0
-}
-
-convert_valid_test(){
- src=$1
- tgt=$2
- for l in $src $tgt; do
- echo "lang: ${l}"
- for o in `ls $orig/*/IWSLT*.TED*.$src-$tgt.$l.xml`; do
- fname=${o##*/}
- f=$tmp/${fname%.*}
- echo "$o => $f"
- grep '<seg id' $o \
- | sed -e 's/<seg id="[0-9]*">\s*//g' \
- | sed -e 's/\s*<\/seg>\s*//g' \
- | sed -e "s/\’/\'/g" \
- > $f
- echo ""
- done
- done
-}
-
-generate_subset(){
- lsrc=$1
- ltgt=$2
- src=${lsrc:0:2}
- tgt=${ltgt:0:2}
- subset=$3
- prefix=$4
- for ll in $lsrc $ltgt; do
- l=${ll:0:2}
- f=$tmp/$prefix.${src}-${tgt}.$l
- if [[ -f $f ]]; then
- cp $f $raw/$subset.${lsrc}-$ltgt.${ll}
- fi
- done
-}
-#################
-
-echo "downloading iwslt training and dev data"
-# using multilingual for it, nl
-download_iwslt "2017-01-trnmted" DeEnItNlRo DeEnItNlRo
-download_iwslt "2017-01-trnted" ar en
-download_iwslt "2017-01-trnted" en ar
-download_iwslt "2017-01-trnted" ko en
-download_iwslt "2017-01-trnted" en ko
-download_iwslt "2015-01" vi en
-download_iwslt "2015-01" en vi
-
-echo "donwloading iwslt test data"
-download_iwslt "2017-01-mted-test" it en "test."
-download_iwslt "2017-01-mted-test" en it "test."
-download_iwslt "2017-01-mted-test" nl en "test."
-download_iwslt "2017-01-mted-test" en nl "test."
-
-download_iwslt "2017-01-ted-test" ar en "test."
-download_iwslt "2017-01-ted-test" en ar "test."
-download_iwslt "2017-01-ted-test" ko en "test."
-download_iwslt "2017-01-ted-test" en ko "test."
-download_iwslt "2015-01-test" vi en "test."
-download_iwslt "2015-01-test" en vi "test."
-
-echo "extract training data tar balls"
-extract_iwslt DeEnItNlRo DeEnItNlRo
-extract_iwslt ar en
-extract_iwslt en ar
-extract_iwslt ko en
-extract_iwslt en ko
-extract_iwslt vi en
-extract_iwslt en vi
-
-
-echo "extracting iwslt test data"
-for lang in $langs; do
- l=${lang:0:2}
- extract_iwslt $l en "test."
- extract_iwslt en $l "test."
-done
-
-echo "convert dev and test data"
-for lang in $langs; do
- s_lang=${lang:0:2}
- convert_valid_test $s_lang en
- convert_valid_test en $s_lang
-done
-
-
-
-echo "creating training data into $raw"
-for lang in $langs; do
- generate_train $lang en_XX
- generate_train en_XX $lang
-done
-
-echo "creating iwslt dev data into raw"
-generate_subset en_XX vi_VN valid "IWSLT15.TED.tst2013"
-generate_subset vi_VN en_XX valid "IWSLT15.TED.tst2013"
-
-generate_subset en_XX ar_AR valid "IWSLT17.TED.tst2016"
-generate_subset ar_AR en_XX valid "IWSLT17.TED.tst2016"
-generate_subset en_XX ko_KR valid "IWSLT17.TED.tst2016"
-generate_subset ko_KR en_XX valid "IWSLT17.TED.tst2016"
-
-
-generate_subset en_XX it_IT valid "IWSLT17.TED.tst2010"
-generate_subset it_IT en_XX valid "IWSLT17.TED.tst2010"
-generate_subset en_XX nl_XX valid "IWSLT17.TED.tst2010"
-generate_subset nl_XX en_XX valid "IWSLT17.TED.tst2010"
-
-echo "creating iswslt test data into raw"
-generate_subset en_XX vi_VN test "IWSLT15.TED.tst2015"
-generate_subset vi_VN en_XX test "IWSLT15.TED.tst2015"
-
-generate_subset en_XX ar_AR test "IWSLT17.TED.tst2017"
-generate_subset ar_AR en_XX test "IWSLT17.TED.tst2017"
-generate_subset en_XX ko_KR test "IWSLT17.TED.tst2017"
-generate_subset ko_KR en_XX test "IWSLT17.TED.tst2017"
-
-generate_subset en_XX it_IT test "IWSLT17.TED.tst2017.mltlng"
-generate_subset it_IT en_XX test "IWSLT17.TED.tst2017.mltlng"
-generate_subset en_XX nl_XX test "IWSLT17.TED.tst2017.mltlng"
-generate_subset nl_XX en_XX test "IWSLT17.TED.tst2017.mltlng"
-
-# normalze iwslt directions into x-en
-pushd $raw
-for lang in $langs; do
- for split in test valid; do
- x_en_f1=$split.$lang-en_XX.en_XX
- x_en_f2=$split.$lang-en_XX.${lang}
-
- en_x_f1=$split.en_XX-$lang.en_XX
- en_x_f2=$split.en_XX-$lang.${lang}
-
- if [ -f $en_x_f1 ] && [ ! -f $x_en_f1 ]; then
- echo "cp $en_x_f1 $x_en_f1"
- cp $en_x_f1 $x_en_f1
- fi
- if [ -f $x_en_f2 ] && [ ! -f $x_en_f2 ]; then
- echo "cp $en_x_f2 $x_en_f2"
- cp $en_x_f2 $x_en_f2
- fi
- done
-done
-popd
\ No newline at end of file
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/nat/cmlm_transformer.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/nat/cmlm_transformer.py
deleted file mode 100644
index c876e9453c101c00bd8e93e6e6f1fb48dc26f993..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/nat/cmlm_transformer.py
+++ /dev/null
@@ -1,162 +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.
-
-"""
-This file implements:
-Ghazvininejad, Marjan, et al.
-"Constant-time machine translation with conditional masked language models."
-arXiv preprint arXiv:1904.09324 (2019).
-"""
-
-from fairseq.models import register_model, register_model_architecture
-from fairseq.models.nat import NATransformerModel
-from fairseq.utils import new_arange
-
-
-def _skeptical_unmasking(output_scores, output_masks, p):
- sorted_index = output_scores.sort(-1)[1]
- boundary_len = (
- (output_masks.sum(1, keepdim=True).type_as(output_scores) - 2) * p
- ).long()
- skeptical_mask = new_arange(output_masks) < boundary_len
- return skeptical_mask.scatter(1, sorted_index, skeptical_mask)
-
-
-@register_model("cmlm_transformer")
-class CMLMNATransformerModel(NATransformerModel):
- @staticmethod
- def add_args(parser):
- NATransformerModel.add_args(parser)
-
- def forward(
- self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
- ):
- assert not self.decoder.src_embedding_copy, "do not support embedding copy."
-
- # encoding
- encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
- # length prediction
- length_out = self.decoder.forward_length(
- normalize=False, encoder_out=encoder_out
- )
- length_tgt = self.decoder.forward_length_prediction(
- length_out, encoder_out, tgt_tokens
- )
-
- # decoding
- word_ins_out = self.decoder(
- normalize=False,
- prev_output_tokens=prev_output_tokens,
- encoder_out=encoder_out,
- )
- word_ins_mask = prev_output_tokens.eq(self.unk)
-
- return {
- "word_ins": {
- "out": word_ins_out,
- "tgt": tgt_tokens,
- "mask": word_ins_mask,
- "ls": self.args.label_smoothing,
- "nll_loss": True,
- },
- "length": {
- "out": length_out,
- "tgt": length_tgt,
- "factor": self.decoder.length_loss_factor,
- },
- }
-
- def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs):
-
- step = decoder_out.step
- max_step = decoder_out.max_step
-
- output_tokens = decoder_out.output_tokens
- output_scores = decoder_out.output_scores
- history = decoder_out.history
-
- # execute the decoder
- output_masks = output_tokens.eq(self.unk)
- _scores, _tokens = self.decoder(
- normalize=True,
- prev_output_tokens=output_tokens,
- encoder_out=encoder_out,
- ).max(-1)
- output_tokens.masked_scatter_(output_masks, _tokens[output_masks])
- output_scores.masked_scatter_(output_masks, _scores[output_masks])
-
- if history is not None:
- history.append(output_tokens.clone())
-
- # skeptical decoding (depend on the maximum decoding steps.)
- if (step + 1) < max_step:
- skeptical_mask = _skeptical_unmasking(
- output_scores, output_tokens.ne(self.pad), 1 - (step + 1) / max_step
- )
-
- output_tokens.masked_fill_(skeptical_mask, self.unk)
- output_scores.masked_fill_(skeptical_mask, 0.0)
-
- if history is not None:
- history.append(output_tokens.clone())
-
- return decoder_out._replace(
- output_tokens=output_tokens,
- output_scores=output_scores,
- attn=None,
- history=history,
- )
-
-
-@register_model_architecture("cmlm_transformer", "cmlm_transformer")
-def cmlm_base_architecture(args):
- args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
- args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
- args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
- args.encoder_layers = getattr(args, "encoder_layers", 6)
- args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
- args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
- args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
- args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
- args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
- args.decoder_ffn_embed_dim = getattr(
- args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
- )
- args.decoder_layers = getattr(args, "decoder_layers", 6)
- args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
- args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
- args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
- args.attention_dropout = getattr(args, "attention_dropout", 0.0)
- args.activation_dropout = getattr(args, "activation_dropout", 0.0)
- args.activation_fn = getattr(args, "activation_fn", "relu")
- args.dropout = getattr(args, "dropout", 0.1)
- args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
- args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
- args.share_decoder_input_output_embed = getattr(
- args, "share_decoder_input_output_embed", False
- )
- args.share_all_embeddings = getattr(args, "share_all_embeddings", True)
- args.no_token_positional_embeddings = getattr(
- args, "no_token_positional_embeddings", False
- )
- args.adaptive_input = getattr(args, "adaptive_input", False)
- args.apply_bert_init = getattr(args, "apply_bert_init", False)
-
- args.decoder_output_dim = getattr(
- args, "decoder_output_dim", args.decoder_embed_dim
- )
- args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
-
- # --- special arguments ---
- args.sg_length_pred = getattr(args, "sg_length_pred", False)
- args.pred_length_offset = getattr(args, "pred_length_offset", False)
- args.length_loss_factor = getattr(args, "length_loss_factor", 0.1)
- args.ngram_predictor = getattr(args, "ngram_predictor", 1)
- args.src_embedding_copy = getattr(args, "src_embedding_copy", False)
-
-
-@register_model_architecture("cmlm_transformer", "cmlm_transformer_wmt_en_de")
-def cmlm_wmt_en_de(args):
- cmlm_base_architecture(args)
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/fused_lamb.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/fused_lamb.py
deleted file mode 100644
index f4f2bdb0c6c65f7758509b6d4d2f2c48cb6e8b4f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/fused_lamb.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from fairseq.optim import LegacyFairseqOptimizer, register_optimizer
-
-
-@register_optimizer("lamb")
-class FairseqLAMB(LegacyFairseqOptimizer):
- """LAMB optimizer."""
-
- def __init__(self, args, params):
- super().__init__(args)
- try:
- from apex.optimizers import FusedLAMB
-
- self._optimizer = FusedLAMB(params, **self.optimizer_config)
- except ImportError:
- raise ImportError("Please install apex to use LAMB optimizer")
-
- @staticmethod
- def add_args(parser):
- """Add optimizer-specific arguments to the parser."""
- # fmt: off
- parser.add_argument('--lamb-betas', default='(0.9, 0.999)', metavar='B',
- help='betas for LAMB optimizer')
- parser.add_argument('--lamb-eps', type=float, default=1e-8, metavar='D',
- help='epsilon for LAMB optimizer')
- parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
- help='weight decay')
- # fmt: on
-
- @property
- def optimizer_config(self):
- """
- Return a kwarg dictionary that will be used to override optimizer
- args stored in checkpoints. This allows us to load a checkpoint and
- resume training using a different set of optimizer args, e.g., with a
- different learning rate.
- """
- return {
- "lr": self.args.lr[0],
- "betas": eval(self.args.lamb_betas),
- "eps": self.args.lamb_eps,
- "weight_decay": self.args.weight_decay,
- }
-
- @property
- def supports_flat_params(self):
- return False
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/sgd.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/sgd.py
deleted file mode 100644
index 8e34fb99a18fff12ab76be5894a84cbbb2f48176..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/sgd.py
+++ /dev/null
@@ -1,43 +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 torch.optim
-
-from . import LegacyFairseqOptimizer, register_optimizer
-
-
-@register_optimizer("sgd")
-class SGD(LegacyFairseqOptimizer):
- def __init__(self, args, params):
- super().__init__(args)
- self._optimizer = torch.optim.SGD(params, **self.optimizer_config)
-
- @staticmethod
- def add_args(parser):
- """Add optimizer-specific arguments to the parser."""
- # fmt: off
- parser.add_argument('--momentum', default=0.0, type=float, metavar='M',
- help='momentum factor')
- parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
- help='weight decay')
- # fmt: on
-
- @property
- def optimizer_config(self):
- """
- Return a kwarg dictionary that will be used to override optimizer
- args stored in checkpoints. This allows us to load a checkpoint and
- resume training using a different set of optimizer args, e.g., with a
- different learning rate.
- """
- return {
- "lr": self.args.lr[0],
- "momentum": self.args.momentum,
- "weight_decay": self.args.weight_decay,
- }
-
- @property
- def supports_flat_params(self):
- return True
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_multihead_attention.py b/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_multihead_attention.py
deleted file mode 100644
index 620a2d679147bbbb8d15f3323374a39939686ec2..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_multihead_attention.py
+++ /dev/null
@@ -1,73 +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 unittest
-
-import torch
-from fairseq.modules.multihead_attention import MultiheadAttention
-
-
-class TestMultiheadAttention(unittest.TestCase):
- def test_append_prev_key_padding_mask(self):
- bsz = 1
- src_len = 4
-
- cases = [
- # no padding mask
- (None, None, None),
- # current padding mask only
- (
- torch.tensor([[1]]).bool(),
- None,
- torch.tensor([[0, 0, 0, 1]]).bool(),
- ),
- # previous padding mask only
- (
- None,
- torch.tensor([[0, 1, 0]]).bool(),
- torch.tensor([[0, 1, 0, 0]]).bool(),
- ),
- # both padding masks
- (
- torch.tensor([[1]]).bool(),
- torch.tensor([[0, 1, 0]]).bool(),
- torch.tensor([[0, 1, 0, 1]]).bool(),
- ),
- # prev_key_padding_mask already full
- (
- torch.tensor([[0, 1, 0, 1]]).bool(),
- None,
- torch.tensor([[0, 1, 0, 1]]).bool(),
- ),
- # key_padding_mask already full
- (
- None,
- torch.tensor([[0, 1, 0, 1]]).bool(),
- torch.tensor([[0, 1, 0, 1]]).bool(),
- ),
- ]
- for c in cases:
- key_padding_mask = MultiheadAttention._append_prev_key_padding_mask(
- c[0],
- c[1],
- batch_size=bsz,
- src_len=src_len,
- static_kv=False,
- )
-
- if key_padding_mask is not None:
- self.assertTrue(
- torch.all(torch.eq(key_padding_mask, c[2])),
- f"Unexpected resultant key padding mask: {key_padding_mask}"
- f" given current: {c[0]} and previous: {c[1]}",
- )
- self.assertEqual(key_padding_mask.size(0), bsz)
- self.assertEqual(key_padding_mask.size(1), src_len)
- else:
- self.assertIsNone(c[2])
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/ORI-Muchim/BarKeYaeTTS/attentions.py b/spaces/ORI-Muchim/BarKeYaeTTS/attentions.py
deleted file mode 100644
index 86bc73b5fe98cc7b443e9078553920346c996707..0000000000000000000000000000000000000000
--- a/spaces/ORI-Muchim/BarKeYaeTTS/attentions.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-from modules import LayerNorm
-
-
-class Encoder(nn.Module):
- def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.window_size = window_size
-
- self.drop = nn.Dropout(p_dropout)
- self.attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask):
- attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.attn_layers[i](x, x, attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class Decoder(nn.Module):
- def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
-
- self.drop = nn.Dropout(p_dropout)
- self.self_attn_layers = nn.ModuleList()
- self.norm_layers_0 = nn.ModuleList()
- self.encdec_attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
- self.norm_layers_0.append(LayerNorm(hidden_channels))
- self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask, h, h_mask):
- """
- x: decoder input
- h: encoder output
- """
- self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
- encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.self_attn_layers[i](x, x, self_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_0[i](x + y)
-
- y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class MultiHeadAttention(nn.Module):
- def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
- super().__init__()
- assert channels % n_heads == 0
-
- self.channels = channels
- self.out_channels = out_channels
- self.n_heads = n_heads
- self.p_dropout = p_dropout
- self.window_size = window_size
- self.heads_share = heads_share
- self.block_length = block_length
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
- self.attn = None
-
- self.k_channels = channels // n_heads
- self.conv_q = nn.Conv1d(channels, channels, 1)
- self.conv_k = nn.Conv1d(channels, channels, 1)
- self.conv_v = nn.Conv1d(channels, channels, 1)
- self.conv_o = nn.Conv1d(channels, out_channels, 1)
- self.drop = nn.Dropout(p_dropout)
-
- if window_size is not None:
- n_heads_rel = 1 if heads_share else n_heads
- rel_stddev = self.k_channels**-0.5
- self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
- self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
- nn.init.xavier_uniform_(self.conv_q.weight)
- nn.init.xavier_uniform_(self.conv_k.weight)
- nn.init.xavier_uniform_(self.conv_v.weight)
- if proximal_init:
- with torch.no_grad():
- self.conv_k.weight.copy_(self.conv_q.weight)
- self.conv_k.bias.copy_(self.conv_q.bias)
-
- def forward(self, x, c, attn_mask=None):
- q = self.conv_q(x)
- k = self.conv_k(c)
- v = self.conv_v(c)
-
- x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
- x = self.conv_o(x)
- return x
-
- def attention(self, query, key, value, mask=None):
- # reshape [b, d, t] -> [b, n_h, t, d_k]
- b, d, t_s, t_t = (*key.size(), query.size(2))
- query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
- key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
- value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
- scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
- if self.window_size is not None:
- assert t_s == t_t, "Relative attention is only available for self-attention."
- key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
- rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
- scores_local = self._relative_position_to_absolute_position(rel_logits)
- scores = scores + scores_local
- if self.proximal_bias:
- assert t_s == t_t, "Proximal bias is only available for self-attention."
- scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
- if mask is not None:
- scores = scores.masked_fill(mask == 0, -1e4)
- if self.block_length is not None:
- assert t_s == t_t, "Local attention is only available for self-attention."
- block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
- scores = scores.masked_fill(block_mask == 0, -1e4)
- p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
- p_attn = self.drop(p_attn)
- output = torch.matmul(p_attn, value)
- if self.window_size is not None:
- relative_weights = self._absolute_position_to_relative_position(p_attn)
- value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
- output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
- output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
- return output, p_attn
-
- def _matmul_with_relative_values(self, x, y):
- """
- x: [b, h, l, m]
- y: [h or 1, m, d]
- ret: [b, h, l, d]
- """
- ret = torch.matmul(x, y.unsqueeze(0))
- return ret
-
- def _matmul_with_relative_keys(self, x, y):
- """
- x: [b, h, l, d]
- y: [h or 1, m, d]
- ret: [b, h, l, m]
- """
- ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
- return ret
-
- def _get_relative_embeddings(self, relative_embeddings, length):
- max_relative_position = 2 * self.window_size + 1
- # Pad first before slice to avoid using cond ops.
- pad_length = max(length - (self.window_size + 1), 0)
- slice_start_position = max((self.window_size + 1) - length, 0)
- slice_end_position = slice_start_position + 2 * length - 1
- if pad_length > 0:
- padded_relative_embeddings = F.pad(
- relative_embeddings,
- commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
- else:
- padded_relative_embeddings = relative_embeddings
- used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
- return used_relative_embeddings
-
- def _relative_position_to_absolute_position(self, x):
- """
- x: [b, h, l, 2*l-1]
- ret: [b, h, l, l]
- """
- batch, heads, length, _ = x.size()
- # Concat columns of pad to shift from relative to absolute indexing.
- x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
- # Concat extra elements so to add up to shape (len+1, 2*len-1).
- x_flat = x.view([batch, heads, length * 2 * length])
- x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
- # Reshape and slice out the padded elements.
- x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
- return x_final
-
- def _absolute_position_to_relative_position(self, x):
- """
- x: [b, h, l, l]
- ret: [b, h, l, 2*l-1]
- """
- batch, heads, length, _ = x.size()
- # padd along column
- x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
- x_flat = x.view([batch, heads, length**2 + length*(length -1)])
- # add 0's in the beginning that will skew the elements after reshape
- x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
- x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
- return x_final
-
- def _attention_bias_proximal(self, length):
- """Bias for self-attention to encourage attention to close positions.
- Args:
- length: an integer scalar.
- Returns:
- a Tensor with shape [1, 1, length, length]
- """
- r = torch.arange(length, dtype=torch.float32)
- diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
- return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
- def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.activation = activation
- self.causal = causal
-
- if causal:
- self.padding = self._causal_padding
- else:
- self.padding = self._same_padding
-
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
- self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
- self.drop = nn.Dropout(p_dropout)
-
- def forward(self, x, x_mask):
- x = self.conv_1(self.padding(x * x_mask))
- if self.activation == "gelu":
- x = x * torch.sigmoid(1.702 * x)
- else:
- x = torch.relu(x)
- x = self.drop(x)
- x = self.conv_2(self.padding(x * x_mask))
- return x * x_mask
-
- def _causal_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = self.kernel_size - 1
- pad_r = 0
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
-
- def _same_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = (self.kernel_size - 1) // 2
- pad_r = self.kernel_size // 2
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/INSTALL.md b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/INSTALL.md
deleted file mode 100644
index b40768913742ca2b2e11c74d5944561931ecb326..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/INSTALL.md
+++ /dev/null
@@ -1,261 +0,0 @@
-## Installation
-
-### Requirements
-- Linux or macOS with Python ≥ 3.6
-- PyTorch ≥ 1.8 and [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation.
- Install them together at [pytorch.org](https://pytorch.org) to make sure of this
-- OpenCV is optional but needed by demo and visualization
-
-
-### Build Detectron2 from Source
-
-gcc & g++ ≥ 5.4 are required. [ninja](https://ninja-build.org/) is optional but recommended for faster build.
-After having them, run:
-```
-python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
-# (add --user if you don't have permission)
-
-# Or, to install it from a local clone:
-git clone https://github.com/facebookresearch/detectron2.git
-python -m pip install -e detectron2
-
-# On macOS, you may need to prepend the above commands with a few environment variables:
-CC=clang CXX=clang++ ARCHFLAGS="-arch x86_64" python -m pip install ...
-```
-
-To __rebuild__ detectron2 that's built from a local clone, use `rm -rf build/ **/*.so` to clean the
-old build first. You often need to rebuild detectron2 after reinstalling PyTorch.
-
-### Install Pre-Built Detectron2 (Linux only)
-
-Choose from this table to install [v0.6 (Oct 2021)](https://github.com/facebookresearch/detectron2/releases):
-
-<table class="docutils"><tbody><th width="80"> CUDA </th><th valign="bottom" align="left" width="100">torch 1.10</th><th valign="bottom" align="left" width="100">torch 1.9</th><th valign="bottom" align="left" width="100">torch 1.8</th> <tr><td align="left">11.3</td><td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
- https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html
-</code></pre> </details> </td> <td align="left"> </td> <td align="left"> </td> </tr> <tr><td align="left">11.1</td><td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
- https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.10/index.html
-</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
- https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.9/index.html
-</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
- https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.8/index.html
-</code></pre> </details> </td> </tr> <tr><td align="left">10.2</td><td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
- https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.10/index.html
-</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
- https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.9/index.html
-</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
- https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.8/index.html
-</code></pre> </details> </td> </tr> <tr><td align="left">10.1</td><td align="left"> </td> <td align="left"> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
- https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.8/index.html
-</code></pre> </details> </td> </tr> <tr><td align="left">cpu</td><td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
- https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.10/index.html
-</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
- https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.9/index.html
-</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
- https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.8/index.html
-</code></pre> </details> </td> </tr></tbody></table>
-
-Note that:
-1. The pre-built packages have to be used with corresponding version of CUDA and the official package of PyTorch.
- Otherwise, please build detectron2 from source.
-2. New packages are released every few months. Therefore, packages may not contain latest features in the main
- branch and may not be compatible with the main branch of a research project that uses detectron2
- (e.g. those in [projects](projects)).
-
-### Common Installation Issues
-
-Click each issue for its solutions:
-
-<details>
-<summary>
-Undefined symbols that looks like "TH..","at::Tensor...","torch..."
-</summary>
-<br/>
-
-This usually happens when detectron2 or torchvision is not
-compiled with the version of PyTorch you're running.
-
-If the error comes from a pre-built torchvision, uninstall torchvision and pytorch and reinstall them
-following [pytorch.org](http://pytorch.org). So the versions will match.
-
-If the error comes from a pre-built detectron2, check [release notes](https://github.com/facebookresearch/detectron2/releases),
-uninstall and reinstall the correct pre-built detectron2 that matches pytorch version.
-
-If the error comes from detectron2 or torchvision that you built manually from source,
-remove files you built (`build/`, `**/*.so`) and rebuild it so it can pick up the version of pytorch currently in your environment.
-
-If the above instructions do not resolve this problem, please provide an environment (e.g. a dockerfile) that can reproduce the issue.
-</details>
-
-<details>
-<summary>
-Missing torch dynamic libraries, OR segmentation fault immediately when using detectron2.
-</summary>
-This usually happens when detectron2 or torchvision is not
-compiled with the version of PyTorch you're running. See the previous common issue for the solution.
-</details>
-
-<details>
-<summary>
-Undefined C++ symbols (e.g. "GLIBCXX..") or C++ symbols not found.
-</summary>
-<br/>
-Usually it's because the library is compiled with a newer C++ compiler but run with an old C++ runtime.
-
-This often happens with old anaconda.
-It may help to run `conda update libgcc` to upgrade its runtime.
-
-The fundamental solution is to avoid the mismatch, either by compiling using older version of C++
-compiler, or run the code with proper C++ runtime.
-To run the code with a specific C++ runtime, you can use environment variable `LD_PRELOAD=/path/to/libstdc++.so`.
-
-</details>
-
-<details>
-<summary>
-"nvcc not found" or "Not compiled with GPU support" or "Detectron2 CUDA Compiler: not available".
-</summary>
-<br/>
-CUDA is not found when building detectron2.
-You should make sure
-
-```
-python -c 'import torch; from torch.utils.cpp_extension import CUDA_HOME; print(torch.cuda.is_available(), CUDA_HOME)'
-```
-
-print `(True, a directory with cuda)` at the time you build detectron2.
-
-Most models can run inference (but not training) without GPU support. To use CPUs, set `MODEL.DEVICE='cpu'` in the config.
-</details>
-
-<details>
-<summary>
-"invalid device function" or "no kernel image is available for execution".
-</summary>
-<br/>
-Two possibilities:
-
-* You build detectron2 with one version of CUDA but run it with a different version.
-
- To check whether it is the case,
- use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions.
- In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA"
- to contain cuda libraries of the same version.
-
- When they are inconsistent,
- you need to either install a different build of PyTorch (or build by yourself)
- to match your local CUDA installation, or install a different version of CUDA to match PyTorch.
-
-* PyTorch/torchvision/Detectron2 is not built for the correct GPU SM architecture (aka. compute capability).
-
- The architecture included by PyTorch/detectron2/torchvision is available in the "architecture flags" in
- `python -m detectron2.utils.collect_env`. It must include
- the architecture of your GPU, which can be found at [developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus).
-
- If you're using pre-built PyTorch/detectron2/torchvision, they have included support for most popular GPUs already.
- If not supported, you need to build them from source.
-
- When building detectron2/torchvision from source, they detect the GPU device and build for only the device.
- This means the compiled code may not work on a different GPU device.
- To recompile them for the correct architecture, remove all installed/compiled files,
- and rebuild them with the `TORCH_CUDA_ARCH_LIST` environment variable set properly.
- For example, `export TORCH_CUDA_ARCH_LIST="6.0;7.0"` makes it compile for both P100s and V100s.
-</details>
-
-<details>
-<summary>
-Undefined CUDA symbols; Cannot open libcudart.so
-</summary>
-<br/>
-The version of NVCC you use to build detectron2 or torchvision does
-not match the version of CUDA you are running with.
-This often happens when using anaconda's CUDA runtime.
-
-Use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions.
-In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA"
-to contain cuda libraries of the same version.
-
-When they are inconsistent,
-you need to either install a different build of PyTorch (or build by yourself)
-to match your local CUDA installation, or install a different version of CUDA to match PyTorch.
-</details>
-
-
-<details>
-<summary>
-C++ compilation errors from NVCC / NVRTC, or "Unsupported gpu architecture"
-</summary>
-<br/>
-A few possibilities:
-
-1. Local CUDA/NVCC version has to match the CUDA version of your PyTorch. Both can be found in `python collect_env.py`.
- When they are inconsistent, you need to either install a different build of PyTorch (or build by yourself)
- to match your local CUDA installation, or install a different version of CUDA to match PyTorch.
-
-2. Local CUDA/NVCC version shall support the SM architecture (a.k.a. compute capability) of your GPU.
- The capability of your GPU can be found at [developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus).
- The capability supported by NVCC is listed at [here](https://gist.github.com/ax3l/9489132).
- If your NVCC version is too old, this can be workaround by setting environment variable
- `TORCH_CUDA_ARCH_LIST` to a lower, supported capability.
-
-3. The combination of NVCC and GCC you use is incompatible. You need to change one of their versions.
- See [here](https://gist.github.com/ax3l/9489132) for some valid combinations.
- Notably, CUDA<=10.1.105 doesn't support GCC>7.3.
-
- The CUDA/GCC version used by PyTorch can be found by `print(torch.__config__.show())`.
-
-</details>
-
-
-<details>
-<summary>
-"ImportError: cannot import name '_C'".
-</summary>
-<br/>
-Please build and install detectron2 following the instructions above.
-
-Or, if you are running code from detectron2's root directory, `cd` to a different one.
-Otherwise you may not import the code that you installed.
-</details>
-
-
-<details>
-<summary>
-Any issue on windows.
-</summary>
-<br/>
-
-Detectron2 is continuously built on windows with [CircleCI](https://app.circleci.com/pipelines/github/facebookresearch/detectron2?branch=main).
-However we do not provide official support for it.
-PRs that improves code compatibility on windows are welcome.
-</details>
-
-<details>
-<summary>
-ONNX conversion segfault after some "TraceWarning".
-</summary>
-<br/>
-The ONNX package is compiled with a too old compiler.
-
-Please build and install ONNX from its source code using a compiler
-whose version is closer to what's used by PyTorch (available in `torch.__config__.show()`).
-</details>
-
-
-<details>
-<summary>
-"library not found for -lstdc++" on older version of MacOS
-</summary>
-<br/>
-See
-[this stackoverflow answer](https://stackoverflow.com/questions/56083725/macos-build-issues-lstdc-not-found-while-building-python-package).
-
-</details>
-
-
-### Installation inside specific environments:
-
-* __Colab__: see our [Colab Tutorial](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
- which has step-by-step instructions.
-
-* __Docker__: The official [Dockerfile](docker) installs detectron2 with a few simple commands.
-
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/docker/README.md b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/docker/README.md
deleted file mode 100644
index ea709f33b007abd2de044a0338659ec003330725..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/docker/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
-
-## Use the container (with docker ≥ 19.03)
-
-```
-cd docker/
-# Build:
-docker build --build-arg USER_ID=$UID -t detectron2:v0 .
-# Launch (require GPUs):
-docker run --gpus all -it \
- --shm-size=8gb --env="DISPLAY" --volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" \
- --name=detectron2 detectron2:v0
-
-# Grant docker access to host X server to show images
-xhost +local:`docker inspect --format='{{ .Config.Hostname }}' detectron2`
-```
-
-## Use the container (with docker-compose ≥ 1.28.0)
-
-Install docker-compose and nvidia-docker-toolkit, then run:
-```
-cd docker && USER_ID=$UID docker-compose run detectron2
-```
-
-## Use the deployment container (to test C++ examples)
-After building the base detectron2 container as above, do:
-```
-# Build:
-docker build -t detectron2-deploy:v0 -f deploy.Dockerfile .
-# Launch:
-docker run --gpus all -it detectron2-deploy:v0
-```
-
-#### Using a persistent cache directory
-
-You can prevent models from being re-downloaded on every run,
-by storing them in a cache directory.
-
-To do this, add `--volume=$HOME/.torch/fvcore_cache:/tmp:rw` in the run command.
-
-## Install new dependencies
-Add the following to `Dockerfile` to make persistent changes.
-```
-RUN sudo apt-get update && sudo apt-get install -y vim
-```
-Or run them in the container to make temporary changes.
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tests/config/test_lazy_config.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tests/config/test_lazy_config.py
deleted file mode 100644
index 6ff5b6dc117744a9d978e0aff324bddeb496409b..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tests/config/test_lazy_config.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import os
-import unittest
-import tempfile
-from itertools import count
-
-from detectron2.config import LazyConfig, LazyCall as L
-from omegaconf import DictConfig
-
-
-class TestLazyPythonConfig(unittest.TestCase):
- def setUp(self):
- self.root_filename = os.path.join(os.path.dirname(__file__), "root_cfg.py")
-
- def test_load(self):
- cfg = LazyConfig.load(self.root_filename)
-
- self.assertEqual(cfg.dir1a_dict.a, "modified")
- self.assertEqual(cfg.dir1b_dict.a, 1)
- self.assertEqual(cfg.lazyobj.x, "base_a_1")
-
- cfg.lazyobj.x = "new_x"
- # reload
- cfg = LazyConfig.load(self.root_filename)
- self.assertEqual(cfg.lazyobj.x, "base_a_1")
-
- def test_save_load(self):
- cfg = LazyConfig.load(self.root_filename)
- with tempfile.TemporaryDirectory(prefix="detectron2") as d:
- fname = os.path.join(d, "test_config.yaml")
- LazyConfig.save(cfg, fname)
- cfg2 = LazyConfig.load(fname)
-
- self.assertEqual(cfg2.lazyobj._target_, "itertools.count")
- self.assertEqual(cfg.lazyobj._target_, count)
- cfg2.lazyobj.pop("_target_")
- cfg.lazyobj.pop("_target_")
- # the rest are equal
- self.assertEqual(cfg, cfg2)
-
- def test_failed_save(self):
- cfg = DictConfig({"x": lambda: 3}, flags={"allow_objects": True})
- with tempfile.TemporaryDirectory(prefix="detectron2") as d:
- fname = os.path.join(d, "test_config.yaml")
- LazyConfig.save(cfg, fname)
- self.assertTrue(os.path.exists(fname))
- self.assertTrue(os.path.exists(fname + ".pkl"))
-
- def test_overrides(self):
- cfg = LazyConfig.load(self.root_filename)
- LazyConfig.apply_overrides(cfg, ["lazyobj.x=123", 'dir1b_dict.a="123"'])
- self.assertEqual(cfg.dir1b_dict.a, "123")
- self.assertEqual(cfg.lazyobj.x, 123)
-
- def test_invalid_overrides(self):
- cfg = LazyConfig.load(self.root_filename)
- with self.assertRaises(KeyError):
- LazyConfig.apply_overrides(cfg, ["lazyobj.x.xxx=123"])
-
- def test_to_py(self):
- cfg = LazyConfig.load(self.root_filename)
- cfg.lazyobj.x = {"a": 1, "b": 2, "c": L(count)(x={"r": "a", "s": 2.4, "t": [1, 2, 3, "z"]})}
- cfg.list = ["a", 1, "b", 3.2]
- py_str = LazyConfig.to_py(cfg)
- expected = """cfg.dir1a_dict.a = "modified"
-cfg.dir1a_dict.b = 2
-cfg.dir1b_dict.a = 1
-cfg.dir1b_dict.b = 2
-cfg.lazyobj = itertools.count(
- x={
- "a": 1,
- "b": 2,
- "c": itertools.count(x={"r": "a", "s": 2.4, "t": [1, 2, 3, "z"]}),
- },
- y="base_a_1_from_b",
-)
-cfg.list = ["a", 1, "b", 3.2]
-"""
- self.assertEqual(py_str, expected)
diff --git a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/utils/box_ops.py b/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/utils/box_ops.py
deleted file mode 100644
index a2b62ad99ed1fc35cdb10a9e11acdeb0ff1abcc4..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/utils/box_ops.py
+++ /dev/null
@@ -1,133 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Utilities for bounding box manipulation and GIoU.
-"""
-import torch, os
-from torchvision.ops.boxes import box_area
-
-
-def box_cxcywh_to_xyxy(x):
- x_c, y_c, w, h = x.unbind(-1)
- b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
- (x_c + 0.5 * w), (y_c + 0.5 * h)]
- return torch.stack(b, dim=-1)
-
-
-def box_xyxy_to_cxcywh(x):
- x0, y0, x1, y1 = x.unbind(-1)
- b = [(x0 + x1) / 2, (y0 + y1) / 2,
- (x1 - x0), (y1 - y0)]
- return torch.stack(b, dim=-1)
-
-
-# modified from torchvision to also return the union
-def box_iou(boxes1, boxes2):
- area1 = box_area(boxes1)
- area2 = box_area(boxes2)
-
- # import ipdb; ipdb.set_trace()
- lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
- rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
-
- wh = (rb - lt).clamp(min=0) # [N,M,2]
- inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]
-
- union = area1[:, None] + area2 - inter
-
- iou = inter / (union + 1e-6)
- return iou, union
-
-
-def generalized_box_iou(boxes1, boxes2):
- """
- Generalized IoU from https://giou.stanford.edu/
- The boxes should be in [x0, y0, x1, y1] format
- Returns a [N, M] pairwise matrix, where N = len(boxes1)
- and M = len(boxes2)
- """
- # degenerate boxes gives inf / nan results
- # so do an early check
- assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
- assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
- # except:
- # import ipdb; ipdb.set_trace()
- iou, union = box_iou(boxes1, boxes2)
-
- lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
- rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
-
- wh = (rb - lt).clamp(min=0) # [N,M,2]
- area = wh[:, :, 0] * wh[:, :, 1]
-
- return iou - (area - union) / (area + 1e-6)
-
-
-
-# modified from torchvision to also return the union
-def box_iou_pairwise(boxes1, boxes2):
- area1 = box_area(boxes1)
- area2 = box_area(boxes2)
-
- lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # [N,2]
- rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # [N,2]
-
- wh = (rb - lt).clamp(min=0) # [N,2]
- inter = wh[:, 0] * wh[:, 1] # [N]
-
- union = area1 + area2 - inter
-
- iou = inter / union
- return iou, union
-
-
-def generalized_box_iou_pairwise(boxes1, boxes2):
- """
- Generalized IoU from https://giou.stanford.edu/
- Input:
- - boxes1, boxes2: N,4
- Output:
- - giou: N, 4
- """
- # degenerate boxes gives inf / nan results
- # so do an early check
- assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
- assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
- assert boxes1.shape == boxes2.shape
- iou, union = box_iou_pairwise(boxes1, boxes2) # N, 4
-
- lt = torch.min(boxes1[:, :2], boxes2[:, :2])
- rb = torch.max(boxes1[:, 2:], boxes2[:, 2:])
-
- wh = (rb - lt).clamp(min=0) # [N,2]
- area = wh[:, 0] * wh[:, 1]
-
- return iou - (area - union) / area
-
-def masks_to_boxes(masks):
- """Compute the bounding boxes around the provided masks
- The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
- Returns a [N, 4] tensors, with the boxes in xyxy format
- """
- if masks.numel() == 0:
- return torch.zeros((0, 4), device=masks.device)
-
- h, w = masks.shape[-2:]
-
- y = torch.arange(0, h, dtype=torch.float)
- x = torch.arange(0, w, dtype=torch.float)
- y, x = torch.meshgrid(y, x)
-
- x_mask = (masks * x.unsqueeze(0))
- x_max = x_mask.flatten(1).max(-1)[0]
- x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
-
- y_mask = (masks * y.unsqueeze(0))
- y_max = y_mask.flatten(1).max(-1)[0]
- y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
-
- return torch.stack([x_min, y_min, x_max, y_max], 1)
-
-if __name__ == '__main__':
- x = torch.rand(5, 4)
- y = torch.rand(3, 4)
- iou, union = box_iou(x, y)
\ No newline at end of file
diff --git a/spaces/PascalLiu/FNeVR_demo/modules/dense_motion.py b/spaces/PascalLiu/FNeVR_demo/modules/dense_motion.py
deleted file mode 100644
index 81127c4f0ae27d5e1f6168feedec602255e5879d..0000000000000000000000000000000000000000
--- a/spaces/PascalLiu/FNeVR_demo/modules/dense_motion.py
+++ /dev/null
@@ -1,114 +0,0 @@
-from torch import nn
-import torch.nn.functional as F
-import torch
-from modules.util import Hourglass, AntiAliasInterpolation2d, make_coordinate_grid, kp2gaussian
-
-
-class DenseMotionNetwork(nn.Module):
- """
- Module that predicting a dense motion from sparse motion representation given by kp_source and kp_driving
- """
-
- def __init__(self, block_expansion, num_blocks, max_features, num_kp, num_channels, estimate_occlusion_map=False,
- scale_factor=1, kp_variance=0.01):
- super(DenseMotionNetwork, self).__init__()
- self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp + 1) * (num_channels + 1),
- max_features=max_features, num_blocks=num_blocks)
-
- self.mask = nn.Conv2d(self.hourglass.out_filters, num_kp + 1, kernel_size=(7, 7), padding=(3, 3))
-
- if estimate_occlusion_map:
- self.occlusion = nn.Conv2d(self.hourglass.out_filters, 1, kernel_size=(7, 7), padding=(3, 3))
- else:
- self.occlusion = None
-
- self.num_kp = num_kp
- self.scale_factor = scale_factor
- self.kp_variance = kp_variance
-
- if self.scale_factor != 1:
- self.down = AntiAliasInterpolation2d(num_channels, self.scale_factor)
-
- def create_heatmap_representations(self, source_image, kp_driving, kp_source):
- """
- Eq 6. in the paper H_k(z)
- """
- spatial_size = source_image.shape[2:]
- gaussian_driving = kp2gaussian(kp_driving, spatial_size=spatial_size, kp_variance=self.kp_variance)
- gaussian_source = kp2gaussian(kp_source, spatial_size=spatial_size, kp_variance=self.kp_variance)
- heatmap = gaussian_driving - gaussian_source
-
- #adding background feature
- zeros = torch.zeros(heatmap.shape[0], 1, spatial_size[0], spatial_size[1]).type(heatmap.type())
- heatmap = torch.cat([zeros, heatmap], dim=1)
- heatmap = heatmap.unsqueeze(2)
- return heatmap
-
- def create_sparse_motions(self, source_image, kp_driving, kp_source):
- """
- Eq 4. in the paper T_{s<-d}(z)
- """
- bs, _, h, w = source_image.shape
- identity_grid = make_coordinate_grid((h, w), type=kp_source['value'].type())
- identity_grid = identity_grid.view(1, 1, h, w, 2)
- coordinate_grid = identity_grid - kp_driving['value'].view(bs, self.num_kp, 1, 1, 2)
- if 'jacobian' in kp_driving:
- jacobian = torch.matmul(kp_source['jacobian'], torch.inverse(kp_driving['jacobian']))
-
- jacobian = jacobian.unsqueeze(-3).unsqueeze(-3)
- jacobian = jacobian.repeat(1, 1, h, w, 1, 1)
- coordinate_grid = torch.matmul(jacobian, coordinate_grid.unsqueeze(-1))
- coordinate_grid = coordinate_grid.squeeze(-1)
-
- driving_to_source = coordinate_grid + kp_source['value'].view(bs, self.num_kp, 1, 1, 2)
-
- #adding background feature
- identity_grid = identity_grid.repeat(bs, 1, 1, 1, 1)
- sparse_motions = torch.cat([identity_grid, driving_to_source], dim=1)
- return sparse_motions
-
- def create_deformed_source_image(self, source_image, sparse_motions):
- """
- Eq 7. in the paper \hat{T}_{s<-d}(z)
- """
- bs, _, h, w = source_image.shape
- source_repeat = source_image.unsqueeze(1).unsqueeze(1).repeat(1, self.num_kp + 1, 1, 1, 1, 1)
- source_repeat = source_repeat.view(bs * (self.num_kp + 1), -1, h, w)
- sparse_motions = sparse_motions.view((bs * (self.num_kp + 1), h, w, -1))
- sparse_deformed = F.grid_sample(source_repeat, sparse_motions)
- sparse_deformed = sparse_deformed.view((bs, self.num_kp + 1, -1, h, w))
- return sparse_deformed
-
- def forward(self, source_image, kp_driving, kp_source):
- if self.scale_factor != 1:
- source_image = self.down(source_image)
-
- bs, _, h, w = source_image.shape
-
- out_dict = dict()
- heatmap_representation = self.create_heatmap_representations(source_image, kp_driving, kp_source)
- sparse_motion = self.create_sparse_motions(source_image, kp_driving, kp_source)
- deformed_source = self.create_deformed_source_image(source_image, sparse_motion)
- out_dict['sparse_deformed'] = deformed_source
-
- input = torch.cat([heatmap_representation, deformed_source], dim=2)
- input = input.view(bs, -1, h, w)
-
- prediction = self.hourglass(input)
-
- mask = self.mask(prediction)
- mask = F.softmax(mask, dim=1)
- out_dict['mask'] = mask
- mask = mask.unsqueeze(2)
- sparse_motion = sparse_motion.permute(0, 1, 4, 2, 3)
- deformation = (sparse_motion * mask).sum(dim=1)
- deformation = deformation.permute(0, 2, 3, 1)
-
- out_dict['deformation'] = deformation
-
- # Sec. 3.2 in the paper
- if self.occlusion:
- occlusion_map = torch.sigmoid(self.occlusion(prediction))
- out_dict['occlusion_map'] = occlusion_map
-
- return out_dict
diff --git a/spaces/PeWeX47/GPT-2-Lyrics-Generator/README.md b/spaces/PeWeX47/GPT-2-Lyrics-Generator/README.md
deleted file mode 100644
index 726f6daa8e8b2099390a48c95dd517b339549ddc..0000000000000000000000000000000000000000
--- a/spaces/PeWeX47/GPT-2-Lyrics-Generator/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: GPT 2 Lyrics Generator
-emoji: 🏆
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Plachta/VALL-E-X/modules/transformer.py b/spaces/Plachta/VALL-E-X/modules/transformer.py
deleted file mode 100644
index ea8826b193c5053cb8ae74312f65ac95fe440350..0000000000000000000000000000000000000000
--- a/spaces/Plachta/VALL-E-X/modules/transformer.py
+++ /dev/null
@@ -1,683 +0,0 @@
-import copy
-import numbers
-from functools import partial
-from typing import Any, Callable, List, Optional, Tuple, Union
-
-import torch
-from torch import Tensor, nn
-from torch.nn import functional as F
-
-from .activation import MultiheadAttention
-from .scaling import ActivationBalancer, BalancedDoubleSwish
-from .scaling import BasicNorm as _BasicNorm
-
-_shape_t = Union[int, List[int], torch.Size]
-
-
-class LayerNorm(nn.Module):
- __constants__ = ["normalized_shape", "eps", "elementwise_affine"]
- normalized_shape: Tuple[int, ...]
- eps: float
- elementwise_affine: bool
-
- def __init__(
- self,
- normalized_shape: _shape_t,
- eps: float = 1e-5,
- elementwise_affine: bool = True,
- device=None,
- dtype=None,
- ) -> None:
- factory_kwargs = {"device": device, "dtype": dtype}
- super(LayerNorm, self).__init__()
- if isinstance(normalized_shape, numbers.Integral):
- # mypy error: incompatible types in assignment
- normalized_shape = (normalized_shape,) # type: ignore[assignment]
- self.normalized_shape = tuple(normalized_shape) # type: ignore[arg-type]
- self.eps = eps
- self.elementwise_affine = elementwise_affine
- if self.elementwise_affine:
- self.weight = nn.Parameter(
- torch.empty(self.normalized_shape, **factory_kwargs)
- )
- self.bias = nn.Parameter(
- torch.empty(self.normalized_shape, **factory_kwargs)
- )
- else:
- self.register_parameter("weight", None)
- self.register_parameter("bias", None)
-
- self.reset_parameters()
-
- def reset_parameters(self) -> None:
- if self.elementwise_affine:
- nn.init.ones_(self.weight)
- nn.init.zeros_(self.bias)
-
- def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
- if isinstance(input, tuple):
- input, embedding = input
- return (
- F.layer_norm(
- input,
- self.normalized_shape,
- self.weight,
- self.bias,
- self.eps,
- ),
- embedding,
- )
-
- assert embedding is None
- return F.layer_norm(
- input, self.normalized_shape, self.weight, self.bias, self.eps
- )
-
- def extra_repr(self) -> str:
- return (
- "{normalized_shape}, eps={eps}, "
- "elementwise_affine={elementwise_affine}".format(**self.__dict__)
- )
-
-
-class AdaptiveLayerNorm(nn.Module):
- r"""Adaptive Layer Normalization"""
-
- def __init__(self, d_model, norm) -> None:
- super(AdaptiveLayerNorm, self).__init__()
- self.project_layer = nn.Linear(d_model, 2 * d_model)
- self.norm = norm
- self.d_model = d_model
- self.eps = self.norm.eps
-
- def forward(self, input: Tensor, embedding: Tensor = None) -> Tensor:
- if isinstance(input, tuple):
- input, embedding = input
- weight, bias = torch.split(
- self.project_layer(embedding),
- split_size_or_sections=self.d_model,
- dim=-1,
- )
- return (weight * self.norm(input) + bias, embedding)
-
- weight, bias = torch.split(
- self.project_layer(embedding),
- split_size_or_sections=self.d_model,
- dim=-1,
- )
- return weight * self.norm(input) + bias
-
-
-class BasicNorm(_BasicNorm):
- def __init__(
- self,
- d_model: int,
- eps: float = 1e-5,
- device=None,
- dtype=None,
- ):
- super(BasicNorm, self).__init__(d_model, eps=eps)
-
- def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
- if isinstance(input, tuple):
- input, embedding = input
- return (
- super(BasicNorm, self).forward(input),
- embedding,
- )
-
- assert embedding is None
- return super(BasicNorm, self).forward(input)
-
-
-class BalancedBasicNorm(nn.Module):
- def __init__(
- self,
- d_model: int,
- eps: float = 1e-5,
- device=None,
- dtype=None,
- ):
- super(BalancedBasicNorm, self).__init__()
- self.balancer = ActivationBalancer(
- d_model,
- channel_dim=-1,
- min_positive=0.45,
- max_positive=0.55,
- max_abs=6.0,
- )
- self.norm = BasicNorm(d_model, eps, device=device, dtype=dtype)
-
- def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
- if isinstance(input, tuple):
- input, embedding = input
- return self.norm((self.balancer(input), embedding))
-
- assert embedding is None
- return self.norm(self.balancer(input))
-
-
-class IdentityNorm(nn.Module):
- def __init__(
- self,
- d_model: int,
- eps: float = 1e-5,
- device=None,
- dtype=None,
- ) -> None:
- super(IdentityNorm, self).__init__()
-
- def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
- if isinstance(input, tuple):
- return input
-
- assert embedding is None
- return input
-
-
-class TransformerEncoderLayer(nn.Module):
- __constants__ = ["batch_first", "norm_first"]
-
- def __init__(
- self,
- d_model: int,
- nhead: int,
- dim_feedforward: int = 2048,
- dropout: float = 0.1,
- activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
- batch_first: bool = False,
- norm_first: bool = False,
- device=None,
- dtype=None,
- linear1_self_attention_cls: nn.Module = nn.Linear,
- linear2_self_attention_cls: nn.Module = nn.Linear,
- linear1_feedforward_cls: nn.Module = nn.Linear,
- linear2_feedforward_cls: nn.Module = nn.Linear,
- layer_norm_cls: nn.Module = LayerNorm,
- layer_norm_eps: float = 1e-5,
- adaptive_layer_norm=False,
- ) -> None:
- factory_kwargs = {"device": device, "dtype": dtype}
- super(TransformerEncoderLayer, self).__init__()
- self.self_attn = MultiheadAttention(
- d_model,
- nhead,
- dropout=dropout,
- batch_first=batch_first,
- linear1_cls=linear1_self_attention_cls,
- linear2_cls=linear2_self_attention_cls,
- **factory_kwargs,
- )
-
- # Implementation of Feedforward model
- self.linear1 = linear1_feedforward_cls(
- d_model, dim_feedforward, **factory_kwargs
- )
- self.dropout = nn.Dropout(dropout)
- self.linear2 = linear2_feedforward_cls(
- dim_feedforward, d_model, **factory_kwargs
- )
-
- self.norm_first = norm_first
- self.dropout1 = nn.Dropout(dropout)
- self.dropout2 = nn.Dropout(dropout)
-
- # Legacy string support for activation function.
- if isinstance(activation, str):
- activation = _get_activation_fn(activation)
- elif isinstance(activation, partial):
- activation = activation(d_model)
- elif activation == BalancedDoubleSwish:
- activation = BalancedDoubleSwish(d_model)
-
- # # We can't test self.activation in forward() in TorchScript,
- # # so stash some information about it instead.
- # if activation is F.relu or isinstance(activation, torch.nn.ReLU):
- # self.activation_relu_or_gelu = 1
- # elif activation is F.gelu or isinstance(activation, torch.nn.GELU):
- # self.activation_relu_or_gelu = 2
- # else:
- # self.activation_relu_or_gelu = 0
- self.activation = activation
-
- norm1 = layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs)
- if layer_norm_cls == IdentityNorm:
- norm2 = BalancedBasicNorm(
- d_model, eps=layer_norm_eps, **factory_kwargs
- )
- else:
- norm2 = layer_norm_cls(
- d_model, eps=layer_norm_eps, **factory_kwargs
- )
-
- if adaptive_layer_norm:
- self.norm1 = AdaptiveLayerNorm(d_model, norm1)
- self.norm2 = AdaptiveLayerNorm(d_model, norm2)
- else:
- self.norm1 = norm1
- self.norm2 = norm2
-
- def __setstate__(self, state):
- super(TransformerEncoderLayer, self).__setstate__(state)
- if not hasattr(self, "activation"):
- self.activation = F.relu
-
- def forward(
- self,
- src: Tensor,
- src_mask: Optional[Tensor] = None,
- src_key_padding_mask: Optional[Tensor] = None,
- ) -> Tensor:
- r"""Pass the input through the encoder layer.
-
- Args:
- src: the sequence to the encoder layer (required).
- src_mask: the mask for the src sequence (optional).
- src_key_padding_mask: the mask for the src keys per batch (optional).
-
- Shape:
- see the docs in Transformer class.
- """
- x, stage_embedding = src, None
- is_src_tuple = False
- if isinstance(src, tuple):
- x, stage_embedding = src
- is_src_tuple = True
-
- if src_key_padding_mask is not None:
- _skpm_dtype = src_key_padding_mask.dtype
- if _skpm_dtype != torch.bool and not torch.is_floating_point(
- src_key_padding_mask
- ):
- raise AssertionError(
- "only bool and floating types of key_padding_mask are supported"
- )
-
- if self.norm_first:
- x = x + self._sa_block(
- self.norm1(x, stage_embedding),
- src_mask,
- src_key_padding_mask,
- )
- x = x + self._ff_block(self.norm2(x, stage_embedding))
- else:
- x = self.norm1(
- x + self._sa_block(x, src_mask, src_key_padding_mask),
- stage_embedding,
- )
- x = self.norm2(x + self._ff_block(x), stage_embedding)
-
- if is_src_tuple:
- return (x, stage_embedding)
- return x
-
- def infer(
- self,
- src: Tensor,
- src_mask: Optional[Tensor] = None,
- src_key_padding_mask: Optional[Tensor] = None,
- past_kv: Optional[Tensor] = None,
- use_cache: bool = False,
- ):
- x, stage_embedding = src, None
- is_src_tuple = False
- if isinstance(src, tuple):
- x, stage_embedding = src
- is_src_tuple = True
-
- if src_key_padding_mask is not None:
- _skpm_dtype = src_key_padding_mask.dtype
- if _skpm_dtype != torch.bool and not torch.is_floating_point(
- src_key_padding_mask
- ):
- raise AssertionError(
- "only bool and floating types of key_padding_mask are supported"
- )
-
- if self.norm_first:
- x_attn_out, kv = self.self_attn.infer(
- self.norm1(x, stage_embedding),
- attn_mask=src_mask,
- key_padding_mask=src_key_padding_mask,
- need_weights=False,
- past_kv=past_kv,
- use_cache=use_cache,
- )
- x = x + x_attn_out
- x = x + self._ff_block(self.norm2(x, stage_embedding))
-
- if is_src_tuple:
- return (x, stage_embedding)
- return (x, kv)
-
- # self-attention block
- def _sa_block(
- self,
- x: Tensor,
- attn_mask: Optional[Tensor],
- key_padding_mask: Optional[Tensor],
- ) -> Tensor:
- x = self.self_attn(
- x,
- x,
- x,
- attn_mask=attn_mask,
- key_padding_mask=key_padding_mask,
- need_weights=False,
- )[0]
- return self.dropout1(x)
-
- # feed forward block
- def _ff_block(self, x: Tensor) -> Tensor:
- x = self.linear2(self.dropout(self.activation(self.linear1(x))))
- return self.dropout2(x)
-
-
-class TransformerEncoder(nn.Module):
- r"""TransformerEncoder is a stack of N encoder layers. Users can build the
- BERT(https://arxiv.org/abs/1810.04805) model with corresponding parameters.
-
- Args:
- encoder_layer: an instance of the TransformerEncoderLayer() class (required).
- num_layers: the number of sub-encoder-layers in the encoder (required).
- norm: the layer normalization component (optional).
- enable_nested_tensor: if True, input will automatically convert to nested tensor
- (and convert back on output). This will improve the overall performance of
- TransformerEncoder when padding rate is high. Default: ``True`` (enabled).
-
- Examples::
- >>> encoder_layer = TransformerEncoderLayer(d_model=512, nhead=8)
- >>> transformer_encoder = TransformerEncoder(encoder_layer, num_layers=6)
- >>> src = torch.rand(10, 32, 512)
- >>> out = transformer_encoder(src)
- """
- __constants__ = ["norm"]
-
- def __init__(self, encoder_layer, num_layers, norm=None):
- super(TransformerEncoder, self).__init__()
- self.layers = _get_clones(encoder_layer, num_layers)
- self.num_layers = num_layers
- self.norm = norm
-
- def forward(
- self,
- src: Tensor,
- mask: Optional[Tensor] = None,
- src_key_padding_mask: Optional[Tensor] = None,
- return_layer_states: bool = False,
- ) -> Tensor:
- r"""Pass the input through the encoder layers in turn.
-
- Args:
- src: the sequence to the encoder (required).
- mask: the mask for the src sequence (optional).
- src_key_padding_mask: the mask for the src keys per batch (optional).
- return_layer_states: return layers' state (optional).
-
- Shape:
- see the docs in Transformer class.
- """
- if return_layer_states:
- layer_states = [] # layers' output
- output = src
- for mod in self.layers:
- output = mod(
- output,
- src_mask=mask,
- src_key_padding_mask=src_key_padding_mask,
- )
- layer_states.append(output[0])
-
- if self.norm is not None:
- output = self.norm(output)
-
- return layer_states, output
-
- output = src
- for mod in self.layers:
- output = mod(
- output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
- )
-
- if self.norm is not None:
- output = self.norm(output)
-
- return output
-
- def infer(
- self,
- src: Tensor,
- mask: Optional[Tensor] = None,
- src_key_padding_mask: Optional[Tensor] = None,
- return_layer_states: bool = False,
- past_kv: Optional[Tensor] = None,
- use_cache: bool = False,
- ):
- if past_kv is None:
- past_length = 0
- past_kv = tuple([None] * self.num_layers)
- else:
- past_length = past_kv[0][0].size(-2)
- new_kv = () if use_cache else None
- output = src
- for mod, past_layer_kv in zip(self.layers, past_kv):
- output, kv = mod.infer(
- output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, past_kv=past_layer_kv, use_cache=use_cache
- )
- if use_cache:
- new_kv = new_kv + (kv,)
-
- if self.norm is not None:
- output = self.norm(output)
-
- return output, new_kv
-
-
-class TransformerDecoderLayer(nn.Module):
- __constants__ = ["batch_first", "norm_first"]
-
- def __init__(
- self,
- d_model: int,
- nhead: int,
- dim_feedforward: int = 2048,
- dropout: float = 0.1,
- activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
- linear1_self_attention_cls: nn.Module = nn.Linear,
- linear2_self_attention_cls: nn.Module = nn.Linear,
- linear1_feedforward_cls: nn.Module = nn.Linear,
- linear2_feedforward_cls: nn.Module = nn.Linear,
- batch_first: bool = False,
- norm_first: bool = False,
- device=None,
- dtype=None,
- layer_norm_cls: nn.Module = LayerNorm,
- layer_norm_eps: float = 1e-5,
- adaptive_layer_norm=False,
- ) -> None:
- factory_kwargs = {"device": device, "dtype": dtype}
- super(TransformerDecoderLayer, self).__init__()
- self.self_attn = MultiheadAttention(
- d_model,
- nhead,
- dropout=dropout,
- batch_first=batch_first,
- linear1_cls=linear1_self_attention_cls,
- linear2_cls=linear2_self_attention_cls,
- **factory_kwargs,
- )
- self.multihead_attn = MultiheadAttention(
- d_model,
- nhead,
- dropout=dropout,
- batch_first=batch_first,
- linear1_cls=linear1_self_attention_cls,
- linear2_cls=linear2_self_attention_cls,
- **factory_kwargs,
- )
- # Implementation of Feedforward model
- self.linear1 = linear1_feedforward_cls(
- d_model, dim_feedforward, **factory_kwargs
- )
- self.dropout = nn.Dropout(dropout)
- self.linear2 = linear2_feedforward_cls(
- dim_feedforward, d_model, **factory_kwargs
- )
-
- self.norm_first = norm_first
- self.dropout1 = nn.Dropout(dropout)
- self.dropout2 = nn.Dropout(dropout)
- self.dropout3 = nn.Dropout(dropout)
-
- # Legacy string support for activation function.
- if isinstance(activation, str):
- self.activation = _get_activation_fn(activation)
- elif isinstance(activation, partial):
- self.activation = activation(d_model)
- elif activation == BalancedDoubleSwish:
- self.activation = BalancedDoubleSwish(d_model)
- else:
- self.activation = activation
-
- if adaptive_layer_norm:
- norm1 = layer_norm_cls(
- d_model, eps=layer_norm_eps, **factory_kwargs
- )
- norm2 = layer_norm_cls(
- d_model, eps=layer_norm_eps, **factory_kwargs
- )
- norm3 = layer_norm_cls(
- d_model, eps=layer_norm_eps, **factory_kwargs
- )
-
- self.norm1 = AdaptiveLayerNorm(d_model, norm1)
- self.norm2 = AdaptiveLayerNorm(d_model, norm2)
- self.norm3 = AdaptiveLayerNorm(d_model, norm3)
- else:
- self.norm1 = layer_norm_cls(
- d_model, eps=layer_norm_eps, **factory_kwargs
- )
- self.norm2 = layer_norm_cls(
- d_model, eps=layer_norm_eps, **factory_kwargs
- )
- if layer_norm_cls == IdentityNorm:
- self.norm3 = BalancedBasicNorm(
- d_model, eps=layer_norm_eps, **factory_kwargs
- )
- else:
- self.norm3 = layer_norm_cls(
- d_model, eps=layer_norm_eps, **factory_kwargs
- )
-
- def forward(
- self,
- tgt: Tensor,
- memory: Tensor,
- tgt_mask: Optional[Tensor] = None,
- memory_mask: Optional[Tensor] = None,
- tgt_key_padding_mask: Optional[Tensor] = None,
- memory_key_padding_mask: Optional[Tensor] = None,
- ) -> Tensor:
- r"""Pass the inputs (and mask) through the decoder layer.
-
- Args:
- tgt: the sequence to the decoder layer (required).
- memory: the sequence from the last layer of the encoder (required).
- tgt_mask: the mask for the tgt sequence (optional).
- memory_mask: the mask for the memory sequence (optional).
- tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
- memory_key_padding_mask: the mask for the memory keys per batch (optional).
-
- Shape:
- see the docs in Transformer class.
- """
- tgt_is_tuple = False
- if isinstance(tgt, tuple):
- x, stage_embedding = tgt
- tgt_is_tuple = True
- else:
- x, stage_embedding = tgt, None
-
- if self.norm_first:
- x = x + self._sa_block(
- self.norm1(x, stage_embedding), tgt_mask, tgt_key_padding_mask
- )
- x = x + self._mha_block(
- self.norm2(x, stage_embedding),
- memory,
- memory_mask,
- memory_key_padding_mask,
- )
- x = x + self._ff_block(self.norm3(x, stage_embedding))
- else:
- x = self.norm1(
- x + self._sa_block(x, tgt_mask, tgt_key_padding_mask),
- stage_embedding,
- )
- x = self.norm2(
- x
- + self._mha_block(
- x, memory, memory_mask, memory_key_padding_mask
- ),
- stage_embedding,
- )
- x = self.norm3(x + self._ff_block(x), stage_embedding)
-
- if tgt_is_tuple:
- return (x, stage_embedding)
- return x
-
- # self-attention block
- def _sa_block(
- self,
- x: Tensor,
- attn_mask: Optional[Tensor],
- key_padding_mask: Optional[Tensor],
- ) -> Tensor:
- x = self.self_attn(
- x,
- x,
- x,
- attn_mask=attn_mask,
- key_padding_mask=key_padding_mask,
- need_weights=False,
- )[0]
- return self.dropout1(x)
-
- # multihead attention block
- def _mha_block(
- self,
- x: Tensor,
- mem: Tensor,
- attn_mask: Optional[Tensor],
- key_padding_mask: Optional[Tensor],
- ) -> Tensor:
- x = self.multihead_attn(
- x,
- mem,
- mem,
- attn_mask=attn_mask,
- key_padding_mask=key_padding_mask,
- need_weights=False,
- )[0]
- return self.dropout2(x)
-
- # feed forward block
- def _ff_block(self, x: Tensor) -> Tensor:
- x = self.linear2(self.dropout(self.activation(self.linear1(x))))
- return self.dropout3(x)
-
-
-def _get_clones(module, N):
- return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
-
-
-def _get_activation_fn(activation: str) -> Callable[[Tensor], Tensor]:
- if activation == "relu":
- return F.relu
- elif activation == "gelu":
- return F.gelu
-
- raise RuntimeError(
- "activation should be relu/gelu, not {}".format(activation)
- )
diff --git a/spaces/Potanin/12345/lib/infer_pack/transforms.py b/spaces/Potanin/12345/lib/infer_pack/transforms.py
deleted file mode 100644
index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000
--- a/spaces/Potanin/12345/lib/infer_pack/transforms.py
+++ /dev/null
@@ -1,209 +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=1.0,
- 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=1.0,
- 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=0.0,
- right=1.0,
- bottom=0.0,
- top=1.0,
- 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/Qiukai/gpt/check_proxy.py b/spaces/Qiukai/gpt/check_proxy.py
deleted file mode 100644
index 7fdd2b0c5ade76d3a828fb7b5fa6b3dd09e8b04a..0000000000000000000000000000000000000000
--- a/spaces/Qiukai/gpt/check_proxy.py
+++ /dev/null
@@ -1,142 +0,0 @@
-
-def check_proxy(proxies):
- import requests
- proxies_https = proxies['https'] if proxies is not None else '无'
- try:
- response = requests.get("https://ipapi.co/json/",
- proxies=proxies, timeout=4)
- data = response.json()
- print(f'查询代理的地理位置，返回的结果是{data}')
- if 'country_name' in data:
- country = data['country_name']
- result = f"代理配置 {proxies_https}, 代理所在地：{country}"
- elif 'error' in data:
- result = f"代理配置 {proxies_https}, 代理所在地：未知，IP查询频率受限"
- print(result)
- return result
- except:
- result = f"代理配置 {proxies_https}, 代理所在地查询超时，代理可能无效"
- print(result)
- return result
-
-
-def backup_and_download(current_version, remote_version):
- """
- 一键更新协议：备份和下载
- """
- from toolbox import get_conf
- import shutil
- import os
- import requests
- import zipfile
- os.makedirs(f'./history', exist_ok=True)
- backup_dir = f'./history/backup-{current_version}/'
- new_version_dir = f'./history/new-version-{remote_version}/'
- if os.path.exists(new_version_dir):
- return new_version_dir
- os.makedirs(new_version_dir)
- shutil.copytree('./', backup_dir, ignore=lambda x, y: ['history'])
- proxies, = get_conf('proxies')
- r = requests.get(
- 'https://github.com/binary-husky/chatgpt_academic/archive/refs/heads/master.zip', proxies=proxies, stream=True)
- zip_file_path = backup_dir+'/master.zip'
- with open(zip_file_path, 'wb+') as f:
- f.write(r.content)
- dst_path = new_version_dir
- with zipfile.ZipFile(zip_file_path, "r") as zip_ref:
- for zip_info in zip_ref.infolist():
- dst_file_path = os.path.join(dst_path, zip_info.filename)
- if os.path.exists(dst_file_path):
- os.remove(dst_file_path)
- zip_ref.extract(zip_info, dst_path)
- return new_version_dir
-
-
-def patch_and_restart(path):
- """
- 一键更新协议：覆盖和重启
- """
- import distutils
- import shutil
- import os
- import sys
- import time
- from colorful import print亮黄, print亮绿, print亮红
- # if not using config_private, move origin config.py as config_private.py
- if not os.path.exists('config_private.py'):
- print亮黄('由于您没有设置config_private.py私密配置，现将您的现有配置移动至config_private.py以防止配置丢失，',
- '另外您可以随时在history子文件夹下找回旧版的程序。')
- shutil.copyfile('config.py', 'config_private.py')
- distutils.dir_util.copy_tree(path+'/chatgpt_academic-master', './')
- import subprocess
- print亮绿('代码已经更新，即将更新pip包依赖……')
- for i in reversed(range(5)): time.sleep(1); print(i)
- try:
- subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', 'requirements.txt'])
- except:
- print亮红('pip包依赖安装出现问题，需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
- print亮绿('更新完成，您可以随时在history子文件夹下找回旧版的程序，5s之后重启')
- print亮红('假如重启失败，您可能需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
- print(' ------------------------------ -----------------------------------')
- for i in reversed(range(8)): time.sleep(1); print(i)
- os.execl(sys.executable, sys.executable, *sys.argv)
-
-
-def get_current_version():
- import json
- try:
- with open('./version', 'r', encoding='utf8') as f:
- current_version = json.loads(f.read())['version']
- except:
- current_version = ""
- return current_version
-
-
-def auto_update():
- """
- 一键更新协议：查询版本和用户意见
- """
- try:
- from toolbox import get_conf
- import requests
- import time
- import json
- proxies, = get_conf('proxies')
- response = requests.get(
- "https://raw.githubusercontent.com/binary-husky/chatgpt_academic/master/version", proxies=proxies, timeout=1)
- remote_json_data = json.loads(response.text)
- remote_version = remote_json_data['version']
- if remote_json_data["show_feature"]:
- new_feature = "新功能：" + remote_json_data["new_feature"]
- else:
- new_feature = ""
- with open('./version', 'r', encoding='utf8') as f:
- current_version = f.read()
- current_version = json.loads(current_version)['version']
- if (remote_version - current_version) >= 0.01:
- from colorful import print亮黄
- print亮黄(
- f'\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}')
- print('（1）Github更新地址:\nhttps://github.com/binary-husky/chatgpt_academic\n')
- user_instruction = input('（2）是否一键更新代码（Y+回车=确认，输入其他/无输入+回车=不更新）？')
- if user_instruction in ['Y', 'y']:
- path = backup_and_download(current_version, remote_version)
- try:
- patch_and_restart(path)
- except:
- print('更新失败。')
- else:
- print('自动更新程序：已禁用')
- return
- else:
- return
- except:
- print('自动更新程序：已禁用')
-
-
-if __name__ == '__main__':
- import os
- os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
- from toolbox import get_conf
- proxies, = get_conf('proxies')
- check_proxy(proxies)
diff --git a/spaces/RMXK/RVC_HFF/i18n/scan_i18n.py b/spaces/RMXK/RVC_HFF/i18n/scan_i18n.py
deleted file mode 100644
index f3e52cf4f9f06d78877d77d2353f666aa759e36f..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/i18n/scan_i18n.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import ast
-import glob
-import json
-from collections import OrderedDict
-
-
-def extract_i18n_strings(node):
- i18n_strings = []
-
- if (
- isinstance(node, ast.Call)
- and isinstance(node.func, ast.Name)
- and node.func.id == "i18n"
- ):
- for arg in node.args:
- if isinstance(arg, ast.Str):
- i18n_strings.append(arg.s)
-
- for child_node in ast.iter_child_nodes(node):
- i18n_strings.extend(extract_i18n_strings(child_node))
-
- return i18n_strings
-
-
-# scan the directory for all .py files (recursively)
-# for each file, parse the code into an AST
-# for each AST, extract the i18n strings
-
-strings = []
-for filename in glob.iglob("**/*.py", recursive=True):
- with open(filename, "r") as f:
- code = f.read()
- if "I18nAuto" in code:
- tree = ast.parse(code)
- i18n_strings = extract_i18n_strings(tree)
- print(filename, len(i18n_strings))
- strings.extend(i18n_strings)
-code_keys = set(strings)
-"""
-n_i18n.py
-gui_v1.py 26
-app.py 16
-infer-web.py 147
-scan_i18n.py 0
-i18n.py 0
-lib/train/process_ckpt.py 1
-"""
-print()
-print("Total unique:", len(code_keys))
-
-
-standard_file = "i18n/locale/zh_CN.json"
-with open(standard_file, "r", encoding="utf-8") as f:
- standard_data = json.load(f, object_pairs_hook=OrderedDict)
-standard_keys = set(standard_data.keys())
-
-# Define the standard file name
-unused_keys = standard_keys - code_keys
-print("Unused keys:", len(unused_keys))
-for unused_key in unused_keys:
- print("\t", unused_key)
-
-missing_keys = code_keys - standard_keys
-print("Missing keys:", len(missing_keys))
-for missing_key in missing_keys:
- print("\t", missing_key)
-
-code_keys_dict = OrderedDict()
-for s in strings:
- code_keys_dict[s] = s
-
-# write back
-with open(standard_file, "w", encoding="utf-8") as f:
- json.dump(code_keys_dict, f, ensure_ascii=False, indent=4, sort_keys=True)
- f.write("\n")
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/operations/freeze.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/operations/freeze.py
deleted file mode 100644
index 930d4c6005ea87c498711bd8facfc4981bfed3fe..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/operations/freeze.py
+++ /dev/null
@@ -1,254 +0,0 @@
-import collections
-import logging
-import os
-from typing import Container, Dict, Generator, Iterable, List, NamedTuple, Optional, Set
-
-from pip._vendor.packaging.utils import canonicalize_name
-from pip._vendor.packaging.version import Version
-
-from pip._internal.exceptions import BadCommand, InstallationError
-from pip._internal.metadata import BaseDistribution, get_environment
-from pip._internal.req.constructors import (
- install_req_from_editable,
- install_req_from_line,
-)
-from pip._internal.req.req_file import COMMENT_RE
-from pip._internal.utils.direct_url_helpers import direct_url_as_pep440_direct_reference
-
-logger = logging.getLogger(__name__)
-
-
-class _EditableInfo(NamedTuple):
- requirement: str
- comments: List[str]
-
-
-def freeze(
- requirement: Optional[List[str]] = None,
- local_only: bool = False,
- user_only: bool = False,
- paths: Optional[List[str]] = None,
- isolated: bool = False,
- exclude_editable: bool = False,
- skip: Container[str] = (),
-) -> Generator[str, None, None]:
- installations: Dict[str, FrozenRequirement] = {}
-
- dists = get_environment(paths).iter_installed_distributions(
- local_only=local_only,
- skip=(),
- user_only=user_only,
- )
- for dist in dists:
- req = FrozenRequirement.from_dist(dist)
- if exclude_editable and req.editable:
- continue
- installations[req.canonical_name] = req
-
- if requirement:
- # the options that don't get turned into an InstallRequirement
- # should only be emitted once, even if the same option is in multiple
- # requirements files, so we need to keep track of what has been emitted
- # so that we don't emit it again if it's seen again
- emitted_options: Set[str] = set()
- # keep track of which files a requirement is in so that we can
- # give an accurate warning if a requirement appears multiple times.
- req_files: Dict[str, List[str]] = collections.defaultdict(list)
- for req_file_path in requirement:
- with open(req_file_path) as req_file:
- for line in req_file:
- if (
- not line.strip()
- or line.strip().startswith("#")
- or line.startswith(
- (
- "-r",
- "--requirement",
- "-f",
- "--find-links",
- "-i",
- "--index-url",
- "--pre",
- "--trusted-host",
- "--process-dependency-links",
- "--extra-index-url",
- "--use-feature",
- )
- )
- ):
- line = line.rstrip()
- if line not in emitted_options:
- emitted_options.add(line)
- yield line
- continue
-
- if line.startswith("-e") or line.startswith("--editable"):
- if line.startswith("-e"):
- line = line[2:].strip()
- else:
- line = line[len("--editable") :].strip().lstrip("=")
- line_req = install_req_from_editable(
- line,
- isolated=isolated,
- )
- else:
- line_req = install_req_from_line(
- COMMENT_RE.sub("", line).strip(),
- isolated=isolated,
- )
-
- if not line_req.name:
- logger.info(
- "Skipping line in requirement file [%s] because "
- "it's not clear what it would install: %s",
- req_file_path,
- line.strip(),
- )
- logger.info(
- " (add #egg=PackageName to the URL to avoid"
- " this warning)"
- )
- else:
- line_req_canonical_name = canonicalize_name(line_req.name)
- if line_req_canonical_name not in installations:
- # either it's not installed, or it is installed
- # but has been processed already
- if not req_files[line_req.name]:
- logger.warning(
- "Requirement file [%s] contains %s, but "
- "package %r is not installed",
- req_file_path,
- COMMENT_RE.sub("", line).strip(),
- line_req.name,
- )
- else:
- req_files[line_req.name].append(req_file_path)
- else:
- yield str(installations[line_req_canonical_name]).rstrip()
- del installations[line_req_canonical_name]
- req_files[line_req.name].append(req_file_path)
-
- # Warn about requirements that were included multiple times (in a
- # single requirements file or in different requirements files).
- for name, files in req_files.items():
- if len(files) > 1:
- logger.warning(
- "Requirement %s included multiple times [%s]",
- name,
- ", ".join(sorted(set(files))),
- )
-
- yield ("## The following requirements were added by pip freeze:")
- for installation in sorted(installations.values(), key=lambda x: x.name.lower()):
- if installation.canonical_name not in skip:
- yield str(installation).rstrip()
-
-
-def _format_as_name_version(dist: BaseDistribution) -> str:
- if isinstance(dist.version, Version):
- return f"{dist.raw_name}=={dist.version}"
- return f"{dist.raw_name}==={dist.version}"
-
-
-def _get_editable_info(dist: BaseDistribution) -> _EditableInfo:
- """
- Compute and return values (req, comments) for use in
- FrozenRequirement.from_dist().
- """
- editable_project_location = dist.editable_project_location
- assert editable_project_location
- location = os.path.normcase(os.path.abspath(editable_project_location))
-
- from pip._internal.vcs import RemoteNotFoundError, RemoteNotValidError, vcs
-
- vcs_backend = vcs.get_backend_for_dir(location)
-
- if vcs_backend is None:
- display = _format_as_name_version(dist)
- logger.debug(
- 'No VCS found for editable requirement "%s" in: %r',
- display,
- location,
- )
- return _EditableInfo(
- requirement=location,
- comments=[f"# Editable install with no version control ({display})"],
- )
-
- vcs_name = type(vcs_backend).__name__
-
- try:
- req = vcs_backend.get_src_requirement(location, dist.raw_name)
- except RemoteNotFoundError:
- display = _format_as_name_version(dist)
- return _EditableInfo(
- requirement=location,
- comments=[f"# Editable {vcs_name} install with no remote ({display})"],
- )
- except RemoteNotValidError as ex:
- display = _format_as_name_version(dist)
- return _EditableInfo(
- requirement=location,
- comments=[
- f"# Editable {vcs_name} install ({display}) with either a deleted "
- f"local remote or invalid URI:",
- f"# '{ex.url}'",
- ],
- )
- except BadCommand:
- logger.warning(
- "cannot determine version of editable source in %s "
- "(%s command not found in path)",
- location,
- vcs_backend.name,
- )
- return _EditableInfo(requirement=location, comments=[])
- except InstallationError as exc:
- logger.warning("Error when trying to get requirement for VCS system %s", exc)
- else:
- return _EditableInfo(requirement=req, comments=[])
-
- logger.warning("Could not determine repository location of %s", location)
-
- return _EditableInfo(
- requirement=location,
- comments=["## !! Could not determine repository location"],
- )
-
-
-class FrozenRequirement:
- def __init__(
- self,
- name: str,
- req: str,
- editable: bool,
- comments: Iterable[str] = (),
- ) -> None:
- self.name = name
- self.canonical_name = canonicalize_name(name)
- self.req = req
- self.editable = editable
- self.comments = comments
-
- @classmethod
- def from_dist(cls, dist: BaseDistribution) -> "FrozenRequirement":
- editable = dist.editable
- if editable:
- req, comments = _get_editable_info(dist)
- else:
- comments = []
- direct_url = dist.direct_url
- if direct_url:
- # if PEP 610 metadata is present, use it
- req = direct_url_as_pep440_direct_reference(direct_url, dist.raw_name)
- else:
- # name==version requirement
- req = _format_as_name_version(dist)
-
- return cls(dist.raw_name, req, editable, comments=comments)
-
- def __str__(self) -> str:
- req = self.req
- if self.editable:
- req = f"-e {req}"
- return "\n".join(list(self.comments) + [str(req)]) + "\n"
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/chardet/mbcharsetprober.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/chardet/mbcharsetprober.py
deleted file mode 100644
index bf96ad5d490ae127dfc067d2a3cfe21fcc0e1edd..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/chardet/mbcharsetprober.py
+++ /dev/null
@@ -1,95 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Universal charset detector code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 2001
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-# Mark Pilgrim - port to Python
-# Shy Shalom - original C code
-# Proofpoint, Inc.
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301 USA
-######################### END LICENSE BLOCK #########################
-
-from .charsetprober import CharSetProber
-from .enums import MachineState, ProbingState
-
-
-class MultiByteCharSetProber(CharSetProber):
- """
- MultiByteCharSetProber
- """
-
- def __init__(self, lang_filter=None):
- super().__init__(lang_filter=lang_filter)
- self.distribution_analyzer = None
- self.coding_sm = None
- self._last_char = [0, 0]
-
- def reset(self):
- super().reset()
- if self.coding_sm:
- self.coding_sm.reset()
- if self.distribution_analyzer:
- self.distribution_analyzer.reset()
- self._last_char = [0, 0]
-
- @property
- def charset_name(self):
- raise NotImplementedError
-
- @property
- def language(self):
- raise NotImplementedError
-
- def feed(self, byte_str):
- for i, byte in enumerate(byte_str):
- coding_state = self.coding_sm.next_state(byte)
- if coding_state == MachineState.ERROR:
- self.logger.debug(
- "%s %s prober hit error at byte %s",
- self.charset_name,
- self.language,
- i,
- )
- self._state = ProbingState.NOT_ME
- break
- if coding_state == MachineState.ITS_ME:
- self._state = ProbingState.FOUND_IT
- break
- if coding_state == MachineState.START:
- char_len = self.coding_sm.get_current_charlen()
- if i == 0:
- self._last_char[1] = byte
- self.distribution_analyzer.feed(self._last_char, char_len)
- else:
- self.distribution_analyzer.feed(byte_str[i - 1 : i + 1], char_len)
-
- self._last_char[0] = byte_str[-1]
-
- if self.state == ProbingState.DETECTING:
- if self.distribution_analyzer.got_enough_data() and (
- self.get_confidence() > self.SHORTCUT_THRESHOLD
- ):
- self._state = ProbingState.FOUND_IT
-
- return self.state
-
- def get_confidence(self):
- return self.distribution_analyzer.get_confidence()
diff --git a/spaces/Re1e9/Flower_Classification_using_InceptionV3/README.md b/spaces/Re1e9/Flower_Classification_using_InceptionV3/README.md
deleted file mode 100644
index 57f8d4cc96164b41c7826a88b84b5e510f0cc004..0000000000000000000000000000000000000000
--- a/spaces/Re1e9/Flower_Classification_using_InceptionV3/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Flower Classification Using InceptionV3
-emoji: 🌍
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Realcat/image-matching-webui/hloc/matchers/__init__.py b/spaces/Realcat/image-matching-webui/hloc/matchers/__init__.py
deleted file mode 100644
index a9fd381eb391604db3d3c3c03d278c0e08de2531..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/hloc/matchers/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-def get_matcher(matcher):
- mod = __import__(f"{__name__}.{matcher}", fromlist=[""])
- return getattr(mod, "Model")
diff --git a/spaces/Realcat/image-matching-webui/third_party/LightGlue/lightglue/lightglue.py b/spaces/Realcat/image-matching-webui/third_party/LightGlue/lightglue/lightglue.py
deleted file mode 100644
index 4b20300bf9068267e7b4d334dc2d3e85114ddd3e..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/LightGlue/lightglue/lightglue.py
+++ /dev/null
@@ -1,470 +0,0 @@
-from pathlib import Path
-from types import SimpleNamespace
-import warnings
-import numpy as np
-import torch
-from torch import nn
-import torch.nn.functional as F
-from typing import Optional, List, Callable
-
-try:
- from flash_attn.modules.mha import FlashCrossAttention
-except ModuleNotFoundError:
- FlashCrossAttention = None
-
-if FlashCrossAttention or hasattr(F, "scaled_dot_product_attention"):
- FLASH_AVAILABLE = True
-else:
- FLASH_AVAILABLE = False
-
-torch.backends.cudnn.deterministic = True
-
-
-@torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)
-def normalize_keypoints(kpts: torch.Tensor, size: torch.Tensor) -> torch.Tensor:
- if isinstance(size, torch.Size):
- size = torch.tensor(size)[None]
- shift = size.float().to(kpts) / 2
- scale = size.max(1).values.float().to(kpts) / 2
- kpts = (kpts - shift[:, None]) / scale[:, None, None]
- return kpts
-
-
-def rotate_half(x: torch.Tensor) -> torch.Tensor:
- x = x.unflatten(-1, (-1, 2))
- x1, x2 = x.unbind(dim=-1)
- return torch.stack((-x2, x1), dim=-1).flatten(start_dim=-2)
-
-
-def apply_cached_rotary_emb(freqs: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
- return (t * freqs[0]) + (rotate_half(t) * freqs[1])
-
-
-class LearnableFourierPositionalEncoding(nn.Module):
- def __init__(self, M: int, dim: int, F_dim: int = None, gamma: float = 1.0) -> None:
- super().__init__()
- F_dim = F_dim if F_dim is not None else dim
- self.gamma = gamma
- self.Wr = nn.Linear(M, F_dim // 2, bias=False)
- nn.init.normal_(self.Wr.weight.data, mean=0, std=self.gamma**-2)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- """encode position vector"""
- projected = self.Wr(x)
- cosines, sines = torch.cos(projected), torch.sin(projected)
- emb = torch.stack([cosines, sines], 0).unsqueeze(-3)
- return emb.repeat_interleave(2, dim=-1)
-
-
-class TokenConfidence(nn.Module):
- def __init__(self, dim: int) -> None:
- super().__init__()
- self.token = nn.Sequential(nn.Linear(dim, 1), nn.Sigmoid())
-
- def forward(self, desc0: torch.Tensor, desc1: torch.Tensor):
- """get confidence tokens"""
- return (
- self.token(desc0.detach().float()).squeeze(-1),
- self.token(desc1.detach().float()).squeeze(-1),
- )
-
-
-class Attention(nn.Module):
- def __init__(self, allow_flash: bool) -> None:
- super().__init__()
- if allow_flash and not FLASH_AVAILABLE:
- warnings.warn(
- "FlashAttention is not available. For optimal speed, "
- "consider installing torch >= 2.0 or flash-attn.",
- stacklevel=2,
- )
- self.enable_flash = allow_flash and FLASH_AVAILABLE
- if allow_flash and FlashCrossAttention:
- self.flash_ = FlashCrossAttention()
-
- def forward(self, q, k, v) -> torch.Tensor:
- if self.enable_flash and q.device.type == "cuda":
- if FlashCrossAttention:
- q, k, v = [x.transpose(-2, -3) for x in [q, k, v]]
- m = self.flash_(q.half(), torch.stack([k, v], 2).half())
- return m.transpose(-2, -3).to(q.dtype)
- else: # use torch 2.0 scaled_dot_product_attention with flash
- args = [x.half().contiguous() for x in [q, k, v]]
- with torch.backends.cuda.sdp_kernel(enable_flash=True):
- return F.scaled_dot_product_attention(*args).to(q.dtype)
- elif hasattr(F, "scaled_dot_product_attention"):
- args = [x.contiguous() for x in [q, k, v]]
- return F.scaled_dot_product_attention(*args).to(q.dtype)
- else:
- s = q.shape[-1] ** -0.5
- attn = F.softmax(torch.einsum("...id,...jd->...ij", q, k) * s, -1)
- return torch.einsum("...ij,...jd->...id", attn, v)
-
-
-class Transformer(nn.Module):
- def __init__(
- self, embed_dim: int, num_heads: int, flash: bool = False, bias: bool = True
- ) -> None:
- super().__init__()
- self.embed_dim = embed_dim
- self.num_heads = num_heads
- assert self.embed_dim % num_heads == 0
- self.head_dim = self.embed_dim // num_heads
- self.Wqkv = nn.Linear(embed_dim, 3 * embed_dim, bias=bias)
- self.inner_attn = Attention(flash)
- self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
- self.ffn = nn.Sequential(
- nn.Linear(2 * embed_dim, 2 * embed_dim),
- nn.LayerNorm(2 * embed_dim, elementwise_affine=True),
- nn.GELU(),
- nn.Linear(2 * embed_dim, embed_dim),
- )
-
- def _forward(self, x: torch.Tensor, encoding: Optional[torch.Tensor] = None):
- qkv = self.Wqkv(x)
- qkv = qkv.unflatten(-1, (self.num_heads, -1, 3)).transpose(1, 2)
- q, k, v = qkv[..., 0], qkv[..., 1], qkv[..., 2]
- if encoding is not None:
- q = apply_cached_rotary_emb(encoding, q)
- k = apply_cached_rotary_emb(encoding, k)
- context = self.inner_attn(q, k, v)
- message = self.out_proj(context.transpose(1, 2).flatten(start_dim=-2))
- return x + self.ffn(torch.cat([x, message], -1))
-
- def forward(self, x0, x1, encoding0=None, encoding1=None):
- return self._forward(x0, encoding0), self._forward(x1, encoding1)
-
-
-class CrossTransformer(nn.Module):
- def __init__(
- self, embed_dim: int, num_heads: int, flash: bool = False, bias: bool = True
- ) -> None:
- super().__init__()
- self.heads = num_heads
- dim_head = embed_dim // num_heads
- self.scale = dim_head**-0.5
- inner_dim = dim_head * num_heads
- self.to_qk = nn.Linear(embed_dim, inner_dim, bias=bias)
- self.to_v = nn.Linear(embed_dim, inner_dim, bias=bias)
- self.to_out = nn.Linear(inner_dim, embed_dim, bias=bias)
- self.ffn = nn.Sequential(
- nn.Linear(2 * embed_dim, 2 * embed_dim),
- nn.LayerNorm(2 * embed_dim, elementwise_affine=True),
- nn.GELU(),
- nn.Linear(2 * embed_dim, embed_dim),
- )
-
- if flash and FLASH_AVAILABLE:
- self.flash = Attention(True)
- else:
- self.flash = None
-
- def map_(self, func: Callable, x0: torch.Tensor, x1: torch.Tensor):
- return func(x0), func(x1)
-
- def forward(self, x0: torch.Tensor, x1: torch.Tensor) -> List[torch.Tensor]:
- qk0, qk1 = self.map_(self.to_qk, x0, x1)
- v0, v1 = self.map_(self.to_v, x0, x1)
- qk0, qk1, v0, v1 = map(
- lambda t: t.unflatten(-1, (self.heads, -1)).transpose(1, 2),
- (qk0, qk1, v0, v1),
- )
- if self.flash is not None:
- m0 = self.flash(qk0, qk1, v1)
- m1 = self.flash(qk1, qk0, v0)
- else:
- qk0, qk1 = qk0 * self.scale**0.5, qk1 * self.scale**0.5
- sim = torch.einsum("b h i d, b h j d -> b h i j", qk0, qk1)
- attn01 = F.softmax(sim, dim=-1)
- attn10 = F.softmax(sim.transpose(-2, -1).contiguous(), dim=-1)
- m0 = torch.einsum("bhij, bhjd -> bhid", attn01, v1)
- m1 = torch.einsum("bhji, bhjd -> bhid", attn10.transpose(-2, -1), v0)
- m0, m1 = self.map_(lambda t: t.transpose(1, 2).flatten(start_dim=-2), m0, m1)
- m0, m1 = self.map_(self.to_out, m0, m1)
- x0 = x0 + self.ffn(torch.cat([x0, m0], -1))
- x1 = x1 + self.ffn(torch.cat([x1, m1], -1))
- return x0, x1
-
-
-def sigmoid_log_double_softmax(
- sim: torch.Tensor, z0: torch.Tensor, z1: torch.Tensor
-) -> torch.Tensor:
- """create the log assignment matrix from logits and similarity"""
- b, m, n = sim.shape
- certainties = F.logsigmoid(z0) + F.logsigmoid(z1).transpose(1, 2)
- scores0 = F.log_softmax(sim, 2)
- scores1 = F.log_softmax(sim.transpose(-1, -2).contiguous(), 2).transpose(-1, -2)
- scores = sim.new_full((b, m + 1, n + 1), 0)
- scores[:, :m, :n] = scores0 + scores1 + certainties
- scores[:, :-1, -1] = F.logsigmoid(-z0.squeeze(-1))
- scores[:, -1, :-1] = F.logsigmoid(-z1.squeeze(-1))
- return scores
-
-
-class MatchAssignment(nn.Module):
- def __init__(self, dim: int) -> None:
- super().__init__()
- self.dim = dim
- self.matchability = nn.Linear(dim, 1, bias=True)
- self.final_proj = nn.Linear(dim, dim, bias=True)
-
- def forward(self, desc0: torch.Tensor, desc1: torch.Tensor):
- """build assignment matrix from descriptors"""
- mdesc0, mdesc1 = self.final_proj(desc0), self.final_proj(desc1)
- _, _, d = mdesc0.shape
- mdesc0, mdesc1 = mdesc0 / d**0.25, mdesc1 / d**0.25
- sim = torch.einsum("bmd,bnd->bmn", mdesc0, mdesc1)
- z0 = self.matchability(desc0)
- z1 = self.matchability(desc1)
- scores = sigmoid_log_double_softmax(sim, z0, z1)
- return scores, sim
-
- def scores(self, desc0: torch.Tensor, desc1: torch.Tensor):
- m0 = torch.sigmoid(self.matchability(desc0)).squeeze(-1)
- m1 = torch.sigmoid(self.matchability(desc1)).squeeze(-1)
- return m0, m1
-
-
-def filter_matches(scores: torch.Tensor, th: float):
- """obtain matches from a log assignment matrix [Bx M+1 x N+1]"""
- max0, max1 = scores[:, :-1, :-1].max(2), scores[:, :-1, :-1].max(1)
- m0, m1 = max0.indices, max1.indices
- mutual0 = torch.arange(m0.shape[1]).to(m0)[None] == m1.gather(1, m0)
- mutual1 = torch.arange(m1.shape[1]).to(m1)[None] == m0.gather(1, m1)
- max0_exp = max0.values.exp()
- zero = max0_exp.new_tensor(0)
- mscores0 = torch.where(mutual0, max0_exp, zero)
- mscores1 = torch.where(mutual1, mscores0.gather(1, m1), zero)
- if th is not None:
- valid0 = mutual0 & (mscores0 > th)
- else:
- valid0 = mutual0
- valid1 = mutual1 & valid0.gather(1, m1)
- m0 = torch.where(valid0, m0, m0.new_tensor(-1))
- m1 = torch.where(valid1, m1, m1.new_tensor(-1))
- return m0, m1, mscores0, mscores1
-
-
-class LightGlue(nn.Module):
- default_conf = {
- "name": "lightglue", # just for interfacing
- "input_dim": 256, # input descriptor dimension (autoselected from weights)
- "descriptor_dim": 256,
- "n_layers": 9,
- "num_heads": 4,
- "flash": True, # enable FlashAttention if available.
- "mp": False, # enable mixed precision
- "depth_confidence": 0.95, # early stopping, disable with -1
- "width_confidence": 0.99, # point pruning, disable with -1
- "filter_threshold": 0.1, # match threshold
- "weights": None,
- }
-
- required_data_keys = ["image0", "image1"]
-
- version = "v0.1_arxiv"
- url = "https://github.com/cvg/LightGlue/releases/download/{}/{}_lightglue.pth"
-
- features = {
- "superpoint": ("superpoint_lightglue", 256),
- "disk": ("disk_lightglue", 128),
- }
-
- def __init__(self, features="superpoint", **conf) -> None:
- super().__init__()
- self.conf = {**self.default_conf, **conf}
- if features is not None:
- assert features in list(self.features.keys())
- self.conf["weights"], self.conf["input_dim"] = self.features[features]
- self.conf = conf = SimpleNamespace(**self.conf)
-
- if conf.input_dim != conf.descriptor_dim:
- self.input_proj = nn.Linear(conf.input_dim, conf.descriptor_dim, bias=True)
- else:
- self.input_proj = nn.Identity()
-
- head_dim = conf.descriptor_dim // conf.num_heads
- self.posenc = LearnableFourierPositionalEncoding(2, head_dim, head_dim)
-
- h, n, d = conf.num_heads, conf.n_layers, conf.descriptor_dim
- self.self_attn = nn.ModuleList(
- [Transformer(d, h, conf.flash) for _ in range(n)]
- )
- self.cross_attn = nn.ModuleList(
- [CrossTransformer(d, h, conf.flash) for _ in range(n)]
- )
- self.log_assignment = nn.ModuleList([MatchAssignment(d) for _ in range(n)])
- self.token_confidence = nn.ModuleList(
- [TokenConfidence(d) for _ in range(n - 1)]
- )
-
- if features is not None:
- fname = f"{conf.weights}_{self.version}.pth".replace(".", "-")
- state_dict = torch.hub.load_state_dict_from_url(
- self.url.format(self.version, features), file_name=fname
- )
- self.load_state_dict(state_dict, strict=False)
- elif conf.weights is not None:
- path = Path(__file__).parent
- path = path / "weights/{}.pth".format(self.conf.weights)
- state_dict = torch.load(str(path), map_location="cpu")
- self.load_state_dict(state_dict, strict=False)
-
- print("Loaded LightGlue model")
-
- def forward(self, data: dict) -> dict:
- """
- Match keypoints and descriptors between two images
-
- Input (dict):
- image0: dict
- keypoints: [B x M x 2]
- descriptors: [B x M x D]
- image: [B x C x H x W] or image_size: [B x 2]
- image1: dict
- keypoints: [B x N x 2]
- descriptors: [B x N x D]
- image: [B x C x H x W] or image_size: [B x 2]
- Output (dict):
- log_assignment: [B x M+1 x N+1]
- matches0: [B x M]
- matching_scores0: [B x M]
- matches1: [B x N]
- matching_scores1: [B x N]
- matches: List[[Si x 2]], scores: List[[Si]]
- """
- with torch.autocast(enabled=self.conf.mp, device_type="cuda"):
- return self._forward(data)
-
- def _forward(self, data: dict) -> dict:
- for key in self.required_data_keys:
- assert key in data, f"Missing key {key} in data"
- data0, data1 = data["image0"], data["image1"]
- kpts0_, kpts1_ = data0["keypoints"], data1["keypoints"]
- b, m, _ = kpts0_.shape
- b, n, _ = kpts1_.shape
- size0, size1 = data0.get("image_size"), data1.get("image_size")
- size0 = size0 if size0 is not None else data0["image"].shape[-2:][::-1]
- size1 = size1 if size1 is not None else data1["image"].shape[-2:][::-1]
- kpts0 = normalize_keypoints(kpts0_, size=size0)
- kpts1 = normalize_keypoints(kpts1_, size=size1)
-
- assert torch.all(kpts0 >= -1) and torch.all(kpts0 <= 1)
- assert torch.all(kpts1 >= -1) and torch.all(kpts1 <= 1)
-
- desc0 = data0["descriptors"].detach()
- desc1 = data1["descriptors"].detach()
-
- assert desc0.shape[-1] == self.conf.input_dim
- assert desc1.shape[-1] == self.conf.input_dim
-
- if torch.is_autocast_enabled():
- desc0 = desc0.half()
- desc1 = desc1.half()
-
- desc0 = self.input_proj(desc0)
- desc1 = self.input_proj(desc1)
-
- # cache positional embeddings
- encoding0 = self.posenc(kpts0)
- encoding1 = self.posenc(kpts1)
-
- # GNN + final_proj + assignment
- ind0 = torch.arange(0, m).to(device=kpts0.device)[None]
- ind1 = torch.arange(0, n).to(device=kpts0.device)[None]
- prune0 = torch.ones_like(ind0) # store layer where pruning is detected
- prune1 = torch.ones_like(ind1)
- dec, wic = self.conf.depth_confidence, self.conf.width_confidence
- token0, token1 = None, None
- for i in range(self.conf.n_layers):
- # self+cross attention
- desc0, desc1 = self.self_attn[i](desc0, desc1, encoding0, encoding1)
- desc0, desc1 = self.cross_attn[i](desc0, desc1)
- if i == self.conf.n_layers - 1:
- continue # no early stopping or adaptive width at last layer
- if dec > 0: # early stopping
- token0, token1 = self.token_confidence[i](desc0, desc1)
- if self.stop(token0, token1, self.conf_th(i), dec, m + n):
- break
- if wic > 0: # point pruning
- match0, match1 = self.log_assignment[i].scores(desc0, desc1)
- mask0 = self.get_mask(token0, match0, self.conf_th(i), 1 - wic)
- mask1 = self.get_mask(token1, match1, self.conf_th(i), 1 - wic)
- ind0, ind1 = ind0[mask0][None], ind1[mask1][None]
- desc0, desc1 = desc0[mask0][None], desc1[mask1][None]
- if desc0.shape[-2] == 0 or desc1.shape[-2] == 0:
- break
- encoding0 = encoding0[:, :, mask0][:, None]
- encoding1 = encoding1[:, :, mask1][:, None]
- prune0[:, ind0] += 1
- prune1[:, ind1] += 1
-
- if wic > 0: # scatter with indices after pruning
- scores_, _ = self.log_assignment[i](desc0, desc1)
- dt, dev = scores_.dtype, scores_.device
- scores = torch.zeros(b, m + 1, n + 1, dtype=dt, device=dev)
- scores[:, :-1, :-1] = -torch.inf
- scores[:, ind0[0], -1] = scores_[:, :-1, -1]
- scores[:, -1, ind1[0]] = scores_[:, -1, :-1]
- x, y = torch.meshgrid(ind0[0], ind1[0], indexing="ij")
- scores[:, x, y] = scores_[:, :-1, :-1]
- else:
- scores, _ = self.log_assignment[i](desc0, desc1)
-
- m0, m1, mscores0, mscores1 = filter_matches(scores, self.conf.filter_threshold)
-
- matches, mscores = [], []
- for k in range(b):
- valid = m0[k] > -1
- matches.append(torch.stack([torch.where(valid)[0], m0[k][valid]], -1))
- mscores.append(mscores0[k][valid])
-
- return {
- "log_assignment": scores,
- "matches0": m0,
- "matches1": m1,
- "matching_scores0": mscores0,
- "matching_scores1": mscores1,
- "stop": i + 1,
- "prune0": prune0,
- "prune1": prune1,
- "matches": matches,
- "scores": mscores,
- }
-
- def conf_th(self, i: int) -> float:
- """scaled confidence threshold"""
- return np.clip(0.8 + 0.1 * np.exp(-4.0 * i / self.conf.n_layers), 0, 1)
-
- def get_mask(
- self,
- confidence: torch.Tensor,
- match: torch.Tensor,
- conf_th: float,
- match_th: float,
- ) -> torch.Tensor:
- """mask points which should be removed"""
- if conf_th and confidence is not None:
- mask = (
- torch.where(confidence > conf_th, match, match.new_tensor(1.0))
- > match_th
- )
- else:
- mask = match > match_th
- return mask
-
- def stop(
- self,
- token0: torch.Tensor,
- token1: torch.Tensor,
- conf_th: float,
- inl_th: float,
- seql: int,
- ) -> torch.Tensor:
- """evaluate stopping condition"""
- tokens = torch.cat([token0, token1], -1)
- if conf_th:
- pos = 1.0 - (tokens < conf_th).float().sum() / seql
- return pos > inl_th
- else:
- return tokens.mean() > inl_th
diff --git a/spaces/Realcat/image-matching-webui/third_party/SOLD2/sold2/postprocess/__init__.py b/spaces/Realcat/image-matching-webui/third_party/SOLD2/sold2/postprocess/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Realcat/image-matching-webui/third_party/lanet/datasets/hp_loader.py b/spaces/Realcat/image-matching-webui/third_party/lanet/datasets/hp_loader.py
deleted file mode 100644
index f255c87dac6e06e56b67ad0f04f7da5c131f0189..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/lanet/datasets/hp_loader.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-import cv2
-import numpy as np
-
-from torchvision import transforms
-from torch.utils.data import Dataset
-from pathlib import Path
-
-
-class PatchesDataset(Dataset):
- """
- HPatches dataset class.
- # Note: output_shape = (output_width, output_height)
- # Note: this returns Pytorch tensors, resized to output_shape (if specified)
- # Note: the homography will be adjusted according to output_shape.
-
- Parameters
- ----------
- root_dir : str
- Path to the dataset
- use_color : bool
- Return color images or convert to grayscale.
- data_transform : Function
- Transformations applied to the sample
- output_shape: tuple
- If specified, the images and homographies will be resized to the desired shape.
- type: str
- Dataset subset to return from ['i', 'v', 'all']:
- i - illumination sequences
- v - viewpoint sequences
- all - all sequences
- """
-
- def __init__(
- self,
- root_dir,
- use_color=True,
- data_transform=None,
- output_shape=None,
- type="all",
- ):
- super().__init__()
- self.type = type
- self.root_dir = root_dir
- self.data_transform = data_transform
- self.output_shape = output_shape
- self.use_color = use_color
- base_path = Path(root_dir)
- folder_paths = [x for x in base_path.iterdir() if x.is_dir()]
- image_paths = []
- warped_image_paths = []
- homographies = []
- for path in folder_paths:
- if self.type == "i" and path.stem[0] != "i":
- continue
- if self.type == "v" and path.stem[0] != "v":
- continue
- num_images = 5
- file_ext = ".ppm"
- for i in range(2, 2 + num_images):
- image_paths.append(str(Path(path, "1" + file_ext)))
- warped_image_paths.append(str(Path(path, str(i) + file_ext)))
- homographies.append(np.loadtxt(str(Path(path, "H_1_" + str(i)))))
- self.files = {
- "image_paths": image_paths,
- "warped_image_paths": warped_image_paths,
- "homography": homographies,
- }
-
- def scale_homography(self, homography, original_scale, new_scale, pre):
- scales = np.divide(new_scale, original_scale)
- if pre:
- s = np.diag(np.append(scales, 1.0))
- homography = np.matmul(s, homography)
- else:
- sinv = np.diag(np.append(1.0 / scales, 1.0))
- homography = np.matmul(homography, sinv)
- return homography
-
- def __len__(self):
- return len(self.files["image_paths"])
-
- def __getitem__(self, idx):
- def _read_image(path):
- img = cv2.imread(path, cv2.IMREAD_COLOR)
- if self.use_color:
- return img
- gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
- return gray
-
- image = _read_image(self.files["image_paths"][idx])
-
- warped_image = _read_image(self.files["warped_image_paths"][idx])
- homography = np.array(self.files["homography"][idx])
- sample = {
- "image": image,
- "warped_image": warped_image,
- "homography": homography,
- "index": idx,
- }
-
- # Apply transformations
- if self.output_shape is not None:
- sample["homography"] = self.scale_homography(
- sample["homography"],
- sample["image"].shape[:2][::-1],
- self.output_shape,
- pre=False,
- )
- sample["homography"] = self.scale_homography(
- sample["homography"],
- sample["warped_image"].shape[:2][::-1],
- self.output_shape,
- pre=True,
- )
-
- for key in ["image", "warped_image"]:
- sample[key] = cv2.resize(sample[key], self.output_shape)
- if self.use_color is False:
- sample[key] = np.expand_dims(sample[key], axis=2)
-
- transform = transforms.ToTensor()
-
- for key in ["image", "warped_image"]:
- sample[key] = transform(sample[key]).type("torch.FloatTensor")
- return sample
diff --git a/spaces/RedBaron5/PatentSolver/App/bin/ParamProcessor.py b/spaces/RedBaron5/PatentSolver/App/bin/ParamProcessor.py
deleted file mode 100644
index b36af0f8ab4d000053bc18ab3cf9716155c1edc8..0000000000000000000000000000000000000000
--- a/spaces/RedBaron5/PatentSolver/App/bin/ParamProcessor.py
+++ /dev/null
@@ -1,99 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import json
-import os
-import re
-import matplotlib.pyplot as plt
-import numpy as np
-from io import StringIO
-from App4api.bin import constants
-from collections import OrderedDict
-from App4api.bin.InformationExtractor import InformationExtractor
-from App4api.bin.ParameterExtractor import ParameterExtractor
-from App4api.bin.TechnologyFinder import TechnologyFinder
-
-class ParamProcessor(object):
-
- def __init__(self, patents,input_folder, file_extension):
- self.patents = patents
- self.input_folder = input_folder
- self.file_extension = file_extension
- print("Processing started")
-
- def change_keys(self, dictionnary, number):
- number = number+'-'
- if type(dictionnary) is dict:
- return dict([(number+str(k) , self.change_keys(v, number)) for k, v in dictionnary.items()])
- else:
- return dictionnary
-
- def process_corpus(self):
-
- count_patent = 0
- patents = self.patents
- input_folder = self.input_folder
- project_folder = os.path.basename(os.path.normpath(input_folder))
- graph_folder = constants.GRAPH_FOLDER + project_folder+"/"
- output_result = []
- parameters_graph = []
- reduced_content = []
- patent_corpus = []
- source_list = []
- parameters_list =[]
-
-
- for patent_file in patents:
-
- read_patent = StringIO(patent_file)
- patent = json.load(read_patent)
- nNumber = patent['number']
- aAbstract = patent['abstract']
- cClaims = patent['claims']
- dDescription = patent['description']
- source = patent['source']
-
- patent_content = aAbstract + cClaims + dDescription
- patent_content = patent_content.splitlines()
-
- for line in patent_content:
- get_parameters = ParameterExtractor(line)
- parameters = get_parameters.extract_parameters()
- if parameters:
- parameters_list.extend( parameters)
-
-
- parameters_list=list(set(parameters_list))
-
- parameters = dict(enumerate(parameters_list, 1))
-
- parameters = self.change_keys(parameters, nNumber.lower())
-
- parameters_array = OrderedDict({
- "concept": {
- "source": source,
- "valeurs": parameters,
-
- }
-
- })
- pParameters= json.dumps(parameters_array, sort_keys=OrderedDict, indent=4, separators=(',', ': '))
- parameters_graph.append(pParameters)
- count_patent +=1
- source_list.append(source)
- patent_corpus.append(reduced_content)
-
- header = '{'
- parameters_output = '"parameters": [%s]' % ','.join(parameters_graph)
- footer = '}'
- output_result.extend((header, parameters_output, footer))
-
- output_result = "".join(output_result)
- concepts_json = json.loads(output_result)
-
-
- json_write_to_file = json.dumps(concepts_json, sort_keys=False, indent=4, separators=(',', ': '))
-
- with open(graph_folder+"parameters-graph.json", 'w') as json_graph:
- json_graph.write(json_write_to_file)
-
- return concepts_json
\ No newline at end of file
diff --git a/spaces/Ricecake123/RVC-demo/venv.sh b/spaces/Ricecake123/RVC-demo/venv.sh
deleted file mode 100644
index 17f58bf2a93bcd706412386e5fc6c1894cc508ad..0000000000000000000000000000000000000000
--- a/spaces/Ricecake123/RVC-demo/venv.sh
+++ /dev/null
@@ -1 +0,0 @@
-python3 -m venv .venv
diff --git a/spaces/Ritori/TTS_Yui/layers.py b/spaces/Ritori/TTS_Yui/layers.py
deleted file mode 100644
index 615a64a4394cb7482cc19bd0bd26f842d38d9b3d..0000000000000000000000000000000000000000
--- a/spaces/Ritori/TTS_Yui/layers.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import torch
-from librosa.filters import mel as librosa_mel_fn
-from audio_processing import dynamic_range_compression
-from audio_processing import dynamic_range_decompression
-from stft import STFT
-
-
-class LinearNorm(torch.nn.Module):
- def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
- super(LinearNorm, self).__init__()
- self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
-
- torch.nn.init.xavier_uniform_(
- self.linear_layer.weight,
- gain=torch.nn.init.calculate_gain(w_init_gain))
-
- def forward(self, x):
- return self.linear_layer(x)
-
-
-class ConvNorm(torch.nn.Module):
- def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
- padding=None, dilation=1, bias=True, w_init_gain='linear'):
- super(ConvNorm, self).__init__()
- if padding is None:
- assert(kernel_size % 2 == 1)
- padding = int(dilation * (kernel_size - 1) / 2)
-
- self.conv = torch.nn.Conv1d(in_channels, out_channels,
- kernel_size=kernel_size, stride=stride,
- padding=padding, dilation=dilation,
- bias=bias)
-
- torch.nn.init.xavier_uniform_(
- self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
-
- def forward(self, signal):
- conv_signal = self.conv(signal)
- return conv_signal
-
-
-class TacotronSTFT(torch.nn.Module):
- def __init__(self, filter_length=1024, hop_length=256, win_length=1024,
- n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0,
- mel_fmax=8000.0):
- super(TacotronSTFT, self).__init__()
- self.n_mel_channels = n_mel_channels
- self.sampling_rate = sampling_rate
- self.stft_fn = STFT(filter_length, hop_length, win_length)
- mel_basis = librosa_mel_fn(
- sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax)
- mel_basis = torch.from_numpy(mel_basis).float()
- self.register_buffer('mel_basis', mel_basis)
-
- def spectral_normalize(self, magnitudes):
- output = dynamic_range_compression(magnitudes)
- return output
-
- def spectral_de_normalize(self, magnitudes):
- output = dynamic_range_decompression(magnitudes)
- return output
-
- def mel_spectrogram(self, y):
- """Computes mel-spectrograms from a batch of waves
- PARAMS
- ------
- y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
-
- RETURNS
- -------
- mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
- """
- assert(torch.min(y.data) >= -1)
- assert(torch.max(y.data) <= 1)
-
- magnitudes, phases = self.stft_fn.transform(y)
- magnitudes = magnitudes.data
- mel_output = torch.matmul(self.mel_basis, magnitudes)
- mel_output = self.spectral_normalize(mel_output)
- return mel_output
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/core/bbox/coder/yolo_bbox_coder.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/core/bbox/coder/yolo_bbox_coder.py
deleted file mode 100644
index d6d0e82ac780820952938d8751ac9776ea31588a..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/core/bbox/coder/yolo_bbox_coder.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import mmcv
-import torch
-
-from ..builder import BBOX_CODERS
-from .base_bbox_coder import BaseBBoxCoder
-
-
-@BBOX_CODERS.register_module()
-class YOLOBBoxCoder(BaseBBoxCoder):
- """YOLO BBox coder.
-
- Following `YOLO <https://arxiv.org/abs/1506.02640>`_, this coder divide
- image into grids, and encode bbox (x1, y1, x2, y2) into (cx, cy, dw, dh).
- cx, cy in [0., 1.], denotes relative center position w.r.t the center of
- bboxes. dw, dh are the same as :obj:`DeltaXYWHBBoxCoder`.
-
- Args:
- eps (float): Min value of cx, cy when encoding.
- """
-
- def __init__(self, eps=1e-6):
- super(BaseBBoxCoder, self).__init__()
- self.eps = eps
-
- @mmcv.jit(coderize=True)
- def encode(self, bboxes, gt_bboxes, stride):
- """Get box regression transformation deltas that can be used to
- transform the ``bboxes`` into the ``gt_bboxes``.
-
- Args:
- bboxes (torch.Tensor): Source boxes, e.g., anchors.
- gt_bboxes (torch.Tensor): Target of the transformation, e.g.,
- ground-truth boxes.
- stride (torch.Tensor | int): Stride of bboxes.
-
- Returns:
- torch.Tensor: Box transformation deltas
- """
-
- assert bboxes.size(0) == gt_bboxes.size(0)
- assert bboxes.size(-1) == gt_bboxes.size(-1) == 4
- x_center_gt = (gt_bboxes[..., 0] + gt_bboxes[..., 2]) * 0.5
- y_center_gt = (gt_bboxes[..., 1] + gt_bboxes[..., 3]) * 0.5
- w_gt = gt_bboxes[..., 2] - gt_bboxes[..., 0]
- h_gt = gt_bboxes[..., 3] - gt_bboxes[..., 1]
- x_center = (bboxes[..., 0] + bboxes[..., 2]) * 0.5
- y_center = (bboxes[..., 1] + bboxes[..., 3]) * 0.5
- w = bboxes[..., 2] - bboxes[..., 0]
- h = bboxes[..., 3] - bboxes[..., 1]
- w_target = torch.log((w_gt / w).clamp(min=self.eps))
- h_target = torch.log((h_gt / h).clamp(min=self.eps))
- x_center_target = ((x_center_gt - x_center) / stride + 0.5).clamp(
- self.eps, 1 - self.eps)
- y_center_target = ((y_center_gt - y_center) / stride + 0.5).clamp(
- self.eps, 1 - self.eps)
- encoded_bboxes = torch.stack(
- [x_center_target, y_center_target, w_target, h_target], dim=-1)
- return encoded_bboxes
-
- @mmcv.jit(coderize=True)
- def decode(self, bboxes, pred_bboxes, stride):
- """Apply transformation `pred_bboxes` to `boxes`.
-
- Args:
- boxes (torch.Tensor): Basic boxes, e.g. anchors.
- pred_bboxes (torch.Tensor): Encoded boxes with shape
- stride (torch.Tensor | int): Strides of bboxes.
-
- Returns:
- torch.Tensor: Decoded boxes.
- """
- assert pred_bboxes.size(0) == bboxes.size(0)
- assert pred_bboxes.size(-1) == bboxes.size(-1) == 4
- x_center = (bboxes[..., 0] + bboxes[..., 2]) * 0.5
- y_center = (bboxes[..., 1] + bboxes[..., 3]) * 0.5
- w = bboxes[..., 2] - bboxes[..., 0]
- h = bboxes[..., 3] - bboxes[..., 1]
- # Get outputs x, y
- x_center_pred = (pred_bboxes[..., 0] - 0.5) * stride + x_center
- y_center_pred = (pred_bboxes[..., 1] - 0.5) * stride + y_center
- w_pred = torch.exp(pred_bboxes[..., 2]) * w
- h_pred = torch.exp(pred_bboxes[..., 3]) * h
-
- decoded_bboxes = torch.stack(
- (x_center_pred - w_pred / 2, y_center_pred - h_pred / 2,
- x_center_pred + w_pred / 2, y_center_pred + h_pred / 2),
- dim=-1)
-
- return decoded_bboxes
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/htc_roi_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/htc_roi_head.py
deleted file mode 100644
index 5b5c2ec3bc9d579061fbd89f8b320e6e59909143..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/htc_roi_head.py
+++ /dev/null
@@ -1,589 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes,
- merge_aug_masks, multiclass_nms)
-from ..builder import HEADS, build_head, build_roi_extractor
-from .cascade_roi_head import CascadeRoIHead
-
-
-@HEADS.register_module()
-class HybridTaskCascadeRoIHead(CascadeRoIHead):
- """Hybrid task cascade roi head including one bbox head and one mask head.
-
- https://arxiv.org/abs/1901.07518
- """
-
- def __init__(self,
- num_stages,
- stage_loss_weights,
- semantic_roi_extractor=None,
- semantic_head=None,
- semantic_fusion=('bbox', 'mask'),
- interleaved=True,
- mask_info_flow=True,
- **kwargs):
- super(HybridTaskCascadeRoIHead,
- self).__init__(num_stages, stage_loss_weights, **kwargs)
- assert self.with_bbox and self.with_mask
- assert not self.with_shared_head # shared head is not supported
-
- if semantic_head is not None:
- self.semantic_roi_extractor = build_roi_extractor(
- semantic_roi_extractor)
- self.semantic_head = build_head(semantic_head)
-
- self.semantic_fusion = semantic_fusion
- self.interleaved = interleaved
- self.mask_info_flow = mask_info_flow
-
- def init_weights(self, pretrained):
- """Initialize the weights in head.
-
- Args:
- pretrained (str, optional): Path to pre-trained weights.
- Defaults to None.
- """
- super(HybridTaskCascadeRoIHead, self).init_weights(pretrained)
- if self.with_semantic:
- self.semantic_head.init_weights()
-
- @property
- def with_semantic(self):
- """bool: whether the head has semantic head"""
- if hasattr(self, 'semantic_head') and self.semantic_head is not None:
- return True
- else:
- return False
-
- def forward_dummy(self, x, proposals):
- """Dummy forward function."""
- outs = ()
- # semantic head
- if self.with_semantic:
- _, semantic_feat = self.semantic_head(x)
- else:
- semantic_feat = None
- # bbox heads
- rois = bbox2roi([proposals])
- for i in range(self.num_stages):
- bbox_results = self._bbox_forward(
- i, x, rois, semantic_feat=semantic_feat)
- outs = outs + (bbox_results['cls_score'],
- bbox_results['bbox_pred'])
- # mask heads
- if self.with_mask:
- mask_rois = rois[:100]
- mask_roi_extractor = self.mask_roi_extractor[-1]
- mask_feats = mask_roi_extractor(
- x[:len(mask_roi_extractor.featmap_strides)], mask_rois)
- if self.with_semantic and 'mask' in self.semantic_fusion:
- mask_semantic_feat = self.semantic_roi_extractor(
- [semantic_feat], mask_rois)
- mask_feats += mask_semantic_feat
- last_feat = None
- for i in range(self.num_stages):
- mask_head = self.mask_head[i]
- if self.mask_info_flow:
- mask_pred, last_feat = mask_head(mask_feats, last_feat)
- else:
- mask_pred = mask_head(mask_feats)
- outs = outs + (mask_pred, )
- return outs
-
- def _bbox_forward_train(self,
- stage,
- x,
- sampling_results,
- gt_bboxes,
- gt_labels,
- rcnn_train_cfg,
- semantic_feat=None):
- """Run forward function and calculate loss for box head in training."""
- bbox_head = self.bbox_head[stage]
- rois = bbox2roi([res.bboxes for res in sampling_results])
- bbox_results = self._bbox_forward(
- stage, x, rois, semantic_feat=semantic_feat)
-
- bbox_targets = bbox_head.get_targets(sampling_results, gt_bboxes,
- gt_labels, rcnn_train_cfg)
- loss_bbox = bbox_head.loss(bbox_results['cls_score'],
- bbox_results['bbox_pred'], rois,
- *bbox_targets)
-
- bbox_results.update(
- loss_bbox=loss_bbox,
- rois=rois,
- bbox_targets=bbox_targets,
- )
- return bbox_results
-
- def _mask_forward_train(self,
- stage,
- x,
- sampling_results,
- gt_masks,
- rcnn_train_cfg,
- semantic_feat=None):
- """Run forward function and calculate loss for mask head in
- training."""
- mask_roi_extractor = self.mask_roi_extractor[stage]
- mask_head = self.mask_head[stage]
- pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
- mask_feats = mask_roi_extractor(x[:mask_roi_extractor.num_inputs],
- pos_rois)
-
- # semantic feature fusion
- # element-wise sum for original features and pooled semantic features
- if self.with_semantic and 'mask' in self.semantic_fusion:
- mask_semantic_feat = self.semantic_roi_extractor([semantic_feat],
- pos_rois)
- if mask_semantic_feat.shape[-2:] != mask_feats.shape[-2:]:
- mask_semantic_feat = F.adaptive_avg_pool2d(
- mask_semantic_feat, mask_feats.shape[-2:])
- mask_feats += mask_semantic_feat
-
- # mask information flow
- # forward all previous mask heads to obtain last_feat, and fuse it
- # with the normal mask feature
- if self.mask_info_flow:
- last_feat = None
- for i in range(stage):
- last_feat = self.mask_head[i](
- mask_feats, last_feat, return_logits=False)
- mask_pred = mask_head(mask_feats, last_feat, return_feat=False)
- else:
- mask_pred = mask_head(mask_feats, return_feat=False)
-
- mask_targets = mask_head.get_targets(sampling_results, gt_masks,
- rcnn_train_cfg)
- pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
- loss_mask = mask_head.loss(mask_pred, mask_targets, pos_labels)
-
- mask_results = dict(loss_mask=loss_mask)
- return mask_results
-
- def _bbox_forward(self, stage, x, rois, semantic_feat=None):
- """Box head forward function used in both training and testing."""
- bbox_roi_extractor = self.bbox_roi_extractor[stage]
- bbox_head = self.bbox_head[stage]
- bbox_feats = bbox_roi_extractor(
- x[:len(bbox_roi_extractor.featmap_strides)], rois)
- if self.with_semantic and 'bbox' in self.semantic_fusion:
- bbox_semantic_feat = self.semantic_roi_extractor([semantic_feat],
- rois)
- if bbox_semantic_feat.shape[-2:] != bbox_feats.shape[-2:]:
- bbox_semantic_feat = F.adaptive_avg_pool2d(
- bbox_semantic_feat, bbox_feats.shape[-2:])
- bbox_feats += bbox_semantic_feat
- cls_score, bbox_pred = bbox_head(bbox_feats)
-
- bbox_results = dict(cls_score=cls_score, bbox_pred=bbox_pred)
- return bbox_results
-
- def _mask_forward_test(self, stage, x, bboxes, semantic_feat=None):
- """Mask head forward function for testing."""
- mask_roi_extractor = self.mask_roi_extractor[stage]
- mask_head = self.mask_head[stage]
- mask_rois = bbox2roi([bboxes])
- mask_feats = mask_roi_extractor(
- x[:len(mask_roi_extractor.featmap_strides)], mask_rois)
- if self.with_semantic and 'mask' in self.semantic_fusion:
- mask_semantic_feat = self.semantic_roi_extractor([semantic_feat],
- mask_rois)
- if mask_semantic_feat.shape[-2:] != mask_feats.shape[-2:]:
- mask_semantic_feat = F.adaptive_avg_pool2d(
- mask_semantic_feat, mask_feats.shape[-2:])
- mask_feats += mask_semantic_feat
- if self.mask_info_flow:
- last_feat = None
- last_pred = None
- for i in range(stage):
- mask_pred, last_feat = self.mask_head[i](mask_feats, last_feat)
- if last_pred is not None:
- mask_pred = mask_pred + last_pred
- last_pred = mask_pred
- mask_pred = mask_head(mask_feats, last_feat, return_feat=False)
- if last_pred is not None:
- mask_pred = mask_pred + last_pred
- else:
- mask_pred = mask_head(mask_feats)
- return mask_pred
-
- def forward_train(self,
- x,
- img_metas,
- proposal_list,
- gt_bboxes,
- gt_labels,
- gt_bboxes_ignore=None,
- gt_masks=None,
- gt_semantic_seg=None):
- """
- Args:
- x (list[Tensor]): list of multi-level img features.
-
- img_metas (list[dict]): list of image info dict where each dict
- has: 'img_shape', 'scale_factor', 'flip', and may also contain
- 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
- For details on the values of these keys see
- `mmdet/datasets/pipelines/formatting.py:Collect`.
-
- proposal_list (list[Tensors]): list of region proposals.
-
- gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
- shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-
- gt_labels (list[Tensor]): class indices corresponding to each box
-
- gt_bboxes_ignore (None, list[Tensor]): specify which bounding
- boxes can be ignored when computing the loss.
-
- gt_masks (None, Tensor) : true segmentation masks for each box
- used if the architecture supports a segmentation task.
-
- gt_semantic_seg (None, list[Tensor]): semantic segmentation masks
- used if the architecture supports semantic segmentation task.
-
- Returns:
- dict[str, Tensor]: a dictionary of loss components
- """
- # semantic segmentation part
- # 2 outputs: segmentation prediction and embedded features
- losses = dict()
- if self.with_semantic:
- semantic_pred, semantic_feat = self.semantic_head(x)
- loss_seg = self.semantic_head.loss(semantic_pred, gt_semantic_seg)
- losses['loss_semantic_seg'] = loss_seg
- else:
- semantic_feat = None
-
- for i in range(self.num_stages):
- self.current_stage = i
- rcnn_train_cfg = self.train_cfg[i]
- lw = self.stage_loss_weights[i]
-
- # assign gts and sample proposals
- sampling_results = []
- bbox_assigner = self.bbox_assigner[i]
- bbox_sampler = self.bbox_sampler[i]
- num_imgs = len(img_metas)
- if gt_bboxes_ignore is None:
- gt_bboxes_ignore = [None for _ in range(num_imgs)]
-
- for j in range(num_imgs):
- assign_result = bbox_assigner.assign(proposal_list[j],
- gt_bboxes[j],
- gt_bboxes_ignore[j],
- gt_labels[j])
- sampling_result = bbox_sampler.sample(
- assign_result,
- proposal_list[j],
- gt_bboxes[j],
- gt_labels[j],
- feats=[lvl_feat[j][None] for lvl_feat in x])
- sampling_results.append(sampling_result)
-
- # bbox head forward and loss
- bbox_results = \
- self._bbox_forward_train(
- i, x, sampling_results, gt_bboxes, gt_labels,
- rcnn_train_cfg, semantic_feat)
- roi_labels = bbox_results['bbox_targets'][0]
-
- for name, value in bbox_results['loss_bbox'].items():
- losses[f's{i}.{name}'] = (
- value * lw if 'loss' in name else value)
-
- # mask head forward and loss
- if self.with_mask:
- # interleaved execution: use regressed bboxes by the box branch
- # to train the mask branch
- if self.interleaved:
- pos_is_gts = [res.pos_is_gt for res in sampling_results]
- with torch.no_grad():
- proposal_list = self.bbox_head[i].refine_bboxes(
- bbox_results['rois'], roi_labels,
- bbox_results['bbox_pred'], pos_is_gts, img_metas)
- # re-assign and sample 512 RoIs from 512 RoIs
- sampling_results = []
- for j in range(num_imgs):
- assign_result = bbox_assigner.assign(
- proposal_list[j], gt_bboxes[j],
- gt_bboxes_ignore[j], gt_labels[j])
- sampling_result = bbox_sampler.sample(
- assign_result,
- proposal_list[j],
- gt_bboxes[j],
- gt_labels[j],
- feats=[lvl_feat[j][None] for lvl_feat in x])
- sampling_results.append(sampling_result)
- mask_results = self._mask_forward_train(
- i, x, sampling_results, gt_masks, rcnn_train_cfg,
- semantic_feat)
- for name, value in mask_results['loss_mask'].items():
- losses[f's{i}.{name}'] = (
- value * lw if 'loss' in name else value)
-
- # refine bboxes (same as Cascade R-CNN)
- if i < self.num_stages - 1 and not self.interleaved:
- pos_is_gts = [res.pos_is_gt for res in sampling_results]
- with torch.no_grad():
- proposal_list = self.bbox_head[i].refine_bboxes(
- bbox_results['rois'], roi_labels,
- bbox_results['bbox_pred'], pos_is_gts, img_metas)
-
- return losses
-
- def simple_test(self, x, proposal_list, img_metas, rescale=False):
- """Test without augmentation."""
- if self.with_semantic:
- _, semantic_feat = self.semantic_head(x)
- else:
- semantic_feat = None
-
- num_imgs = len(proposal_list)
- img_shapes = tuple(meta['img_shape'] for meta in img_metas)
- ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
- scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
-
- # "ms" in variable names means multi-stage
- ms_bbox_result = {}
- ms_segm_result = {}
- ms_scores = []
- rcnn_test_cfg = self.test_cfg
-
- rois = bbox2roi(proposal_list)
- for i in range(self.num_stages):
- bbox_head = self.bbox_head[i]
- bbox_results = self._bbox_forward(
- i, x, rois, semantic_feat=semantic_feat)
- # split batch bbox prediction back to each image
- cls_score = bbox_results['cls_score']
- bbox_pred = bbox_results['bbox_pred']
- num_proposals_per_img = tuple(len(p) for p in proposal_list)
- rois = rois.split(num_proposals_per_img, 0)
- cls_score = cls_score.split(num_proposals_per_img, 0)
- bbox_pred = bbox_pred.split(num_proposals_per_img, 0)
- ms_scores.append(cls_score)
-
- if i < self.num_stages - 1:
- bbox_label = [s[:, :-1].argmax(dim=1) for s in cls_score]
- rois = torch.cat([
- bbox_head.regress_by_class(rois[i], bbox_label[i],
- bbox_pred[i], img_metas[i])
- for i in range(num_imgs)
- ])
-
- # average scores of each image by stages
- cls_score = [
- sum([score[i] for score in ms_scores]) / float(len(ms_scores))
- for i in range(num_imgs)
- ]
-
- # apply bbox post-processing to each image individually
- det_bboxes = []
- det_labels = []
- for i in range(num_imgs):
- det_bbox, det_label = self.bbox_head[-1].get_bboxes(
- rois[i],
- cls_score[i],
- bbox_pred[i],
- img_shapes[i],
- scale_factors[i],
- rescale=rescale,
- cfg=rcnn_test_cfg)
- det_bboxes.append(det_bbox)
- det_labels.append(det_label)
- bbox_result = [
- bbox2result(det_bboxes[i], det_labels[i],
- self.bbox_head[-1].num_classes)
- for i in range(num_imgs)
- ]
- ms_bbox_result['ensemble'] = bbox_result
-
- if self.with_mask:
- if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
- mask_classes = self.mask_head[-1].num_classes
- segm_results = [[[] for _ in range(mask_classes)]
- for _ in range(num_imgs)]
- else:
- if rescale and not isinstance(scale_factors[0], float):
- scale_factors = [
- torch.from_numpy(scale_factor).to(det_bboxes[0].device)
- for scale_factor in scale_factors
- ]
- _bboxes = [
- det_bboxes[i][:, :4] *
- scale_factors[i] if rescale else det_bboxes[i]
- for i in range(num_imgs)
- ]
- mask_rois = bbox2roi(_bboxes)
- aug_masks = []
- mask_roi_extractor = self.mask_roi_extractor[-1]
- mask_feats = mask_roi_extractor(
- x[:len(mask_roi_extractor.featmap_strides)], mask_rois)
- if self.with_semantic and 'mask' in self.semantic_fusion:
- mask_semantic_feat = self.semantic_roi_extractor(
- [semantic_feat], mask_rois)
- mask_feats += mask_semantic_feat
- last_feat = None
-
- num_bbox_per_img = tuple(len(_bbox) for _bbox in _bboxes)
- for i in range(self.num_stages):
- mask_head = self.mask_head[i]
- if self.mask_info_flow:
- mask_pred, last_feat = mask_head(mask_feats, last_feat)
- else:
- mask_pred = mask_head(mask_feats)
-
- # split batch mask prediction back to each image
- mask_pred = mask_pred.split(num_bbox_per_img, 0)
- aug_masks.append(
- [mask.sigmoid().cpu().numpy() for mask in mask_pred])
-
- # apply mask post-processing to each image individually
- segm_results = []
- for i in range(num_imgs):
- if det_bboxes[i].shape[0] == 0:
- segm_results.append(
- [[]
- for _ in range(self.mask_head[-1].num_classes)])
- else:
- aug_mask = [mask[i] for mask in aug_masks]
- merged_mask = merge_aug_masks(
- aug_mask, [[img_metas[i]]] * self.num_stages,
- rcnn_test_cfg)
- segm_result = self.mask_head[-1].get_seg_masks(
- merged_mask, _bboxes[i], det_labels[i],
- rcnn_test_cfg, ori_shapes[i], scale_factors[i],
- rescale)
- segm_results.append(segm_result)
- ms_segm_result['ensemble'] = segm_results
-
- if self.with_mask:
- results = list(
- zip(ms_bbox_result['ensemble'], ms_segm_result['ensemble']))
- else:
- results = ms_bbox_result['ensemble']
-
- return results
-
- def aug_test(self, img_feats, proposal_list, img_metas, rescale=False):
- """Test with augmentations.
-
- If rescale is False, then returned bboxes and masks will fit the scale
- of imgs[0].
- """
- if self.with_semantic:
- semantic_feats = [
- self.semantic_head(feat)[1] for feat in img_feats
- ]
- else:
- semantic_feats = [None] * len(img_metas)
-
- rcnn_test_cfg = self.test_cfg
- aug_bboxes = []
- aug_scores = []
- for x, img_meta, semantic in zip(img_feats, img_metas, semantic_feats):
- # only one image in the batch
- img_shape = img_meta[0]['img_shape']
- scale_factor = img_meta[0]['scale_factor']
- flip = img_meta[0]['flip']
- flip_direction = img_meta[0]['flip_direction']
-
- proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
- scale_factor, flip, flip_direction)
- # "ms" in variable names means multi-stage
- ms_scores = []
-
- rois = bbox2roi([proposals])
- for i in range(self.num_stages):
- bbox_head = self.bbox_head[i]
- bbox_results = self._bbox_forward(
- i, x, rois, semantic_feat=semantic)
- ms_scores.append(bbox_results['cls_score'])
-
- if i < self.num_stages - 1:
- bbox_label = bbox_results['cls_score'].argmax(dim=1)
- rois = bbox_head.regress_by_class(
- rois, bbox_label, bbox_results['bbox_pred'],
- img_meta[0])
-
- cls_score = sum(ms_scores) / float(len(ms_scores))
- bboxes, scores = self.bbox_head[-1].get_bboxes(
- rois,
- cls_score,
- bbox_results['bbox_pred'],
- img_shape,
- scale_factor,
- rescale=False,
- cfg=None)
- aug_bboxes.append(bboxes)
- aug_scores.append(scores)
-
- # after merging, bboxes will be rescaled to the original image size
- merged_bboxes, merged_scores = merge_aug_bboxes(
- aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
- det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
- rcnn_test_cfg.score_thr,
- rcnn_test_cfg.nms,
- rcnn_test_cfg.max_per_img)
-
- bbox_result = bbox2result(det_bboxes, det_labels,
- self.bbox_head[-1].num_classes)
-
- if self.with_mask:
- if det_bboxes.shape[0] == 0:
- segm_result = [[[]
- for _ in range(self.mask_head[-1].num_classes)]
- ]
- else:
- aug_masks = []
- aug_img_metas = []
- for x, img_meta, semantic in zip(img_feats, img_metas,
- semantic_feats):
- img_shape = img_meta[0]['img_shape']
- scale_factor = img_meta[0]['scale_factor']
- flip = img_meta[0]['flip']
- flip_direction = img_meta[0]['flip_direction']
- _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
- scale_factor, flip, flip_direction)
- mask_rois = bbox2roi([_bboxes])
- mask_feats = self.mask_roi_extractor[-1](
- x[:len(self.mask_roi_extractor[-1].featmap_strides)],
- mask_rois)
- if self.with_semantic:
- semantic_feat = semantic
- mask_semantic_feat = self.semantic_roi_extractor(
- [semantic_feat], mask_rois)
- if mask_semantic_feat.shape[-2:] != mask_feats.shape[
- -2:]:
- mask_semantic_feat = F.adaptive_avg_pool2d(
- mask_semantic_feat, mask_feats.shape[-2:])
- mask_feats += mask_semantic_feat
- last_feat = None
- for i in range(self.num_stages):
- mask_head = self.mask_head[i]
- if self.mask_info_flow:
- mask_pred, last_feat = mask_head(
- mask_feats, last_feat)
- else:
- mask_pred = mask_head(mask_feats)
- aug_masks.append(mask_pred.sigmoid().cpu().numpy())
- aug_img_metas.append(img_meta)
- merged_masks = merge_aug_masks(aug_masks, aug_img_metas,
- self.test_cfg)
-
- ori_shape = img_metas[0][0]['ori_shape']
- segm_result = self.mask_head[-1].get_seg_masks(
- merged_masks,
- det_bboxes,
- det_labels,
- rcnn_test_cfg,
- ori_shape,
- scale_factor=1.0,
- rescale=False)
- return [(bbox_result, segm_result)]
- else:
- return [bbox_result]
diff --git a/spaces/Rohit001/emotion_detection/templates/index.html b/spaces/Rohit001/emotion_detection/templates/index.html
deleted file mode 100644
index 4d91b0a409bf8c46d5d73b2e8efca41b192357ef..0000000000000000000000000000000000000000
--- a/spaces/Rohit001/emotion_detection/templates/index.html
+++ /dev/null
@@ -1,29 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-
-<head>
- <meta charset="UTF-8">
- <meta http-equiv="X-UA-Compatible" content="IE=edge">
- <meta name="viewport" content="width=device-width, initial-scale=1.0">
- <title>Document
-
-
-
-
-
-
-
-
-
-
-
-
- emotion:
-
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-