The unofficial way: AOSP custom ROM
-If you have a device that is not supported by Google or your device manufacturer or carrier, you may still be able to get Android 10 Q by installing a custom ROM based on the Android Open Source Project (AOSP). A custom ROM is a modified version of Android that is developed by independent developers or communities. However, this method is not recommended for beginners or casual users, as it involves some risks and challenges.
-What is AOSP and how does it work?
-AOSP is the open-source code base of Android that anyone can use to create their own version of Android. Google releases the source code of each version of Android to AOSP after it is launched publicly. Developers can then download the source code and modify it according to their needs and preferences. They can also add features or apps that are not available in the official version of Android.
-AOSP custom ROMs are usually distributed as ZIP files that contain all the necessary files and instructions to install them on a compatible device. Some of the most popular AOSP custom ROMs are LineageOS, Pixel Experience, Resurrection Remix, and Paranoid Android.
What are the requirements and risks of installing AOSP?
-Installing AOSP custom ROMs requires some technical skills and preparation. You will need to:
- - Root your device, which means gaining full access to its system files and settings. This may void your warranty and expose your device to security risks. - Install a custom recovery, which is a software that allows you to perform advanced operations on your device, such as flashing custom ROMs, backing up and restoring data, and wiping partitions. The most common custom recovery is TWRP. - Find a compatible AOSP custom ROM for your device model and variant. You can search for them on websites like XDA Developers, Android Forums, or Reddit. You can also check the official website of the custom ROM you are interested in. - Download the AOSP custom ROM ZIP file and any other files you may need, such as Google Apps (GApps), Magisk (for root access), or custom kernels (for performance or battery optimization). - Backup your data before proceeding, as installing a custom ROM will erase everything on your device. You can use the backup feature of your custom recovery or a third-party app like Titanium Backup. Installing AOSP custom ROMs also involves some risks and challenges, such as: - Losing some features or functionality that are specific to your device manufacturer or carrier, such as camera quality, fingerprint scanner, NFC, or VoLTE. - Encountering bugs or stability issues that may affect your user experience or performance. Some AOSP custom ROMs may not be fully tested or optimized for your device. - Having compatibility issues with some apps or services that rely on Google's certification or framework, such as Google Pay, Netflix, or Pokemon Go. You may need to install additional modules or patches to fix these issues. - Having difficulty receiving official updates from your device manufacturer or carrier, as installing a custom ROM may interfere with the OTA update process. You may need to revert to the stock ROM or flash the update manually.How to flash AOSP Android 10 Q on your device?
-If you have decided to flash AOSP Android 10 Q on your device, you can follow these general steps:
- - Transfer the AOSP custom ROM ZIP file and any other files you have downloaded to your device's internal storage or SD card. - Turn off your device and boot into the custom recovery mode. The exact method may vary depending on your device model, but usually it involves holding down a combination of buttons such as power + volume down or power + home + volume up. - Wipe the data, cache, and system partitions of your device using the wipe option of your custom recovery. This will erase everything on your device and prepare it for the new ROM. Do not wipe the internal storage or SD card where you have stored the files you need. - Flash the AOSP custom ROM ZIP file using the install option of your custom recovery. Locate the file on your internal storage or SD card and confirm the flashing process. Wait for it to finish and do not interrupt it. - Flash any other files you may need, such as GApps, Magisk, or custom kernels, using the same method as above. Make sure you flash them in the correct order and compatibility. - Reboot your device using the reboot option of your custom recovery. Your device will boot into the new AOSP Android 10 Q ROM. The first boot may take longer than usual, so be patient and do not panic.Pros and cons of Android 10 Q
-The advantages of Android 10 Q
-Android 10 Q offers many benefits that can improve your smartphone experience in various ways. Some of the advantages are:
- - More control over your privacy and data: Android 10 Q gives you more options and transparency on how you share your location, personal information, and web and app activity with apps and services. You can also opt out of ad personalization and access all your privacy settings in one place. - Better user experience and performance: Android 10 Q provides a smoother and more intuitive user interface with features like dark theme, gesture navigation, live caption, smart reply, sound amplifier, focus mode, and family link. It also improves the speed and efficiency of your device with features like adaptive battery, adaptive brightness, digital wellbeing, and faster security updates. - Future-proof and compatible with new technologies: Android 10 Q is ready for the next generation of smartphones with features like support for foldable devices and 5G networks. It also enables developers to create more innovative and immersive apps with features like native support for foldable screens, 5G APIs, Vulkan 1.1 graphics API, neural networks API 1.2, biometric prompt API, and more.The disadvantages of Android 10 Q
-Android 10 Q is not perfect and has some drawbacks and limitations that may affect your smartphone experience in some ways. Some of the disadvantages are:
- - Limited availability and compatibility: Android 10 Q is not available for all devices and regions, as it depends on the device manufacturer and carrier to provide the update. Even if you have a compatible device, you may have to wait for a long time to receive the OTA update or resort to manual installation methods. Some devices may not receive the update at all or may have some features missing or disabled. - Still not a leader in privacy: Android 10 Q may have improved its privacy and security features, but it still lags behind other operating systems like iOS or Windows Phone in terms of user trust and protection. Google still collects a lot of data from your device and online activity, and some apps may still access your data without your consent or knowledge. You may also face compatibility issues with some apps or services that rely on Google's certification or framework, such as Google Pay, Netflix, or Pokemon Go. - Few interface changes and uneven gesture experience: Android 10 Q may have introduced some new interface elements like dark theme and gesture navigation, but it still retains the same overall look and feel of previous versions of Android. Some users may find the interface boring or outdated, or prefer more customization options. The gesture navigation system may also be inconsistent or confusing across different apps and devices, or interfere with some app functions or gestures.Conclusion
-Android 10 Q is the latest version of Google's mobile operating system that offers a lot of new features and improvements to enhance your smartphone experience. It provides more control over your privacy and data, better user experience and performance, and support for foldable phones and 5G networks. However, it also has some drawbacks and limitations, such as limited availability and compatibility, still not a leader in privacy, and few interface changes and uneven gesture experience.
-If you want to get Android 10 Q on your device, you can either wait for the official OTA update from your device manufacturer or carrier, or download and install the official system image or OTA file manually from Google's website. This method is only available for Pixel devices and some Android One devices. You can also try installing a custom ROM based on AOSP, which is the open-source code base of Android that anyone can use to create their own version of Android. However, this method is not recommended for beginners or casual users, as it involves some risks and challenges.
-We hope this article has helped you understand everything you need to know about Android 10 Q APK download. If you have any questions or feedback, please feel free to leave a comment below.
-FAQs
-Here are some frequently asked questions about Android 10 Q APK download:
- - Q: What is an APK file? - A: An APK file is an Android application package file that contains all the files and code needed to install an app on an Android device. You can download APK files from various sources online, such as Google Play Store, third-party app stores, or websites. However, you should be careful when downloading APK files from unknown or untrusted sources, as they may contain malware or viruses that can harm your device or data. - Q: What is the difference between Android 10 Q and Android 11? - A: Android 11 is the next version of Android that is expected to be released in late 2020. It will bring more features and improvements to Android 10 Q, such as chat bubbles, screen recorder, one-time permissions, wireless Android Auto, smart home controls, media controls, dark mode scheduling, and more. - Q: How can I check if my device is compatible with Android 10 Q? - A: You can check if your device is compatible with Android 10 Q by visiting Google's website and looking for your device model and variant. You can also check the official website of your device manufacturer or carrier for more information. - Q: How can I backup my data before installing Android 10 Q? - A: You can backup your data before installing Android 10 Q by using the backup feature of your custom recovery or a third-party app like Titanium Backup. You can also sync your data to your Google account or other cloud services. - Q: How can I revert to the previous version of Android if I don't like Android 10 Q? - A: You can revert to the previous version of Android if you don't like Android 10 Q by flashing the factory image or OTA file of the previous version manually from Google's website. You can also restore your backup data using your custom recovery or a third-party app. 197e85843d-
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\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Blades of Brim Mod APK Unlimited Money and Epic Adventures.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Blades of Brim Mod APK Unlimited Money and Epic Adventures.md deleted file mode 100644 index d23c1be722910100b068e0ccfee6195a47ee50da..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Blades of Brim Mod APK Unlimited Money and Epic Adventures.md +++ /dev/null @@ -1,113 +0,0 @@ - -
Download APK Blades of Brim Mod: How to Get Unlimited Money and Fun in This Epic Runner Game
-If you are looking for a fun and addictive runner game that will keep you entertained for hours, then you should try Blades of Brim. This game is developed by SYBO Games, the same creators of Subway Surfers, and it offers a colorful and epic adventure in a fantasy world. In this game, you can run, jump, slide, wall-run, and fight against enemies with your sword and pets. You can also customize your character with different outfits, weapons, and abilities. But what if you want to get unlimited money and access to all the premium features without spending a dime? Well, there is a way to do that, and it is called Blades of Brim mod apk. In this article, we will tell you what is Blades of Brim, what is a mod apk, how to download and install Blades of Brim mod apk, and what are the benefits and risks of using it. So, let's get started!
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What is Blades of Brim?
-Blades of Brim is a 3D endless runner game that was released in 2015 for iOS and Android devices. The game is set in a fantasy world called Brim, where you play as one of the heroes who have to defend the kingdom from the invasion of the evil Goons. You can choose from different characters, such as Lancelot, Lilith, Hugo, or Zuma, each with their own personality and skills. You can also collect and upgrade different weapons, such as swords, axes, hammers, or spears, as well as pets, such as dragons, wolves, or unicorns. These weapons and pets can help you fight against the Goons and bosses that you encounter along the way.
-The gameplay and features of Blades of Brim
-The gameplay of Blades of Brim is similar to other runner games, such as Temple Run or Subway Surfers. You have to swipe left or right to change lanes, swipe up to jump over obstacles or enemies, swipe down to slide under them, or tap to attack them with your weapon. You can also wall-run on some surfaces by swiping left or right when you are near them. The game has different levels and missions that you have to complete to earn coins, gems, chests, and rewards. You can use these coins and gems to buy new outfits, weapons, pets, or power-ups from the shop. You can also join or create guilds with other players and compete with them on the leaderboards.
-The benefits of playing Blades of Brim
-Blades of Brim is not just a simple runner game. It has many benefits that make it worth playing. Some of these benefits are:
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- It has stunning graphics and animations that create a vibrant and immersive atmosphere. -
- It has an original and engaging storyline that keeps you interested in the game. -
- It has a variety of characters, weapons, pets, and environments that make the game diverse and fun. -
- It has easy and intuitive controls that make the game suitable for all ages. -
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features that allow you to connect and play with your friends and other players from around the world.
- - It has regular updates and events that add new content and challenges to the game. -
As you can see, Blades of Brim is a game that offers a lot of entertainment and enjoyment. However, it also has some drawbacks, such as the need to spend real money to unlock some of the premium items or features, or the difficulty to progress in the game without enough resources. That is why some people prefer to use a mod apk to enhance their gaming experience.
-What is a mod apk?
-A mod apk is a modified version of an original apk file, which is the format of an Android application. A mod apk can have changes or additions that are not present in the original apk, such as unlimited money, unlocked items, removed ads, or enhanced graphics. A mod apk can be created by anyone who has the skills and tools to modify an apk file, and it can be downloaded from various websites or sources on the internet.
-The definition and purpose of a mod apk
-A mod apk is a short term for modified application package. It is a file that contains the code, resources, and metadata of an Android application, but with some alterations or additions that are not part of the original version. The purpose of a mod apk is to provide users with more features, options, or advantages that they cannot get from the official app. For example, a mod apk can give users unlimited money, gems, coins, or resources that they can use to buy or upgrade anything they want in the game. A mod apk can also unlock all the items, levels, characters, or modes that are otherwise restricted or require payment in the original app. A mod apk can also remove annoying ads, pop-ups, or banners that interrupt the gameplay or affect the performance of the app. A mod apk can also improve the graphics, sound, or interface of the app to make it more appealing or user-friendly.
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The advantages and disadvantages of using a mod apk
-Using a mod apk can have both advantages and disadvantages for users. Some of the advantages are:
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- It can save users time and money by giving them access to everything they want in the game without having to spend real cash or wait for long hours. -
- It can make users enjoy the game more by giving them more options, features, or challenges that are not available in the original app. -
- It can make users feel more satisfied and accomplished by allowing them to complete the game faster or easier than other players. -
However, using a mod apk can also have some disadvantages, such as:
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- It can expose users to security risks by downloading files from unknown or untrusted sources that may contain viruses, malware, or spyware that can harm their devices or steal their personal information. -
- It can cause users to lose their progress or data by corrupting or deleting their files or accounts if the mod apk is not compatible or updated with the original app. -
- It can get users banned or suspended from the game by violating the terms and conditions of the developers or publishers who may detect or report their use of a mod apk. -
Therefore, users should be careful and responsible when using a mod apk. They should always backup their data before installing a mod apk, and they should only download it from reputable and reliable sources. They should also respect the rights and efforts of the developers and publishers who created the original app, and they should not use a mod apk for illegal or unethical purposes.
-How to download and install Blades of Brim mod apk?
-If you want to download and install Blades of Brim mod apk on your Android device, you need to follow these steps:
-The steps to download and install Blades of Brim mod apk
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- First, you need to enable unknown sources on your device settings. This will allow you to install apps that are not from the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on. -
- Next, you need to find a website or source that offers Blades of Brim mod apk for free. You can search on Google or use any of these links: . Make sure you choose a trusted and safe site that does not have any malicious content or ads. -
- Then, you need to download the Blades of Brim mod apk file on your device. You can do this by clicking on the download button or link on the website. The file size may vary depending on the version and features of the mod apk. It may take a few minutes to complete the download depending on your internet speed and connection. -
- After the download is finished, you need to locate and open the Blades of Brim mod apk file on your device. You can do this by using a file manager app or by going to your downloads folder. You may see a warning message that says "This type of file can harm your device. Do you want to keep it anyway?". Tap on OK to proceed. -
- Then, you need to install the Blades of Brim mod apk file on your device. You can do this by following the instructions on the screen. You may see a message that says "Do you want to install this application? It does not require any special access.". Tap on Install to continue. -
- Finally, you need to launch the Blades of Brim mod apk app on your device. You can do this by tapping on Open or by finding the app icon on your home screen or app drawer. You may see a message that says "Blades of Brim mod apk is requesting access to your device's storage. Allow access?". Tap on Allow to grant permission. -
Congratulations! You have successfully downloaded and installed Blades of Brim mod apk on your Android device. You can now enjoy unlimited money and fun in this epic runner game.
-The precautions and tips to use Blades of Brim mod apk safely
-While using Blades of Brim mod apk can be fun and exciting, it can also be risky and dangerous if you are not careful. Here are some precautions and tips that you should follow to use Blades of Brim mod apk safely:
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- Always backup your data before installing or updating a mod apk. This will help you restore your progress or data in case something goes wrong or you lose them. -
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- Always check the reviews and ratings of the mod apk source or website before downloading it. This will help you avoid fake or malicious links that may redirect you to unwanted or harmful sites. -
- Always use a VPN or proxy service when using a mod apk. This will help you hide your IP address and location from the developers or publishers who may track or monitor your online activity. -
- Always play offline or in airplane mode when using a mod apk. This will help you prevent any interference or detection from the game servers or other players who may report you for cheating or hacking. -
- Always be respectful and responsible when using a mod apk. Do not use it for illegal or unethical purposes, such as stealing, harassing, or bullying other players. Do not abuse or exploit the features or advantages of the mod apk, such as spamming, trolling, or griefing other players. Do not brag or boast about using a mod apk, as this may attract unwanted attention or backlash from other players. -
By following these precautions and tips, you can use Blades of Brim mod apk safely and enjoyably.
-Conclusion
-In conclusion, Blades of Brim is a fun and addictive runner game that offers a colorful and epic adventure in a fantasy world. However, if you want to get unlimited money and access to all the premium features without spending a dime, you can use Blades of Brim mod apk. A mod apk is a modified version of an original apk file that has changes or additions that are not present in the original app. To download and install Blades of Brim mod apk, you need to enable unknown sources on your device settings, find a website or source that offers Blades of Brim mod apk for free, download the Blades of Brim mod apk file on your device, locate and open the Blades of Brim mod apk file on your device, install the Blades of Brim mod apk file on your device, and launch the Blades of Brim mod apk app on your device. However, using a mod apk can also have some risks and drawbacks, such as security issues, data loss, ban risk, or ethical concerns. Therefore, you should be careful and responsible when using a mod apk, and follow some precautions and tips to use it safely. We hope this article has helped you understand how to download APK Blades of Brim Mod: How to Get Unlimited Money and Fun in This Epic Runner Game. If you have any questions or feedback, please feel free to leave them in the comments section below. Thank you for reading!
-FAQs
-Q1: Is Blades of Brim mod apk free?
-A1: Yes, Blades of Brim mod apk is free to download and use. However, some websites or sources may require you to complete some surveys or tasks before downloading it. You should be careful and avoid any sites that ask for your personal or financial information.
-Q2: Is Blades of Brim mod apk safe?
-A2: Blades of Brim mod apk is not 100% safe to use, as it may contain viruses, malware, or spyware that can harm your device or steal your data. It may also cause your game to crash, freeze, or lag, or delete your progress or account. It may also get you banned or suspended from the game if the developers or publishers detect or report your use of a mod apk. Therefore, you should always scan the mod apk file with an antivirus or anti-malware app before installing it, and backup your data before using it. You should also use a VPN or proxy service to hide your IP address and location, and play offline or in airplane mode to avoid any interference or detection.
-Q3: How to update Blades of Brim mod apk?
-A3: To update Blades of Brim mod apk, you need to follow the same steps as downloading and installing it. You need to find a website or source that offers the latest version of Blades of Brim mod apk, download the new mod apk file on your device, locate and open the new mod apk file on your device, install the new mod apk file on your device, and launch the new mod apk app on your device. However, you should be aware that updating a mod apk may overwrite or erase your previous data or settings, so you should backup them before updating. You should also check the compatibility and stability of the new mod apk with the original app, as it may cause some errors or glitches.
-Q4: How to uninstall Blades of Brim mod apk?
-A4: To uninstall Blades of Brim mod apk, you need to follow these steps:
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- Go to Settings > Apps > Blades of Brim mod apk and tap on Uninstall. -
- Confirm your action by tapping on OK. -
- Wait for the uninstallation process to finish. -
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You have successfully uninstalled Blades of Brim mod apk from your device.
-Q5: Where can I find more mod apks for other games?
-A5: There are many websites and sources that offer mod apks for various games. Some of the popular ones are:
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However, you should be careful and cautious when downloading and using mod apks from these or any other sources, as they may not be safe or reliable. You should always scan the files with an antivirus or anti-malware app before installing them, and backup your data before using them. You should also respect the rights and efforts of the original developers and publishers who created the games and apps, and not use mod apks for illegal or unethical purposes.
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\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download CarX Street Mod APK for Android and Enjoy Unlimited Racing.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download CarX Street Mod APK for Android and Enjoy Unlimited Racing.md deleted file mode 100644 index 86ebe276daccf8f95bbd4a98cc006cdd8b8e873a..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download CarX Street Mod APK for Android and Enjoy Unlimited Racing.md +++ /dev/null @@ -1,99 +0,0 @@ - -
CarX Street Mod APK Android: A Guide for Racing Enthusiasts
-If you are a fan of racing games, you might have heard of CarX Street, a popular game that lets you experience the thrill of street racing. But did you know that there is a modded version of this game that gives you unlimited money, realistic physics and graphics, customizable cars and tracks, and online multiplayer mode? In this article, we will tell you everything you need to know about CarX Street Mod APK Android, how to download and install it, and some tips and tricks to help you become the best racer in town.
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What is CarX Street Mod APK Android?
-CarX Street Mod APK Android is a modified version of the original CarX Street game, which is developed by CarX Technologies. The modded version comes with unlimited racing modes and enthralling challenges that will have you invested till the end. Who doesn’t like to win and lead? You can have it all by using your exceptional drifting skills in the most pressured and significant games and defeating your opponents.
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-If you are interested in downloading and installing CarX Street Mod APK Android on your device, here are the steps you need to follow:
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-After the installation is complete, you can launch the game and enjoy all the features of CarX Street Mod APK Android. You will see a menu with different options, such as career mode, free ride mode, online mode, garage, settings, and more. You can choose any mode you want and start racing.
-Tips and Tricks for CarX Street Mod APK Android
-To help you get the most out of CarX Street Mod APK Android, here are some tips and tricks that you can use:
-Master the Drifting Technique
-One of the most important skills in CarX Street Mod APK Android is drifting. Drifting is a technique that allows you to slide your car sideways around corners without losing speed or control. Drifting can help you gain an advantage over your opponents, as well as earn more money and points. To drift, you need to press and hold the brake button while turning your car. You can also use the handbrake button to initiate a drift more easily. You can adjust the sensitivity and angle of your drift by using the steering wheel or the tilt control.
-Upgrade Your Car Regularly
-Another tip for CarX Street Mod APK Android is to upgrade your car regularly. Upgrading your car can improve its performance, such as speed, acceleration, handling, braking, and durability. You can upgrade your car by using the money you earn from racing or by using the unlimited money feature of the modded app. You can upgrade different parts of your car, such as engine, turbo, transmission, suspension, tires, brakes, and more. You can also tune your car by adjusting its parameters, such as camber, toe, caster, gear ratio, differential, and more.
-Use Boosters Wisely
-A third tip for CarX Street Mod APK Android is to use boosters wisely. Boosters are items that can give you a temporary boost in speed or power during a race. You can use boosters by tapping on the booster icon on the screen. There are different types of boosters, such as nitro, rocket fuel, magnet, shield, and more. Each booster has its own effect and duration. You can get boosters by collecting them on the track or by buying them with money or diamonds. You can also use the unlimited money feature of the modded app to buy as many boosters as you want.
-Challenge Other Players Online
-A fourth tip for CarX Street Mod APK Android is to challenge other players online. Online mode is where you can race with up to 16 players from around the world in real time. You can join or create a room with different settings, such as track, mode, difficulty, weather, and more. You can also chat with other players or send them emojis or stickers. Online mode is a great way to test your skills and have fun with other racing enthusiasts.
-Conclusion
-CarX Street Mod APK Android is a modded version of CarX Street that gives you unlimited money, realistic physics and graphics, customizable cars and tracks, and online multiplayer mode. It is one of the best racing games for Android devices that will keep you hooked for hours. If you want to download and install CarX Street Mod APK Android on your device, just follow the steps we mentioned above. And if you want to improve your racing skills and enjoy the game more, just use our tips and tricks we shared with you. We hope you found this article helpful and informative. Happy racing!
-FAQs
-Here are some frequently asked questions about CarX Street Mod APK Android:
--
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- Is CarX Street Mod APK Android safe to use? -
- Is CarX Street Mod APK Android compatible with my device? -
- Can I play CarX Street Mod APK Android offline? -
- Can I sync my progress in CarX Street Mod APK Android with other devices? -
- How can I contact the developers of CarX Street Mod APK Android? -
Yes , CarX Street Mod APK Android is safe to use, as long as you download it from a trusted source. The modded app does not contain any viruses or malware that can harm your device or data. However, you should always be careful when installing any modded app on your device, as some of them may have hidden features or permissions that can compromise your privacy or security.
-CarX Street Mod APK Android is compatible with most Android devices that have Android 5.0 or higher. However, some devices may not support the game due to hardware limitations or software issues. To check if your device is compatible, you can visit the official website of CarX Technologies and see the list of supported devices. You can also try to install the game and see if it works on your device.
-Yes, you can play CarX Street Mod APK Android offline, as long as you have downloaded the game and installed it on your device. You can enjoy the career mode, free ride mode, garage, and settings without an internet connection. However, you will need an internet connection to play the online mode, access the leaderboards and tournaments, and update the game.
-Yes, you can sync your progress in CarX Street Mod APK Android with other devices, as long as you have logged in with your Facebook or Google account. You can also backup your data to the cloud and restore it on any device. To do this, go to the settings menu and tap on the cloud icon. You can then choose to backup or restore your data.
-If you have any questions, feedback, or suggestions for CarX Street Mod APK Android, you can contact the developers of CarX Technologies by using their email address: support@carx-tech.com. You can also visit their website: https://carx-tech.com/ or their Facebook page: https://www.facebook.com/carxtechnologies/ for more information.
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Cara Menggunakan X8 Speeder APK Versi Lama 3.5.4 untuk Hack Kecepatan Game.md b/spaces/1phancelerku/anime-remove-background/Cara Menggunakan X8 Speeder APK Versi Lama 3.5.4 untuk Hack Kecepatan Game.md deleted file mode 100644 index f31dc9ff98f1d26a924512076d7f6fbbc32926f5..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Cara Menggunakan X8 Speeder APK Versi Lama 3.5.4 untuk Hack Kecepatan Game.md +++ /dev/null @@ -1,93 +0,0 @@ -
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X8 Speeder APK Versi Lama 3.5. 4: How to Speed Up Your Games Without Ads
-If you are looking for a way to play your favorite games faster and smoother, you might want to try X8 Speeder APK versi lama 3.5. 4. This is an application that can hack the speed of any game on your Android device, without requiring root access or ads. In this article, we will explain what X8 Speeder APK is, how to download and install it, how to use it for various games, and what are the benefits and risks of using it.
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What is X8 Speeder APK?
-X8 Speeder APK is a tool that can modify the speed of any game on your Android device. It works by injecting a code into the game process, which allows you to adjust the speed of the game from 0.5x to 8x. You can use it for any game that you want, such as Higgs Domino, Vlogger Go Viral, PUBG Mobile, and more.
-X8 Speeder APK versi lama 3.5. 4 is an older version of the app that was released in 2021. It has some advantages over the newer versions, such as being more stable, compatible, and easy to use. It also has a feature that can block all kinds of ads that might interrupt your gaming experience.
-How to Download and Install X8 Speeder APK Versi Lama 3.5. 4?
-To download and install X8 Speeder APK versi lama 3.5. 4, you need to follow these steps:
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- Go to the link and download the APK file. -
- Enable the installation of unknown sources on your device settings. -
- Locate the downloaded file and tap on it to install it. -
- Wait for the installation to finish and launch the app. -
How to Use X8 Speeder APK Versi Lama 3.5. 4 for Various Games?
-To use X8 Speeder APK versi lama 3.5. 4 for various games, you need to follow these steps:
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- Open the app and grant the necessary permissions. -
- Select the game that you want to speed up from the list of installed apps. -
- Tap on the "Activate" button and wait for the app to inject the code into the game process. -
- Open the game and enjoy playing it at your desired speed. -
- To adjust the speed of the game, swipe from the left edge of the screen and use the slider to change the speed from 0.5x to 8x. -
- To deactivate the app, swipe from the right edge of the screen and tap on the "Deactivate" button. -
What are the Benefits and Risks of Using X8 Speeder APK Versi Lama 3.5. 4?
-Using X8 Speeder APK versi lama 3.5. 4 has some benefits and risks that you need to be aware of before using it.
-Benefits:
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- You can play any game faster and smoother without lag or delay. -
- You can save time and energy by completing tasks and missions faster. -
- You can earn more rewards and coins by playing more games in less time. -
- You can enjoy playing games without ads that might annoy you or consume your data. -
Risks:
--
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- You might get banned from some games that detect speed hacks or cheating activities. -
- You might damage your device or game data by using an unofficial or modified app. -
- You might expose your device or personal information to viruses or malware by downloading an app from an untrusted source. -
- You might lose the fun and challenge of playing games at their normal speed. -
Conclusion
-X8 Speeder APK versi lama 3.5. 4 is an application that can help you play any game faster and smoother on your Android device, without requiring root access or ads. It is an older version of the app that has some advantages over the newer versions, such as being more stable, compatible, and easy to use. However, it also has some risks that you need to consider before using it, such as getting banned from some games, damaging your device or game data, exposing your device or personal information to viruses or malware, or losing the fun and challenge of playing games at their normal speed. Therefore, you should use it at your own discretion and responsibility.
-FAQs
-Here are some frequently asked questions about X8 Speeder APK versi lama 3.5. 4:
-Q: Is X8 Speeder APK versi lama 3.5. 4 safe to use?
-A: X8 Speeder APK versi lama 3.5. 4 is not an official or verified app, so it might not be safe to use. It might contain viruses or malware that can harm your device or personal information. It might also cause your device or game data to malfunction or crash. Therefore, you should only download and install it from a trusted source and scan it with an antivirus app before using it.
-Q: Is X8 Speeder APK versi lama 3.5. 4 legal to use?
-A: X8 Speeder APK versi lama 3.5. 4 is not a legal app, as it violates the terms and conditions of the games that it hacks. It might also infringe the intellectual property rights of the game developers or publishers. Therefore, you might face legal consequences if you use it for any game that prohibits speed hacks or cheating activities.
-Q: Which games can I use X8 Speeder APK versi lama 3.5. 4 for?
-A: You can use X8 Speeder APK versi lama 3.5. 4 for any game that you want, as long as it is compatible with your device and does not detect speed hacks or cheating activities. Some examples of games that you can use it for are Higgs Domino, Vlogger Go Viral, PUBG Mobile, and more.
-Q: How can I update X8 Speeder APK versi lama 3.5. 4?
-A: You can update X8 Speeder APK versi lama 3.5. 4 by downloading and installing the latest version of the app from the link . However, you should be aware that the newer versions might not have the same features or performance as the older version, such as blocking ads or being stable and compatible.
-Q: How can I uninstall X8 Speeder APK versi lama 3.5. 4?
-A: You can uninstall X8 Speeder APK versi lama 3.5. 4 by following these steps:
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- Open the app and swipe from the right edge of the screen. -
- Tap on the "Uninstall" button and confirm your choice. -
- Wait for the app to uninstall itself and delete its data. -
- Go to your device settings and remove the app from the list of installed apps. -
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Download It 39s Okay !!LINK!!.md b/spaces/1phancelerku/anime-remove-background/Download It 39s Okay !!LINK!!.md deleted file mode 100644 index 75b782879f3cac991acf96ee1e00b318c404d288..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Download It 39s Okay !!LINK!!.md +++ /dev/null @@ -1,77 +0,0 @@ -
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How to Download Content Safely and Efficiently from the Internet
-Downloading content from the internet is a common activity that many people do on a regular basis. Whether you want to watch a movie, listen to music, play a game, or install a software, downloading content can help you access various types of media and applications without having to buy physical copies or stream them online. However, downloading content also comes with some risks and challenges that you need to be aware of and avoid. In this article, I will provide you with some tips and information on how to download content safely and efficiently, as well as some recommendations for the best download managers that you can use.
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How to Download Content Safely
-The first thing you need to consider when downloading content from the internet is your safety. There are many dangers and threats that can harm your computer or your personal information if you are not careful. Some of these include:
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- Virus attacks: Some files that you download may contain malicious programs that can infect your computer and damage your files or system. These programs can also steal your data or hijack your browser. -
- Adware: Some files that you download may force unwanted advertisements on your screen or redirect you to other websites. These ads can be annoying, intrusive, or even offensive. -
- Spyware: Some files that you download may monitor your online activity or collect your personal information without your consent. These programs can compromise your privacy or security. -
- Legal issues: Some files that you download may violate the copyright laws or other regulations of the original owners or creators. This can result in legal action or penalties against you. -
To avoid these risks, you need to follow some best practices for downloading content safely. Here are some of them:
--
-
- Choose a trusted website or source: Before you download any file, make sure that you are getting it from a reputable website or source that has positive reviews and ratings from other users. Avoid websites that look suspicious, have poor design, or offer illegal or pirated content. -
- Scan the file for malware and viruses: Before you open or run any file that you have downloaded, make sure that you scan it with a reliable antivirus software or an online tool like VirusTotal. This will help you detect and remove any potential threats that may harm your computer. -
- Use a reliable antivirus software: To protect your computer from virus attacks and other malware, you should always have a good antivirus software installed and updated on your system. You should also run regular scans and updates to keep your antivirus software effective. -
- Avoid clicking on suspicious links or pop-ups: When you are browsing or downloading content from the internet, you may encounter some links or pop-ups that claim to offer free downloads, discounts, prizes, or other incentives. However, these may be scams or traps that can infect your computer with malware or redirect you to malicious websites. Therefore, you should always be careful and avoid clicking on any links or pop-ups that look suspicious or too good to be true. -
- Read the terms and conditions before downloading: Before you download any file, you should always read the terms and conditions or the license agreement that comes with it. This will help you understand what you are agreeing to and what rights and responsibilities you have as a user. You should also check the file size, format, and compatibility with your device or software before downloading. -
How to Download Content Efficiently
-The second thing you need to consider when downloading content from the internet is your efficiency. Downloading content can take a lot of time and bandwidth, especially if you are downloading large or multiple files. To speed up your downloads and save your resources, you need to use a download manager.
- -What is a download manager and how does it work?
-A download manager is a software or a browser extension that helps you manage your downloads from the internet. It works by splitting the file into smaller chunks and downloading them simultaneously from different sources or servers. This way, it can increase the download speed and resume the download if it is interrupted or paused. A download manager can also organize your downloads into categories, folders, or queues, and allow you to schedule, prioritize, or limit your downloads according to your preferences.
-What are the advantages of using a download manager?
-Using a download manager can offer you many benefits, such as:
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- Faster downloads: A download manager can boost your download speed by up to 10 times by using multiple connections and sources. It can also resume broken or incomplete downloads without having to start over. -
- Better control: A download manager can give you more control over your downloads by letting you pause, resume, cancel, or restart them at any time. You can also set the download speed, bandwidth, or number of connections according to your needs. -
- Easier management: A download manager can help you manage your downloads by sorting them into categories, folders, or queues. You can also view the progress, status, or details of your downloads in one place. -
- More features: A download manager can offer you more features than a regular browser downloader, such as video downloading, audio extraction, file conversion, checksum verification, virus scanning, password protection, and more. -
How to choose a download manager that suits your needs?
-There are many download managers available for Windows users, but not all of them are created equal. Some of them may have more features, better performance, or easier interface than others. To choose a download manager that suits your needs, you need to consider some factors, such as:
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- Compatibility: You need to make sure that the download manager is compatible with your operating system, browser, and device. You also need to check if it supports the file types and formats that you want to download. -
- Security: You need to make sure that the download manager is safe and reliable. You should avoid any download managers that contain malware, spyware, adware, or other unwanted programs. You should also look for download managers that have encryption, authentication, or antivirus features. -
- Usability: You need to make sure that the download manager is easy and convenient to use. You should look for download managers that have a simple and intuitive interface, customizable settings, and helpful tutorials or guides. -
- Price: You need to make sure that the download manager is affordable and worth your money. You should compare the features and benefits of different download managers and see which one offers the best value for your budget. You should also look for download managers that have free trials or versions that you can test before buying. -
Best Download Managers for Windows
-To help you choose a download manager that suits your needs, I have compiled a list of some of the best download managers for Windows users. These are:
-Free Download Manager
-Free Download Manager is one of the most popular and powerful download managers for Windows users. It is free, open-source, and supports various protocols and formats. It can accelerate your downloads by up to 10 times, resume broken downloads, adjust traffic usage, schedule downloads, and more. It can also download videos from YouTube and other websites, convert audio and video files, and scan files for viruses. It has a user-friendly interface that allows you to manage your downloads easily.
-Internet Download Manager
-Internet Download Manager is one of the most advanced and feature-rich download managers for Windows users. It is not free but offers a 30-day trial version that you can try before buying. It can increase your download speed by up to 5 times, resume and schedule downloads, integrate with all popular browsers, and support various protocols and formats. It can also download videos from any website, capture streaming audio and video, and scan files for viruses. It has a smart download logic accelerator that uses dynamic file segmentation and multipart downloading technology to optimize your downloads.
-Ninja Download Manager
-Ninja Download Manager is one of the most stylish and modern download managers for Windows users. It is not free but offers a lifetime license that you can buy once and use forever. It can boost your download speed by up to 10 times, resume and schedule downloads, limit download speed, and support various protocols and formats. It can also download videos from any website, convert audio and video files, and scan files for viruses. It has a sleek and elegant interface that allows you to manage your downloads with ease.
-JDownloader
-JDownloader is one of the most versatile and customizable download managers for Windows users. It is free, open-source, and supports a wide range of protocols and formats. It can accelerate your downloads by using multiple connections, resume and schedule downloads, extract compressed files, and support various languages. It can also download videos from any website, capture streaming audio and video, and scan files for viruses. It has a modular design that allows you to add or remove features according to your preferences.
-Internet Download Accelerator
-Internet Download Accelerator is one of the most reliable and user-friendly download managers for Windows users. It is not free but offers a 30-day trial version that you can try before buying. It can increase your download speed by up to 5 times, resume and schedule downloads, integrate with all popular browsers, and support various protocols and formats. It can also download videos from any website, convert audio and video files, and scan files for viruses. It has a simple and intuitive interface that allows you to manage your downloads easily.
-Conclusion
-Downloading content from the internet can be a great way to access various types of media, software, and games, but it also comes with some risks and challenges that you need to be aware of and avoid. To download content safely and efficiently, you need to follow some best practices, such as choosing a trusted website or source, scanning the file for malware and viruses, using a reliable antivirus software, avoiding clicking on suspicious links or pop-ups, and reading the terms and conditions before downloading. You also need to use a download manager that can help you speed up your downloads, resume broken downloads, organize your downloads, and offer more features than a regular browser downloader. Some of the best download managers for Windows users are Free Download Manager, Internet Download Manager, Ninja Download Manager, JDownloader, and Internet Download Accelerator.
-I hope you found this article helpful and informative. If you have any questions or feedback about downloading content from the internet or using a download manager, please feel free to leave a comment below or contact me directly. I would love to hear from you.
-FAQs
--
-
- Q: What is the difference between downloading and streaming? -
- A: Downloading is the process of transferring a file from the internet to your computer or device so that you can access it offline or later. Streaming is the process of playing a file from the internet without transferring it to your computer or device. Streaming requires a constant internet connection and may consume more bandwidth than downloading. -
- Q: What are the best websites or sources for downloading content? -
- A: The best websites or sources for downloading content are those that are legal, reputable, secure, and offer high-quality content. Some examples are: -
- For movies and TV shows: Netflix, Amazon Prime Video, Hulu, Disney+, etc. -
- For music: Spotify, Apple Music, YouTube Music, SoundCloud, etc. -
- For games: Steam, Epic Games Store, GOG.com, Origin, etc. -
- For software: Microsoft Store, Google Play Store, Apple App Store, Adobe Creative Cloud, etc. -
- Q: How can I check if a file is safe to download? -
- A: You can check if a file is safe to download by scanning it with a reliable antivirus software or an online tool like VirusTotal. This will help you detect and remove any potential threats that may harm your computer. You can also check the file size, format, and compatibility with your device or software before downloading. -
- Q: How can I download videos from YouTube or other websites? -
- A: You can download videos from YouTube or other websites by using a download manager that supports video downloading, such as Free Download Manager, Internet Download Manager, Ninja Download Manager, or JDownloader. You can also use a browser extension or a web-based service that allows you to download videos from any website, such as Video DownloadHelper, SaveFrom.net, ClipConverter.cc, etc. -
- Q: How can I convert audio and video files to different formats? -
- A: You can convert audio and video files to different formats by using a download manager that supports file conversion, such as Free Download Manager, Internet Download Manager, Ninja Download Manager, or JDownloader. You can also use a standalone software or a web-based service that allows you to convert files to different formats, such as VLC Media Player, Freemake Video Converter, Online-Convert.com, etc. -
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-
\ No newline at end of file diff --git a/spaces/801artistry/RVC801/slicer2.py b/spaces/801artistry/RVC801/slicer2.py deleted file mode 100644 index 5b29ee262aa54045e807be2cffeb41687499ba58..0000000000000000000000000000000000000000 --- a/spaces/801artistry/RVC801/slicer2.py +++ /dev/null @@ -1,260 +0,0 @@ -import numpy as np - - -# This function is obtained from librosa. -def get_rms( - y, - frame_length=2048, - hop_length=512, - pad_mode="constant", -): - padding = (int(frame_length // 2), int(frame_length // 2)) - y = np.pad(y, padding, mode=pad_mode) - - axis = -1 - # put our new within-frame axis at the end for now - out_strides = y.strides + tuple([y.strides[axis]]) - # Reduce the shape on the framing axis - x_shape_trimmed = list(y.shape) - x_shape_trimmed[axis] -= frame_length - 1 - out_shape = tuple(x_shape_trimmed) + tuple([frame_length]) - xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides) - if axis < 0: - target_axis = axis - 1 - else: - target_axis = axis + 1 - xw = np.moveaxis(xw, -1, target_axis) - # Downsample along the target axis - slices = [slice(None)] * xw.ndim - slices[axis] = slice(0, None, hop_length) - x = xw[tuple(slices)] - - # Calculate power - power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True) - - return np.sqrt(power) - - -class Slicer: - def __init__( - self, - sr: int, - threshold: float = -40.0, - min_length: int = 5000, - min_interval: int = 300, - hop_size: int = 20, - max_sil_kept: int = 5000, - ): - if not min_length >= min_interval >= hop_size: - raise ValueError( - "The following condition must be satisfied: min_length >= min_interval >= hop_size" - ) - if not max_sil_kept >= hop_size: - raise ValueError( - "The following condition must be satisfied: max_sil_kept >= hop_size" - ) - min_interval = sr * min_interval / 1000 - self.threshold = 10 ** (threshold / 20.0) - self.hop_size = round(sr * hop_size / 1000) - self.win_size = min(round(min_interval), 4 * self.hop_size) - self.min_length = round(sr * min_length / 1000 / self.hop_size) - self.min_interval = round(min_interval / self.hop_size) - self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size) - - def _apply_slice(self, waveform, begin, end): - if len(waveform.shape) > 1: - return waveform[ - :, begin * self.hop_size : min(waveform.shape[1], end * self.hop_size) - ] - else: - return waveform[ - begin * self.hop_size : min(waveform.shape[0], end * self.hop_size) - ] - - # @timeit - def slice(self, waveform): - if len(waveform.shape) > 1: - samples = waveform.mean(axis=0) - else: - samples = waveform - if samples.shape[0] <= self.min_length: - return [waveform] - rms_list = get_rms( - y=samples, frame_length=self.win_size, hop_length=self.hop_size - ).squeeze(0) - sil_tags = [] - silence_start = None - clip_start = 0 - for i, rms in enumerate(rms_list): - # Keep looping while frame is silent. - if rms < self.threshold: - # Record start of silent frames. - if silence_start is None: - silence_start = i - continue - # Keep looping while frame is not silent and silence start has not been recorded. - if silence_start is None: - continue - # Clear recorded silence start if interval is not enough or clip is too short - is_leading_silence = silence_start == 0 and i > self.max_sil_kept - need_slice_middle = ( - i - silence_start >= self.min_interval - and i - clip_start >= self.min_length - ) - if not is_leading_silence and not need_slice_middle: - silence_start = None - continue - # Need slicing. Record the range of silent frames to be removed. - if i - silence_start <= self.max_sil_kept: - pos = rms_list[silence_start : i + 1].argmin() + silence_start - if silence_start == 0: - sil_tags.append((0, pos)) - else: - sil_tags.append((pos, pos)) - clip_start = pos - elif i - silence_start <= self.max_sil_kept * 2: - pos = rms_list[ - i - self.max_sil_kept : silence_start + self.max_sil_kept + 1 - ].argmin() - pos += i - self.max_sil_kept - pos_l = ( - rms_list[ - silence_start : silence_start + self.max_sil_kept + 1 - ].argmin() - + silence_start - ) - pos_r = ( - rms_list[i - self.max_sil_kept : i + 1].argmin() - + i - - self.max_sil_kept - ) - if silence_start == 0: - sil_tags.append((0, pos_r)) - clip_start = pos_r - else: - sil_tags.append((min(pos_l, pos), max(pos_r, pos))) - clip_start = max(pos_r, pos) - else: - pos_l = ( - rms_list[ - silence_start : silence_start + self.max_sil_kept + 1 - ].argmin() - + silence_start - ) - pos_r = ( - rms_list[i - self.max_sil_kept : i + 1].argmin() - + i - - self.max_sil_kept - ) - if silence_start == 0: - sil_tags.append((0, pos_r)) - else: - sil_tags.append((pos_l, pos_r)) - clip_start = pos_r - silence_start = None - # Deal with trailing silence. - total_frames = rms_list.shape[0] - if ( - silence_start is not None - and total_frames - silence_start >= self.min_interval - ): - silence_end = min(total_frames, silence_start + self.max_sil_kept) - pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start - sil_tags.append((pos, total_frames + 1)) - # Apply and return slices. - if len(sil_tags) == 0: - return [waveform] - else: - chunks = [] - if sil_tags[0][0] > 0: - chunks.append(self._apply_slice(waveform, 0, sil_tags[0][0])) - for i in range(len(sil_tags) - 1): - chunks.append( - self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0]) - ) - if sil_tags[-1][1] < total_frames: - chunks.append( - self._apply_slice(waveform, sil_tags[-1][1], total_frames) - ) - return chunks - - -def main(): - import os.path - from argparse import ArgumentParser - - import librosa - import soundfile - - parser = ArgumentParser() - parser.add_argument("audio", type=str, help="The audio to be sliced") - parser.add_argument( - "--out", type=str, help="Output directory of the sliced audio clips" - ) - parser.add_argument( - "--db_thresh", - type=float, - required=False, - default=-40, - help="The dB threshold for silence detection", - ) - parser.add_argument( - "--min_length", - type=int, - required=False, - default=5000, - help="The minimum milliseconds required for each sliced audio clip", - ) - parser.add_argument( - "--min_interval", - type=int, - required=False, - default=300, - help="The minimum milliseconds for a silence part to be sliced", - ) - parser.add_argument( - "--hop_size", - type=int, - required=False, - default=10, - help="Frame length in milliseconds", - ) - parser.add_argument( - "--max_sil_kept", - type=int, - required=False, - default=500, - help="The maximum silence length kept around the sliced clip, presented in milliseconds", - ) - args = parser.parse_args() - out = args.out - if out is None: - out = os.path.dirname(os.path.abspath(args.audio)) - audio, sr = librosa.load(args.audio, sr=None, mono=False) - slicer = Slicer( - sr=sr, - threshold=args.db_thresh, - min_length=args.min_length, - min_interval=args.min_interval, - hop_size=args.hop_size, - max_sil_kept=args.max_sil_kept, - ) - chunks = slicer.slice(audio) - if not os.path.exists(out): - os.makedirs(out) - for i, chunk in enumerate(chunks): - if len(chunk.shape) > 1: - chunk = chunk.T - soundfile.write( - os.path.join( - out, - f"%s_%d.wav" - % (os.path.basename(args.audio).rsplit(".", maxsplit=1)[0], i), - ), - chunk, - sr, - ) - - -if __name__ == "__main__": - main() diff --git a/spaces/801artistry/RVC801/train/losses.py b/spaces/801artistry/RVC801/train/losses.py deleted file mode 100644 index b89038f14d06d7fae43628183e9ffb465e4edafd..0000000000000000000000000000000000000000 --- a/spaces/801artistry/RVC801/train/losses.py +++ /dev/null @@ -1,59 +0,0 @@ -import torch -from torch.nn import functional as F - - -def feature_loss(fmap_r, fmap_g): - loss = 0 - for dr, dg in zip(fmap_r, fmap_g): - for rl, gl in zip(dr, dg): - rl = rl.float().detach() - gl = gl.float() - loss += torch.mean(torch.abs(rl - gl)) - - return loss * 2 - - -def discriminator_loss(disc_real_outputs, disc_generated_outputs): - loss = 0 - r_losses = [] - g_losses = [] - for dr, dg in zip(disc_real_outputs, disc_generated_outputs): - dr = dr.float() - dg = dg.float() - r_loss = torch.mean((1 - dr) ** 2) - g_loss = torch.mean(dg**2) - loss += r_loss + g_loss - r_losses.append(r_loss.item()) - g_losses.append(g_loss.item()) - - return loss, r_losses, g_losses - - -def generator_loss(disc_outputs): - loss = 0 - gen_losses = [] - for dg in disc_outputs: - dg = dg.float() - l = torch.mean((1 - dg) ** 2) - gen_losses.append(l) - loss += l - - return loss, gen_losses - - -def kl_loss(z_p, logs_q, m_p, logs_p, z_mask): - """ - z_p, logs_q: [b, h, t_t] - m_p, logs_p: [b, h, t_t] - """ - z_p = z_p.float() - logs_q = logs_q.float() - m_p = m_p.float() - logs_p = logs_p.float() - z_mask = z_mask.float() - - kl = logs_p - logs_q - 0.5 - kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p) - kl = torch.sum(kl * z_mask) - l = kl / torch.sum(z_mask) - return l diff --git a/spaces/AEUPH/CosmosTV/README.md b/spaces/AEUPH/CosmosTV/README.md deleted file mode 100644 index c5f80934843b99015cd7f9ace88a7a2d5ac4dbab..0000000000000000000000000000000000000000 --- a/spaces/AEUPH/CosmosTV/README.md +++ /dev/null @@ -1,44 +0,0 @@ ---- -title: AI WebTV -emoji: 🔮 -colorFrom: purple -colorTo: white -sdk: docker -app_port: 7860 -duplicated_from: jbilcke-hf/AI-WebTV ---- - -A generative AI WebTV, powered by Zeroscope and Hugging Face. - -This is just the frontend part, you will need the media-server (also open source) to make it work. - -Warning: this is an experimental, proof-of-concept project made in a few days. - -It is not ready for production use by other people! Also, this use models that should only be used for research purposes (no commercial usage). - -Note: because the stream uses FLV, it doesn't work on iPhone. There is however a [Twitch mirror here](https://www.twitch.tv/ai_webtv). - -The main code of the webtv is located inside the [media-server](https://huggingface.co/spaces/jbilcke-hf/media-server/tree/main) : - -manual steps: -- human input to write a short paragraph describing a multi-shot video sequence -- manual submit it to GPT-4 to generate a list of video captions for each shot (the system instructions are extracts from a stable diffusion guide) -- commit the captions to the [playlist database](https://huggingface.co/spaces/jbilcke-hf/media-server/raw/main/database.json) - -Inside the `media-server` space (generation process running in the background): -- for each prompt in the database -- generate a silent 3 seconds video clip with Zeroscope V2 576w (hosted on Hugging Face Spaces) -- upscale the clip with Zeroscope V2 XL (also a HF Space) -- perform frame interpolation with FILM (also a HF Space) -- storage in the Persistent Storage of the media-server Space - -Inside the `media-server` space (streaming process running in the foreground): -- for each video file in the persistent storage folder -- add it to a new FFmpeg playlist (it's just a .txt file) -- broadcast it over the RTMP protocol using FFmpeg (in FLV format) -- diffusion of the stream using node-media-server - -Inside the `AI-WebTV` space: -- display the stream using `mpegts.js` -- this doesn't work on iPhone, but now there is also a Twitch mirror - diff --git a/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/training/params.py b/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/training/params.py deleted file mode 100644 index 0cc1a0e2d982e900988cf5a4b24b2e59b093537b..0000000000000000000000000000000000000000 --- a/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/training/params.py +++ /dev/null @@ -1,563 +0,0 @@ -import argparse - - -def get_default_params(model_name): - # Params from paper (https://arxiv.org/pdf/2103.00020.pdf) - model_name = model_name.lower() - if "vit" in model_name: - return {"lr": 5.0e-4, "beta1": 0.9, "beta2": 0.98, "eps": 1.0e-6} - else: - return {"lr": 5.0e-4, "beta1": 0.9, "beta2": 0.999, "eps": 1.0e-8} - - -def parse_args(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--train-data", - type=str, - default=None, - help="Path to h5 filewith training data", - ) - parser.add_argument( - "--val-data", - type=str, - default=None, - help="Path to h5 file with validation data", - ) - parser.add_argument( - "--freeze-text", - default=False, - action="store_true", - help="if you need to freeze the text encoder, make this True", - ) - parser.add_argument( - "--freeze-text-after", - type=int, - default=-1, - help="if you need to freeze the text encoder after (include) epoch x, set this param to x. Set -1 to disable it", - ) - parser.add_argument( - "--train-ipc", - type=str, - default=None, - help="Path to npy file of the number of instance per class in training data", - ) - parser.add_argument( - "--val-ipc", - type=str, - default=None, - help="Path to npy file of the number of instance per class in validation data", - ) - parser.add_argument( - "--train-num-samples", - type=int, - default=None, - help="Number of samples in dataset. Required for webdataset if not available in info file.", - ) - parser.add_argument( - "--val-num-samples", - type=int, - default=None, - help="Number of samples in dataset. Useful for webdataset if not available in info file.", - ) - parser.add_argument( - "--dataset-type", - choices=["webdataset", "csv", "auto", "toy"], - default="auto", - help="Which type of dataset to process.", - ) - parser.add_argument( - "--csv-separator", - type=str, - default="\t", - help="For csv-like datasets, which separator to use.", - ) - parser.add_argument( - "--csv-img-key", - type=str, - default="filepath", - help="For csv-like datasets, the name of the key for the image paths.", - ) - parser.add_argument( - "--csv-caption-key", - type=str, - default="title", - help="For csv-like datasets, the name of the key for the captions.", - ) - parser.add_argument( - "--imagenet-val", - type=str, - default=None, - help="Path to imagenet val set for conducting zero shot evaluation.", - ) - parser.add_argument( - "--imagenet-v2", - type=str, - default=None, - help="Path to imagenet v2 for conducting zero shot evaluation.", - ) - parser.add_argument( - "--datasetnames", - nargs="+", - default=None, - help="If loading webdataset, spedify the dataset names to load. Can be some of these: Clotho, audioset, audiocaps, BBCSoundEffects", - ) - parser.add_argument( - "--full-train-dataset", - nargs="+", - default=None, - help="Which dataset will be trained with all the subsets. (train+test)", - ) - parser.add_argument( - "--exclude-eval-dataset", - nargs="+", - default=None, - help="Which dataset will be excluded with evaluation", - ) - parser.add_argument( - "--datasetinfos", - nargs="+", - default=None, - help="If loading webdataset, spedify the dataset types to load. Can be some of these: train, test, valid, unbalanced_train, balanced_train, eval", - ) - parser.add_argument( - "--dataset-proportion", - type=float, - default=1.0, - help="How much proportion of dataset we want to train.", - ) - parser.add_argument( - "--remotedata", - default=False, - action="store_true", - help="if the dataset is remote, set this flag", - ) - parser.add_argument( - "--class-label-path", - type=str, - default=None, - help="The path of the class label pickle or csv.", - ) - parser.add_argument( - "--datasetpath", - type=str, - default="/mnt/audio_clip/webdataset_tar", - help="The path to the dataset", - ) - parser.add_argument( - "--logs", - type=str, - default="./logs/", - help="Where to store tensorboard logs. Use None to avoid storing logs.", - ) - parser.add_argument( - "--log-local", - action="store_true", - default=False, - help="log files on local master, otherwise global master only.", - ) - parser.add_argument( - "--name", - type=str, - default=None, - help="Optional identifier for the experiment when storing logs. Otherwise use current time.", - ) - parser.add_argument( - "--workers", type=int, default=1, help="Number of workers per GPU." - ) - parser.add_argument( - "--batch-size", type=int, default=64, help="Batch size per GPU." - ) - parser.add_argument( - "--epochs", type=int, default=32, help="Number of epochs to train for." - ) - parser.add_argument("--lr", type=float, default=None, help="Learning rate.") - parser.add_argument("--beta1", type=float, default=None, help="Adam beta 1.") - parser.add_argument("--beta2", type=float, default=None, help="Adam beta 2.") - parser.add_argument("--eps", type=float, default=None, help="Adam epsilon.") - parser.add_argument("--momentum", type=float, default=None, help="SGD epsilon.") - parser.add_argument("--wd", type=float, default=0.2, help="Weight decay.") - - parser.add_argument( - "--split-opt", - action="store_true", - default=False, - help="Use this flag to skip the learning rate decay.", - ) - parser.add_argument( - "--lr-pretrained", type=float, default=None, help="Learning rate for text." - ) - parser.add_argument( - "--beta1-pretrained", type=float, default=None, help="Adam beta 1 for text." - ) - parser.add_argument( - "--beta2-pretrained", type=float, default=None, help="Adam beta 2 for text." - ) - parser.add_argument( - "--eps-pretrained", type=float, default=None, help="Adam epsilon for text." - ) - parser.add_argument( - "--wd-pretrained", type=float, default=0.2, help="Weight decay for text." - ) - parser.add_argument( - "--momentum-pretrained", type=float, default=0.9, help="Momentum for text." - ) - parser.add_argument( - "--lr-new", type=float, default=None, help="Learning rate for audio." - ) - parser.add_argument( - "--beta1-new", type=float, default=None, help="Adam beta 1 for audio." - ) - parser.add_argument( - "--beta2-new", type=float, default=None, help="Adam beta 2 for audio." - ) - parser.add_argument( - "--eps-new", type=float, default=None, help="Adam epsilon for audio." - ) - parser.add_argument( - "--wd-new", type=float, default=0.2, help="Weight decay for audio." - ) - parser.add_argument( - "--momentum-new", type=float, default=0.9, help="Momentum for audio." - ) - parser.add_argument( - "--warmup", type=int, default=10000, help="Number of steps to warmup for." - ) - parser.add_argument( - "--use-bn-sync", - default=False, - action="store_true", - help="Whether to use batch norm sync.", - ) - parser.add_argument( - "--skip-scheduler", - action="store_true", - default=False, - help="Use this flag to skip the learning rate decay.", - ) - parser.add_argument( - "--save-frequency", type=int, default=1, help="How often to save checkpoints." - ) - parser.add_argument( - "--save-top-performance", - type=int, - default=0, - help="Save the top x performance weights if the value >0", - ) - parser.add_argument( - "--save-most-recent", - action="store_true", - default=False, - help="Always save the most recent model trained to epoch_latest.pt.", - ) - parser.add_argument( - "--zeroshot-frequency", type=int, default=2, help="How often to run zero shot." - ) - parser.add_argument( - "--val-frequency", - type=int, - default=1, - help="How often to run evaluation with val data.", - ) - parser.add_argument( - "--resume", - default=None, - type=str, - help="path to latest checkpoint (default: none)", - ) - parser.add_argument( - "--precision", - choices=["amp", "fp16", "fp32"], - default="amp", - help="Floating point precision.", - ) - parser.add_argument( - "--amodel", - type=str, - default="RN50", - help="Name of the audio backbone to use.", - ) - parser.add_argument( - "--tmodel", - type=str, - default="transformer", - help="Name of the text backbone to use. Can be [transformer, bert, roberta, bart]", - ) - parser.add_argument( - "--pretrained-audio", - default="", - type=str, - help="Use a pretrained audio model weights for the audio encoder of CLAP", - ) - parser.add_argument( - "--pretrained-text", - default="", - type=str, - help="Use a pretrained text model weights for the text encoder of CLAP", - ) - parser.add_argument( - "--pretrained", - default="", - type=str, - help="Use a pretrained CLIP model weights with the specified tag or file path.", - ) - parser.add_argument( - "--pretrained-image", - default=False, - action="store_true", - help="Load imagenet pretrained weights for image tower backbone if available.", - ) - parser.add_argument( - "--lock-image", - default=False, - action="store_true", - help="Lock full image tower by disabling gradients.", - ) - parser.add_argument( - "--lock-image-unlocked-groups", - type=int, - default=0, - help="Leave last n image tower layer groups unlocked.", - ) - parser.add_argument( - "--lock-image-freeze-bn-stats", - default=False, - action="store_true", - help="Freeze BatchNorm running stats in image tower for any locked layers.", - ) - parser.add_argument( - "--local-loss", - default=False, - action="store_true", - help="calculate loss w/ local features @ global (instead of realizing full global @ global matrix)", - ) - parser.add_argument( - "--gather-with-grad", - default=False, - action="store_true", - help="enable full distributed gradient for feature gather", - ) - parser.add_argument( - "--force-quick-gelu", - default=False, - action="store_true", - help="Force use of QuickGELU activation for non-OpenAI transformer models.", - ) - parser.add_argument( - "--torchscript", - default=False, - action="store_true", - help="torch.jit.script the model, also uses jit version of OpenAI models if pretrained=='openai'", - ) - parser.add_argument( - "--trace", - default=False, - action="store_true", - help="torch.jit.trace the model for inference / eval only", - ) - # arguments for distributed training - parser.add_argument( - "--dist-url", - default="env://", - type=str, - help="url used to set up distributed training", - ) - parser.add_argument( - "--dist-backend", default="nccl", type=str, help="distributed backend" - ) - parser.add_argument( - "--report-to", - default="", - type=str, - help="Options are ['wandb', 'tensorboard', 'wandb,tensorboard']", - ) - parser.add_argument( - "--wandb-notes", default="", type=str, help="Notes if logging with wandb" - ) - parser.add_argument( - "--C", type=float, default=3.16, help="inverse regularizer for logistic reg." - ) - parser.add_argument( - "--debug", - default=False, - action="store_true", - help="If true, more information is logged.", - ) - parser.add_argument( - "--copy-codebase", - default=False, - action="store_true", - help="If true, we copy the entire base on the log diretory, and execute from there.", - ) - parser.add_argument( - "--horovod", - default=False, - action="store_true", - help="Use horovod for distributed training.", - ) - parser.add_argument( - "--ddp-static-graph", - default=False, - action="store_true", - help="Enable static graph optimization for DDP in PyTorch >= 1.11.", - ) - parser.add_argument( - "--no-set-device-rank", - default=False, - action="store_true", - help="Don't set device index from local rank (when CUDA_VISIBLE_DEVICES restricted to one per proc).", - ) - parser.add_argument("--seed", type=int, default=4242, help="Default random seed.") - - parser.add_argument( - "--top-k-checkpoint-select-dataset", - type=str, - default="all", - help="The dataset of selecting top-k checkpoint.", - ) - - # @R10, @R@5, @R1, mAP@10 - parser.add_argument( - "--top-k-checkpoint-select-metric", - type=str, - default="_R@10", - help="The metric for selecting top-k checkpoint.", - ) - parser.add_argument( - "--openai-model-cache-dir", - type=str, - default="~/.cache/clip", - help="Directory to download OpenAI models.", - ) - parser.add_argument( - "--optimizer", - type=str, - default="adamw", - help="can be AdamW or SGD", - ) - parser.add_argument( - "--parallel-eval", - default=False, - action="store_true", - help="Eval in parallel (multi-GPU, multi-node).", - ) - - parser.add_argument( - "--no-eval", - default=False, - action="store_true", - help="Training without evaluation.", - ) - - parser.add_argument( - "--lp-mlp", - default=False, - action="store_true", - help="Linear Probe using MLP layer or not.", - ) - - parser.add_argument( - "--lp-freeze", - default=False, - action="store_true", - help="Linear Probe using Freeze CLAP or not", - ) - - parser.add_argument( - "--lp-act", - default="None", - type=str, - help="Options are ['relu','elu','prelu','softmax','sigmoid']", - ) - - parser.add_argument( - "--lp-loss", type=str, default="bce", help="Loss func of Linear Probe." - ) - - parser.add_argument( - "--lp-metrics", - type=str, - default="map,mauc,acc", - help="Metrics of Linear Probe.", - ) - - parser.add_argument( - "--lp-lr", type=float, default=1e-4, help="learning rate of linear probe" - ) - parser.add_argument( - "--kappa", - type=float, - default=0, - help="the kappa in the weighted contrastive loss, default is to turn off the weighted contrastive loss", - ) - - parser.add_argument( - "--data-filling", - type=str, - default="pad", - help="type of data filling when the audio length is shorter than the max length." - "Can be one of the following: repeat, repeatpad, pad", - ) - parser.add_argument( - "--data-truncating", - type=str, - default="rand_trunc", - help="type of data truncation when the audio length is longer than the max length." - "Can be one of the following: rand_trunc, fusion", - ) - - parser.add_argument( - "--clap-mlploss", - default=False, - action="store_true", - help="Using MLP loss for CLAP model or not", - ) - - parser.add_argument( - "--wandb-id", - type=str, - default=None, - help="the id of wandb experiment to restore.", - ) - - parser.add_argument( - "--sleep", type=float, default=0, help="sleep n seconds before start training" - ) - - # variable length processing - parser.add_argument( - "--enable-fusion", - default=False, - action="store_true", - help="Enable feature funsion for variable-length data", - ) - - parser.add_argument( - "--fusion-type", - type=str, - default="None", - help="Type is among ['channel_map', 'daf_1d','aff_1d','iaff_1d','daf_2d','aff_2d','iaff_2d']", - ) - - parser.add_argument( - "--mixup", - default=False, - action="store_true", - help="Enable mixup in finetuning training.", - ) - parser.add_argument( - "--text-augment-selection", - type=str, - default=None, - help="For selecting levels of augmented text. Type is among ['all', 'augment_only', 'none']", - ) - - args = parser.parse_args() - - # If some params are not passed, we use the default values based on model name. - default_params = get_default_params(args.amodel) - for name, val in default_params.items(): - if getattr(args, name) is None: - setattr(args, name, val) - - return args diff --git a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/create_black_list.py b/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/create_black_list.py deleted file mode 100644 index fadbe94599997e3476f37f8c4cdd30ca86a8720e..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/create_black_list.py +++ /dev/null @@ -1,64 +0,0 @@ -import argparse -import csv -import os - -from utilities import create_folder - - -def dcase2017task4(args): - """Create black list. Black list is a list of audio ids that will be - skipped in training. - """ - - # Augments & parameters - workspace = args.workspace - - # Black list from DCASE 2017 Task 4 - test_weak_csv = 'metadata/black_list/groundtruth_weak_label_testing_set.csv' - evaluation_weak_csv = 'metadata/black_list/groundtruth_weak_label_evaluation_set.csv' - - black_list_csv = os.path.join(workspace, 'black_list', 'dcase2017task4.csv') - create_folder(os.path.dirname(black_list_csv)) - - def get_id_sets(csv_path): - with open(csv_path, 'r') as fr: - reader = csv.reader(fr, delimiter='\t') - lines = list(reader) - - ids_set = [] - - for line in lines: - """line: ['-5QrBL6MzLg_60.000_70.000.wav', '60.000', '70.000', 'Train horn']""" - ids_set.append(line[0][0 : 11]) - - ids_set = list(set(ids_set)) - return ids_set - - test_ids_set = get_id_sets(test_weak_csv) - evaluation_ids_set = get_id_sets(evaluation_weak_csv) - - full_ids_set = test_ids_set + evaluation_ids_set - - # Write black list - fw = open(black_list_csv, 'w') - - for id in full_ids_set: - fw.write('{}\n'.format(id)) - - print('Write black list to {}'.format(black_list_csv)) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='') - subparsers = parser.add_subparsers(dest='mode') - - parser_dcase2017task4 = subparsers.add_parser('dcase2017task4') - parser_dcase2017task4.add_argument('--workspace', type=str, required=True) - - args = parser.parse_args() - - if args.mode == 'dcase2017task4': - dcase2017task4(args) - - else: - raise Exception('Error argument!') \ No newline at end of file diff --git a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/image_degradation/__init__.py b/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/image_degradation/__init__.py deleted file mode 100644 index 7836cada81f90ded99c58d5942eea4c3477f58fc..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/image_degradation/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr -from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light diff --git a/spaces/Aaaaaaaabdualh/topic2poem/app.py b/spaces/Aaaaaaaabdualh/topic2poem/app.py deleted file mode 100644 index 25116ecea621b15e1c098f85fc813210fc80cade..0000000000000000000000000000000000000000 --- a/spaces/Aaaaaaaabdualh/topic2poem/app.py +++ /dev/null @@ -1,48 +0,0 @@ -from transformers import BertTokenizer, EncoderDecoderModel -import gradio as gr - -tokenizerM = BertTokenizer.from_pretrained("mareloraby/BERTShared-PoetryGen-arV01") -bertSharedM = EncoderDecoderModel.from_pretrained("mareloraby/BERTShared-PoetryGen-arV01") -# bertSharedM.cuda() - - -def generate_response(text, k = 70, p = 0.9, nb = 4): - prompt = f"{text}" - encoded_prompt = tokenizerM.encode_plus(prompt, return_tensors = 'pt')#.to(device) - gneration = bertSharedM.generate( - input_ids = encoded_prompt.input_ids, - attention_mask = encoded_prompt.attention_mask, - do_sample = True, - top_k= k, - top_p = p, - num_beams= nb, - max_length =130, - repetition_penalty = 2.0, - no_repeat_ngram_size = 2, - early_stopping=True) - - generated_text = tokenizerM.decode(gneration[0], skip_special_tokens=True) - bayts = generated_text.split("[BSEP]") - while("FSEP" not in bayts[-1]): - bayts = bayts[:-1] - bayts = bayts[:-1] - temp_poem = '' - for b in range(len(bayts)): - temp_line = bayts[b].split('[FSEP]') - temp_poem = temp_poem + temp_line[1] + ' - ' + temp_line[0] +'\n' - - return temp_poem - -iface = gr.Interface(fn=generate_response, - title = 'BERTShared - topic based generation', - - inputs=[ - gr.inputs.Radio(['حزينه','هجاء','عتاب','غزل','مدح','رومنسيه','دينية'],label='Choose Topic'), - gr.inputs.Slider(10, 200, step=10,default = 70, label='Top-K'), - gr.inputs.Slider(0.10, 0.99, step=0.02, default = 0.90, label='Top-P'), - #gr.inputs.Slider(1, 20, step=1, default = 4, label='Beams'), - - ], - outputs="text") - -iface.launch() \ No newline at end of file diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/deprecated/DfeHub.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/deprecated/DfeHub.py deleted file mode 100644 index 4ea7501f760faabd48c1657d491577dff6cf099d..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/deprecated/DfeHub.py +++ /dev/null @@ -1,77 +0,0 @@ -from __future__ import annotations - -import json -import re -import time - -import requests - -from ...typing import Any, CreateResult -from ..base_provider import BaseProvider - - -class DfeHub(BaseProvider): - url = "https://chat.dfehub.com/" - supports_stream = True - supports_gpt_35_turbo = True - - @staticmethod - def create_completion( - model: str, - messages: list[dict[str, str]], - stream: bool, **kwargs: Any) -> CreateResult: - - headers = { - "authority" : "chat.dfehub.com", - "accept" : "*/*", - "accept-language" : "en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3", - "content-type" : "application/json", - "origin" : "https://chat.dfehub.com", - "referer" : "https://chat.dfehub.com/", - "sec-ch-ua" : '"Not.A/Brand";v="8", "Chromium";v="114", "Google Chrome";v="114"', - "sec-ch-ua-mobile" : "?0", - "sec-ch-ua-platform": '"macOS"', - "sec-fetch-dest" : "empty", - "sec-fetch-mode" : "cors", - "sec-fetch-site" : "same-origin", - "user-agent" : "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36", - "x-requested-with" : "XMLHttpRequest", - } - - json_data = { - "messages" : messages, - "model" : "gpt-3.5-turbo", - "temperature" : kwargs.get("temperature", 0.5), - "presence_penalty" : kwargs.get("presence_penalty", 0), - "frequency_penalty" : kwargs.get("frequency_penalty", 0), - "top_p" : kwargs.get("top_p", 1), - "stream" : True - } - - response = requests.post("https://chat.dfehub.com/api/openai/v1/chat/completions", - headers=headers, json=json_data, timeout=3) - - for chunk in response.iter_lines(): - if b"detail" in chunk: - delay = re.findall(r"\d+\.\d+", chunk.decode()) - delay = float(delay[-1]) - time.sleep(delay) - yield from DfeHub.create_completion(model, messages, stream, **kwargs) - if b"content" in chunk: - data = json.loads(chunk.decode().split("data: ")[1]) - yield (data["choices"][0]["delta"]["content"]) - - @classmethod - @property - def params(cls): - params = [ - ("model", "str"), - ("messages", "list[dict[str, str]]"), - ("stream", "bool"), - ("temperature", "float"), - ("presence_penalty", "int"), - ("frequency_penalty", "int"), - ("top_p", "int"), - ] - param = ", ".join([": ".join(p) for p in params]) - return f"g4f.provider.{cls.__name__} supports: ({param})" diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/deprecated/GetGpt.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/deprecated/GetGpt.py deleted file mode 100644 index a5de1d296a5d6abada13030ceabcd181e2f90497..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/deprecated/GetGpt.py +++ /dev/null @@ -1,88 +0,0 @@ -from __future__ import annotations - -import json -import os -import uuid - -import requests -from Crypto.Cipher import AES - -from ...typing import Any, CreateResult -from ..base_provider import BaseProvider - - -class GetGpt(BaseProvider): - url = 'https://chat.getgpt.world/' - supports_stream = True - working = False - supports_gpt_35_turbo = True - - @staticmethod - def create_completion( - model: str, - messages: list[dict[str, str]], - stream: bool, **kwargs: Any) -> CreateResult: - - headers = { - 'Content-Type' : 'application/json', - 'Referer' : 'https://chat.getgpt.world/', - 'user-agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36', - } - - data = json.dumps( - { - 'messages' : messages, - 'frequency_penalty' : kwargs.get('frequency_penalty', 0), - 'max_tokens' : kwargs.get('max_tokens', 4000), - 'model' : 'gpt-3.5-turbo', - 'presence_penalty' : kwargs.get('presence_penalty', 0), - 'temperature' : kwargs.get('temperature', 1), - 'top_p' : kwargs.get('top_p', 1), - 'stream' : True, - 'uuid' : str(uuid.uuid4()) - } - ) - - res = requests.post('https://chat.getgpt.world/api/chat/stream', - headers=headers, json={'signature': _encrypt(data)}, stream=True) - - res.raise_for_status() - for line in res.iter_lines(): - if b'content' in line: - line_json = json.loads(line.decode('utf-8').split('data: ')[1]) - yield (line_json['choices'][0]['delta']['content']) - - @classmethod - @property - def params(cls): - params = [ - ('model', 'str'), - ('messages', 'list[dict[str, str]]'), - ('stream', 'bool'), - ('temperature', 'float'), - ('presence_penalty', 'int'), - ('frequency_penalty', 'int'), - ('top_p', 'int'), - ('max_tokens', 'int'), - ] - param = ', '.join([': '.join(p) for p in params]) - return f'g4f.provider.{cls.__name__} supports: ({param})' - - -def _encrypt(e: str): - t = os.urandom(8).hex().encode('utf-8') - n = os.urandom(8).hex().encode('utf-8') - r = e.encode('utf-8') - - cipher = AES.new(t, AES.MODE_CBC, n) - ciphertext = cipher.encrypt(_pad_data(r)) - - return ciphertext.hex() + t.decode('utf-8') + n.decode('utf-8') - - -def _pad_data(data: bytes) -> bytes: - block_size = AES.block_size - padding_size = block_size - len(data) % block_size - padding = bytes([padding_size] * padding_size) - - return data + padding diff --git a/spaces/Adapter/T2I-Adapter/ldm/models/diffusion/dpm_solver/sampler.py b/spaces/Adapter/T2I-Adapter/ldm/models/diffusion/dpm_solver/sampler.py deleted file mode 100644 index fc2c96baf2bf5f8de3684c198bcd1b0df5b51149..0000000000000000000000000000000000000000 --- a/spaces/Adapter/T2I-Adapter/ldm/models/diffusion/dpm_solver/sampler.py +++ /dev/null @@ -1,87 +0,0 @@ -"""SAMPLING ONLY.""" -import torch - -from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver - - -MODEL_TYPES = { - "eps": "noise", - "v": "v" -} - - -class DPMSolverSampler(object): - def __init__(self, model, **kwargs): - super().__init__() - self.model = model - to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device) - self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod)) - - def register_buffer(self, name, attr): - if type(attr) == torch.Tensor: - if attr.device != torch.device("cuda"): - attr = attr.to(torch.device("cuda")) - setattr(self, name, attr) - - @torch.no_grad() - def sample(self, - S, - batch_size, - shape, - conditioning=None, - callback=None, - normals_sequence=None, - img_callback=None, - quantize_x0=False, - eta=0., - mask=None, - x0=None, - temperature=1., - noise_dropout=0., - score_corrector=None, - corrector_kwargs=None, - verbose=True, - x_T=None, - log_every_t=100, - unconditional_guidance_scale=1., - unconditional_conditioning=None, - # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... - **kwargs - ): - if conditioning is not None: - if isinstance(conditioning, dict): - cbs = conditioning[list(conditioning.keys())[0]].shape[0] - if cbs != batch_size: - print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") - else: - if conditioning.shape[0] != batch_size: - print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") - - # sampling - C, H, W = shape - size = (batch_size, C, H, W) - - print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}') - - device = self.model.betas.device - if x_T is None: - img = torch.randn(size, device=device) - else: - img = x_T - - ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod) - - model_fn = model_wrapper( - lambda x, t, c: self.model.apply_model(x, t, c), - ns, - model_type=MODEL_TYPES[self.model.parameterization], - guidance_type="classifier-free", - condition=conditioning, - unconditional_condition=unconditional_conditioning, - guidance_scale=unconditional_guidance_scale, - ) - - dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False) - x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True) - - return x.to(device), None diff --git a/spaces/Adapter/T2I-Adapter/style.css b/spaces/Adapter/T2I-Adapter/style.css deleted file mode 100644 index c4739b4ea5fc35e774a049e3dacc443f7f0eac19..0000000000000000000000000000000000000000 --- a/spaces/Adapter/T2I-Adapter/style.css +++ /dev/null @@ -1,3 +0,0 @@ -h1 { - text-align: center; -} diff --git a/spaces/Adapter/T2I-Adapter/train_depth.py b/spaces/Adapter/T2I-Adapter/train_depth.py deleted file mode 100644 index af9a203bdd8b0904440bdcc55f2127c26aab7ebd..0000000000000000000000000000000000000000 --- a/spaces/Adapter/T2I-Adapter/train_depth.py +++ /dev/null @@ -1,281 +0,0 @@ -import argparse -import logging -import os -import os.path as osp -import torch -from basicsr.utils import (get_env_info, get_root_logger, get_time_str, - scandir) -from basicsr.utils.options import copy_opt_file, dict2str -from omegaconf import OmegaConf - -from ldm.data.dataset_depth import DepthDataset -from basicsr.utils.dist_util import get_dist_info, init_dist, master_only -from ldm.modules.encoders.adapter import Adapter -from ldm.util import load_model_from_config - - -@master_only -def mkdir_and_rename(path): - """mkdirs. If path exists, rename it with timestamp and create a new one. - - Args: - path (str): Folder path. - """ - if osp.exists(path): - new_name = path + '_archived_' + get_time_str() - print(f'Path already exists. Rename it to {new_name}', flush=True) - os.rename(path, new_name) - os.makedirs(path, exist_ok=True) - os.makedirs(osp.join(path, 'models')) - os.makedirs(osp.join(path, 'training_states')) - os.makedirs(osp.join(path, 'visualization')) - - -def load_resume_state(opt): - resume_state_path = None - if opt.auto_resume: - state_path = osp.join('experiments', opt.name, 'training_states') - if osp.isdir(state_path): - states = list(scandir(state_path, suffix='state', recursive=False, full_path=False)) - if len(states) != 0: - states = [float(v.split('.state')[0]) for v in states] - resume_state_path = osp.join(state_path, f'{max(states):.0f}.state') - opt.resume_state_path = resume_state_path - - if resume_state_path is None: - resume_state = None - else: - device_id = torch.cuda.current_device() - resume_state = torch.load(resume_state_path, map_location=lambda storage, loc: storage.cuda(device_id)) - return resume_state - - -def parsr_args(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--bsize", - type=int, - default=8, - ) - parser.add_argument( - "--epochs", - type=int, - default=10000, - ) - parser.add_argument( - "--num_workers", - type=int, - default=8, - ) - parser.add_argument( - "--plms", - action='store_true', - help="use plms sampling", - ) - parser.add_argument( - "--auto_resume", - action='store_true', - help="use plms sampling", - ) - parser.add_argument( - "--ckpt", - type=str, - default="models/sd-v1-4.ckpt", - help="path to checkpoint of model", - ) - parser.add_argument( - "--config", - type=str, - default="configs/stable-diffusion/sd-v1-train.yaml", - help="path to config which constructs model", - ) - parser.add_argument( - "--name", - type=str, - default="train_depth", - help="experiment name", - ) - parser.add_argument( - "--print_fq", - type=int, - default=100, - help="path to config which constructs model", - ) - parser.add_argument( - "--H", - type=int, - default=512, - help="image height, in pixel space", - ) - parser.add_argument( - "--W", - type=int, - default=512, - help="image width, in pixel space", - ) - parser.add_argument( - "--C", - type=int, - default=4, - help="latent channels", - ) - parser.add_argument( - "--f", - type=int, - default=8, - help="downsampling factor", - ) - parser.add_argument( - "--sample_steps", - type=int, - default=50, - help="number of ddim sampling steps", - ) - parser.add_argument( - "--n_samples", - type=int, - default=1, - help="how many samples to produce for each given prompt. A.k.a. batch size", - ) - parser.add_argument( - "--scale", - type=float, - default=7.5, - help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))", - ) - parser.add_argument( - "--gpus", - default=[0, 1, 2, 3], - help="gpu idx", - ) - parser.add_argument( - '--local_rank', - default=0, - type=int, - help='node rank for distributed training' - ) - parser.add_argument( - '--launcher', - default='pytorch', - type=str, - help='node rank for distributed training' - ) - opt = parser.parse_args() - return opt - - -def main(): - opt = parsr_args() - config = OmegaConf.load(f"{opt.config}") - - # distributed setting - init_dist(opt.launcher) - torch.backends.cudnn.benchmark = True - device = 'cuda' - torch.cuda.set_device(opt.local_rank) - - # dataset - train_dataset = DepthDataset('datasets/laion_depth_meta_v1.txt') - train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) - train_dataloader = torch.utils.data.DataLoader( - train_dataset, - batch_size=opt.bsize, - shuffle=(train_sampler is None), - num_workers=opt.num_workers, - pin_memory=True, - sampler=train_sampler) - - # stable diffusion - model = load_model_from_config(config, f"{opt.ckpt}").to(device) - - # depth encoder - model_ad = Adapter(cin=3 * 64, channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False).to( - device) - - # to gpus - model_ad = torch.nn.parallel.DistributedDataParallel( - model_ad, - device_ids=[opt.local_rank], - output_device=opt.local_rank) - model = torch.nn.parallel.DistributedDataParallel( - model, - device_ids=[opt.local_rank], - output_device=opt.local_rank) - - # optimizer - params = list(model_ad.parameters()) - optimizer = torch.optim.AdamW(params, lr=config['training']['lr']) - - experiments_root = osp.join('experiments', opt.name) - - # resume state - resume_state = load_resume_state(opt) - if resume_state is None: - mkdir_and_rename(experiments_root) - start_epoch = 0 - current_iter = 0 - # WARNING: should not use get_root_logger in the above codes, including the called functions - # Otherwise the logger will not be properly initialized - log_file = osp.join(experiments_root, f"train_{opt.name}_{get_time_str()}.log") - logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file) - logger.info(get_env_info()) - logger.info(dict2str(config)) - else: - # WARNING: should not use get_root_logger in the above codes, including the called functions - # Otherwise the logger will not be properly initialized - log_file = osp.join(experiments_root, f"train_{opt.name}_{get_time_str()}.log") - logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file) - logger.info(get_env_info()) - logger.info(dict2str(config)) - resume_optimizers = resume_state['optimizers'] - optimizer.load_state_dict(resume_optimizers) - logger.info(f"Resuming training from epoch: {resume_state['epoch']}, " f"iter: {resume_state['iter']}.") - start_epoch = resume_state['epoch'] - current_iter = resume_state['iter'] - - # copy the yml file to the experiment root - copy_opt_file(opt.config, experiments_root) - - # training - logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter}') - for epoch in range(start_epoch, opt.epochs): - train_dataloader.sampler.set_epoch(epoch) - # train - for _, data in enumerate(train_dataloader): - current_iter += 1 - with torch.no_grad(): - c = model.module.get_learned_conditioning(data['sentence']) - z = model.module.encode_first_stage((data['im'] * 2 - 1.).to(device)) - z = model.module.get_first_stage_encoding(z) - - optimizer.zero_grad() - model.zero_grad() - features_adapter = model_ad(data['depth'].to(device)) - l_pixel, loss_dict = model(z, c=c, features_adapter=features_adapter) - l_pixel.backward() - optimizer.step() - - if (current_iter + 1) % opt.print_fq == 0: - logger.info(loss_dict) - - # save checkpoint - rank, _ = get_dist_info() - if (rank == 0) and ((current_iter + 1) % config['training']['save_freq'] == 0): - save_filename = f'model_ad_{current_iter + 1}.pth' - save_path = os.path.join(experiments_root, 'models', save_filename) - save_dict = {} - state_dict = model_ad.state_dict() - for key, param in state_dict.items(): - if key.startswith('module.'): # remove unnecessary 'module.' - key = key[7:] - save_dict[key] = param.cpu() - torch.save(save_dict, save_path) - # save state - state = {'epoch': epoch, 'iter': current_iter + 1, 'optimizers': optimizer.state_dict()} - save_filename = f'{current_iter + 1}.state' - save_path = os.path.join(experiments_root, 'training_states', save_filename) - torch.save(state, save_path) - - -if __name__ == '__main__': - main() diff --git a/spaces/Aditya9790/yolo7-object-tracking/utils/aws/resume.py b/spaces/Aditya9790/yolo7-object-tracking/utils/aws/resume.py deleted file mode 100644 index 338685b19c19ddb47aa2fde22a535a8efcf17802..0000000000000000000000000000000000000000 --- a/spaces/Aditya9790/yolo7-object-tracking/utils/aws/resume.py +++ /dev/null @@ -1,37 +0,0 @@ -# Resume all interrupted trainings in yolor/ dir including DDP trainings -# Usage: $ python utils/aws/resume.py - -import os -import sys -from pathlib import Path - -import torch -import yaml - -sys.path.append('./') # to run '$ python *.py' files in subdirectories - -port = 0 # --master_port -path = Path('').resolve() -for last in path.rglob('*/**/last.pt'): - ckpt = torch.load(last) - if ckpt['optimizer'] is None: - continue - - # Load opt.yaml - with open(last.parent.parent / 'opt.yaml') as f: - opt = yaml.load(f, Loader=yaml.SafeLoader) - - # Get device count - d = opt['device'].split(',') # devices - nd = len(d) # number of devices - ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel - - if ddp: # multi-GPU - port += 1 - cmd = f'python -m torch.distributed.launch --nproc_per_node {nd} --master_port {port} train.py --resume {last}' - else: # single-GPU - cmd = f'python train.py --resume {last}' - - cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread - print(cmd) - os.system(cmd) diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/ScrollMethods.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/ScrollMethods.js deleted file mode 100644 index 1e9b6f2cbd994bc57c8ee6f36b16bb6a5dbd9424..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/ScrollMethods.js +++ /dev/null @@ -1,13 +0,0 @@ -export default { - scrollToRow(rowIndex) { - var table = this.childrenMap.child; - table.scrollToRow(rowIndex); - return this; - }, - - scrollToNextRow(rowCount) { - var table = this.childrenMap.child; - table.scrollToNextRow(rowCount); - return this; - } -} \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateNumberBar.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateNumberBar.js deleted file mode 100644 index e2044db5632f62fe028c72adbb5007c3196db47c..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateNumberBar.js +++ /dev/null @@ -1,20 +0,0 @@ -import MergeStyle from './utils/MergeStyle.js'; -import NumberBar from '../../numberbar/NumberBar.js'; -import CreateChild from './utils/CreateChild.js'; -import ReplaceSliderConfig from './utils/ReplaceSliderConfig.js'; - -var CreateNumberBar = function (scene, data, view, styles, customBuilders) { - data = MergeStyle(data, styles); - - // Replace data by child game object - CreateChild(scene, data, 'background', view, styles, customBuilders); - CreateChild(scene, data, 'icon', view, styles, customBuilders); - ReplaceSliderConfig(scene, data.slider, view, styles, customBuilders); - CreateChild(scene, data, 'text', view, styles, customBuilders); - - var gameObject = new NumberBar(scene, data); - scene.add.existing(gameObject); - return gameObject; -}; - -export default CreateNumberBar; \ No newline at end of file diff --git a/spaces/Alpaca233/SadTalker/src/face3d/models/losses.py b/spaces/Alpaca233/SadTalker/src/face3d/models/losses.py deleted file mode 100644 index 09d6a85870af1ef2b857e4a3fdd4b2f7fc991317..0000000000000000000000000000000000000000 --- a/spaces/Alpaca233/SadTalker/src/face3d/models/losses.py +++ /dev/null @@ -1,113 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -from kornia.geometry import warp_affine -import torch.nn.functional as F - -def resize_n_crop(image, M, dsize=112): - # image: (b, c, h, w) - # M : (b, 2, 3) - return warp_affine(image, M, dsize=(dsize, dsize), align_corners=True) - -### perceptual level loss -class PerceptualLoss(nn.Module): - def __init__(self, recog_net, input_size=112): - super(PerceptualLoss, self).__init__() - self.recog_net = recog_net - self.preprocess = lambda x: 2 * x - 1 - self.input_size=input_size - def forward(imageA, imageB, M): - """ - 1 - cosine distance - Parameters: - imageA --torch.tensor (B, 3, H, W), range (0, 1) , RGB order - imageB --same as imageA - """ - - imageA = self.preprocess(resize_n_crop(imageA, M, self.input_size)) - imageB = self.preprocess(resize_n_crop(imageB, M, self.input_size)) - - # freeze bn - self.recog_net.eval() - - id_featureA = F.normalize(self.recog_net(imageA), dim=-1, p=2) - id_featureB = F.normalize(self.recog_net(imageB), dim=-1, p=2) - cosine_d = torch.sum(id_featureA * id_featureB, dim=-1) - # assert torch.sum((cosine_d > 1).float()) == 0 - return torch.sum(1 - cosine_d) / cosine_d.shape[0] - -def perceptual_loss(id_featureA, id_featureB): - cosine_d = torch.sum(id_featureA * id_featureB, dim=-1) - # assert torch.sum((cosine_d > 1).float()) == 0 - return torch.sum(1 - cosine_d) / cosine_d.shape[0] - -### image level loss -def photo_loss(imageA, imageB, mask, eps=1e-6): - """ - l2 norm (with sqrt, to ensure backward stabililty, use eps, otherwise Nan may occur) - Parameters: - imageA --torch.tensor (B, 3, H, W), range (0, 1), RGB order - imageB --same as imageA - """ - loss = torch.sqrt(eps + torch.sum((imageA - imageB) ** 2, dim=1, keepdims=True)) * mask - loss = torch.sum(loss) / torch.max(torch.sum(mask), torch.tensor(1.0).to(mask.device)) - return loss - -def landmark_loss(predict_lm, gt_lm, weight=None): - """ - weighted mse loss - Parameters: - predict_lm --torch.tensor (B, 68, 2) - gt_lm --torch.tensor (B, 68, 2) - weight --numpy.array (1, 68) - """ - if not weight: - weight = np.ones([68]) - weight[28:31] = 20 - weight[-8:] = 20 - weight = np.expand_dims(weight, 0) - weight = torch.tensor(weight).to(predict_lm.device) - loss = torch.sum((predict_lm - gt_lm)**2, dim=-1) * weight - loss = torch.sum(loss) / (predict_lm.shape[0] * predict_lm.shape[1]) - return loss - - -### regulization -def reg_loss(coeffs_dict, opt=None): - """ - l2 norm without the sqrt, from yu's implementation (mse) - tf.nn.l2_loss https://www.tensorflow.org/api_docs/python/tf/nn/l2_loss - Parameters: - coeffs_dict -- a dict of torch.tensors , keys: id, exp, tex, angle, gamma, trans - - """ - # coefficient regularization to ensure plausible 3d faces - if opt: - w_id, w_exp, w_tex = opt.w_id, opt.w_exp, opt.w_tex - else: - w_id, w_exp, w_tex = 1, 1, 1, 1 - creg_loss = w_id * torch.sum(coeffs_dict['id'] ** 2) + \ - w_exp * torch.sum(coeffs_dict['exp'] ** 2) + \ - w_tex * torch.sum(coeffs_dict['tex'] ** 2) - creg_loss = creg_loss / coeffs_dict['id'].shape[0] - - # gamma regularization to ensure a nearly-monochromatic light - gamma = coeffs_dict['gamma'].reshape([-1, 3, 9]) - gamma_mean = torch.mean(gamma, dim=1, keepdims=True) - gamma_loss = torch.mean((gamma - gamma_mean) ** 2) - - return creg_loss, gamma_loss - -def reflectance_loss(texture, mask): - """ - minimize texture variance (mse), albedo regularization to ensure an uniform skin albedo - Parameters: - texture --torch.tensor, (B, N, 3) - mask --torch.tensor, (N), 1 or 0 - - """ - mask = mask.reshape([1, mask.shape[0], 1]) - texture_mean = torch.sum(mask * texture, dim=1, keepdims=True) / torch.sum(mask) - loss = torch.sum(((texture - texture_mean) * mask)**2) / (texture.shape[0] * torch.sum(mask)) - return loss - diff --git a/spaces/Amitesh007/elevenlabs-stt/README.md b/spaces/Amitesh007/elevenlabs-stt/README.md deleted file mode 100644 index 0196b270535ded141518d59c2af26dea90653350..0000000000000000000000000000000000000000 --- a/spaces/Amitesh007/elevenlabs-stt/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Elevenlabs Stt -emoji: 🌖 -colorFrom: blue -colorTo: indigo -sdk: streamlit -sdk_version: 1.19.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_diffusion/image_variation.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_diffusion/image_variation.md deleted file mode 100644 index 4895ababf5bd19fdd02578647ecec6f4885423f5..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_diffusion/image_variation.md +++ /dev/null @@ -1,37 +0,0 @@ - - -# Image variation - -The Stable Diffusion model can also generate variations from an input image. It uses a fine-tuned version of a Stable Diffusion model by [Justin Pinkney](https://www.justinpinkney.com/) from [Lambda](https://lambdalabs.com/). - -The original codebase can be found at [LambdaLabsML/lambda-diffusers](https://github.com/LambdaLabsML/lambda-diffusers#stable-diffusion-image-variations) and additional official checkpoints for image variation can be found at [lambdalabs/sd-image-variations-diffusers](https://huggingface.co/lambdalabs/sd-image-variations-diffusers). - -
fine-tuning** | **Comments** | -| :-------------------------------------------------: | :----------------: | :-------------------------------------: | :---------------------------------------------------------------------------------------------: | -| [Instruct Pix2Pix](#instruct-pix2pix) | ✅ | ❌ | Can additionally be
fine-tuned for better
performance on specific
edit instructions. | -| [Pix2Pix Zero](#pix2pixzero) | ✅ | ❌ | | -| [Attend and Excite](#attend-and-excite) | ✅ | ❌ | | -| [Semantic Guidance](#semantic-guidance) | ✅ | ❌ | | -| [Self-attention Guidance](#self-attention-guidance) | ✅ | ❌ | | -| [Depth2Image](#depth2image) | ✅ | ❌ | | -| [MultiDiffusion Panorama](#multidiffusion-panorama) | ✅ | ❌ | | -| [DreamBooth](#dreambooth) | ❌ | ✅ | | -| [Textual Inversion](#textual-inversion) | ❌ | ✅ | | -| [ControlNet](#controlnet) | ✅ | ❌ | A ControlNet can be
trained/fine-tuned on
a custom conditioning. | -| [Prompt Weighting](#prompt-weighting) | ✅ | ❌ | | -| [Custom Diffusion](#custom-diffusion) | ❌ | ✅ | | -| [Model Editing](#model-editing) | ✅ | ❌ | | -| [DiffEdit](#diffedit) | ✅ | ❌ | | -| [T2I-Adapter](#t2i-adapter) | ✅ | ❌ | | - -## Instruct Pix2Pix - -[Paper](https://arxiv.org/abs/2211.09800) - -[Instruct Pix2Pix](../api/pipelines/stable_diffusion/pix2pix) is fine-tuned from stable diffusion to support editing input images. It takes as inputs an image and a prompt describing an edit, and it outputs the edited image. -Instruct Pix2Pix has been explicitly trained to work well with [InstructGPT](https://openai.com/blog/instruction-following/)-like prompts. - -See [here](../api/pipelines/stable_diffusion/pix2pix) for more information on how to use it. - -## Pix2Pix Zero - -[Paper](https://arxiv.org/abs/2302.03027) - -[Pix2Pix Zero](../api/pipelines/stable_diffusion/pix2pix_zero) allows modifying an image so that one concept or subject is translated to another one while preserving general image semantics. - -The denoising process is guided from one conceptual embedding towards another conceptual embedding. The intermediate latents are optimized during the denoising process to push the attention maps towards reference attention maps. The reference attention maps are from the denoising process of the input image and are used to encourage semantic preservation. - -Pix2Pix Zero can be used both to edit synthetic images as well as real images. - -- To edit synthetic images, one first generates an image given a caption. - Next, we generate image captions for the concept that shall be edited and for the new target concept. We can use a model like [Flan-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5) for this purpose. Then, "mean" prompt embeddings for both the source and target concepts are created via the text encoder. Finally, the pix2pix-zero algorithm is used to edit the synthetic image. -- To edit a real image, one first generates an image caption using a model like [BLIP](https://huggingface.co/docs/transformers/model_doc/blip). Then one applies ddim inversion on the prompt and image to generate "inverse" latents. Similar to before, "mean" prompt embeddings for both source and target concepts are created and finally the pix2pix-zero algorithm in combination with the "inverse" latents is used to edit the image. - -
Voleibol Arena: Un juego móvil divertido y competitivo
-Si usted está buscando una experiencia única de voleibol en su dispositivo móvil, es posible que desee echar un vistazo a Volleyball Arena, un nuevo juego en línea de ritmo rápido 1v1 donde cada segundo realmente cuenta. En este juego, puedes disfrutar de gráficos increíbles, controles simples y un juego atractivo mientras desafías a tus oponentes a un juego divertido y casual de voleibol. También puedes dominar el campo de juego y enorgullecerte de mostrar los personajes especiales y los premios que desbloquearás en el camino. En este artículo, le diremos todo lo que necesita saber sobre Volleyball Arena, incluyendo cómo jugarlo, cómo ganar más partidos, cómo actualizar y personalizar a sus jugadores y poderes, cómo jugar a través de diferentes arenas, cómo descargar un mod APK para características adicionales, y cuáles son los pros y los contras del juego basado en los comentarios de los usuarios.
-Cómo jugar Voleibol Arena
-Volleyball Arena es un juego que es fácil de aprender pero difícil de dominar. Las reglas y controles básicos son simples: tienes que volear, aplastar, remar y anotar puntos usando tus manos, cabeza o superpoderes para golpear la pelota sobre la red y hacerla tocar el suelo del lado de tu oponente. También puede bucear si la pelota está demasiado lejos para alcanzarla. Tiene 30 segundos para anotar tantos puntos como sea posible. El jugador con más puntos al final del partido gana.
-apk mod de la arena de voleibol
Download File ☆ https://bltlly.com/2v6KR8
-
El juego utiliza controles táctiles intuitivos que te permiten mover a tu personaje hacia la izquierda o hacia la derecha deslizando el dedo sobre la pantalla. Para saltar o bucear, tienes que tocar la pantalla. Para golpear la pelota con la mano o la cabeza, tienes que deslizar hacia arriba o hacia abajo en la pantalla. Para usar una superpotencia, tienes que tocar su icono en la parte inferior de la pantalla.
-Consejos y trucos para ganar más partidos
-Aunque jugar Volleyball Arena es divertido y fácil, ganar partidos puede ser un reto si no sabes algunos consejos y trucos. Estos son algunos de ellos:
--
-
-
- Mejora tus habilidades. Las habilidades son habilidades pasivas que
Aquí está la continuación del artículo:
--
-
- Aprende de tus oponentes. Volleyball Arena es un juego en línea donde puedes jugar contra jugadores reales de todo el mundo. Puedes aprender de sus movimientos, tácticas y errores, y usarlos para mejorar tus propias habilidades. También puedes chatear con ellos después del partido e intercambiar consejos o cumplidos. -
- Divertirse y disfrutar del juego. Voleibol Arena es un juego casual que está destinado a ser divertido y entretenido. No te lo tomes demasiado en serio ni te frustres si pierdes. En su lugar, céntrate en los aspectos positivos del juego, como los gráficos, los personajes, los potenciadores y las arenas. También puedes invitar a tus amigos a jugar contigo y tener una competencia amistosa. -
Cómo actualizar y personalizar tus reproductores y poderes
-Una de las características más emocionantes de Volleyball Arena es que puede actualizar y personalizar a sus jugadores y poderes para adaptarse a su estilo y preferencias. Así es como:
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-
- Desbloquear y actualizar todos los jugadores. Hay 16 jugadores diferentes que puedes desbloquear y usar en el juego, cada uno con sus propias estadísticas, habilidades y personalidades. Puedes desbloquearlos ganando partidas, completando logros o comprándolos con monedas o gemas. También puedes actualizar sus estadísticas, como velocidad, salto, potencia y resistencia, gastando monedas o gemas. -
- Desbloquear y utilizar diferentes elementos. Hay varios artículos que puedes desbloquear y usar en el juego, como trajes, sombreros, gafas, zapatos, bolas, etc. Puedes desbloquearlos abriendo cofres, completando logros o comprándolos con monedas o gemas. También puede equipar a sus jugadores para cambiar su apariencia y aumentar sus estadísticas. - -
Cómo jugar a través de diferentes arenas
-Voleibol Arena no se trata solo de jugar al voleibol en una cancha regular. También puedes viajar alrededor del mundo y competir en varias canchas con diferentes recompensas. Así es como:
--
-
- Desbloquear y jugar a través de diferentes arenas. Hay 6 escenarios diferentes que puedes desbloquear y jugar en el juego, como Londres, Río de Janeiro, Tokio, Beijing, Nueva York y París. Puedes desbloquearlos alcanzando ciertos niveles o comprándolos con monedas o gemas. También puede jugar a través de ellos en orden o elegir cualquier arena que desee. -
- Gana recompensas de diferentes arenas. Cada arena tiene sus propias recompensas que puedes ganar al ganar partidas o completar logros. Por ejemplo, puedes ganar monedas, gemas, cofres, objetos, poderes, jugadores, etc. de diferentes arenas. También puedes ganar trofeos de cada arena que aumentarán tu rango y reputación. -
- Disfruta del paisaje de diferentes arenas. Cada arena tiene su propio tema y diseño que refleja su ubicación y cultura. Por ejemplo, se puede ver Big Ben en Londres, Cristo Redentor en Río de Janeiro,
Aquí está la continuación del artículo:
- -Monte Fuji en Tokio, la Gran Muralla China en Beijing, la Estatua de la Libertad en Nueva York y la Torre Eiffel en París. También puedes disfrutar de los diferentes sonidos y música de cada arena que te harán sentir como si estuvieras realmente allí.
-Voleibol Arena Mod APK: ¿Qué es y cómo descargarlo
-Si desea disfrutar de Volleyball Arena con más características y beneficios, es posible que desee probar la descarga de un mod APK para el juego. Un mod APK es una versión modificada del archivo APK original que puede darle acceso a recursos ilimitados, elementos desbloqueados, características premium y más. Sin embargo, antes de descargar un mod APK, usted debe saber lo que es, cómo funciona, y cuáles son los riesgos y precauciones de su uso.
-Características de Voleibol Arena Mod APK
- --
-
- Dinero ilimitado. Puedes obtener monedas y gemas ilimitadas que puedes usar para comprar y actualizar cualquier cosa en el juego. -
- Skins desbloqueados. Puedes obtener todas las skins para tus jugadores y bolas que puedes usar para personalizar tu apariencia. -
- Salto ilimitado. Puedes saltar tan alto como quieras sin ningún límite o tiempo de reutilización. -
- Sin anuncios. Puedes jugar el juego sin ningún anuncio molesto que pueda interrumpir tu juego. -
- Sin raíz. No es necesario rootear el dispositivo para usar el mod APK. -
Cómo descargar e instalar Voleibol Arena Mod APK
-Si quieres descargar e instalar un mod APK para Volleyball Arena, tienes que seguir estos pasos:
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-
- Encontrar una fuente confiable para el mod APK. Usted puede buscar en línea para sitios web o foros que ofrecen mod APK para Voleibol Arena. Asegúrese de leer los comentarios y calificaciones del mod APK antes de descargarlo. -
- Descargar el archivo APK mod a su dispositivo. Puede utilizar cualquier navegador o aplicación de descarga para descargar el archivo APK mod. Asegúrese de verificar el tamaño y el nombre del archivo antes de descargarlo. -
- Habilitar fuentes desconocidas en el dispositivo. Debe habilitar fuentes desconocidas en la configuración de su dispositivo para permitir la instalación de aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. -
- Instale el archivo APK mod en su dispositivo. Puede usar cualquier aplicación de administrador de archivos para localizar e instalar el archivo APK mod. Asegúrate de conceder todos los permisos que pide la app. -
- Inicie el juego y disfrute. Ahora puede lanzar Volleyball Arena desde el cajón de su aplicación y disfrutar de las características modificadas. -
Requisitos para descargar e instalar Voleibol Arena Mod APK
-Para descargar e instalar un mod APK para Voleibol Arena, usted tiene que cumplir con estos requisitos mínimos:
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-
- Tu dispositivo debe tener Android 4.4 o una versión superior. - -
- Su dispositivo debe tener una conexión a Internet estable. -
Precauciones para descargar e instalar Voleibol Arena Mod APK
-Si bien descargar e instalar un mod APK para Volleyball Arena puede ser tentador, también debe ser consciente de los riesgos y precauciones de su uso. Algunos de ellos son:
--
-
- Puedes tener malware o virus en tu dispositivo. Algunos APK mod pueden contener código malicioso o archivos que pueden dañar tu dispositivo o robar tus datos. Para evitar esto, debe escanear el archivo APK mod con una aplicación antivirus antes de instalarlo. -
- Usted puede ser expulsado del juego o perder su cuenta. Algunos mod APK pueden violar los términos y condiciones del juego o Google Play Store, lo que puede resultar en la prohibición o suspensión de su cuenta. Para evitar esto, debe usar una aplicación VPN o una cuenta falsa al jugar con un mod APK. -
- Puedes perder tu progreso o datos en el juego. Algunos APK mod pueden sobrescribir o eliminar los datos originales del juego o el progreso, lo que puede causar que pierda sus logros o elementos. Para evitar esto, usted debe copia de seguridad de los datos del juego antes de instalar un mod APK. -
Revisión del juego de voleibol Arena
-Volleyball Arena es un juego que ha recibido críticas mixtas de usuarios y críticos por igual. Algunas personas lo aman por sus gráficos, jugabilidad, factor de diversión y variedad, mientras que otros lo odian por sus controles, anuncios, problemas técnicos y elementos de pago para ganar. Aquí hay un resumen de los pros y los contras de Volleyball Arena basado en las opiniones y calificaciones de los usuarios:
-Pros de
Aquí está la continuación del artículo:
-Pros de Voleibol Arena
-Algunos de los aspectos positivos de Voleibol Arena son:
--
-
- Tiene gráficos y animaciones increíbles que hacen que el juego se vea realista y animado. Los personajes, las bolas, las canchas y los fondos están bien diseñados y detallados. El juego también funciona sin problemas y no tiene problemas de retraso o tartamudez. - -
- Tiene un juego divertido y atractivo que te mantiene enganchado y entretenido. Puedes jugar contra jugadores reales de todo el mundo en partidas rápidas de 1v1 que duran 30 segundos. También puedes usar diferentes potenciadores, habilidades y estrategias para vencer a tus oponentes y anotar más puntos. -
- Tiene mucha variedad y contenido que hacen que el juego sea diverso e interesante. Puedes desbloquear y usar 16 jugadores diferentes, 12 poderes diferentes y varios elementos que pueden cambiar tu apariencia y rendimiento. También puedes viajar y jugar a través de 6 arenas diferentes que tienen diferentes temas y recompensas. -
Contras de Voleibol Arena
-Algunos de los aspectos negativos de Voleibol Arena son:
--
-
- Tiene controles pobres y frustrantes que hacen que el juego sea difícil de jugar y disfrutar. Puedes deslizar, tocar y arrastrar la pantalla para mover, saltar, sumergirte, golpear y usar poderes. Sin embargo, a veces el juego no registra tus entradas o responde con un retraso. Esto puede hacer que pierdas la pelota, la golpees fuera de los límites o pierdas tus potenciadores. -
- Tiene demasiados anuncios que interrumpen tu juego y te molestan. Tienes que ver un anuncio cada vez que terminas un partido, abrir un cofre o reclamar una recompensa. También puedes obtener anuncios emergentes que aparecen aleatoriamente en tu pantalla. La única manera de deshacerse de ellos es pagar una suscripción premium o apagar su conexión a Internet. -
- Tiene muchos fallos y errores que arruinan su experiencia de juego y causan que pierda partidos o progreso. Por ejemplo, a veces el juego se bloquea o se congela, la pelota pasa por la red o el suelo, los power-ups no funcionan o desaparecen, los oponentes no se mueven o devuelven el golpe, etc. - -
Conclusión
-Volleyball Arena es un juego que puede ser divertido y competitivo si te gusta el voleibol y los juegos en línea. Tiene gráficos increíbles, controles simples, un juego atractivo y mucha variedad y contenido. Sin embargo, también tiene controles deficientes, demasiados anuncios, fallos y errores, y un elemento de pago para ganar que puede ser frustrante y molesto. Por lo tanto, le recomendamos que lo pruebe por sí mismo y vea si le gusta o no. Se puede descargar de forma gratuita desde Google Play Store o utilizar un mod APK para características adicionales.
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre Volleyball Arena y sus respuestas:
--
-
- P: ¿Cómo puedo jugar con mis amigos en la Arena de Voleibol? -
- A: Puedes jugar con tus amigos en la Arena de Voleibol invitándolos a unirse a tu equipo o desafiándolos a un partido. Para ello, tienes que conectar tu cuenta de juego a Facebook o Google Play Games. Luego, puedes ir a la pestaña Amigos en el menú principal y seleccionar a tus amigos desde allí. -
- P: ¿Cómo puedo obtener más monedas y gemas en la Arena de Voleibol? -
- A: Usted puede conseguir más monedas y gemas en la arena de voleibol ganando partidos, abriendo cofres,
Aquí está la continuación del artículo:
--
-
- Q: ¿Cómo puedo obtener más potenciadores en la Arena de Voleibol? -
- A: Puedes obtener más potenciadores en la Arena de Voleibol al desbloquearlos con monedas o gemas, o al encontrarlos al azar durante el partido. También puedes obtener más potenciadores viendo anuncios o completando logros. -
- Q: ¿Cómo puedo cambiar mi carácter o pelota en la Arena de Voleibol? -
- A: Puede cambiar su personaje o pelota en la Arena de Voleibol yendo a la pestaña Tienda en el menú principal y seleccionando el personaje o pelota que desea usar. También puedes cambiar tu personaje o bola antes del partido tocando sus iconos en la parte inferior de la pantalla. -
- Q: ¿Cómo puedo contactar a los desarrolladores de Volleyball Arena? - -
-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Caja Monster.md b/spaces/Benson/text-generation/Examples/Caja Monster.md deleted file mode 100644 index 07e26385d4d9e3357390a3c367cf0e20b63c3c23..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Caja Monster.md +++ /dev/null @@ -1,78 +0,0 @@ - -¿Qué es una caja monstruo y por qué necesita uno
-¿Alguna vez has oído hablar de una caja monstruo? Si no, te estás perdiendo una gran manera de disfrutar de los juegos o invertir en metales preciosos. Una caja de monstruos es un término que puede referirse a diferentes cosas dependiendo del contexto, pero todos tienen una cosa en común: son cajas que contienen monstruos. En este artículo, explicaremos qué es una caja monstruosa, qué tipos de cajas monstruosas existen, qué beneficios ofrecen y cómo puedes obtener una por ti mismo.
-Definición y Tipos de Monster Box
-Una caja de monstruos es una caja que contiene monstruos. Suena simple, ¿verdad? ¿Pero de qué clase de monstruos estamos hablando? Bueno, hay dos tipos principales de cajas monstruosas que debes conocer: cajas monstruosas en juegos y cajas monstruosas en metales preciosos.
-Caja Monster
Download ✦ https://bltlly.com/2v6MHP
-Caja de monstruos en juegos
-Una caja de monstruos en juegos es una caja que contiene monstruos digitales que puedes capturar, recolectar y usar para batallas. Estos monstruos suelen ser lindos, coloridos y tienen diferentes habilidades y personalidades. Un ejemplo de un juego que cuenta con cajas de monstruos es Monster Box, un juego casual donde capturas monstruos en tus cápsulas y los usas para defenderte. También puedes aceptar desafíos de otros entrenadores, recompensar a tus monstruos con juegos y golosinas, y crear tu mejor equipo.
-Caja de monstruos en metales preciosos
-Una caja monstruo en metales preciosos es una caja que contiene monedas físicas o barras hechas de oro, plata, platino u otros metales. Estas monedas o barras son generalmente acuñadas por las mentas oficiales como la U.S. Mint, la Royal Canadian Mint, la Austrian Mint, y más. Un ejemplo de un producto que viene en una caja monstruo es el American Silver Eagle, una moneda de plata que es la moneda oficial de lingotes de plata de los Estados Unidos. Una caja monstruosa de águilas plateadas americanas contiene 500 monedas en 25 tubos de 20 monedas cada uno.
-Beneficios de tener una caja de monstruos
- -Caja de monstruos para juegos
-Si eres un fanático de los juegos, especialmente los juegos casuales que son divertidos y fáciles de jugar, tener una caja de monstruos puede proporcionarte horas de entretenimiento y disfrute. Estos son algunos de los beneficios de tener una caja monstruo para juegos:
-Recoger y monstruos de batalla
-Una de las principales atracciones de tener una caja de monstruos para los juegos es que usted puede recoger y monstruos de batalla. Puedes capturar diferentes tipos de monstruos con diferentes habilidades y características, como fuego, agua, tierra, aire, luz, oscuridad, etc. También puedes personalizar tus monstruos con diferentes atuendos, accesorios y nombres. A continuación, puede utilizar sus monstruos para luchar contra otros entrenadores o enemigos en varios modos y arenas.
-Desafía y recompensa a tus monstruos
-Otro beneficio de tener una caja de monstruos para jugar es que puedes desafiar y recompensar a tus monstruos. Puedes aceptar misiones y misiones de otros entrenadores o PNJ (personajes no jugadores) que pondrán a prueba tus habilidades y estrategia. También puedes recompensar a tus monstruos con juegos y golosinas que los harán felices y leales. También puedes desbloquear logros y recompensas que mejorarán tu experiencia de juego.
-Crea tu mejor equipo
-Un beneficio final de tener una caja monstruo para juegos es que puedes crear tu mejor equipo. Puedes mezclar y combinar diferentes monstruos para formar un equipo equilibrado y potente. También puedes mejorar tus monstruos con objetos y habilidades que aumentarán su rendimiento. También puedes intercambiar y compartir tus monstruos con otros jugadores para ampliar tu colección y hacer nuevos amigos.
-Caja de monstruos para metales preciosos
-Si eres un fan de invertir en metales preciosos, especialmente monedas o barras que son acuñadas por mentas oficiales, entonces tener una caja monstruo puede proporcionarte seguridad y valor. Estos son algunos de los beneficios de tener una caja monstruosa para metales preciosos:
- -Almacenar y proteger sus metales
- -Ahorre dinero y tiempo
-Otro beneficio de tener una caja monstruo para metales preciosos es que puede ahorrar dinero y tiempo. Comprar una caja monstruo de monedas o barras suele ser más barato que comprar piezas individuales, ya que puede obtener un descuento a granel o una prima más baja. También puede ahorrar tiempo ordenando en línea o visitando a un distribuidor que tiene la caja monstruo en stock, en lugar de buscar diferentes productos o mentas.
-Aumente el valor de su inversión
-Un beneficio final de tener una caja monstruosa para metales preciosos es que puede aumentar su valor de inversión. Una caja monstruosa de monedas o barras es un activo tangible que tiene un valor intrínseco y un suministro limitado. El valor de sus metales depende del precio de mercado, la demanda y la oferta, la rareza y la calidad, y la menta y el diseño. También puede beneficiarse de la apreciación, diversificación, liquidez y cobertura de sus metales contra la inflación, la devaluación de la moneda o la inestabilidad económica.
-Cómo obtener una caja de monstruos
-Ahora que sabes lo que es una caja monstruo y qué beneficios ofrece, es posible que te estés preguntando cómo conseguir una para ti. Bueno, el proceso es diferente dependiendo de si quieres una caja monstruo para juegos o metales preciosos.
-Caja de monstruos para juegos
-Si quieres una caja monstruo para juegos, aquí están los pasos que debes seguir:
-Descargar la aplicación o jugar en línea
-El primer paso es descargar la aplicación o jugar en línea el juego que cuenta con cajas de monstruos. Por ejemplo, puede descargar Kongregate. También puedes ver otros juegos que tienen características similares, como Pokémon Go, Monster Legends, o APMEX, JM Bullion, o Liberty Coin, Bullion Exchanges, o BB", severity_fatal, description_unknown_ca) - msg_len = len(msg) - record_type_alert = 0x15 - record = struct.pack(">BBBH", record_type_alert, ver_maj, ver_min, msg_len) + msg - return record diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/anchor_generator.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/anchor_generator.py deleted file mode 100644 index c0ae9bfc61f5c8483727dded627dfd8addc19cd0..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/anchor_generator.py +++ /dev/null @@ -1,365 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -import copy -import math -from typing import List -import torch -from torch import nn - -from detectron2.layers import ShapeSpec -from detectron2.structures import Boxes, RotatedBoxes -from detectron2.utils.registry import Registry - -ANCHOR_GENERATOR_REGISTRY = Registry("ANCHOR_GENERATOR") -ANCHOR_GENERATOR_REGISTRY.__doc__ = """ -Registry for modules that creates object detection anchors for feature maps. - -The registered object will be called with `obj(cfg, input_shape)`. -""" - - -class BufferList(nn.Module): - """ - Similar to nn.ParameterList, but for buffers - """ - - def __init__(self, buffers=None): - super(BufferList, self).__init__() - if buffers is not None: - self.extend(buffers) - - def extend(self, buffers): - offset = len(self) - for i, buffer in enumerate(buffers): - self.register_buffer(str(offset + i), buffer) - return self - - def __len__(self): - return len(self._buffers) - - def __iter__(self): - return iter(self._buffers.values()) - - -def _create_grid_offsets(size, stride, offset, device): - grid_height, grid_width = size - shifts_x = torch.arange( - offset * stride, grid_width * stride, step=stride, dtype=torch.float32, device=device - ) - shifts_y = torch.arange( - offset * stride, grid_height * stride, step=stride, dtype=torch.float32, device=device - ) - - shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) - shift_x = shift_x.reshape(-1) - shift_y = shift_y.reshape(-1) - return shift_x, shift_y - - -@ANCHOR_GENERATOR_REGISTRY.register() -class DefaultAnchorGenerator(nn.Module): - """ - For a set of image sizes and feature maps, computes a set of anchors. - """ - - def __init__(self, cfg, input_shape: List[ShapeSpec]): - super().__init__() - # fmt: off - sizes = cfg.MODEL.ANCHOR_GENERATOR.SIZES - aspect_ratios = cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS - self.strides = [x.stride for x in input_shape] - self.offset = cfg.MODEL.ANCHOR_GENERATOR.OFFSET - - assert 0.0 <= self.offset < 1.0, self.offset - - # fmt: on - """ - sizes (list[list[int]]): sizes[i] is the list of anchor sizes to use - for the i-th feature map. If len(sizes) == 1, then the same list of - anchor sizes, given by sizes[0], is used for all feature maps. Anchor - sizes are given in absolute lengths in units of the input image; - they do not dynamically scale if the input image size changes. - aspect_ratios (list[list[float]]): aspect_ratios[i] is the list of - anchor aspect ratios to use for the i-th feature map. If - len(aspect_ratios) == 1, then the same list of anchor aspect ratios, - given by aspect_ratios[0], is used for all feature maps. - strides (list[int]): stride of each input feature. - """ - - self.num_features = len(self.strides) - self.cell_anchors = self._calculate_anchors(sizes, aspect_ratios) - - def _calculate_anchors(self, sizes, aspect_ratios): - # If one size (or aspect ratio) is specified and there are multiple feature - # maps, then we "broadcast" anchors of that single size (or aspect ratio) - # over all feature maps. - if len(sizes) == 1: - sizes *= self.num_features - if len(aspect_ratios) == 1: - aspect_ratios *= self.num_features - assert self.num_features == len(sizes) - assert self.num_features == len(aspect_ratios) - - cell_anchors = [ - self.generate_cell_anchors(s, a).float() for s, a in zip(sizes, aspect_ratios) - ] - - return BufferList(cell_anchors) - - @property - def box_dim(self): - """ - Returns: - int: the dimension of each anchor box. - """ - return 4 - - @property - def num_cell_anchors(self): - """ - Returns: - list[int]: Each int is the number of anchors at every pixel - location, on that feature map. - For example, if at every pixel we use anchors of 3 aspect - ratios and 5 sizes, the number of anchors is 15. - (See also ANCHOR_GENERATOR.SIZES and ANCHOR_GENERATOR.ASPECT_RATIOS in config) - - In standard RPN models, `num_cell_anchors` on every feature map is the same. - """ - return [len(cell_anchors) for cell_anchors in self.cell_anchors] - - def grid_anchors(self, grid_sizes): - anchors = [] - for size, stride, base_anchors in zip(grid_sizes, self.strides, self.cell_anchors): - shift_x, shift_y = _create_grid_offsets(size, stride, self.offset, base_anchors.device) - shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) - - anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) - - return anchors - - def generate_cell_anchors(self, sizes=(32, 64, 128, 256, 512), aspect_ratios=(0.5, 1, 2)): - """ - Generate a tensor storing anchor boxes, which are continuous geometric rectangles - centered on one feature map point sample. We can later build the set of anchors - for the entire feature map by tiling these tensors; see `meth:grid_anchors`. - - Args: - sizes (tuple[float]): Absolute size (i.e. sqrt of area) of the anchors in the units - of pixels on the input image (the input received by the network, after - undergoing necessary scaling). - aspect_ratios (tuple[float]]): Aspect ratios of the boxes computed as box - height / width. - - Returns: - Tensor of shape (len(sizes) * len(aspect_ratios), 4) storing anchor boxes - in XYXY format. - """ - - # This is different from the anchor generator defined in the original Faster R-CNN - # code or Detectron. They yield the same AP, however the old version defines cell - # anchors in a less natural way with a shift relative to the feature grid and - # quantization that results in slightly different sizes for different aspect ratios. - # See also https://github.com/facebookresearch/Detectron/issues/227 - - anchors = [] - for size in sizes: - area = size ** 2.0 - for aspect_ratio in aspect_ratios: - # s * s = w * h - # a = h / w - # ... some algebra ... - # w = sqrt(s * s / a) - # h = a * w - w = math.sqrt(area / aspect_ratio) - h = aspect_ratio * w - x0, y0, x1, y1 = -w / 2.0, -h / 2.0, w / 2.0, h / 2.0 - anchors.append([x0, y0, x1, y1]) - return torch.tensor(anchors) - - def forward(self, features): - """ - Args: - features (list[Tensor]): list of backbone feature maps on which to generate anchors. - - Returns: - list[list[Boxes]]: a list of #image elements. Each is a list of #feature level Boxes. - The Boxes contains anchors of this image on the specific feature level. - """ - num_images = len(features[0]) - grid_sizes = [feature_map.shape[-2:] for feature_map in features] - anchors_over_all_feature_maps = self.grid_anchors(grid_sizes) - - anchors_in_image = [] - for anchors_per_feature_map in anchors_over_all_feature_maps: - boxes = Boxes(anchors_per_feature_map) - anchors_in_image.append(boxes) - - anchors = [copy.deepcopy(anchors_in_image) for _ in range(num_images)] - return anchors - - -@ANCHOR_GENERATOR_REGISTRY.register() -class RotatedAnchorGenerator(nn.Module): - """ - The anchor generator used by Rotated RPN (RRPN). - """ - - def __init__(self, cfg, input_shape: List[ShapeSpec]): - super().__init__() - # fmt: off - sizes = cfg.MODEL.ANCHOR_GENERATOR.SIZES - aspect_ratios = cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS - angles = cfg.MODEL.ANCHOR_GENERATOR.ANGLES - self.strides = [x.stride for x in input_shape] - self.offset = cfg.MODEL.ANCHOR_GENERATOR.OFFSET - - assert 0.0 <= self.offset < 1.0, self.offset - - # fmt: on - - self.num_features = len(self.strides) - self.cell_anchors = self._calculate_anchors(sizes, aspect_ratios, angles, self.strides) - - def _calculate_anchors(self, sizes, aspect_ratios, angles, feature_strides): - """ - Args: - sizes (list[list[int]]): sizes[i] is the list of anchor sizes to use - for the i-th feature map. If len(sizes) == 1, then the same list of - anchor sizes, given by sizes[0], is used for all feature maps. Anchor - sizes are given in absolute lengths in units of the input image; - they do not dynamically scale if the input image size changes. - aspect_ratios (list[list[float]]): aspect_ratios[i] is the list of - anchor aspect ratios to use for the i-th feature map. If - len(aspect_ratios) == 1, then the same list of anchor aspect ratios, - given by aspect_ratios[0], is used for all feature maps. - angles (list[list[float]]): angles[i] is the list of - anchor angles to use for the i-th feature map. If - len(angles) == 1, then the same list of anchor angles, - given by angles[0], is used for all feature maps. - feature_strides (list[number]): list of feature map strides (with respect - to the input image) for each input feature map. - """ - - # If one size (or aspect ratio) is specified and there are multiple feature - # maps, then we "broadcast" anchors of that single size - # (or aspect ratio/angle) over all feature maps. - - if len(sizes) == 1: - sizes *= self.num_features - if len(aspect_ratios) == 1: - aspect_ratios *= self.num_features - if len(angles) == 1: - angles *= self.num_features - assert self.num_features == len(sizes) - assert self.num_features == len(aspect_ratios) - assert self.num_features == len(angles) - - cell_anchors = [ - self.generate_cell_anchors(size, aspect_ratio, angle).float() - for size, aspect_ratio, angle in zip(sizes, aspect_ratios, angles) - ] - - return BufferList(cell_anchors) - - @property - def box_dim(self): - """ - Returns: - int: the dimension of each anchor box. - """ - return 5 - - @property - def num_cell_anchors(self): - """ - Returns: - list[int]: Each int is the number of anchors at every pixel - location, on that feature map. - For example, if at every pixel we use anchors of 3 aspect - ratios, 2 sizes and 5 angles, the number of anchors is 30. - (See also ANCHOR_GENERATOR.SIZES, ANCHOR_GENERATOR.ASPECT_RATIOS - and ANCHOR_GENERATOR.ANGLES in config) - - In standard RRPN models, `num_cell_anchors` on every feature map is the same. - """ - return [len(cell_anchors) for cell_anchors in self.cell_anchors] - - def grid_anchors(self, grid_sizes): - anchors = [] - for size, stride, base_anchors in zip(grid_sizes, self.strides, self.cell_anchors): - shift_x, shift_y = _create_grid_offsets(size, stride, self.offset, base_anchors.device) - zeros = torch.zeros_like(shift_x) - shifts = torch.stack((shift_x, shift_y, zeros, zeros, zeros), dim=1) - - anchors.append((shifts.view(-1, 1, 5) + base_anchors.view(1, -1, 5)).reshape(-1, 5)) - - return anchors - - def generate_cell_anchors( - self, - sizes=(32, 64, 128, 256, 512), - aspect_ratios=(0.5, 1, 2), - angles=(-90, -60, -30, 0, 30, 60, 90), - ): - """ - Generate a tensor storing anchor boxes, which are continuous geometric rectangles - centered on one feature map point sample. We can later build the set of anchors - for the entire feature map by tiling these tensors; see `meth:grid_anchors`. - - Args: - sizes (tuple[float]): Absolute size of the anchors in the units of the input - image (the input received by the network, after undergoing necessary scaling). - The absolute size is given as the side length of a box. - aspect_ratios (tuple[float]]): Aspect ratios of the boxes computed as box - height / width. - angles (tuple[float]]): Angles of boxes indicating how many degrees - the boxes are rotated counter-clockwise. - - Returns: - Tensor of shape (len(sizes) * len(aspect_ratios) * len(angles), 5) - storing anchor boxes in (x_ctr, y_ctr, w, h, angle) format. - """ - anchors = [] - for size in sizes: - area = size ** 2.0 - for aspect_ratio in aspect_ratios: - # s * s = w * h - # a = h / w - # ... some algebra ... - # w = sqrt(s * s / a) - # h = a * w - w = math.sqrt(area / aspect_ratio) - h = aspect_ratio * w - anchors.extend([0, 0, w, h, a] for a in angles) - - return torch.tensor(anchors) - - def forward(self, features): - """ - Args: - features (list[Tensor]): list of backbone feature maps on which to generate anchors. - - Returns: - list[list[RotatedBoxes]]: - a list of #image elements. Each is a list of #feature level RotatedBoxes. - The RotatedBoxes contains anchors of this image on the specific feature level. - """ - num_images = len(features[0]) - grid_sizes = [feature_map.shape[-2:] for feature_map in features] - anchors_over_all_feature_maps = self.grid_anchors(grid_sizes) - - anchors_in_image = [] - for anchors_per_feature_map in anchors_over_all_feature_maps: - boxes = RotatedBoxes(anchors_per_feature_map) - anchors_in_image.append(boxes) - - anchors = [copy.deepcopy(anchors_in_image) for _ in range(num_images)] - return anchors - - -def build_anchor_generator(cfg, input_shape): - """ - Built an anchor generator from `cfg.MODEL.ANCHOR_GENERATOR.NAME`. - """ - anchor_generator = cfg.MODEL.ANCHOR_GENERATOR.NAME - return ANCHOR_GENERATOR_REGISTRY.get(anchor_generator)(cfg, input_shape) diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/dev/run_inference_tests.sh b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/dev/run_inference_tests.sh deleted file mode 100644 index 34f47d5a07a90c411e830c98a346845fa618f836..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/dev/run_inference_tests.sh +++ /dev/null @@ -1,33 +0,0 @@ -#!/bin/bash -e -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved - -BIN="python train_net.py" -OUTPUT="inference_test_output" -NUM_GPUS=2 -IMS_PER_GPU=2 -IMS_PER_BATCH=$(( NUM_GPUS * IMS_PER_GPU )) - -CFG_LIST=( "${@:1}" ) - -if [ ${#CFG_LIST[@]} -eq 0 ]; then - CFG_LIST=( ./configs/quick_schedules/*inference_acc_test.yaml ) -fi - -echo "========================================================================" -echo "Configs to run:" -echo "${CFG_LIST[@]}" -echo "========================================================================" - -for cfg in "${CFG_LIST[@]}"; do - echo "========================================================================" - echo "Running $cfg ..." - echo "========================================================================" - $BIN \ - --eval-only \ - --num-gpus $NUM_GPUS \ - --config-file "$cfg" \ - OUTPUT_DIR "$OUTPUT" \ - SOLVER.IMS_PER_BATCH $IMS_PER_BATCH - rm -rf $OUTPUT -done - diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TensorMask/tensormask/config.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TensorMask/tensormask/config.py deleted file mode 100644 index 44479f211811bd4060c6afef9ed86791b0dcd0d4..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TensorMask/tensormask/config.py +++ /dev/null @@ -1,50 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved - -from detectron2.config import CfgNode as CN - - -def add_tensormask_config(cfg): - """ - Add config for TensorMask. - """ - cfg.MODEL.TENSOR_MASK = CN() - - # Anchor parameters - cfg.MODEL.TENSOR_MASK.IN_FEATURES = ["p2", "p3", "p4", "p5", "p6", "p7"] - - # Convolutions to use in the towers - cfg.MODEL.TENSOR_MASK.NUM_CONVS = 4 - - # Number of foreground classes. - cfg.MODEL.TENSOR_MASK.NUM_CLASSES = 80 - # Channel size for the classification tower - cfg.MODEL.TENSOR_MASK.CLS_CHANNELS = 256 - - cfg.MODEL.TENSOR_MASK.SCORE_THRESH_TEST = 0.05 - # Only the top (1000 * #levels) candidate boxes across all levels are - # considered jointly during test (to improve speed) - cfg.MODEL.TENSOR_MASK.TOPK_CANDIDATES_TEST = 6000 - cfg.MODEL.TENSOR_MASK.NMS_THRESH_TEST = 0.5 - - # Box parameters - # Channel size for the box tower - cfg.MODEL.TENSOR_MASK.BBOX_CHANNELS = 128 - # Weights on (dx, dy, dw, dh) - cfg.MODEL.TENSOR_MASK.BBOX_REG_WEIGHTS = (1.5, 1.5, 0.75, 0.75) - - # Loss parameters - cfg.MODEL.TENSOR_MASK.FOCAL_LOSS_GAMMA = 3.0 - cfg.MODEL.TENSOR_MASK.FOCAL_LOSS_ALPHA = 0.3 - - # Mask parameters - # Channel size for the mask tower - cfg.MODEL.TENSOR_MASK.MASK_CHANNELS = 128 - # Mask loss weight - cfg.MODEL.TENSOR_MASK.MASK_LOSS_WEIGHT = 2.0 - # weight on positive pixels within the mask - cfg.MODEL.TENSOR_MASK.POSITIVE_WEIGHT = 1.5 - # Whether to predict in the aligned representation - cfg.MODEL.TENSOR_MASK.ALIGNED_ON = False - # Whether to use the bipyramid architecture - cfg.MODEL.TENSOR_MASK.BIPYRAMID_ON = False diff --git a/spaces/ChenyangSi/FreeU/README.md b/spaces/ChenyangSi/FreeU/README.md deleted file mode 100644 index 04908d93f7418d405bb05ed9a7a04461e2d366a4..0000000000000000000000000000000000000000 --- a/spaces/ChenyangSi/FreeU/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: FreeU -emoji: 🐠 -colorFrom: gray -colorTo: red -sdk: gradio -sdk_version: 3.44.0 -app_file: app.py -pinned: false -hf_oauth: true ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/CofAI/chat/client/css/button.css b/spaces/CofAI/chat/client/css/button.css deleted file mode 100644 index 5f604a8460d048458249f78be9dc544ade84801e..0000000000000000000000000000000000000000 --- a/spaces/CofAI/chat/client/css/button.css +++ /dev/null @@ -1,26 +0,0 @@ -.button { - display: flex; - padding: 8px 12px; - align-items: center; - justify-content: center; - border: 1px solid var(--conversations); - border-radius: var(--border-radius-1); - width: 100%; - background: transparent; - cursor: pointer; -} - -.button span { - color: var(--colour-3); - font-size: 0.875rem; -} - -.button i::before { - margin-right: 8px; -} - -@media screen and (max-width: 990px) { - .button span { - font-size: 0.75rem; - } -} diff --git a/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/cldm/cldm.py b/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/cldm/cldm.py deleted file mode 100644 index 0b3ac7a575cf4933fc14dfc15dd3cca41cb3f3e8..0000000000000000000000000000000000000000 --- a/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/cldm/cldm.py +++ /dev/null @@ -1,435 +0,0 @@ -import einops -import torch -import torch as th -import torch.nn as nn - -from ldm.modules.diffusionmodules.util import ( - conv_nd, - linear, - zero_module, - timestep_embedding, -) - -from einops import rearrange, repeat -from torchvision.utils import make_grid -from ldm.modules.attention import SpatialTransformer -from ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock -from ldm.models.diffusion.ddpm import LatentDiffusion -from ldm.util import log_txt_as_img, exists, instantiate_from_config -from ldm.models.diffusion.ddim import DDIMSampler - - -class ControlledUnetModel(UNetModel): - def forward(self, x, timesteps=None, context=None, control=None, only_mid_control=False, **kwargs): - hs = [] - with torch.no_grad(): - t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) - emb = self.time_embed(t_emb) - h = x.type(self.dtype) - for module in self.input_blocks: - h = module(h, emb, context) - hs.append(h) - h = self.middle_block(h, emb, context) - - if control is not None: - h += control.pop() - - for i, module in enumerate(self.output_blocks): - if only_mid_control or control is None: - h = torch.cat([h, hs.pop()], dim=1) - else: - h = torch.cat([h, hs.pop() + control.pop()], dim=1) - h = module(h, emb, context) - - h = h.type(x.dtype) - return self.out(h) - - -class ControlNet(nn.Module): - def __init__( - self, - image_size, - in_channels, - model_channels, - hint_channels, - num_res_blocks, - attention_resolutions, - dropout=0, - channel_mult=(1, 2, 4, 8), - conv_resample=True, - dims=2, - use_checkpoint=False, - use_fp16=False, - num_heads=-1, - num_head_channels=-1, - num_heads_upsample=-1, - use_scale_shift_norm=False, - resblock_updown=False, - use_new_attention_order=False, - use_spatial_transformer=False, # custom transformer support - transformer_depth=1, # custom transformer support - context_dim=None, # custom transformer support - n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model - legacy=True, - disable_self_attentions=None, - num_attention_blocks=None, - disable_middle_self_attn=False, - use_linear_in_transformer=False, - ): - super().__init__() - if use_spatial_transformer: - assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' - - if context_dim is not None: - assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' - from omegaconf.listconfig import ListConfig - if type(context_dim) == ListConfig: - context_dim = list(context_dim) - - if num_heads_upsample == -1: - num_heads_upsample = num_heads - - if num_heads == -1: - assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' - - if num_head_channels == -1: - assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' - - self.dims = dims - self.image_size = image_size - self.in_channels = in_channels - self.model_channels = model_channels - if isinstance(num_res_blocks, int): - self.num_res_blocks = len(channel_mult) * [num_res_blocks] - else: - if len(num_res_blocks) != len(channel_mult): - raise ValueError("provide num_res_blocks either as an int (globally constant) or " - "as a list/tuple (per-level) with the same length as channel_mult") - self.num_res_blocks = num_res_blocks - if disable_self_attentions is not None: - # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not - assert len(disable_self_attentions) == len(channel_mult) - if num_attention_blocks is not None: - assert len(num_attention_blocks) == len(self.num_res_blocks) - assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks)))) - print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. " - f"This option has LESS priority than attention_resolutions {attention_resolutions}, " - f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, " - f"attention will still not be set.") - - self.attention_resolutions = attention_resolutions - self.dropout = dropout - self.channel_mult = channel_mult - self.conv_resample = conv_resample - self.use_checkpoint = use_checkpoint - self.dtype = th.float16 if use_fp16 else th.float32 - self.num_heads = num_heads - self.num_head_channels = num_head_channels - self.num_heads_upsample = num_heads_upsample - self.predict_codebook_ids = n_embed is not None - - time_embed_dim = model_channels * 4 - self.time_embed = nn.Sequential( - linear(model_channels, time_embed_dim), - nn.SiLU(), - linear(time_embed_dim, time_embed_dim), - ) - - self.input_blocks = nn.ModuleList( - [ - TimestepEmbedSequential( - conv_nd(dims, in_channels, model_channels, 3, padding=1) - ) - ] - ) - self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)]) - - self.input_hint_block = TimestepEmbedSequential( - conv_nd(dims, hint_channels, 16, 3, padding=1), - nn.SiLU(), - conv_nd(dims, 16, 16, 3, padding=1), - nn.SiLU(), - conv_nd(dims, 16, 32, 3, padding=1, stride=2), - nn.SiLU(), - conv_nd(dims, 32, 32, 3, padding=1), - nn.SiLU(), - conv_nd(dims, 32, 96, 3, padding=1, stride=2), - nn.SiLU(), - conv_nd(dims, 96, 96, 3, padding=1), - nn.SiLU(), - conv_nd(dims, 96, 256, 3, padding=1, stride=2), - nn.SiLU(), - zero_module(conv_nd(dims, 256, model_channels, 3, padding=1)) - ) - - self._feature_size = model_channels - input_block_chans = [model_channels] - ch = model_channels - ds = 1 - for level, mult in enumerate(channel_mult): - for nr in range(self.num_res_blocks[level]): - layers = [ - ResBlock( - ch, - time_embed_dim, - dropout, - out_channels=mult * model_channels, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - ) - ] - ch = mult * model_channels - if ds in attention_resolutions: - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - # num_heads = 1 - dim_head = ch // num_heads if use_spatial_transformer else num_head_channels - if exists(disable_self_attentions): - disabled_sa = disable_self_attentions[level] - else: - disabled_sa = False - - if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: - layers.append( - AttentionBlock( - ch, - use_checkpoint=use_checkpoint, - num_heads=num_heads, - num_head_channels=dim_head, - use_new_attention_order=use_new_attention_order, - ) if not use_spatial_transformer else SpatialTransformer( - ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, - disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint - ) - ) - self.input_blocks.append(TimestepEmbedSequential(*layers)) - self.zero_convs.append(self.make_zero_conv(ch)) - self._feature_size += ch - input_block_chans.append(ch) - if level != len(channel_mult) - 1: - out_ch = ch - self.input_blocks.append( - TimestepEmbedSequential( - ResBlock( - ch, - time_embed_dim, - dropout, - out_channels=out_ch, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - down=True, - ) - if resblock_updown - else Downsample( - ch, conv_resample, dims=dims, out_channels=out_ch - ) - ) - ) - ch = out_ch - input_block_chans.append(ch) - self.zero_convs.append(self.make_zero_conv(ch)) - ds *= 2 - self._feature_size += ch - - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - # num_heads = 1 - dim_head = ch // num_heads if use_spatial_transformer else num_head_channels - self.middle_block = TimestepEmbedSequential( - ResBlock( - ch, - time_embed_dim, - dropout, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - ), - AttentionBlock( - ch, - use_checkpoint=use_checkpoint, - num_heads=num_heads, - num_head_channels=dim_head, - use_new_attention_order=use_new_attention_order, - ) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn - ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, - disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint - ), - ResBlock( - ch, - time_embed_dim, - dropout, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - ), - ) - self.middle_block_out = self.make_zero_conv(ch) - self._feature_size += ch - - def make_zero_conv(self, channels): - return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0))) - - def forward(self, x, hint, timesteps, context, **kwargs): - t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) - emb = self.time_embed(t_emb) - - guided_hint = self.input_hint_block(hint, emb, context) - - outs = [] - - h = x.type(self.dtype) - for module, zero_conv in zip(self.input_blocks, self.zero_convs): - if guided_hint is not None: - h = module(h, emb, context) - h += guided_hint - guided_hint = None - else: - h = module(h, emb, context) - outs.append(zero_conv(h, emb, context)) - - h = self.middle_block(h, emb, context) - outs.append(self.middle_block_out(h, emb, context)) - - return outs - - -class ControlLDM(LatentDiffusion): - - def __init__(self, control_stage_config, control_key, only_mid_control, *args, **kwargs): - super().__init__(*args, **kwargs) - self.control_model = instantiate_from_config(control_stage_config) - self.control_key = control_key - self.only_mid_control = only_mid_control - self.control_scales = [1.0] * 13 - - @torch.no_grad() - def get_input(self, batch, k, bs=None, *args, **kwargs): - x, c = super().get_input(batch, self.first_stage_key, *args, **kwargs) - control = batch[self.control_key] - if bs is not None: - control = control[:bs] - control = control.to(self.device) - control = einops.rearrange(control, 'b h w c -> b c h w') - control = control.to(memory_format=torch.contiguous_format).float() - return x, dict(c_crossattn=[c], c_concat=[control]) - - def apply_model(self, x_noisy, t, cond, *args, **kwargs): - assert isinstance(cond, dict) - diffusion_model = self.model.diffusion_model - - cond_txt = torch.cat(cond['c_crossattn'], 1) - - if cond['c_concat'] is None: - eps = diffusion_model(x=x_noisy, timesteps=t, context=cond_txt, control=None, only_mid_control=self.only_mid_control) - else: - control = self.control_model(x=x_noisy, hint=torch.cat(cond['c_concat'], 1), timesteps=t, context=cond_txt) - control = [c * scale for c, scale in zip(control, self.control_scales)] - eps = diffusion_model(x=x_noisy, timesteps=t, context=cond_txt, control=control, only_mid_control=self.only_mid_control) - - return eps - - @torch.no_grad() - def get_unconditional_conditioning(self, N): - return self.get_learned_conditioning([""] * N) - - @torch.no_grad() - def log_images(self, batch, N=4, n_row=2, sample=False, ddim_steps=50, ddim_eta=0.0, return_keys=None, - quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, - plot_diffusion_rows=False, unconditional_guidance_scale=9.0, unconditional_guidance_label=None, - use_ema_scope=True, - **kwargs): - use_ddim = ddim_steps is not None - - log = dict() - z, c = self.get_input(batch, self.first_stage_key, bs=N) - c_cat, c = c["c_concat"][0][:N], c["c_crossattn"][0][:N] - N = min(z.shape[0], N) - n_row = min(z.shape[0], n_row) - log["reconstruction"] = self.decode_first_stage(z) - log["control"] = c_cat * 2.0 - 1.0 - log["conditioning"] = log_txt_as_img((512, 512), batch[self.cond_stage_key], size=16) - - if plot_diffusion_rows: - # get diffusion row - diffusion_row = list() - z_start = z[:n_row] - for t in range(self.num_timesteps): - if t % self.log_every_t == 0 or t == self.num_timesteps - 1: - t = repeat(torch.tensor([t]), '1 -> b', b=n_row) - t = t.to(self.device).long() - noise = torch.randn_like(z_start) - z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) - diffusion_row.append(self.decode_first_stage(z_noisy)) - - diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W - diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') - diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') - diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) - log["diffusion_row"] = diffusion_grid - - if sample: - # get denoise row - samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, - batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta) - x_samples = self.decode_first_stage(samples) - log["samples"] = x_samples - if plot_denoise_rows: - denoise_grid = self._get_denoise_row_from_list(z_denoise_row) - log["denoise_row"] = denoise_grid - - if unconditional_guidance_scale > 1.0: - uc_cross = self.get_unconditional_conditioning(N) - uc_cat = c_cat # torch.zeros_like(c_cat) - uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]} - samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, - batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=uc_full, - ) - x_samples_cfg = self.decode_first_stage(samples_cfg) - log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg - - return log - - @torch.no_grad() - def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs): - ddim_sampler = DDIMSampler(self) - b, c, h, w = cond["c_concat"][0].shape - shape = (self.channels, h // 8, w // 8) - samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size, shape, cond, verbose=False, **kwargs) - return samples, intermediates - - def configure_optimizers(self): - lr = self.learning_rate - params = list(self.control_model.parameters()) - if not self.sd_locked: - params += list(self.model.diffusion_model.output_blocks.parameters()) - params += list(self.model.diffusion_model.out.parameters()) - opt = torch.optim.AdamW(params, lr=lr) - return opt - - def low_vram_shift(self, is_diffusing): - if is_diffusing: - self.model = self.model.cuda() - self.control_model = self.control_model.cuda() - self.first_stage_model = self.first_stage_model.cpu() - self.cond_stage_model = self.cond_stage_model.cpu() - else: - self.model = self.model.cpu() - self.control_model = self.control_model.cpu() - self.first_stage_model = self.first_stage_model.cuda() - self.cond_stage_model = self.cond_stage_model.cuda() diff --git a/spaces/Cvandi/remake/realesrgan/models/realesrgan_model.py b/spaces/Cvandi/remake/realesrgan/models/realesrgan_model.py deleted file mode 100644 index c298a09c42433177f90001a0a31d029576072ccd..0000000000000000000000000000000000000000 --- a/spaces/Cvandi/remake/realesrgan/models/realesrgan_model.py +++ /dev/null @@ -1,258 +0,0 @@ -import numpy as np -import random -import torch -from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt -from basicsr.data.transforms import paired_random_crop -from basicsr.models.srgan_model import SRGANModel -from basicsr.utils import DiffJPEG, USMSharp -from basicsr.utils.img_process_util import filter2D -from basicsr.utils.registry import MODEL_REGISTRY -from collections import OrderedDict -from torch.nn import functional as F - - -@MODEL_REGISTRY.register() -class RealESRGANModel(SRGANModel): - """RealESRGAN Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. - - It mainly performs: - 1. randomly synthesize LQ images in GPU tensors - 2. optimize the networks with GAN training. - """ - - def __init__(self, opt): - super(RealESRGANModel, self).__init__(opt) - self.jpeger = DiffJPEG(differentiable=False).cuda() # simulate JPEG compression artifacts - self.usm_sharpener = USMSharp().cuda() # do usm sharpening - self.queue_size = opt.get('queue_size', 180) - - @torch.no_grad() - def _dequeue_and_enqueue(self): - """It is the training pair pool for increasing the diversity in a batch. - - Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a - batch could not have different resize scaling factors. Therefore, we employ this training pair pool - to increase the degradation diversity in a batch. - """ - # initialize - b, c, h, w = self.lq.size() - if not hasattr(self, 'queue_lr'): - assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}' - self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda() - _, c, h, w = self.gt.size() - self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda() - self.queue_ptr = 0 - if self.queue_ptr == self.queue_size: # the pool is full - # do dequeue and enqueue - # shuffle - idx = torch.randperm(self.queue_size) - self.queue_lr = self.queue_lr[idx] - self.queue_gt = self.queue_gt[idx] - # get first b samples - lq_dequeue = self.queue_lr[0:b, :, :, :].clone() - gt_dequeue = self.queue_gt[0:b, :, :, :].clone() - # update the queue - self.queue_lr[0:b, :, :, :] = self.lq.clone() - self.queue_gt[0:b, :, :, :] = self.gt.clone() - - self.lq = lq_dequeue - self.gt = gt_dequeue - else: - # only do enqueue - self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone() - self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone() - self.queue_ptr = self.queue_ptr + b - - @torch.no_grad() - def feed_data(self, data): - """Accept data from dataloader, and then add two-order degradations to obtain LQ images. - """ - if self.is_train and self.opt.get('high_order_degradation', True): - # training data synthesis - self.gt = data['gt'].to(self.device) - self.gt_usm = self.usm_sharpener(self.gt) - - self.kernel1 = data['kernel1'].to(self.device) - self.kernel2 = data['kernel2'].to(self.device) - self.sinc_kernel = data['sinc_kernel'].to(self.device) - - ori_h, ori_w = self.gt.size()[2:4] - - # ----------------------- The first degradation process ----------------------- # - # blur - out = filter2D(self.gt_usm, self.kernel1) - # random resize - updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0] - if updown_type == 'up': - scale = np.random.uniform(1, self.opt['resize_range'][1]) - elif updown_type == 'down': - scale = np.random.uniform(self.opt['resize_range'][0], 1) - else: - scale = 1 - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate(out, scale_factor=scale, mode=mode) - # add noise - gray_noise_prob = self.opt['gray_noise_prob'] - if np.random.uniform() < self.opt['gaussian_noise_prob']: - out = random_add_gaussian_noise_pt( - out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob) - else: - out = random_add_poisson_noise_pt( - out, - scale_range=self.opt['poisson_scale_range'], - gray_prob=gray_noise_prob, - clip=True, - rounds=False) - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range']) - out = torch.clamp(out, 0, 1) # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts - out = self.jpeger(out, quality=jpeg_p) - - # ----------------------- The second degradation process ----------------------- # - # blur - if np.random.uniform() < self.opt['second_blur_prob']: - out = filter2D(out, self.kernel2) - # random resize - updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0] - if updown_type == 'up': - scale = np.random.uniform(1, self.opt['resize_range2'][1]) - elif updown_type == 'down': - scale = np.random.uniform(self.opt['resize_range2'][0], 1) - else: - scale = 1 - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate( - out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode) - # add noise - gray_noise_prob = self.opt['gray_noise_prob2'] - if np.random.uniform() < self.opt['gaussian_noise_prob2']: - out = random_add_gaussian_noise_pt( - out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob) - else: - out = random_add_poisson_noise_pt( - out, - scale_range=self.opt['poisson_scale_range2'], - gray_prob=gray_noise_prob, - clip=True, - rounds=False) - - # JPEG compression + the final sinc filter - # We also need to resize images to desired sizes. We group [resize back + sinc filter] together - # as one operation. - # We consider two orders: - # 1. [resize back + sinc filter] + JPEG compression - # 2. JPEG compression + [resize back + sinc filter] - # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines. - if np.random.uniform() < 0.5: - # resize back + the final sinc filter - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode) - out = filter2D(out, self.sinc_kernel) - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2']) - out = torch.clamp(out, 0, 1) - out = self.jpeger(out, quality=jpeg_p) - else: - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2']) - out = torch.clamp(out, 0, 1) - out = self.jpeger(out, quality=jpeg_p) - # resize back + the final sinc filter - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode) - out = filter2D(out, self.sinc_kernel) - - # clamp and round - self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255. - - # random crop - gt_size = self.opt['gt_size'] - (self.gt, self.gt_usm), self.lq = paired_random_crop([self.gt, self.gt_usm], self.lq, gt_size, - self.opt['scale']) - - # training pair pool - self._dequeue_and_enqueue() - # sharpen self.gt again, as we have changed the self.gt with self._dequeue_and_enqueue - self.gt_usm = self.usm_sharpener(self.gt) - self.lq = self.lq.contiguous() # for the warning: grad and param do not obey the gradient layout contract - else: - # for paired training or validation - self.lq = data['lq'].to(self.device) - if 'gt' in data: - self.gt = data['gt'].to(self.device) - self.gt_usm = self.usm_sharpener(self.gt) - - def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): - # do not use the synthetic process during validation - self.is_train = False - super(RealESRGANModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img) - self.is_train = True - - def optimize_parameters(self, current_iter): - # usm sharpening - l1_gt = self.gt_usm - percep_gt = self.gt_usm - gan_gt = self.gt_usm - if self.opt['l1_gt_usm'] is False: - l1_gt = self.gt - if self.opt['percep_gt_usm'] is False: - percep_gt = self.gt - if self.opt['gan_gt_usm'] is False: - gan_gt = self.gt - - # optimize net_g - for p in self.net_d.parameters(): - p.requires_grad = False - - self.optimizer_g.zero_grad() - self.output = self.net_g(self.lq) - - l_g_total = 0 - loss_dict = OrderedDict() - if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): - # pixel loss - if self.cri_pix: - l_g_pix = self.cri_pix(self.output, l1_gt) - l_g_total += l_g_pix - loss_dict['l_g_pix'] = l_g_pix - # perceptual loss - if self.cri_perceptual: - l_g_percep, l_g_style = self.cri_perceptual(self.output, percep_gt) - if l_g_percep is not None: - l_g_total += l_g_percep - loss_dict['l_g_percep'] = l_g_percep - if l_g_style is not None: - l_g_total += l_g_style - loss_dict['l_g_style'] = l_g_style - # gan loss - fake_g_pred = self.net_d(self.output) - l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False) - l_g_total += l_g_gan - loss_dict['l_g_gan'] = l_g_gan - - l_g_total.backward() - self.optimizer_g.step() - - # optimize net_d - for p in self.net_d.parameters(): - p.requires_grad = True - - self.optimizer_d.zero_grad() - # real - real_d_pred = self.net_d(gan_gt) - l_d_real = self.cri_gan(real_d_pred, True, is_disc=True) - loss_dict['l_d_real'] = l_d_real - loss_dict['out_d_real'] = torch.mean(real_d_pred.detach()) - l_d_real.backward() - # fake - fake_d_pred = self.net_d(self.output.detach().clone()) # clone for pt1.9 - l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True) - loss_dict['l_d_fake'] = l_d_fake - loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach()) - l_d_fake.backward() - self.optimizer_d.step() - - if self.ema_decay > 0: - self.model_ema(decay=self.ema_decay) - - self.log_dict = self.reduce_loss_dict(loss_dict) diff --git a/spaces/DAMO-NLP-SG/CLEX-Chat/README.md b/spaces/DAMO-NLP-SG/CLEX-Chat/README.md deleted file mode 100644 index 50ce3572b5fd93d26e6e0c3cf97307833cf00a09..0000000000000000000000000000000000000000 --- a/spaces/DAMO-NLP-SG/CLEX-Chat/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: CLEX Chat -emoji: 📈 -colorFrom: purple -colorTo: purple -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/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/merge/tables.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/merge/tables.py deleted file mode 100644 index 394541b8a4d3355784ef84a04aaaa7501e4dc201..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/merge/tables.py +++ /dev/null @@ -1,338 +0,0 @@ -# Copyright 2013 Google, Inc. All Rights Reserved. -# -# Google Author(s): Behdad Esfahbod, Roozbeh Pournader - -from fontTools import ttLib, cffLib -from fontTools.misc.psCharStrings import T2WidthExtractor -from fontTools.ttLib.tables.DefaultTable import DefaultTable -from fontTools.merge.base import add_method, mergeObjects -from fontTools.merge.cmap import computeMegaCmap -from fontTools.merge.util import * -import logging - - -log = logging.getLogger("fontTools.merge") - - -ttLib.getTableClass("maxp").mergeMap = { - "*": max, - "tableTag": equal, - "tableVersion": equal, - "numGlyphs": sum, - "maxStorage": first, - "maxFunctionDefs": first, - "maxInstructionDefs": first, - # TODO When we correctly merge hinting data, update these values: - # maxFunctionDefs, maxInstructionDefs, maxSizeOfInstructions -} - -headFlagsMergeBitMap = { - "size": 16, - "*": bitwise_or, - 1: bitwise_and, # Baseline at y = 0 - 2: bitwise_and, # lsb at x = 0 - 3: bitwise_and, # Force ppem to integer values. FIXME? - 5: bitwise_and, # Font is vertical - 6: lambda bit: 0, # Always set to zero - 11: bitwise_and, # Font data is 'lossless' - 13: bitwise_and, # Optimized for ClearType - 14: bitwise_and, # Last resort font. FIXME? equal or first may be better - 15: lambda bit: 0, # Always set to zero -} - -ttLib.getTableClass("head").mergeMap = { - "tableTag": equal, - "tableVersion": max, - "fontRevision": max, - "checkSumAdjustment": lambda lst: 0, # We need *something* here - "magicNumber": equal, - "flags": mergeBits(headFlagsMergeBitMap), - "unitsPerEm": equal, - "created": current_time, - "modified": current_time, - "xMin": min, - "yMin": min, - "xMax": max, - "yMax": max, - "macStyle": first, - "lowestRecPPEM": max, - "fontDirectionHint": lambda lst: 2, - "indexToLocFormat": first, - "glyphDataFormat": equal, -} - -ttLib.getTableClass("hhea").mergeMap = { - "*": equal, - "tableTag": equal, - "tableVersion": max, - "ascent": max, - "descent": min, - "lineGap": max, - "advanceWidthMax": max, - "minLeftSideBearing": min, - "minRightSideBearing": min, - "xMaxExtent": max, - "caretSlopeRise": first, - "caretSlopeRun": first, - "caretOffset": first, - "numberOfHMetrics": recalculate, -} - -ttLib.getTableClass("vhea").mergeMap = { - "*": equal, - "tableTag": equal, - "tableVersion": max, - "ascent": max, - "descent": min, - "lineGap": max, - "advanceHeightMax": max, - "minTopSideBearing": min, - "minBottomSideBearing": min, - "yMaxExtent": max, - "caretSlopeRise": first, - "caretSlopeRun": first, - "caretOffset": first, - "numberOfVMetrics": recalculate, -} - -os2FsTypeMergeBitMap = { - "size": 16, - "*": lambda bit: 0, - 1: bitwise_or, # no embedding permitted - 2: bitwise_and, # allow previewing and printing documents - 3: bitwise_and, # allow editing documents - 8: bitwise_or, # no subsetting permitted - 9: bitwise_or, # no embedding of outlines permitted -} - - -def mergeOs2FsType(lst): - lst = list(lst) - if all(item == 0 for item in lst): - return 0 - - # Compute least restrictive logic for each fsType value - for i in range(len(lst)): - # unset bit 1 (no embedding permitted) if either bit 2 or 3 is set - if lst[i] & 0x000C: - lst[i] &= ~0x0002 - # set bit 2 (allow previewing) if bit 3 is set (allow editing) - elif lst[i] & 0x0008: - lst[i] |= 0x0004 - # set bits 2 and 3 if everything is allowed - elif lst[i] == 0: - lst[i] = 0x000C - - fsType = mergeBits(os2FsTypeMergeBitMap)(lst) - # unset bits 2 and 3 if bit 1 is set (some font is "no embedding") - if fsType & 0x0002: - fsType &= ~0x000C - return fsType - - -ttLib.getTableClass("OS/2").mergeMap = { - "*": first, - "tableTag": equal, - "version": max, - "xAvgCharWidth": first, # Will be recalculated at the end on the merged font - "fsType": mergeOs2FsType, # Will be overwritten - "panose": first, # FIXME: should really be the first Latin font - "ulUnicodeRange1": bitwise_or, - "ulUnicodeRange2": bitwise_or, - "ulUnicodeRange3": bitwise_or, - "ulUnicodeRange4": bitwise_or, - "fsFirstCharIndex": min, - "fsLastCharIndex": max, - "sTypoAscender": max, - "sTypoDescender": min, - "sTypoLineGap": max, - "usWinAscent": max, - "usWinDescent": max, - # Version 1 - "ulCodePageRange1": onlyExisting(bitwise_or), - "ulCodePageRange2": onlyExisting(bitwise_or), - # Version 2, 3, 4 - "sxHeight": onlyExisting(max), - "sCapHeight": onlyExisting(max), - "usDefaultChar": onlyExisting(first), - "usBreakChar": onlyExisting(first), - "usMaxContext": onlyExisting(max), - # version 5 - "usLowerOpticalPointSize": onlyExisting(min), - "usUpperOpticalPointSize": onlyExisting(max), -} - - -@add_method(ttLib.getTableClass("OS/2")) -def merge(self, m, tables): - DefaultTable.merge(self, m, tables) - if self.version < 2: - # bits 8 and 9 are reserved and should be set to zero - self.fsType &= ~0x0300 - if self.version >= 3: - # Only one of bits 1, 2, and 3 may be set. We already take - # care of bit 1 implications in mergeOs2FsType. So unset - # bit 2 if bit 3 is already set. - if self.fsType & 0x0008: - self.fsType &= ~0x0004 - return self - - -ttLib.getTableClass("post").mergeMap = { - "*": first, - "tableTag": equal, - "formatType": max, - "isFixedPitch": min, - "minMemType42": max, - "maxMemType42": lambda lst: 0, - "minMemType1": max, - "maxMemType1": lambda lst: 0, - "mapping": onlyExisting(sumDicts), - "extraNames": lambda lst: [], -} - -ttLib.getTableClass("vmtx").mergeMap = ttLib.getTableClass("hmtx").mergeMap = { - "tableTag": equal, - "metrics": sumDicts, -} - -ttLib.getTableClass("name").mergeMap = { - "tableTag": equal, - "names": first, # FIXME? Does mixing name records make sense? -} - -ttLib.getTableClass("loca").mergeMap = { - "*": recalculate, - "tableTag": equal, -} - -ttLib.getTableClass("glyf").mergeMap = { - "tableTag": equal, - "glyphs": sumDicts, - "glyphOrder": sumLists, - "axisTags": equal, -} - - -@add_method(ttLib.getTableClass("glyf")) -def merge(self, m, tables): - for i, table in enumerate(tables): - for g in table.glyphs.values(): - if i: - # Drop hints for all but first font, since - # we don't map functions / CVT values. - g.removeHinting() - # Expand composite glyphs to load their - # composite glyph names. - if g.isComposite() or g.isVarComposite(): - g.expand(table) - return DefaultTable.merge(self, m, tables) - - -ttLib.getTableClass("prep").mergeMap = lambda self, lst: first(lst) -ttLib.getTableClass("fpgm").mergeMap = lambda self, lst: first(lst) -ttLib.getTableClass("cvt ").mergeMap = lambda self, lst: first(lst) -ttLib.getTableClass("gasp").mergeMap = lambda self, lst: first( - lst -) # FIXME? Appears irreconcilable - - -@add_method(ttLib.getTableClass("CFF ")) -def merge(self, m, tables): - if any(hasattr(table.cff[0], "FDSelect") for table in tables): - raise NotImplementedError("Merging CID-keyed CFF tables is not supported yet") - - for table in tables: - table.cff.desubroutinize() - - newcff = tables[0] - newfont = newcff.cff[0] - private = newfont.Private - newDefaultWidthX, newNominalWidthX = private.defaultWidthX, private.nominalWidthX - storedNamesStrings = [] - glyphOrderStrings = [] - glyphOrder = set(newfont.getGlyphOrder()) - - for name in newfont.strings.strings: - if name not in glyphOrder: - storedNamesStrings.append(name) - else: - glyphOrderStrings.append(name) - - chrset = list(newfont.charset) - newcs = newfont.CharStrings - log.debug("FONT 0 CharStrings: %d.", len(newcs)) - - for i, table in enumerate(tables[1:], start=1): - font = table.cff[0] - defaultWidthX, nominalWidthX = ( - font.Private.defaultWidthX, - font.Private.nominalWidthX, - ) - widthsDiffer = ( - defaultWidthX != newDefaultWidthX or nominalWidthX != newNominalWidthX - ) - font.Private = private - fontGlyphOrder = set(font.getGlyphOrder()) - for name in font.strings.strings: - if name in fontGlyphOrder: - glyphOrderStrings.append(name) - cs = font.CharStrings - gs = table.cff.GlobalSubrs - log.debug("Font %d CharStrings: %d.", i, len(cs)) - chrset.extend(font.charset) - if newcs.charStringsAreIndexed: - for i, name in enumerate(cs.charStrings, start=len(newcs)): - newcs.charStrings[name] = i - newcs.charStringsIndex.items.append(None) - for name in cs.charStrings: - if widthsDiffer: - c = cs[name] - defaultWidthXToken = object() - extractor = T2WidthExtractor([], [], nominalWidthX, defaultWidthXToken) - extractor.execute(c) - width = extractor.width - if width is not defaultWidthXToken: - c.program.pop(0) - else: - width = defaultWidthX - if width != newDefaultWidthX: - c.program.insert(0, width - newNominalWidthX) - newcs[name] = cs[name] - - newfont.charset = chrset - newfont.numGlyphs = len(chrset) - newfont.strings.strings = glyphOrderStrings + storedNamesStrings - - return newcff - - -@add_method(ttLib.getTableClass("cmap")) -def merge(self, m, tables): - # TODO Handle format=14. - if not hasattr(m, "cmap"): - computeMegaCmap(m, tables) - cmap = m.cmap - - cmapBmpOnly = {uni: gid for uni, gid in cmap.items() if uni <= 0xFFFF} - self.tables = [] - module = ttLib.getTableModule("cmap") - if len(cmapBmpOnly) != len(cmap): - # format-12 required. - cmapTable = module.cmap_classes[12](12) - cmapTable.platformID = 3 - cmapTable.platEncID = 10 - cmapTable.language = 0 - cmapTable.cmap = cmap - self.tables.append(cmapTable) - # always create format-4 - cmapTable = module.cmap_classes[4](4) - cmapTable.platformID = 3 - cmapTable.platEncID = 1 - cmapTable.language = 0 - cmapTable.cmap = cmapBmpOnly - # ordered by platform then encoding - self.tables.insert(0, cmapTable) - self.tableVersion = 0 - self.numSubTables = len(self.tables) - return self diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_c_m_a_p.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_c_m_a_p.py deleted file mode 100644 index 6c00aaf63dea48bd96e718809319f3e27c08567e..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_c_m_a_p.py +++ /dev/null @@ -1,1578 +0,0 @@ -from fontTools.misc.textTools import bytesjoin, safeEval, readHex -from fontTools.misc.encodingTools import getEncoding -from fontTools.ttLib import getSearchRange -from fontTools.unicode import Unicode -from . import DefaultTable -import sys -import struct -import array -import logging - - -log = logging.getLogger(__name__) - - -def _make_map(font, chars, gids): - assert len(chars) == len(gids) - glyphNames = font.getGlyphNameMany(gids) - cmap = {} - for char, gid, name in zip(chars, gids, glyphNames): - if gid == 0: - continue - cmap[char] = name - return cmap - - -class table__c_m_a_p(DefaultTable.DefaultTable): - """Character to Glyph Index Mapping Table - - This class represents the `cmap
- - """) - - openai_api_key_textbox = gr.Textbox(placeholder="Paste your OpenAI API key (sk-...) and hit Enter", - show_label=False, lines=1, type='password') - - with gr.Row(): - with gr.Column(scale=1, min_width=TALKING_HEAD_WIDTH, visible=True): - # speak_text_cb = gr.Checkbox(label="Enable speech", value=False) - # speak_text_cb.change(update_foo, inputs=[speak_text_cb, speak_text_state], - # outputs=[speak_text_state]) - - my_file = gr.File(label="Upload a file", type="file", visible=False) - tmp_file = gr.File(LOOPING_TALKING_HEAD, visible=False) - # tmp_file_url = "/file=" + tmp_file.value['name'] - htm_video = create_html_video(LOOPING_TALKING_HEAD, TALKING_HEAD_WIDTH) - video_html = gr.HTML(htm_video) - - # my_aud_file = gr.File(label="Audio file", type="file", visible=True) - tmp_aud_file = gr.File("audios/tempfile.mp3", visible=False) - tmp_aud_file_url = "/file=" + tmp_aud_file.value['name'] - htm_audio = f'' - audio_html = gr.HTML(htm_audio) - - with gr.Column(scale=7): - chatbot = gr.Chatbot() - - with gr.Row(): - message = gr.Textbox(label="What's on your mind??", - placeholder="What's the answer to life, the universe, and everything?", - lines=1) - submit = gr.Button(value="Send", variant="secondary").style(full_width=False) - - # UNCOMMENT TO USE WHISPER - with gr.Row(): - audio_comp = gr.Microphone(source="microphone", type="filepath", label="Just say it!", - interactive=True, streaming=False) - audio_comp.change(transcribe, inputs=[audio_comp, whisper_lang_state], outputs=[message]) - - # TEMPORARY FOR TESTING - # with gr.Row(): - # audio_comp_tb = gr.Textbox(label="Just say it!", lines=1) - # audio_comp_tb.submit(transcribe_dummy, inputs=[audio_comp_tb, whisper_lang_state], outputs=[message]) - - gr.Examples( - examples=["How many people live in Canada?", - "What is 2 to the 30th power?", - "If x+y=10 and x-y=4, what are x and y?", - "How much did it rain in SF today?", - "Get me information about the movie 'Avatar'", - "What are the top tech headlines in the US?", - "On the desk, you see two blue booklets, two purple booklets, and two yellow pairs of sunglasses - " - "if I remove all the pairs of sunglasses from the desk, how many purple items remain on it?"], - inputs=message - ) - - # with gr.Tab("Settings"): - # tools_cb_group = gr.CheckboxGroup(label="Tools:", choices=TOOLS_LIST, - # value=TOOLS_DEFAULT_LIST) - # tools_cb_group.change(update_selected_tools, - # inputs=[tools_cb_group, tools_list_state, llm_state], - # outputs=[tools_list_state, llm_state, chain_state, express_chain_state]) - - # trace_chain_cb = gr.Checkbox(label="Show reasoning chain in chat bubble", value=False) - # trace_chain_cb.change(update_foo, inputs=[trace_chain_cb, trace_chain_state], - # outputs=[trace_chain_state]) - - # force_translate_cb = gr.Checkbox(label="Force translation to selected Output Language", - # value=FORCE_TRANSLATE_DEFAULT) - # force_translate_cb.change(update_foo, inputs=[force_translate_cb, force_translate_state], - # outputs=[force_translate_state]) - - # # speak_text_cb = gr.Checkbox(label="Speak text from agent", value=False) - # # speak_text_cb.change(update_foo, inputs=[speak_text_cb, speak_text_state], - # # outputs=[speak_text_state]) - - # talking_head_cb = gr.Checkbox(label="Show talking head", value=True) - # talking_head_cb.change(update_talking_head, inputs=[talking_head_cb, talking_head_state], - # outputs=[talking_head_state, video_html]) - - # monologue_cb = gr.Checkbox(label="Babel fish mode (translate/restate what you enter, no conversational agent)", - # value=False) - # monologue_cb.change(update_foo, inputs=[monologue_cb, monologue_state], - # outputs=[monologue_state]) - - # use_gpt4_cb = gr.Checkbox(label="Use GPT-4 (experimental) if your OpenAI API has access to it", - # value=USE_GPT4_DEFAULT) - # use_gpt4_cb.change(set_openai_api_key, - # inputs=[openai_api_key_textbox, use_gpt4_cb], - # outputs=[chain_state, express_chain_state, llm_state, embeddings_state, - # qa_chain_state, memory_state, use_gpt4_state]) - - # reset_btn = gr.Button(value="Reset chat", variant="secondary").style(full_width=False) - # reset_btn.click(reset_memory, inputs=[history_state, memory_state], - # outputs=[chatbot, history_state, memory_state]) - - # with gr.Tab("Whisper STT"): - # whisper_lang_radio = gr.Radio(label="Whisper speech-to-text language:", choices=[ - # WHISPER_DETECT_LANG, "Arabic", "Arabic (Gulf)", "Catalan", "Chinese (Cantonese)", "Chinese (Mandarin)", - # "Danish", "Dutch", "English (Australian)", "English (British)", "English (Indian)", "English (New Zealand)", - # "English (South African)", "English (US)", "English (Welsh)", "Finnish", "French", "French (Canadian)", - # "German", "German (Austrian)", "Georgian", "Hindi", "Icelandic", "Indonesian", "Italian", "Japanese", - # "Korean", "Norwegian", "Polish", - # "Portuguese (Brazilian)", "Portuguese (European)", "Romanian", "Russian", "Spanish (European)", - # "Spanish (Mexican)", "Spanish (US)", "Swedish", "Turkish", "Ukrainian", "Welsh"], - # value=WHISPER_DETECT_LANG) - - # whisper_lang_radio.change(update_foo, - # inputs=[whisper_lang_radio, whisper_lang_state], - # outputs=[whisper_lang_state]) - - # with gr.Tab("Output Language"): - # lang_level_radio = gr.Radio(label="Language level:", choices=[ - # LANG_LEVEL_DEFAULT, "1st grade", "2nd grade", "3rd grade", "4th grade", "5th grade", "6th grade", - # "7th grade", "8th grade", "9th grade", "10th grade", "11th grade", "12th grade", "University"], - # value=LANG_LEVEL_DEFAULT) - # lang_level_radio.change(update_foo, inputs=[lang_level_radio, lang_level_state], - # outputs=[lang_level_state]) - - # translate_to_radio = gr.Radio(label="Language:", choices=[ - # TRANSLATE_TO_DEFAULT, "Arabic", "Arabic (Gulf)", "Catalan", "Chinese (Cantonese)", "Chinese (Mandarin)", - # "Danish", "Dutch", "English (Australian)", "English (British)", "English (Indian)", "English (New Zealand)", - # "English (South African)", "English (US)", "English (Welsh)", "Finnish", "French", "French (Canadian)", - # "German", "German (Austrian)", "Georgian", "Hindi", "Icelandic", "Indonesian", "Italian", "Japanese", - # "Korean", "Norwegian", "Polish", - # "Portuguese (Brazilian)", "Portuguese (European)", "Romanian", "Russian", "Spanish (European)", - # "Spanish (Mexican)", "Spanish (US)", "Swedish", "Turkish", "Ukrainian", "Welsh", - # "emojis", "Gen Z slang", "how the stereotypical Karen would say it", "Klingon", "Neanderthal", - # "Pirate", "Strange Planet expospeak technical talk", "Yoda"], - # value=TRANSLATE_TO_DEFAULT) - - # translate_to_radio.change(update_foo, - # inputs=[translate_to_radio, translate_to_state], - # outputs=[translate_to_state]) - - # with gr.Tab("Formality"): - # formality_radio = gr.Radio(label="Formality:", - # choices=[FORMALITY_DEFAULT, "Casual", "Polite", "Honorific"], - # value=FORMALITY_DEFAULT) - # formality_radio.change(update_foo, - # inputs=[formality_radio, formality_state], - # outputs=[formality_state]) - - # with gr.Tab("Lit Style"): - # literary_style_radio = gr.Radio(label="Literary style:", choices=[ - # LITERARY_STYLE_DEFAULT, "Prose", "Story", "Summary", "Outline", "Bullets", "Poetry", "Haiku", "Limerick", - # "Rap", - # "Joke", "Knock-knock", "FAQ"], - # value=LITERARY_STYLE_DEFAULT) - - # literary_style_radio.change(update_foo, - # inputs=[literary_style_radio, literary_style_state], - # outputs=[literary_style_state]) - - # with gr.Tab("Emotions"): - # anticipation_level_radio = gr.Radio(label="Anticipation level:", - # choices=[EMOTION_DEFAULT, "Interest", "Anticipation", "Vigilance"], - # value=EMOTION_DEFAULT) - # anticipation_level_radio.change(update_foo, - # inputs=[anticipation_level_radio, anticipation_level_state], - # outputs=[anticipation_level_state]) - - # joy_level_radio = gr.Radio(label="Joy level:", - # choices=[EMOTION_DEFAULT, "Serenity", "Joy", "Ecstasy"], - # value=EMOTION_DEFAULT) - # joy_level_radio.change(update_foo, - # inputs=[joy_level_radio, joy_level_state], - # outputs=[joy_level_state]) - - # trust_level_radio = gr.Radio(label="Trust level:", - # choices=[EMOTION_DEFAULT, "Acceptance", "Trust", "Admiration"], - # value=EMOTION_DEFAULT) - # trust_level_radio.change(update_foo, - # inputs=[trust_level_radio, trust_level_state], - # outputs=[trust_level_state]) - - # fear_level_radio = gr.Radio(label="Fear level:", - # choices=[EMOTION_DEFAULT, "Apprehension", "Fear", "Terror"], - # value=EMOTION_DEFAULT) - # fear_level_radio.change(update_foo, - # inputs=[fear_level_radio, fear_level_state], - # outputs=[fear_level_state]) - - # surprise_level_radio = gr.Radio(label="Surprise level:", - # choices=[EMOTION_DEFAULT, "Distraction", "Surprise", "Amazement"], - # value=EMOTION_DEFAULT) - # surprise_level_radio.change(update_foo, - # inputs=[surprise_level_radio, surprise_level_state], - # outputs=[surprise_level_state]) - - # sadness_level_radio = gr.Radio(label="Sadness level:", - # choices=[EMOTION_DEFAULT, "Pensiveness", "Sadness", "Grief"], - # value=EMOTION_DEFAULT) - # sadness_level_radio.change(update_foo, - # inputs=[sadness_level_radio, sadness_level_state], - # outputs=[sadness_level_state]) - - # disgust_level_radio = gr.Radio(label="Disgust level:", - # choices=[EMOTION_DEFAULT, "Boredom", "Disgust", "Loathing"], - # value=EMOTION_DEFAULT) - # disgust_level_radio.change(update_foo, - # inputs=[disgust_level_radio, disgust_level_state], - # outputs=[disgust_level_state]) - - # anger_level_radio = gr.Radio(label="Anger level:", - # choices=[EMOTION_DEFAULT, "Annoyance", "Anger", "Rage"], - # value=EMOTION_DEFAULT) - # anger_level_radio.change(update_foo, - # inputs=[anger_level_radio, anger_level_state], - # outputs=[anger_level_state]) - - # with gr.Tab("Max Words"): - # num_words_slider = gr.Slider(label="Max number of words to generate (0 for don't care)", - # value=NUM_WORDS_DEFAULT, minimum=0, maximum=MAX_WORDS, step=10) - # num_words_slider.change(update_foo, - # inputs=[num_words_slider, num_words_state], - # outputs=[num_words_state]) - - # with gr.Tab("Embeddings"): - # embeddings_text_box = gr.Textbox(label="Enter text for embeddings and hit Create:", - # lines=20) - - # with gr.Row(): - # use_embeddings_cb = gr.Checkbox(label="Use embeddings", value=False) - # use_embeddings_cb.change(update_use_embeddings, inputs=[use_embeddings_cb, use_embeddings_state], - 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-#include "inpaint.h" - -namespace { - static std::vector kDistance2Similarity; - - void init_kDistance2Similarity() { - double base[11] = {1.0, 0.99, 0.96, 0.83, 0.38, 0.11, 0.02, 0.005, 0.0006, 0.0001, 0}; - int length = (PatchDistanceMetric::kDistanceScale + 1); - kDistance2Similarity.resize(length); - for (int i = 0; i < length; ++i) { - double t = (double) i / length; - int j = (int) (100 * t); - int k = j + 1; - double vj = (j < 11) ? base[j] : 0; - double vk = (k < 11) ? base[k] : 0; - kDistance2Similarity[i] = vj + (100 * t - j) * (vk - vj); - } - } - - - inline void _weighted_copy(const MaskedImage &source, int ys, int xs, cv::Mat &target, int yt, int xt, double weight) { - if (source.is_masked(ys, xs)) return; - if (source.is_globally_masked(ys, xs)) return; - - auto source_ptr = source.get_image(ys, xs); - auto target_ptr = target.ptr (yt, xt); - -#pragma unroll - for (int c = 0; c < 3; ++c) - target_ptr[c] += static_cast (source_ptr[c]) * weight; - target_ptr[3] += weight; - } -} - -/** - * This algorithme uses a version proposed by Xavier Philippeau. - */ - -Inpainting::Inpainting(cv::Mat image, cv::Mat mask, const PatchDistanceMetric *metric) - : m_initial(image, mask), m_distance_metric(metric), m_pyramid(), m_source2target(), m_target2source() { - _initialize_pyramid(); -} - -Inpainting::Inpainting(cv::Mat image, cv::Mat mask, cv::Mat global_mask, const PatchDistanceMetric *metric) - : m_initial(image, mask, global_mask), m_distance_metric(metric), m_pyramid(), m_source2target(), m_target2source() { - _initialize_pyramid(); -} - -void Inpainting::_initialize_pyramid() { - auto source = m_initial; - m_pyramid.push_back(source); - while (source.size().height > m_distance_metric->patch_size() && source.size().width > m_distance_metric->patch_size()) { - source = source.downsample(); - m_pyramid.push_back(source); - } - - if (kDistance2Similarity.size() == 0) { - init_kDistance2Similarity(); - } -} - -cv::Mat Inpainting::run(bool verbose, bool verbose_visualize, unsigned int random_seed) { - srand(random_seed); - const int nr_levels = m_pyramid.size(); - - MaskedImage source, target; - for (int level = nr_levels - 1; level >= 0; --level) { - if (verbose) std::cerr << "Inpainting level: " << level << std::endl; - - source = m_pyramid[level]; - - if (level == nr_levels - 1) { - target = source.clone(); - target.clear_mask(); - m_source2target = NearestNeighborField(source, target, m_distance_metric); - m_target2source = NearestNeighborField(target, source, m_distance_metric); - } else { - m_source2target = NearestNeighborField(source, target, m_distance_metric, m_source2target); - m_target2source = NearestNeighborField(target, source, m_distance_metric, m_target2source); - } - - if (verbose) std::cerr << "Initialization done." << std::endl; - - if (verbose_visualize) { - auto visualize_size = m_initial.size(); - cv::Mat source_visualize(visualize_size, m_initial.image().type()); - cv::resize(source.image(), source_visualize, visualize_size); - cv::imshow("Source", source_visualize); - cv::Mat target_visualize(visualize_size, m_initial.image().type()); - cv::resize(target.image(), target_visualize, visualize_size); - cv::imshow("Target", target_visualize); - cv::waitKey(0); - } - - target = _expectation_maximization(source, target, level, verbose); - } - - return target.image(); -} - -// EM-Like algorithm (see "PatchMatch" - page 6). -// Returns a double sized target image (unless level = 0). -MaskedImage Inpainting::_expectation_maximization(MaskedImage source, MaskedImage target, int level, bool verbose) { - const int nr_iters_em = 1 + 2 * level; - const int nr_iters_nnf = static_cast (std::min(7, 1 + level)); - const int patch_size = m_distance_metric->patch_size(); - - MaskedImage new_source, new_target; - - for (int iter_em = 0; iter_em < nr_iters_em; ++iter_em) { - if (iter_em != 0) { - m_source2target.set_target(new_target); - m_target2source.set_source(new_target); - target = new_target; - } - - if (verbose) std::cerr << "EM Iteration: " << iter_em << std::endl; - - auto size = source.size(); - for (int i = 0; i < size.height; ++i) { - for (int j = 0; j < size.width; ++j) { - if (!source.contains_mask(i, j, patch_size)) { - m_source2target.set_identity(i, j); - m_target2source.set_identity(i, j); - } - } - } - if (verbose) std::cerr << " NNF minimization started." << std::endl; - m_source2target.minimize(nr_iters_nnf); - m_target2source.minimize(nr_iters_nnf); - if (verbose) std::cerr << " NNF minimization finished." << std::endl; - - // Instead of upsizing the final target, we build the last target from the next level source image. - // Thus, the final target is less blurry (see "Space-Time Video Completion" - page 5). - bool upscaled = false; - if (level >= 1 && iter_em == nr_iters_em - 1) { - new_source = m_pyramid[level - 1]; - new_target = target.upsample(new_source.size().width, new_source.size().height, m_pyramid[level - 1].global_mask()); - upscaled = true; - } else { - new_source = m_pyramid[level]; - new_target = target.clone(); - } - - auto vote = cv::Mat(new_target.size(), CV_64FC4); - vote.setTo(cv::Scalar::all(0)); - - // Votes for best patch from NNF Source->Target (completeness) and Target->Source (coherence). - _expectation_step(m_source2target, 1, vote, new_source, upscaled); - if (verbose) std::cerr << " Expectation source to target finished." << std::endl; - _expectation_step(m_target2source, 0, vote, new_source, upscaled); - if (verbose) std::cerr << " Expectation target to source finished." << std::endl; - - // Compile votes and update pixel values. - _maximization_step(new_target, vote); - if (verbose) std::cerr << " Minimization step finished." << std::endl; - } - - return new_target; -} - -// Expectation step: vote for best estimations of each pixel. -void Inpainting::_expectation_step( - const NearestNeighborField &nnf, bool source2target, - cv::Mat &vote, const MaskedImage &source, bool upscaled -) { - auto source_size = nnf.source_size(); - auto target_size = nnf.target_size(); - const int patch_size = m_distance_metric->patch_size(); - - for (int i = 0; i < source_size.height; ++i) { - for (int j = 0; j < source_size.width; ++j) { - if (nnf.source().is_globally_masked(i, j)) continue; - int yp = nnf.at(i, j, 0), xp = nnf.at(i, j, 1), dp = nnf.at(i, j, 2); - double w = kDistance2Similarity[dp]; - - for (int di = -patch_size; di <= patch_size; ++di) { - for (int dj = -patch_size; dj <= patch_size; ++dj) { - int ys = i + di, xs = j + dj, yt = yp + di, xt = xp + dj; - if (!(ys >= 0 && ys < source_size.height && xs >= 0 && xs < source_size.width)) continue; - if (nnf.source().is_globally_masked(ys, xs)) continue; - if (!(yt >= 0 && yt < target_size.height && xt >= 0 && xt < target_size.width)) continue; - if (nnf.target().is_globally_masked(yt, xt)) continue; - - if (!source2target) { - std::swap(ys, yt); - std::swap(xs, xt); - } - - if (upscaled) { - for (int uy = 0; uy < 2; ++uy) { - for (int ux = 0; ux < 2; ++ux) { - _weighted_copy(source, 2 * ys + uy, 2 * xs + ux, vote, 2 * yt + uy, 2 * xt + ux, w); - } - } - } else { - _weighted_copy(source, ys, xs, vote, yt, xt, w); - } - } - } - } - } -} - -// Maximization Step: maximum likelihood of target pixel. -void Inpainting::_maximization_step(MaskedImage &target, const cv::Mat &vote) { - auto target_size = target.size(); - for (int i = 0; i < target_size.height; ++i) { - for (int j = 0; j < target_size.width; ++j) { - const double *source_ptr = vote.ptr (i, j); - unsigned char *target_ptr = target.get_mutable_image(i, j); - - if (target.is_globally_masked(i, j)) { - continue; - } - - if (source_ptr[3] > 0) { - unsigned char r = cv::saturate_cast (source_ptr[0] / source_ptr[3]); - unsigned char g = cv::saturate_cast (source_ptr[1] / source_ptr[3]); - unsigned char b = cv::saturate_cast (source_ptr[2] / source_ptr[3]); - target_ptr[0] = r, target_ptr[1] = g, target_ptr[2] = b; - } else { - target.set_mask(i, j, 0); - } - } - } -} - diff --git a/spaces/Giuliano/T0/README.md b/spaces/Giuliano/T0/README.md deleted file mode 100644 index b5165460814bdd52d1a341a483a9463380f42dff..0000000000000000000000000000000000000000 --- a/spaces/Giuliano/T0/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: T0 -emoji: 🏢 -colorFrom: pink -colorTo: indigo -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/Gradio-Blocks/anime-colorization/test_danbooru_cascade.sh b/spaces/Gradio-Blocks/anime-colorization/test_danbooru_cascade.sh deleted file mode 100644 index 39ec5efd62103685f5df061cbcc92d47f6d431a8..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/anime-colorization/test_danbooru_cascade.sh +++ /dev/null @@ -1,6 +0,0 @@ - -MODEL_FLAGS="--image_size 32 --small_size 32 --large_size 128 --guide_size 128 --num_channels 128 --num_channels2 64 --num_res_blocks 3 --learn_sigma True --dropout 0.0 --use_attention2 False" -DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule cosine" -TEST_FLAGS="--batch_size 4 --seed 233" - -OPENAI_LOGDIR="./danbooru2017_guided_cascaded_test_log" python scripts/cascaded_pixel_guide_sample.py --data_dir data/danbooru2017/anime --guide_dir data/danbooru2017/anime_sketch --timestep_respacing ddim25 --use_ddim True --model_path danbooru2017_guided_log/ema_0.9999_360000.pt --model_path2 danbooru2017_guided_sr_log/ema_0.9999_360000.pt $MODEL_FLAGS $DIFFUSION_FLAGS $TEST_FLAGS diff --git a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/model/tf/shape_helpers.py b/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/model/tf/shape_helpers.py deleted file mode 100644 index be2926a63bce7ca5db3effe63d5264620aa1dcf8..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/model/tf/shape_helpers.py +++ /dev/null @@ -1,47 +0,0 @@ -# Copyright 2021 DeepMind Technologies Limited -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Utilities for dealing with shapes of TensorFlow tensors.""" -import tensorflow.compat.v1 as tf - - -def shape_list(x): - """Return list of dimensions of a tensor, statically where possible. - - Like `x.shape.as_list()` but with tensors instead of `None`s. - - Args: - x: A tensor. - Returns: - A list with length equal to the rank of the tensor. The n-th element of the - list is an integer when that dimension is statically known otherwise it is - the n-th element of `tf.shape(x)`. - """ - x = tf.convert_to_tensor(x) - - # If unknown rank, return dynamic shape - if x.get_shape().dims is None: - return tf.shape(x) - - static = x.get_shape().as_list() - shape = tf.shape(x) - - ret = [] - for i in range(len(static)): - dim = static[i] - if dim is None: - dim = shape[i] - ret.append(dim) - return ret - diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py deleted file mode 100644 index 497267b6b50b3c160a4f8807230d4f986cf8eb3f..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py +++ /dev/null @@ -1,13 +0,0 @@ -_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' -conv_cfg = dict(type='ConvWS') -norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) -model = dict( - pretrained='open-mmlab://jhu/resnet50_gn_ws', - backbone=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), - neck=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), - roi_head=dict( - bbox_head=dict( - type='Shared4Conv1FCBBoxHead', - conv_out_channels=256, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg))) diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py deleted file mode 100644 index f275e430d1b57c4d9df57387b8f3ae6f0ff68cf1..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py +++ /dev/null @@ -1,157 +0,0 @@ -import numpy as np -import torch - -from ..builder import BBOX_SAMPLERS -from .random_sampler import RandomSampler - - -@BBOX_SAMPLERS.register_module() -class IoUBalancedNegSampler(RandomSampler): - """IoU Balanced Sampling. - - arXiv: https://arxiv.org/pdf/1904.02701.pdf (CVPR 2019) - - Sampling proposals according to their IoU. `floor_fraction` of needed RoIs - are sampled from proposals whose IoU are lower than `floor_thr` randomly. - The others are sampled from proposals whose IoU are higher than - `floor_thr`. These proposals are sampled from some bins evenly, which are - split by `num_bins` via IoU evenly. - - Args: - num (int): number of proposals. - pos_fraction (float): fraction of positive proposals. - floor_thr (float): threshold (minimum) IoU for IoU balanced sampling, - set to -1 if all using IoU balanced sampling. - floor_fraction (float): sampling fraction of proposals under floor_thr. - num_bins (int): number of bins in IoU balanced sampling. - """ - - def __init__(self, - num, - pos_fraction, - floor_thr=-1, - floor_fraction=0, - num_bins=3, - **kwargs): - super(IoUBalancedNegSampler, self).__init__(num, pos_fraction, - **kwargs) - assert floor_thr >= 0 or floor_thr == -1 - assert 0 <= floor_fraction <= 1 - assert num_bins >= 1 - - self.floor_thr = floor_thr - self.floor_fraction = floor_fraction - self.num_bins = num_bins - - def sample_via_interval(self, max_overlaps, full_set, num_expected): - """Sample according to the iou interval. - - Args: - max_overlaps (torch.Tensor): IoU between bounding boxes and ground - truth boxes. - full_set (set(int)): A full set of indices of boxes。 - num_expected (int): Number of expected samples。 - - Returns: - np.ndarray: Indices of samples - """ - max_iou = max_overlaps.max() - iou_interval = (max_iou - self.floor_thr) / self.num_bins - per_num_expected = int(num_expected / self.num_bins) - - sampled_inds = [] - for i in range(self.num_bins): - start_iou = self.floor_thr + i * iou_interval - end_iou = self.floor_thr + (i + 1) * iou_interval - tmp_set = set( - np.where( - np.logical_and(max_overlaps >= start_iou, - max_overlaps < end_iou))[0]) - tmp_inds = list(tmp_set & full_set) - if len(tmp_inds) > per_num_expected: - tmp_sampled_set = self.random_choice(tmp_inds, - per_num_expected) - else: - tmp_sampled_set = np.array(tmp_inds, dtype=np.int) - sampled_inds.append(tmp_sampled_set) - - sampled_inds = np.concatenate(sampled_inds) - if len(sampled_inds) < num_expected: - num_extra = num_expected - len(sampled_inds) - extra_inds = np.array(list(full_set - set(sampled_inds))) - if len(extra_inds) > num_extra: - extra_inds = self.random_choice(extra_inds, num_extra) - sampled_inds = np.concatenate([sampled_inds, extra_inds]) - - return sampled_inds - - def _sample_neg(self, assign_result, num_expected, **kwargs): - """Sample negative boxes. - - Args: - assign_result (:obj:`AssignResult`): The assigned results of boxes. - num_expected (int): The number of expected negative samples - - Returns: - Tensor or ndarray: sampled indices. - """ - neg_inds = torch.nonzero(assign_result.gt_inds == 0, as_tuple=False) - if neg_inds.numel() != 0: - neg_inds = neg_inds.squeeze(1) - if len(neg_inds) <= num_expected: - return neg_inds - else: - max_overlaps = assign_result.max_overlaps.cpu().numpy() - # balance sampling for negative samples - neg_set = set(neg_inds.cpu().numpy()) - - if self.floor_thr > 0: - floor_set = set( - np.where( - np.logical_and(max_overlaps >= 0, - max_overlaps < self.floor_thr))[0]) - iou_sampling_set = set( - np.where(max_overlaps >= self.floor_thr)[0]) - elif self.floor_thr == 0: - floor_set = set(np.where(max_overlaps == 0)[0]) - iou_sampling_set = set( - np.where(max_overlaps > self.floor_thr)[0]) - else: - floor_set = set() - iou_sampling_set = set( - np.where(max_overlaps > self.floor_thr)[0]) - # for sampling interval calculation - self.floor_thr = 0 - - floor_neg_inds = list(floor_set & neg_set) - iou_sampling_neg_inds = list(iou_sampling_set & neg_set) - num_expected_iou_sampling = int(num_expected * - (1 - self.floor_fraction)) - if len(iou_sampling_neg_inds) > num_expected_iou_sampling: - if self.num_bins >= 2: - iou_sampled_inds = self.sample_via_interval( - max_overlaps, set(iou_sampling_neg_inds), - num_expected_iou_sampling) - else: - iou_sampled_inds = self.random_choice( - iou_sampling_neg_inds, num_expected_iou_sampling) - else: - iou_sampled_inds = np.array( - iou_sampling_neg_inds, dtype=np.int) - num_expected_floor = num_expected - len(iou_sampled_inds) - if len(floor_neg_inds) > num_expected_floor: - sampled_floor_inds = self.random_choice( - floor_neg_inds, num_expected_floor) - else: - sampled_floor_inds = np.array(floor_neg_inds, dtype=np.int) - sampled_inds = np.concatenate( - (sampled_floor_inds, iou_sampled_inds)) - if len(sampled_inds) < num_expected: - num_extra = num_expected - len(sampled_inds) - extra_inds = np.array(list(neg_set - set(sampled_inds))) - if len(extra_inds) > num_extra: - extra_inds = self.random_choice(extra_inds, num_extra) - sampled_inds = np.concatenate((sampled_inds, extra_inds)) - sampled_inds = torch.from_numpy(sampled_inds).long().to( - assign_result.gt_inds.device) - return sampled_inds diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50-d8_480x480_40k_pascal_context_59.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50-d8_480x480_40k_pascal_context_59.py deleted file mode 100644 index 038993c6a434d843ddcd1f754bec191ae9da983e..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50-d8_480x480_40k_pascal_context_59.py +++ /dev/null @@ -1,10 +0,0 @@ -_base_ = [ - '../_base_/models/deeplabv3_r50-d8.py', - '../_base_/datasets/pascal_context_59.py', '../_base_/default_runtime.py', - '../_base_/schedules/schedule_40k.py' -] -model = dict( - decode_head=dict(num_classes=59), - auxiliary_head=dict(num_classes=59), - test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320))) -optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001) diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/task_balancing.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/task_balancing.py deleted file mode 100644 index 2ebdbbc820fd62af464f214e496471fbadc09a06..0000000000000000000000000000000000000000 --- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/task_balancing.py +++ /dev/null @@ -1,44 +0,0 @@ -# Copyright (c) EPFL VILAB. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn - - -class NoWeightingStrategy(nn.Module): - """No weighting strategy - """ - - def __init__(self, **kwargs): - super(NoWeightingStrategy, self).__init__() - - def forward(self, task_losses): - return task_losses - -class UncertaintyWeightingStrategy(nn.Module): - """Uncertainty weighting strategy - """ - - def __init__(self, tasks): - super(UncertaintyWeightingStrategy, self).__init__() - - self.tasks = tasks - self.log_vars = nn.Parameter(torch.zeros(len(tasks))) - - def forward(self, task_losses): - losses_tensor = torch.stack(list(task_losses.values())) - non_zero_losses_mask = (losses_tensor != 0.0) - - # calculate weighted losses - losses_tensor = torch.exp(-self.log_vars) * losses_tensor + self.log_vars - - # if some loss was 0 (i.e. task was dropped), weighted loss should also be 0 and not just log_var as no information was gained - losses_tensor *= non_zero_losses_mask - - # return dictionary of weighted task losses - weighted_task_losses = task_losses.copy() - weighted_task_losses.update(zip(weighted_task_losses, losses_tensor)) - return weighted_task_losses diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/hubert/hubert_asr.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/hubert/hubert_asr.py deleted file mode 100644 index dce899c9de3ab68341c0b21bea749a3ee29e0d8a..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/hubert/hubert_asr.py +++ /dev/null @@ -1,376 +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 contextlib -from argparse import Namespace -from typing import Any - -import torch -import torch.nn as nn -from dataclasses import dataclass, field -from fairseq import checkpoint_utils, tasks, utils -from fairseq.dataclass import FairseqDataclass -from fairseq.dataclass.utils import convert_namespace_to_omegaconf -from fairseq.models import BaseFairseqModel, FairseqEncoder, register_model -from fairseq.models.hubert.hubert import MASKING_DISTRIBUTION_CHOICES -from fairseq.tasks import FairseqTask -from omegaconf import II, MISSING - - -@dataclass -class HubertAsrConfig(FairseqDataclass): - w2v_path: str = field( - default=MISSING, metadata={"help": "path to hubert model"} - ) - no_pretrained_weights: bool = field( - default=False, - metadata={"help": "if true, does not load pretrained weights"}, - ) - dropout_input: float = field( - default=0.0, - metadata={"help": "dropout to apply to the input (after feat extr)"}, - ) - final_dropout: float = field( - default=0.0, - metadata={ - "help": "dropout after transformer and before final projection" - }, - ) - dropout: float = field( - default=0.0, - metadata={"help": "dropout probability inside hubert model"}, - ) - attention_dropout: float = field( - default=0.0, - metadata={ - "help": "dropout probability for attention weights " - "inside hubert model" - }, - ) - activation_dropout: float = field( - default=0.0, - metadata={ - "help": "dropout probability after activation in FFN " - "inside hubert model" - }, - ) - - # masking - apply_mask: bool = field( - default=False, metadata={"help": "apply masking during fine-tuning"} - ) - mask_length: int = field( - default=10, metadata={"help": "repeat the mask indices multiple times"} - ) - mask_prob: float = field( - default=0.5, - metadata={ - "help": "probability of replacing a token with mask " - "(normalized by length)" - }, - ) - mask_selection: MASKING_DISTRIBUTION_CHOICES = field( - default="static", metadata={"help": "how to choose masks"} - ) - mask_other: float = field( - default=0, - metadata={ - "help": "secondary mask argument " - "(used for more complex distributions), " - "see help in compute_mask_indices" - }, - ) - no_mask_overlap: bool = field( - default=False, metadata={"help": "whether to allow masks to overlap"} - ) - - # channel masking - mask_channel_length: int = field( - default=10, - metadata={"help": "length of the mask for features (channels)"}, - ) - mask_channel_prob: float = field( - default=0.0, - metadata={"help": "probability of replacing a feature with 0"}, - ) - mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( - default="static", - metadata={"help": "how to choose mask length for channel masking"}, - ) - mask_channel_other: float = field( - default=0, - metadata={ - "help": "secondary mask argument " - "(used for more complex distributions), " - "see help in compute_mask_indices" - }, - ) - no_mask_channel_overlap: bool = field( - default=False, - metadata={"help": "whether to allow channel masks to overlap"}, - ) - freeze_finetune_updates: int = field( - default=0, - metadata={"help": "dont finetune hubert for this many updates"}, - ) - feature_grad_mult: float = field( - default=0.0, - metadata={"help": "reset feature grad mult in hubert to this"}, - ) - layerdrop: float = field( - default=0.0, - metadata={"help": "probability of dropping a layer in hubert"}, - ) - normalize: bool = II("task.normalize") - data: str = II("task.data") - - # this holds the loaded hubert args - w2v_args: Any = None - - -@dataclass -class HubertCtcConfig(HubertAsrConfig): - pass - - -@register_model("hubert_ctc", dataclass=HubertCtcConfig) -class HubertCtc(BaseFairseqModel): - def __init__(self, cfg: HubertCtcConfig, w2v_encoder: BaseFairseqModel): - super().__init__() - self.cfg = cfg - self.w2v_encoder = w2v_encoder - - def upgrade_state_dict_named(self, state_dict, name): - super().upgrade_state_dict_named(state_dict, name) - return state_dict - - @classmethod - def build_model(cls, cfg: HubertCtcConfig, task: FairseqTask): - """Build a new model instance.""" - w2v_encoder = HubertEncoder(cfg, task.target_dictionary) - return cls(cfg, w2v_encoder) - - def get_normalized_probs(self, net_output, log_probs): - """Get normalized probabilities (or log probs) from a net's output.""" - - logits = net_output["encoder_out"] - if log_probs: - return utils.log_softmax(logits.float(), dim=-1) - else: - return utils.softmax(logits.float(), dim=-1) - - def get_logits(self, net_output): - logits = net_output["encoder_out"] - padding = net_output["encoder_padding_mask"] - if padding is not None and padding.any(): - padding = padding.T - logits[padding][..., 0] = 0 - logits[padding][..., 1:] = float("-inf") - - return logits - - def forward(self, **kwargs): - x = self.w2v_encoder(**kwargs) - return x - - -@dataclass -class HubertSeq2SeqConfig(HubertAsrConfig): - decoder_embed_dim: int = field( - default=768, metadata={"help": "decoder embedding dimension"} - ) - decoder_ffn_embed_dim: int = field( - default=3072, metadata={"help": "decoder embedding dimension for FFN"} - ) - decoder_layers: int = field( - default=6, metadata={"help": "num of decoder layers"} - ) - decoder_layerdrop: float = field( - default=0.0, metadata={"help": "decoder layerdrop chance"} - ) - decoder_attention_heads: int = field( - default=4, metadata={"help": "num decoder attention heads"} - ) - decoder_learned_pos: bool = field( - default=False, - metadata={"help": "use learned positional embeddings in the decoder"}, - ) - decoder_normalize_before: bool = field( - default=False, - metadata={"help": "apply layernorm before each decoder block"}, - ) - no_token_positional_embeddings: bool = field( - default=False, - metadata={ - "help": "if set, disables positional embeddings " - "(outside self attention)" - }, - ) - decoder_dropout: float = field( - default=0.0, metadata={"help": "dropout probability in the decoder"} - ) - decoder_attention_dropout: float = field( - default=0.0, - metadata={ - "help": "dropout probability for attention weights " - "inside the decoder" - }, - ) - decoder_activation_dropout: float = field( - default=0.0, - metadata={ - "help": "dropout probability after activation in FFN " - "inside the decoder" - }, - ) - max_target_positions: int = field( - default=2048, metadata={"help": "max target positions"} - ) - share_decoder_input_output_embed: bool = field( - default=False, - metadata={"help": "share decoder input and output embeddings"}, - ) - - -class HubertEncoder(FairseqEncoder): - def __init__(self, cfg: HubertAsrConfig, tgt_dict=None): - self.apply_mask = cfg.apply_mask - - arg_overrides = { - "dropout": cfg.dropout, - "activation_dropout": cfg.activation_dropout, - "dropout_input": cfg.dropout_input, - "attention_dropout": cfg.attention_dropout, - "mask_length": cfg.mask_length, - "mask_prob": cfg.mask_prob, - "mask_selection": cfg.mask_selection, - "mask_other": cfg.mask_other, - "no_mask_overlap": cfg.no_mask_overlap, - "mask_channel_length": cfg.mask_channel_length, - "mask_channel_prob": cfg.mask_channel_prob, - "mask_channel_selection": cfg.mask_channel_selection, - "mask_channel_other": cfg.mask_channel_other, - "no_mask_channel_overlap": cfg.no_mask_channel_overlap, - "encoder_layerdrop": cfg.layerdrop, - "feature_grad_mult": cfg.feature_grad_mult, - } - - if cfg.w2v_args is None: - state = checkpoint_utils.load_checkpoint_to_cpu( - cfg.w2v_path, arg_overrides - ) - w2v_args = state.get("cfg", None) - if w2v_args is None: - w2v_args = convert_namespace_to_omegaconf(state["args"]) - cfg.w2v_args = w2v_args - else: - state = None - w2v_args = cfg.w2v_args - if isinstance(w2v_args, Namespace): - cfg.w2v_args = w2v_args = convert_namespace_to_omegaconf( - w2v_args - ) - - assert cfg.normalize == w2v_args.task.normalize, ( - "Fine-tuning works best when data normalization is the same. " - "Please check that --normalize is set or unset for " - "both pre-training and here" - ) - - w2v_args.task.data = cfg.data - task = tasks.setup_task(w2v_args.task) - if state is not None and "task_state" in state: - # This will load the stored "dictionaries" object - task.load_state_dict(state["task_state"]) - model = task.build_model(w2v_args.model) - - if state is not None and not cfg.no_pretrained_weights: - # set strict=False because we omit some modules - model.load_state_dict(state["model"], strict=False) - - model.remove_pretraining_modules() - - super().__init__(task.source_dictionary) - - d = w2v_args.model.encoder_embed_dim - - self.w2v_model = model - - self.final_dropout = nn.Dropout(cfg.final_dropout) - self.freeze_finetune_updates = cfg.freeze_finetune_updates - self.num_updates = 0 - - if tgt_dict is not None: - self.proj = Linear(d, len(tgt_dict)) - elif getattr(cfg, "decoder_embed_dim", d) != d: - self.proj = Linear(d, cfg.decoder_embed_dim) - else: - self.proj = None - - def set_num_updates(self, num_updates): - """Set the number of parameters updates.""" - super().set_num_updates(num_updates) - self.num_updates = num_updates - - def forward(self, source, padding_mask, tbc=True, **kwargs): - - w2v_args = { - "source": source, - "padding_mask": padding_mask, - "mask": self.apply_mask and self.training, - } - - ft = self.freeze_finetune_updates <= self.num_updates - - with torch.no_grad() if not ft else contextlib.ExitStack(): - x, padding_mask = self.w2v_model.extract_features(**w2v_args) - - if tbc: - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - x = self.final_dropout(x) - - if self.proj: - x = self.proj(x) - - return { - "encoder_out": x, # T x B x C - "encoder_padding_mask": padding_mask, # B x T - "padding_mask": padding_mask, - } - - def reorder_encoder_out(self, encoder_out, new_order): - if encoder_out["encoder_out"] is not None: - encoder_out["encoder_out"] = encoder_out[ - "encoder_out" - ].index_select(1, new_order) - if encoder_out["encoder_padding_mask"] is not None: - encoder_out["encoder_padding_mask"] = encoder_out[ - "encoder_padding_mask" - ].index_select(0, new_order) - return encoder_out - - def max_positions(self): - """Maximum input length supported by the encoder.""" - return None - - def upgrade_state_dict_named(self, state_dict, name): - return state_dict - - -def Embedding(num_embeddings, embedding_dim, padding_idx): - m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) - nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5) - nn.init.constant_(m.weight[padding_idx], 0) - return m - - -def Linear(in_features, out_features, bias=True): - m = nn.Linear(in_features, out_features, bias) - nn.init.xavier_uniform_(m.weight) - if bias: - nn.init.constant_(m.bias, 0.0) - return m diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/transformer_align.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/transformer_align.py deleted file mode 100644 index eaf585bd10e630ae6cd89920f197cd165f55ad58..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/transformer_align.py +++ /dev/null @@ -1,93 +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.models import register_model, register_model_architecture -from fairseq.models.transformer import ( - TransformerModel, - base_architecture, - transformer_wmt_en_de_big, -) - - -@register_model("transformer_align") -class TransformerAlignModel(TransformerModel): - """ - See "Jointly Learning to Align and Translate with Transformer - Models" (Garg et al., EMNLP 2019). - """ - - def __init__(self, encoder, decoder, args): - super().__init__(args, encoder, decoder) - self.alignment_heads = args.alignment_heads - self.alignment_layer = args.alignment_layer - self.full_context_alignment = args.full_context_alignment - - @staticmethod - def add_args(parser): - # fmt: off - super(TransformerAlignModel, TransformerAlignModel).add_args(parser) - parser.add_argument('--alignment-heads', type=int, metavar='D', - help='Number of cross attention heads per layer to supervised with alignments') - parser.add_argument('--alignment-layer', type=int, metavar='D', - help='Layer number which has to be supervised. 0 corresponding to the bottommost layer.') - parser.add_argument('--full-context-alignment', action='store_true', - help='Whether or not alignment is supervised conditioned on the full target context.') - # fmt: on - - @classmethod - def build_model(cls, args, task): - # set any default arguments - transformer_align(args) - - transformer_model = TransformerModel.build_model(args, task) - return TransformerAlignModel( - transformer_model.encoder, transformer_model.decoder, args - ) - - def forward(self, src_tokens, src_lengths, prev_output_tokens): - encoder_out = self.encoder(src_tokens, src_lengths) - return self.forward_decoder(prev_output_tokens, encoder_out) - - def forward_decoder( - self, - prev_output_tokens, - encoder_out=None, - incremental_state=None, - features_only=False, - **extra_args, - ): - attn_args = { - "alignment_layer": self.alignment_layer, - "alignment_heads": self.alignment_heads, - } - decoder_out = self.decoder(prev_output_tokens, encoder_out, **attn_args) - - if self.full_context_alignment: - attn_args["full_context_alignment"] = self.full_context_alignment - _, alignment_out = self.decoder( - prev_output_tokens, - encoder_out, - features_only=True, - **attn_args, - **extra_args, - ) - decoder_out[1]["attn"] = alignment_out["attn"] - - return decoder_out - - -@register_model_architecture("transformer_align", "transformer_align") -def transformer_align(args): - args.alignment_heads = getattr(args, "alignment_heads", 1) - args.alignment_layer = getattr(args, "alignment_layer", 4) - args.full_context_alignment = getattr(args, "full_context_alignment", False) - base_architecture(args) - - -@register_model_architecture("transformer_align", "transformer_wmt_en_de_big_align") -def transformer_wmt_en_de_big_align(args): - args.alignment_heads = getattr(args, "alignment_heads", 1) - args.alignment_layer = getattr(args, "alignment_layer", 4) - transformer_wmt_en_de_big(args) diff --git a/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/src/hifi_gan/utils.py b/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/src/hifi_gan/utils.py deleted file mode 100644 index 71e9b2c99e053e2d4239074a67d64b834898c348..0000000000000000000000000000000000000000 --- a/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/src/hifi_gan/utils.py +++ /dev/null @@ -1,57 +0,0 @@ -import glob -import os -import matplotlib -import torch -from torch.nn.utils import weight_norm - -matplotlib.use("Agg") -import matplotlib.pylab as plt - - -def plot_spectrogram(spectrogram): - fig, ax = plt.subplots(figsize=(10, 2)) - im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none") - plt.colorbar(im, ax=ax) - - fig.canvas.draw() - plt.close() - - return fig - - -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) - - -def apply_weight_norm(m): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - weight_norm(m) - - -def get_padding(kernel_size, dilation=1): - return int((kernel_size * dilation - dilation) / 2) - - -def load_checkpoint(filepath, device): - assert os.path.isfile(filepath) - print("Loading '{}'".format(filepath)) - checkpoint_dict = torch.load(filepath, map_location=device) - print("Complete.") - return checkpoint_dict - - -def save_checkpoint(filepath, obj): - print("Saving checkpoint to {}".format(filepath)) - torch.save(obj, filepath) - print("Complete.") - - -def scan_checkpoint(cp_dir, prefix): - pattern = os.path.join(cp_dir, prefix + "????????") - cp_list = glob.glob(pattern) - if len(cp_list) == 0: - return None - return sorted(cp_list)[-1] diff --git a/spaces/Hina4867/bingo/src/state/index.ts b/spaces/Hina4867/bingo/src/state/index.ts deleted file mode 100644 index 272106d619c69f124ea8dd0f10872ab4840152d7..0000000000000000000000000000000000000000 --- a/spaces/Hina4867/bingo/src/state/index.ts +++ /dev/null @@ -1,118 +0,0 @@ -import { BingWebBot } from '@/lib/bots/bing' -import { BingConversationStyle, ChatMessageModel, BotId } from '@/lib/bots/bing/types' -import { nanoid } from '@/lib/utils' -import { atom } from 'jotai' -import { atomWithImmer } from 'jotai-immer' -import { atomWithStorage } from 'jotai/utils' -import { atomFamily } from 'jotai/utils' -import { atomWithHash, atomWithLocation } from 'jotai-location' - -const initialMessages: ChatMessageModel[] = [ - { author: 'system', text: 'conversation between user and robot', id: '1' }, - { author: 'user', text: '销量最高的 3 种宠物吸尘器有哪些优点和缺点? ', id: '2' }, - { - author: 'bot', text: ` -您好，这是必应。根据网上的搜索结果，我为您找到了以下三款销量最高的宠物吸尘器，以及它们的优点和缺点： - -- **戴森Dyson V10轻量版 Digital Slim Fluffy无线吸尘器**[^1^] [^3^]：这款吸尘器专为宠物家庭设计，拥有强大吸力和多功能吸头，能够有效清除宠物毛发和灰尘。 - - 优点：轻便易用，续航时间长，过滤系统先进，能够有效去除过敏原； - - 缺点：价格较高，噪音较大，需要定期清洁滤网。 -- **飞利浦FC6908/82（W7）无线吸尘器**[^2^] [^4^]：这是一款手持式干吸无线吸尘器，专门为宠物家庭设计。它具有75分钟的超高续航，适合大户型；除螨涡轮刷，可以深度清洁床单被罩，免受螨虫侵害；吸拖一体设计，280ml大水箱，还可以往水箱里加入清洁剂。 - - 优点：清洁性能强劲，操作方便，适用多种场景； - - 缺点：噪音较大，达到84dB（A）；尘杯容量较小，只有0.6L；充电时长较长，需要5小时。 -- **必胜（Bissell） 宠物吸尘器**[^2^]：这是一款便携式手持真空吸尘器，专为清理宠物毛发而设计。它配备特殊的橡胶喷嘴和软毛刷头，可以轻松拾取宠物毛发和灰尘；几乎静音运行。 - - 优点：轻巧易用，重量只有1.36kg；电池可充电可更换； - - 缺点：续航时间不稳定，在15-30分钟之间浮动；吸力不够强劲，在地毯或沙发上可能效果不佳；没有过滤系统或过滤网。 - -希望这些信息对您有所帮助。如果您还有其他问题，请随时提问。😊 - `, id: '3' }, - { author: 'user', text: '今天的新闻', id: '4' }, - { - author: 'bot', text: `你好，这是Bing。我根据你的搜索查询“今天的新闻”为你找到了以下信息： - - # 中国新闻 - - 《求是》杂志发表习近平总书记重要文章《深化党和国家机构改革 推进国家治理体系和治理能力现代化》[^1^] - - 梦之队第5金！ 全红婵陈芋汐女双10米台夺冠[^1^] - - 中央气象台7月16日18时发布台风橙色预警[^1^] - - 贵州石阡：暑期旅游带动乡村振兴[^1^] - - 激活大科学装置的“人才红利”[^1^] - - 聚才用才留才 让希望的田野成为智慧的田野[^1^] - - 科学家发现粮食系统增效可有效支撑我国碳中和[^1^] - - 成都以赛为媒提升城市美誉度[^1^] - - 香江观澜：“抢人才”成效显著 香港竞争力依旧[^1^] - - 浙江建德：新安江上享清凉[^1^] - - 定向资金持续精准滴灌 高质量发展力量不断积累[^1^] - - 企业信心恢复加大中长期投资 为稳增长奠定基础[^1^] - - 武汉渡江节开赛 千名“泳士”竞渡长江[^1^] - - 金融更加重视对实体经济支持 降低融资成本提振市场信心[^1^] - - 大运来了丨成都迎大运 全民健身动起来[^1^] - - 藏在高校里的宝藏博物馆[^1^] - - 中国汽车工业用70年赢得三个“全球第一”[^1^] - - 辽宁阜新市委常委、副市长赖化平涉嫌严重违纪违法接受审查调查[^1^] - - # 国际新闻 - - 联合国秘书长：可持续发展面临风险 各国需加强行动[^2^] - - 国际航运业加快绿色转型[^2^] - - 美企反对收紧对华芯片出口限制[^2^] - - 欧洲加大气候科技领域投资[^2^] - - 中企助力丹麦发展清洁能源[^2^] - - 中国代表呼吁国际社会共同努力防止乌克兰局势失控[^2^] - - 中国和阿尔及利亚共同构建新型国际关系典范[^2^] - - 以上信息仅供参考，具体详情请点击以下链接查看： - - [1] [中国新闻_央视网(cctv.com)](https://news.cctv.com/china/) - [2] [人民网_网上的人民日报](http://www.people.com.cn/)`, id: '5' }, - { author: 'user', text: '写一个快排', id: '6' }, - { author: 'bot', text: "你好，这是Bing。我可以帮你用python写一个快排。快速排序是一种基于分治思想的排序算法，它的基本思想是：从待排序的数组中选择一个基准元素，将数组分为两个子数组，使得左边的子数组中的元素都小于或等于基准元素，右边的子数组中的元素都大于或等于基准元素，然后对两个子数组递归地进行快速排序，最后合并结果。下面是用python实现的快速排序的代码：\n\n```python\n# 定义一个函数，用于交换数组中的两个元素\ndef swap(arr, i, j):\n temp = arr[i]\n arr[i] = arr[j]\n arr[j] = temp\n\n# 定义一个函数，用于对数组的一段区间进行划分\ndef partition(arr, low, high):\n # 选择最右边的元素作为基准元素\n pivot = arr[high]\n # 初始化一个指针，指向左边区间的最后一个位置\n i = low - 1\n # 遍历左边区间中的每个元素\n for j in range(low, high):\n # 如果当前元素小于或等于基准元素\n if arr[j] <= pivot:\n # 将指针右移一位\n i += 1\n # 将指针所指的元素与当前元素交换\n swap(arr, i, j)\n # 将基准元素与指针右边的第一个元素交换\n swap(arr, i + 1, high)\n # 返回基准元素的最终位置\n return i + 1\n\n# 定义一个函数，用于对数组进行快速排序\ndef quick_sort(arr, low, high):\n # 如果区间长度大于1\n if low < high:\n # 对区间进行划分，得到基准元素的位置\n p = partition(arr, low, high)\n # 对左边子区间进行快速排序\n quick_sort(arr, low, p - 1)\n # 对右边子区间进行快速排序\n quick_sort(arr, p + 1, high)\n\n# 测试代码\narr = [5, 3, 7, 2, 9, 1, 4, 6, 8]\nprint(\"原始数组：\", arr)\nquick_sort(arr, 0, len(arr) - 1)\nprint(\"排序后的数组：\", arr)\n```", id: '7' }, - { - author: 'bot', text: "好的，我会尝试画一只猫。\n > ", - id: '8' - } -] - -export const GreetMessages = [ - '谢谢你! 知道你什么时候准备好继续前进总是很有帮助的。我现在能为你回答什么问题?', - '重新开始总是很棒。问我任何问题!', - '当然，我很乐意重新开始。我现在可以为你提供哪些帮助?', - '当然，我已准备好进行新的挑战。我现在可以为你做什么?', - '很好，让我们来更改主题。你在想什么?', - '不用担心，我很高兴尝试一些新内容。我现在可以为你回答什么问题?', - '好的，我准备好了！感谢重置。我们应该了解哪些内容？', - '感谢刷新！你有新的话题吗？', - '明白了，让我们重新开始。接下来应该讨论什么？', - '下一步！我可以为你做什么？', - '好的，我已准备好新话题。我们应该一起了解哪些内容？' -] - -export const bingConversationStyleAtom = atomWithStorage ('bingConversationStyle', BingConversationStyle.Creative, undefined, { unstable_getOnInit: true }) -export const voiceAtom = atomWithStorage ('enableTTS', false, undefined, { unstable_getOnInit: true }) - -type Param = { botId: BotId; page: string } - -const createBotInstance = () => { - return new BingWebBot({ - cookie: ' ', - ua: ' ', - }) -} - -export const chatFamily = atomFamily( - (param: Param) => { - return atomWithImmer({ - botId: param.botId, - bot: createBotInstance(), - messages: [] as ChatMessageModel[], - generatingMessageId: '', - abortController: undefined as AbortController | undefined, - conversationId: nanoid(), - }) - }, - (a, b) => a.botId === b.botId && a.page === b.page, -) - -export const hashAtom = atomWithHash('dialog', '') - -export const locationAtom = atomWithLocation() - -export const voiceListenAtom = atom(false) diff --git a/spaces/Huu-Mon12/test01/Dockerfile b/spaces/Huu-Mon12/test01/Dockerfile deleted file mode 100644 index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000 --- a/spaces/Huu-Mon12/test01/Dockerfile +++ /dev/null @@ -1,21 +0,0 @@ -FROM node:18-bullseye-slim - -RUN apt-get update && \ - -apt-get install -y git - -RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app - -WORKDIR /app - -RUN npm install - -COPY Dockerfile greeting.md* .env* ./ - -RUN npm run build - -EXPOSE 7860 - -ENV NODE_ENV=production - -CMD [ "npm", "start" ] \ No newline at end of file diff --git a/spaces/ICML2022/OFA/fairseq/examples/speech_text_joint_to_text/docs/ende-mustc.md b/spaces/ICML2022/OFA/fairseq/examples/speech_text_joint_to_text/docs/ende-mustc.md deleted file mode 100644 index 2897c4e27b053d4fd65b37fb7e586679dffed1ba..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/examples/speech_text_joint_to_text/docs/ende-mustc.md +++ /dev/null @@ -1,112 +0,0 @@ -[[Back]](..) - -# Joint Speech Text Training for the MuST-C English to German Speech Translation task - -Joint Training Baseline: it is based on paper ["A general multi-task learning framework to leverage text data for speech to text tasks"](https://arxiv.org/pdf/2010.11338.pdf) - -Enhanced Joint Training: the joint training is enhanced with pre-trained models, cross attentive regularization and online knowledge distillation based on paper ["Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task"](https://research.fb.com/publications/improving-speech-translation-by-understanding-and-learning-from-the-auxiliary-text-translation-task) - -## Prepare Data -#### Download files -- Sentence piece model [spm.model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/spm.model) -- Dictionary [dict.txt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/dict.txt) -- config [config.yaml](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/config.yaml) -#### Prepare MuST-C data set -- [Please follow the data preparation in the S2T example](https://github.com/pytorch/fairseq/blob/main/examples/speech_to_text/docs/mustc_example.md) -- Append src_text in the tsv file with phoneme representation. -```bash - python examples/speech_text_joint_to_text/scripts/g2p_encode.py \ - --lower-case --do-filter --use-word-start --no-punc \ - --reserve-word examples/speech_text_joint_to_text/configs/mustc_noise.list \ - --data-path ${must_c_en_de_src_text} \ - --out-path ${must_c_en_de_src_text_pho} -``` -- Update tsv data with src_text generated above and save to $MANIFEST_ROOT -- Prepare phoneme dictionary and save to $MANIFEST_ROOT as [src_dict.txt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/src_dict.txt) -#### Prepare WMT text data -- [Download wmt data](https://github.com/pytorch/fairseq/blob/main/examples/translation/prepare-wmt14en2de.sh) -- Convert source text (English) into phoneme representation as above -- Generate binary parallel file for training (as translation example) and save data in $parallel_text_data - -## Training -The model is trained with 8 v100 GPUs. - -#### Download pretrained models -- [pretrain_encoder](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_multilingual_asr_transformer_m.pt) -- [pretrain_nmt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/checkpoint_mt.pt) - -#### Training scripts -- Jointly trained model from scratch -```bash -python train.py ${MANIFEST_ROOT} \ - --save-dir ${save_dir} \ - --num-workers 8 \ - --task speech_text_joint_to_text \ - --arch dualinputs2ttransformer_s \ - --user-dir examples/speech_text_joint_to_text \ - --max-epoch 100 --update-mix-data \ - --optimizer adam --lr-scheduler inverse_sqrt \ - --lr 0.001 --update-freq 4 --clip-norm 10.0 \ - --criterion guided_label_smoothed_cross_entropy_with_accuracy \ - --label-smoothing 0.1 --max-tokens 10000 --max-tokens-text 10000 \ - --max-positions-text 400 --seed 2 --speech-encoder-layers 12 \ - --text-encoder-layers 6 --encoder-shared-layers 6 --decoder-layers 6 \ - --dropout 0.1 --warmup-updates 20000 \ - --text-sample-ratio 0.25 --parallel-text-data ${parallel_text_data} \ - --text-input-cost-ratio 0.5 --enc-grad-mult 2.0 --add-speech-eos \ - --log-format json --langpairs en-de --noise-token '"'"'▁NOISE'"'"' \ - --mask-text-ratio 0.0 --max-tokens-valid 20000 --ddp-backend no_c10d \ - --log-interval 100 --data-buffer-size 50 --config-yaml config.yaml \ - --keep-last-epochs 10 -``` -- Jointly trained model with good initialization, cross attentive loss and online knowledge distillation -```bash -python train.py ${MANIFEST_ROOT} \ - --save-dir ${save_dir} \ - --num-workers 8 \ - --task speech_text_joint_to_text \ - --arch dualinputs2ttransformer_m \ - --user-dir examples/speech_text_joint_to_text \ - --max-epoch 100 --update-mix-data \ - --optimizer adam --lr-scheduler inverse_sqrt \ - --lr 0.002 --update-freq 4 --clip-norm 10.0 \ - --criterion guided_label_smoothed_cross_entropy_with_accuracy \ - --guide-alpha 0.8 --disable-text-guide-update-num 5000 \ - --label-smoothing 0.1 --max-tokens 10000 --max-tokens-text 10000 \ - --max-positions-text 400 --seed 2 --speech-encoder-layers 12 \ - --text-encoder-layers 6 --encoder-shared-layers 6 --decoder-layers 6 \ - --dropout 0.1 --warmup-updates 20000 --attentive-cost-regularization 0.02 \ - --text-sample-ratio 0.25 --parallel-text-data ${parallel_text_data} \ - --text-input-cost-ratio 0.5 --enc-grad-mult 2.0 --add-speech-eos \ - --log-format json --langpairs en-de --noise-token '"'"'▁NOISE'"'"' \ - --mask-text-ratio 0.0 --max-tokens-valid 20000 --ddp-backend no_c10d \ - --log-interval 100 --data-buffer-size 50 --config-yaml config.yaml \ - --load-pretrain-speech-encoder ${pretrain_encoder} \ - --load-pretrain-decoder ${pretrain_nmt} \ - --load-pretrain-text-encoder-last ${pretrain_nmt} \ - --keep-last-epochs 10 -``` - -## Evaluation -```bash -python ./fairseq_cli/generate.py \ - ${MANIFEST_ROOT} \ - --task speech_text_joint_to_text \ - --max-tokens 25000 \ - --nbest 1 \ - --results-path ${infer_results} \ - --batch-size 512 \ - --path ${model} \ - --gen-subset tst-COMMON \ - --config-yaml config_spm.yaml \ - --scoring sacrebleu \ - --beam 5 --lenpen 1.0 \ - --user-dir examples/speech_text_joint_to_text \ - --load-speech-only -``` - -## Results (Joint training with initialization + CAR + online KD) -|Direction|En-De | En-Es | En-Fr | -|---|---|---|---| -|BLEU|27.4| 31.2 | 37.6 | -|checkpoint | [link](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/checkpoint_ave_10.pt) |[link](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_es/checkpoint_ave_10.pt)|[link](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_fr/checkpoint_ave_10.pt)| diff --git a/spaces/IDEA-CCNL/ziya2-13B-base/app.py b/spaces/IDEA-CCNL/ziya2-13B-base/app.py deleted file mode 100644 index 011c0fdd461a98a9f256eef00c6d413a63655b20..0000000000000000000000000000000000000000 --- a/spaces/IDEA-CCNL/ziya2-13B-base/app.py +++ /dev/null @@ -1,72 +0,0 @@ -import gradio as gr -import plotly.express as px -import numpy as np -import wandb -import os -import pandas as pd - -wandb_key = os.getenv("wandb_key") -proj_name = os.getenv("proj_name") -entity_proj_name = os.getenv("entity_proj_name") -wandb.login(key=wandb_key) -api = wandb.Api() - -step=[] -loss=[] -runs = api.runs(entity_proj_name) -for run in runs: - # if run.id not in ['dys9531f','kakfv1ab']: - # continue - # print(run) - history = run.scan_history(keys=['train/lm_loss','_step']) - tmp_step=[] - tmp_loss=[] - for row in history: - # if row['_step'] not in step - tmp_step.append(row['_step']) - tmp_loss.append(row['train/lm_loss']) - if len(tmp_step)>35: - step.extend(tmp_step) - loss.extend(tmp_loss) - - -def get_plot(period=1): - # run = api.run(proj_name) - # df = run.history() - # history = run.scan_history(keys=['train/lm_loss','_step']) - # step=[] - # loss=[] - # for row in history: - # step.append(row['_step']) - # loss.append(row['train/lm_loss']) - - # df=pd.DataFrame({'_step':step,'train/lm_loss':loss}) - # df.shape - - runs = api.runs(entity_proj_name) - for run in runs: - df = run.history() - for i,row in df.iterrows(): - if row['_step'] not in step: - step.append(row['_step']) - loss.append(row['train/lm_loss']) - - df=pd.DataFrame({'_step':step,'train/lm_loss':loss}) - df=df.sort_values('_step', ascending=True) - - fig = px.line(df, x='_step', y='train/lm_loss', range_y=[0.8, 3.8], render_mode='webgl') - return fig - - -with gr.Blocks() as demo: - with gr.Row(): - with gr.Column(): - gr.Markdown("Ziya-Llama2-13B/train/lm_loss") - plot = gr.Plot(label="Plot (updates every 12 second)") - - dep = demo.load(get_plot, None, plot, every=100) - - - -if __name__ == "__main__": - demo.queue().launch() \ No newline at end of file diff --git a/spaces/Iceclear/StableSR/StableSR/ldm/modules/image_degradation/utils_image.py b/spaces/Iceclear/StableSR/StableSR/ldm/modules/image_degradation/utils_image.py deleted file mode 100644 index 0175f155ad900ae33c3c46ed87f49b352e3faf98..0000000000000000000000000000000000000000 --- a/spaces/Iceclear/StableSR/StableSR/ldm/modules/image_degradation/utils_image.py +++ /dev/null @@ -1,916 +0,0 @@ -import os -import math -import random -import numpy as np -import torch -import cv2 -from torchvision.utils import make_grid -from datetime import datetime -#import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py - - -os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" - - -''' -# -------------------------------------------- -# Kai Zhang (github: https://github.com/cszn) -# 03/Mar/2019 -# -------------------------------------------- -# https://github.com/twhui/SRGAN-pyTorch -# https://github.com/xinntao/BasicSR -# -------------------------------------------- -''' - - -IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif'] - - -def is_image_file(filename): - return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) - - -def get_timestamp(): - return datetime.now().strftime('%y%m%d-%H%M%S') - - -def imshow(x, title=None, cbar=False, figsize=None): - plt.figure(figsize=figsize) - plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray') - if title: - plt.title(title) - if cbar: - plt.colorbar() - plt.show() - - -def surf(Z, cmap='rainbow', figsize=None): - plt.figure(figsize=figsize) - ax3 = plt.axes(projection='3d') - - w, h = Z.shape[:2] - xx = np.arange(0,w,1) - yy = np.arange(0,h,1) - X, Y = np.meshgrid(xx, yy) - ax3.plot_surface(X,Y,Z,cmap=cmap) - #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap) - plt.show() - - -''' -# -------------------------------------------- -# get image pathes -# -------------------------------------------- -''' - - -def get_image_paths(dataroot): - paths = None # return None if dataroot is None - if dataroot is not None: - paths = sorted(_get_paths_from_images(dataroot)) - return paths - - -def _get_paths_from_images(path): - assert os.path.isdir(path), '{:s} is not a valid directory'.format(path) - images = [] - for dirpath, _, fnames in sorted(os.walk(path)): - for fname in sorted(fnames): - if is_image_file(fname): - img_path = os.path.join(dirpath, fname) - images.append(img_path) - assert images, '{:s} has no valid image file'.format(path) - return images - - -''' -# -------------------------------------------- -# split large images into small images -# -------------------------------------------- -''' - - -def patches_from_image(img, p_size=512, p_overlap=64, p_max=800): - w, h = img.shape[:2] - patches = [] - if w > p_max and h > p_max: - w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int)) - h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int)) - w1.append(w-p_size) - h1.append(h-p_size) -# print(w1) -# print(h1) - for i in w1: - for j in h1: - patches.append(img[i:i+p_size, j:j+p_size,:]) - else: - patches.append(img) - - return patches - - -def imssave(imgs, img_path): - """ - imgs: list, N images of size WxHxC - """ - img_name, ext = os.path.splitext(os.path.basename(img_path)) - - for i, img in enumerate(imgs): - if img.ndim == 3: - img = img[:, :, [2, 1, 0]] - new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png') - cv2.imwrite(new_path, img) - - -def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000): - """ - split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size), - and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max) - will be splitted. - Args: - original_dataroot: - taget_dataroot: - p_size: size of small images - p_overlap: patch size in training is a good choice - p_max: images with smaller size than (p_max)x(p_max) keep unchanged. - """ - paths = get_image_paths(original_dataroot) - for img_path in paths: - # img_name, ext = os.path.splitext(os.path.basename(img_path)) - img = imread_uint(img_path, n_channels=n_channels) - patches = patches_from_image(img, p_size, p_overlap, p_max) - imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path))) - #if original_dataroot == taget_dataroot: - #del img_path - -''' -# -------------------------------------------- -# makedir -# -------------------------------------------- -''' - - -def mkdir(path): - if not os.path.exists(path): - os.makedirs(path) - - -def mkdirs(paths): - if isinstance(paths, str): - mkdir(paths) - else: - for path in paths: - mkdir(path) - - -def mkdir_and_rename(path): - if os.path.exists(path): - new_name = path + '_archived_' + get_timestamp() - print('Path already exists. Rename it to [{:s}]'.format(new_name)) - os.rename(path, new_name) - os.makedirs(path) - - -''' -# -------------------------------------------- -# read image from path -# opencv is fast, but read BGR numpy image -# -------------------------------------------- -''' - - -# -------------------------------------------- -# get uint8 image of size HxWxn_channles (RGB) -# -------------------------------------------- -def imread_uint(path, n_channels=3): - # input: path - # output: HxWx3(RGB or GGG), or HxWx1 (G) - if n_channels == 1: - img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE - img = np.expand_dims(img, axis=2) # HxWx1 - elif n_channels == 3: - img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G - if img.ndim == 2: - img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG - else: - img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB - return img - - -# -------------------------------------------- -# matlab's imwrite -# -------------------------------------------- -def imsave(img, img_path): - img = np.squeeze(img) - if img.ndim == 3: - img = img[:, :, [2, 1, 0]] - cv2.imwrite(img_path, img) - -def imwrite(img, img_path): - img = np.squeeze(img) - if img.ndim == 3: - img = img[:, :, [2, 1, 0]] - cv2.imwrite(img_path, img) - - - -# -------------------------------------------- -# get single image of size HxWxn_channles (BGR) -# -------------------------------------------- -def read_img(path): - # read image by cv2 - # return: Numpy float32, HWC, BGR, [0,1] - img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE - img = img.astype(np.float32) / 255. - if img.ndim == 2: - img = np.expand_dims(img, axis=2) - # some images have 4 channels - if img.shape[2] > 3: - img = img[:, :, :3] - return img - - -''' -# -------------------------------------------- -# image format conversion -# -------------------------------------------- -# numpy(single) <---> numpy(unit) -# numpy(single) <---> tensor -# numpy(unit) <---> tensor -# -------------------------------------------- -''' - - -# -------------------------------------------- -# numpy(single) [0, 1] <---> numpy(unit) -# -------------------------------------------- - - -def uint2single(img): - - return np.float32(img/255.) - - -def single2uint(img): - - return np.uint8((img.clip(0, 1)*255.).round()) - - -def uint162single(img): - - return np.float32(img/65535.) - - -def single2uint16(img): - - return np.uint16((img.clip(0, 1)*65535.).round()) - - -# -------------------------------------------- -# numpy(unit) (HxWxC or HxW) <---> tensor -# -------------------------------------------- - - -# convert uint to 4-dimensional torch tensor -def uint2tensor4(img): - if img.ndim == 2: - img = np.expand_dims(img, axis=2) - return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0) - - -# convert uint to 3-dimensional torch tensor -def uint2tensor3(img): - if img.ndim == 2: - img = np.expand_dims(img, axis=2) - return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.) - - -# convert 2/3/4-dimensional torch tensor to uint -def tensor2uint(img): - img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy() - if img.ndim == 3: - img = np.transpose(img, (1, 2, 0)) - return np.uint8((img*255.0).round()) - - -# -------------------------------------------- -# numpy(single) (HxWxC) <---> tensor -# -------------------------------------------- - - -# convert single (HxWxC) to 3-dimensional torch tensor -def single2tensor3(img): - return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float() - - -# convert single (HxWxC) to 4-dimensional torch tensor -def single2tensor4(img): - return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0) - - -# convert torch tensor to single -def tensor2single(img): - img = img.data.squeeze().float().cpu().numpy() - if img.ndim == 3: - img = np.transpose(img, (1, 2, 0)) - - return img - -# convert torch tensor to single -def tensor2single3(img): - img = img.data.squeeze().float().cpu().numpy() - if img.ndim == 3: - img = np.transpose(img, (1, 2, 0)) - elif img.ndim == 2: - img = np.expand_dims(img, axis=2) - return img - - -def single2tensor5(img): - return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0) - - -def single32tensor5(img): - return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0) - - -def single42tensor4(img): - return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float() - - -# from skimage.io import imread, imsave -def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)): - ''' - Converts a torch Tensor into an image Numpy array of BGR channel order - Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order - Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default) - ''' - tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp - tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1] - n_dim = tensor.dim() - if n_dim == 4: - n_img = len(tensor) - img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy() - img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR - elif n_dim == 3: - img_np = tensor.numpy() - img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR - elif n_dim == 2: - img_np = tensor.numpy() - else: - raise TypeError( - 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim)) - if out_type == np.uint8: - img_np = (img_np * 255.0).round() - # Important. Unlike matlab, numpy.unit8() WILL NOT round by default. - return img_np.astype(out_type) - - -''' -# -------------------------------------------- -# Augmentation, flipe and/or rotate -# -------------------------------------------- -# The following two are enough. -# (1) augmet_img: numpy image of WxHxC or WxH -# (2) augment_img_tensor4: tensor image 1xCxWxH -# -------------------------------------------- -''' - - -def augment_img(img, mode=0): - '''Kai Zhang (github: https://github.com/cszn) - ''' - if mode == 0: - return img - elif mode == 1: - return np.flipud(np.rot90(img)) - elif mode == 2: - return np.flipud(img) - elif mode == 3: - return np.rot90(img, k=3) - elif mode == 4: - return np.flipud(np.rot90(img, k=2)) - elif mode == 5: - return np.rot90(img) - elif mode == 6: - return np.rot90(img, k=2) - elif mode == 7: - return np.flipud(np.rot90(img, k=3)) - - -def augment_img_tensor4(img, mode=0): - '''Kai Zhang (github: https://github.com/cszn) - ''' - if mode == 0: - return img - elif mode == 1: - return img.rot90(1, [2, 3]).flip([2]) - elif mode == 2: - return img.flip([2]) - elif mode == 3: - return img.rot90(3, [2, 3]) - elif mode == 4: - return img.rot90(2, [2, 3]).flip([2]) - elif mode == 5: - return img.rot90(1, [2, 3]) - elif mode == 6: - return img.rot90(2, [2, 3]) - elif mode == 7: - return img.rot90(3, [2, 3]).flip([2]) - - -def augment_img_tensor(img, mode=0): - '''Kai Zhang (github: https://github.com/cszn) - ''' - img_size = img.size() - img_np = img.data.cpu().numpy() - if len(img_size) == 3: - img_np = np.transpose(img_np, (1, 2, 0)) - elif len(img_size) == 4: - img_np = np.transpose(img_np, (2, 3, 1, 0)) - img_np = augment_img(img_np, mode=mode) - img_tensor = torch.from_numpy(np.ascontiguousarray(img_np)) - if len(img_size) == 3: - img_tensor = img_tensor.permute(2, 0, 1) - elif len(img_size) == 4: - img_tensor = img_tensor.permute(3, 2, 0, 1) - - return img_tensor.type_as(img) - - -def augment_img_np3(img, mode=0): - if mode == 0: - return img - elif mode == 1: - return img.transpose(1, 0, 2) - elif mode == 2: - return img[::-1, :, :] - elif mode == 3: - img = img[::-1, :, :] - img = img.transpose(1, 0, 2) - return img - elif mode == 4: - return img[:, ::-1, :] - elif mode == 5: - img = img[:, ::-1, :] - img = img.transpose(1, 0, 2) - return img - elif mode == 6: - img = img[:, ::-1, :] - img = img[::-1, :, :] - return img - elif mode == 7: - img = img[:, ::-1, :] - img = img[::-1, :, :] - img = img.transpose(1, 0, 2) - return img - - -def augment_imgs(img_list, hflip=True, rot=True): - # horizontal flip OR rotate - hflip = hflip and random.random() < 0.5 - vflip = rot and random.random() < 0.5 - rot90 = rot and random.random() < 0.5 - - def _augment(img): - if hflip: - img = img[:, ::-1, :] - if vflip: - img = img[::-1, :, :] - if rot90: - img = img.transpose(1, 0, 2) - return img - - return [_augment(img) for img in img_list] - - -''' -# -------------------------------------------- -# modcrop and shave -# -------------------------------------------- -''' - - -def modcrop(img_in, scale): - # img_in: Numpy, HWC or HW - img = np.copy(img_in) - if img.ndim == 2: - H, W = img.shape - H_r, W_r = H % scale, W % scale - img = img[:H - H_r, :W - W_r] - elif img.ndim == 3: - H, W, C = img.shape - H_r, W_r = H % scale, W % scale - img = img[:H - H_r, :W - W_r, :] - else: - raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim)) - return img - - -def shave(img_in, border=0): - # img_in: Numpy, HWC or HW - img = np.copy(img_in) - h, w = img.shape[:2] - img = img[border:h-border, border:w-border] - return img - - -''' -# -------------------------------------------- -# image processing process on numpy image -# channel_convert(in_c, tar_type, img_list): -# rgb2ycbcr(img, only_y=True): -# bgr2ycbcr(img, only_y=True): -# ycbcr2rgb(img): -# -------------------------------------------- -''' - - -def rgb2ycbcr(img, only_y=True): - '''same as matlab rgb2ycbcr - only_y: only return Y channel - Input: - uint8, [0, 255] - float, [0, 1] - ''' - in_img_type = img.dtype - img.astype(np.float32) - if in_img_type != np.uint8: - img *= 255. - # convert - if only_y: - rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0 - else: - rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], - [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128] - if in_img_type == np.uint8: - rlt = rlt.round() - else: - rlt /= 255. - return rlt.astype(in_img_type) - - -def ycbcr2rgb(img): - '''same as matlab ycbcr2rgb - Input: - uint8, [0, 255] - float, [0, 1] - ''' - in_img_type = img.dtype - img.astype(np.float32) - if in_img_type != np.uint8: - img *= 255. - # convert - rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071], - [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836] - if in_img_type == np.uint8: - rlt = rlt.round() - else: - rlt /= 255. - return rlt.astype(in_img_type) - - -def bgr2ycbcr(img, only_y=True): - '''bgr version of rgb2ycbcr - only_y: only return Y channel - Input: - uint8, [0, 255] - float, [0, 1] - ''' - in_img_type = img.dtype - img.astype(np.float32) - if in_img_type != np.uint8: - img *= 255. - # convert - if only_y: - rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0 - else: - rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], - [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128] - if in_img_type == np.uint8: - rlt = rlt.round() - else: - rlt /= 255. - return rlt.astype(in_img_type) - - -def channel_convert(in_c, tar_type, img_list): - # conversion among BGR, gray and y - if in_c == 3 and tar_type == 'gray': # BGR to gray - gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list] - return [np.expand_dims(img, axis=2) for img in gray_list] - elif in_c == 3 and tar_type == 'y': # BGR to y - y_list = [bgr2ycbcr(img, only_y=True) for img in img_list] - return [np.expand_dims(img, axis=2) for img in y_list] - elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR - return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list] - else: - return img_list - - -''' -# -------------------------------------------- -# metric, PSNR and SSIM -# -------------------------------------------- -''' - - -# -------------------------------------------- -# PSNR -# -------------------------------------------- -def calculate_psnr(img1, img2, border=0): - # img1 and img2 have range [0, 255] - #img1 = img1.squeeze() - #img2 = img2.squeeze() - if not img1.shape == img2.shape: - raise ValueError('Input images must have the same dimensions.') - h, w = img1.shape[:2] - img1 = img1[border:h-border, border:w-border] - img2 = img2[border:h-border, border:w-border] - - img1 = img1.astype(np.float64) - img2 = img2.astype(np.float64) - mse = np.mean((img1 - img2)**2) - if mse == 0: - return float('inf') - return 20 * math.log10(255.0 / math.sqrt(mse)) - - -# -------------------------------------------- -# SSIM -# -------------------------------------------- -def calculate_ssim(img1, img2, border=0): - '''calculate SSIM - the same outputs as MATLAB's - img1, img2: [0, 255] - ''' - #img1 = img1.squeeze() - #img2 = img2.squeeze() - if not img1.shape == img2.shape: - raise ValueError('Input images must have the same dimensions.') - h, w = img1.shape[:2] - img1 = img1[border:h-border, border:w-border] - img2 = img2[border:h-border, border:w-border] - - if img1.ndim == 2: - return ssim(img1, img2) - elif img1.ndim == 3: - if img1.shape[2] == 3: - ssims = [] - for i in range(3): - ssims.append(ssim(img1[:,:,i], img2[:,:,i])) - return np.array(ssims).mean() - elif img1.shape[2] == 1: - return ssim(np.squeeze(img1), np.squeeze(img2)) - else: - raise ValueError('Wrong input image dimensions.') - - -def ssim(img1, img2): - C1 = (0.01 * 255)**2 - C2 = (0.03 * 255)**2 - - img1 = img1.astype(np.float64) - img2 = img2.astype(np.float64) - kernel = cv2.getGaussianKernel(11, 1.5) - window = np.outer(kernel, kernel.transpose()) - - mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid - mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] - mu1_sq = mu1**2 - mu2_sq = mu2**2 - mu1_mu2 = mu1 * mu2 - sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq - sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq - sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 - - ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * - (sigma1_sq + sigma2_sq + C2)) - return ssim_map.mean() - - -''' -# -------------------------------------------- -# matlab's bicubic imresize (numpy and torch) [0, 1] -# -------------------------------------------- -''' - - -# matlab 'imresize' function, now only support 'bicubic' -def cubic(x): - absx = torch.abs(x) - absx2 = absx**2 - absx3 = absx**3 - return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \ - (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx)) - - -def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing): - if (scale < 1) and (antialiasing): - # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width - kernel_width = kernel_width / scale - - # Output-space coordinates - x = torch.linspace(1, out_length, out_length) - - # Input-space coordinates. Calculate the inverse mapping such that 0.5 - # in output space maps to 0.5 in input space, and 0.5+scale in output - # space maps to 1.5 in input space. - u = x / scale + 0.5 * (1 - 1 / scale) - - # What is the left-most pixel that can be involved in the computation? - left = torch.floor(u - kernel_width / 2) - - # What is the maximum number of pixels that can be involved in the - # computation? Note: it's OK to use an extra pixel here; if the - # corresponding weights are all zero, it will be eliminated at the end - # of this function. - P = math.ceil(kernel_width) + 2 - - # The indices of the input pixels involved in computing the k-th output - # pixel are in row k of the indices matrix. - indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view( - 1, P).expand(out_length, P) - - # The weights used to compute the k-th output pixel are in row k of the - # weights matrix. - distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices - # apply cubic kernel - if (scale < 1) and (antialiasing): - weights = scale * cubic(distance_to_center * scale) - else: - weights = cubic(distance_to_center) - # Normalize the weights matrix so that each row sums to 1. - weights_sum = torch.sum(weights, 1).view(out_length, 1) - weights = weights / weights_sum.expand(out_length, P) - - # If a column in weights is all zero, get rid of it. only consider the first and last column. - weights_zero_tmp = torch.sum((weights == 0), 0) - if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6): - indices = indices.narrow(1, 1, P - 2) - weights = weights.narrow(1, 1, P - 2) - if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6): - indices = indices.narrow(1, 0, P - 2) - weights = weights.narrow(1, 0, P - 2) - weights = weights.contiguous() - indices = indices.contiguous() - sym_len_s = -indices.min() + 1 - sym_len_e = indices.max() - in_length - indices = indices + sym_len_s - 1 - return weights, indices, int(sym_len_s), int(sym_len_e) - - -# -------------------------------------------- -# imresize for tensor image [0, 1] -# -------------------------------------------- -def imresize(img, scale, antialiasing=True): - # Now the scale should be the same for H and W - # input: img: pytorch tensor, CHW or HW [0,1] - # output: CHW or HW [0,1] w/o round - need_squeeze = True if img.dim() == 2 else False - if need_squeeze: - img.unsqueeze_(0) - in_C, in_H, in_W = img.size() - out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) - kernel_width = 4 - kernel = 'cubic' - - # Return the desired dimension order for performing the resize. The - # strategy is to perform the resize first along the dimension with the - # smallest scale factor. - # Now we do not support this. - - # get weights and indices - weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( - in_H, out_H, scale, kernel, kernel_width, antialiasing) - weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( - in_W, out_W, scale, kernel, kernel_width, antialiasing) - # process H dimension - # symmetric copying - img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W) - img_aug.narrow(1, sym_len_Hs, in_H).copy_(img) - - sym_patch = img[:, :sym_len_Hs, :] - inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(1, inv_idx) - img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv) - - sym_patch = img[:, -sym_len_He:, :] - inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(1, inv_idx) - img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) - - out_1 = torch.FloatTensor(in_C, out_H, in_W) - kernel_width = weights_H.size(1) - for i in range(out_H): - idx = int(indices_H[i][0]) - for j in range(out_C): - out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i]) - - # process W dimension - # symmetric copying - out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We) - out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1) - - sym_patch = out_1[:, :, :sym_len_Ws] - inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(2, inv_idx) - out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv) - - sym_patch = out_1[:, :, -sym_len_We:] - inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(2, inv_idx) - out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) - - out_2 = torch.FloatTensor(in_C, out_H, out_W) - kernel_width = weights_W.size(1) - for i in range(out_W): - idx = int(indices_W[i][0]) - for j in range(out_C): - out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i]) - if need_squeeze: - out_2.squeeze_() - return out_2 - - -# -------------------------------------------- -# imresize for numpy image [0, 1] -# -------------------------------------------- -def imresize_np(img, scale, antialiasing=True): - # Now the scale should be the same for H and W - # input: img: Numpy, HWC or HW [0,1] - # output: HWC or HW [0,1] w/o round - img = torch.from_numpy(img) - need_squeeze = True if img.dim() == 2 else False - if need_squeeze: - img.unsqueeze_(2) - - in_H, in_W, in_C = img.size() - out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) - kernel_width = 4 - kernel = 'cubic' - - # Return the desired dimension order for performing the resize. The - # strategy is to perform the resize first along the dimension with the - # smallest scale factor. - # Now we do not support this. - - # get weights and indices - weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( - in_H, out_H, scale, kernel, kernel_width, antialiasing) - weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( - in_W, out_W, scale, kernel, kernel_width, antialiasing) - # process H dimension - # symmetric copying - img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C) - img_aug.narrow(0, sym_len_Hs, in_H).copy_(img) - - sym_patch = img[:sym_len_Hs, :, :] - inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(0, inv_idx) - img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv) - - sym_patch = img[-sym_len_He:, :, :] - inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(0, inv_idx) - img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) - - out_1 = torch.FloatTensor(out_H, in_W, in_C) - kernel_width = weights_H.size(1) - for i in range(out_H): - idx = int(indices_H[i][0]) - for j in range(out_C): - out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i]) - - # process W dimension - # symmetric copying - out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C) - out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1) - - sym_patch = out_1[:, :sym_len_Ws, :] - inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(1, inv_idx) - out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv) - - sym_patch = out_1[:, -sym_len_We:, :] - inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(1, inv_idx) - out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) - - out_2 = torch.FloatTensor(out_H, out_W, in_C) - kernel_width = weights_W.size(1) - for i in range(out_W): - idx = int(indices_W[i][0]) - for j in range(out_C): - out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i]) - if need_squeeze: - out_2.squeeze_() - - return out_2.numpy() - - -if __name__ == '__main__': - print('---') -# img = imread_uint('test.bmp', 3) -# img = uint2single(img) -# img_bicubic = imresize_np(img, 1/4) \ No newline at end of file diff --git a/spaces/Iceclear/StableSR/StableSR/taming/models/dummy_cond_stage.py b/spaces/Iceclear/StableSR/StableSR/taming/models/dummy_cond_stage.py deleted file mode 100644 index 6e19938078752e09b926a3e749907ee99a258ca0..0000000000000000000000000000000000000000 --- a/spaces/Iceclear/StableSR/StableSR/taming/models/dummy_cond_stage.py +++ /dev/null @@ -1,22 +0,0 @@ -from torch import Tensor - - -class DummyCondStage: - def __init__(self, conditional_key): - self.conditional_key = conditional_key - self.train = None - - def eval(self): - return self - - @staticmethod - def encode(c: Tensor): - return c, None, (None, None, c) - - @staticmethod - def decode(c: Tensor): - return c - - @staticmethod - def to_rgb(c: Tensor): - return c diff --git a/spaces/IoannisTr/Tech_Stocks_Trading_Assistant/app.py b/spaces/IoannisTr/Tech_Stocks_Trading_Assistant/app.py deleted file mode 100644 index f5048d568fa498102fafde0f97598843bb80ce78..0000000000000000000000000000000000000000 --- a/spaces/IoannisTr/Tech_Stocks_Trading_Assistant/app.py +++ /dev/null @@ -1,91 +0,0 @@ -from stocks import * -from functions import * -from datetime import datetime -import streamlit as st - -st.set_page_config(layout="wide") - -st.title("Tech Stocks Trading Assistant") - -left_column, right_column = st.columns(2) - -with left_column: - - all_tickers = { - "Apple":"AAPL", - "Microsoft":"MSFT", - "Nvidia":"NVDA", - "Paypal":"PYPL", - "Amazon":"AMZN", - "Spotify":"SPOT", - #"Twitter":"TWTR", - "adanipower":"adanipower.ns", - "Uber":"UBER", - "Google":"GOOG" - } - - st.subheader("Technical Analysis Methods") - option_name = st.selectbox('Choose a stock:', all_tickers.keys()) - option_ticker = all_tickers[option_name] - execution_timestamp = datetime.now() - 'You selected: ', option_name, "(",option_ticker,")" - 'Last execution:', execution_timestamp - - s = Stock_Data() - t = s.Ticker(tick=option_ticker) - - m = Models() - - with st.spinner('Loading stock data...'): - - technical_analysis_methods_outputs = { - 'Technical Analysis Method': [ - 'Bollinger Bands (20 days & 2 stand. deviations)', - 'Bollinger Bands (10 days & 1.5 stand. deviations)', - 'Bollinger Bands (50 days & 3 stand. deviations)', - 'Moving Average Convergence Divergence (MACD)' - ], - 'Outlook': [ - m.bollinger_bands_20d_2std(t), - m.bollinger_bands_10d_1point5std(t), - m.bollinger_bands_50d_3std(t), - m.MACD(t) - ], - 'Timeframe of Method': [ - "Medium-term", - "Short-term", - "Long-term", - "Short-term" - ] - } - - df = pd.DataFrame(technical_analysis_methods_outputs) - - - def color_survived(val): - color = "" - if (val=="Sell" or val=="Downtrend and sell signal" or val=="Downtrend and no signal"): - color="#EE3B3B" - elif (val=="Buy" or val=="Uptrend and buy signal" or val=="Uptrend and no signal"): - color="#3D9140" - else: - color="#CD950C" - return f'background-color: {color}' - - - st.table(df.sort_values(['Timeframe of Method'], ascending=False). - reset_index(drop=True).style.applymap(color_survived, subset=['Outlook'])) - -with right_column: - - st.subheader("FinBERT-based Sentiment Analysis") - - with st.spinner("Connecting with www.marketwatch.com..."): - st.plotly_chart(m.finbert_headlines_sentiment(t)["fig"]) - "Current sentiment:", m.finbert_headlines_sentiment(t)["current_sentiment"], "%" - - st.subheader("LSTM-based 7-day stock price prediction model") - - with st.spinner("Compiling LSTM model.."): - st.plotly_chart(m.LSTM_7_days_price_predictor(t)) - \ No newline at end of file diff --git a/spaces/ItsJayQz/Classic_Telltale_Diffusion/README.md b/spaces/ItsJayQz/Classic_Telltale_Diffusion/README.md deleted file mode 100644 index f08f5c7040c7d0150c79ade5416a3afa7499fb73..0000000000000000000000000000000000000000 --- a/spaces/ItsJayQz/Classic_Telltale_Diffusion/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Classic Telltale Diffusion -emoji: 🔥 -colorFrom: green -colorTo: indigo -sdk: gradio -sdk_version: 3.14.0 -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/Real-Time-Voice-Cloning/demo_toolbox.py b/spaces/Kevin676/Real-Time-Voice-Cloning/demo_toolbox.py deleted file mode 100644 index ea30a29275965c7e2b815cd703e891a5ca53e97b..0000000000000000000000000000000000000000 --- a/spaces/Kevin676/Real-Time-Voice-Cloning/demo_toolbox.py +++ /dev/null @@ -1,43 +0,0 @@ -from pathlib import Path -from toolbox import Toolbox -from utils.argutils import print_args -from utils.modelutils import check_model_paths -import argparse -import os - - -if __name__ == '__main__': - parser = argparse.ArgumentParser( - description="Runs the toolbox", - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) - - parser.add_argument("-d", "--datasets_root", type=Path, help= \ - "Path to the directory containing your datasets. See toolbox/__init__.py for a list of " - "supported datasets.", default=None) - parser.add_argument("-e", "--enc_models_dir", type=Path, default="encoder/saved_models", - help="Directory containing saved encoder models") - parser.add_argument("-s", "--syn_models_dir", type=Path, default="synthesizer/saved_models", - help="Directory containing saved synthesizer models") - parser.add_argument("-v", "--voc_models_dir", type=Path, default="vocoder/saved_models", - help="Directory containing saved vocoder models") - parser.add_argument("--cpu", action="store_true", help=\ - "If True, processing is done on CPU, even when a GPU is available.") - parser.add_argument("--seed", type=int, default=None, help=\ - "Optional random number seed value to make toolbox deterministic.") - parser.add_argument("--no_mp3_support", action="store_true", help=\ - "If True, no mp3 files are allowed.") - args = parser.parse_args() - print_args(args, parser) - - if args.cpu: - # Hide GPUs from Pytorch to force CPU processing - os.environ["CUDA_VISIBLE_DEVICES"] = "-1" - del args.cpu - - ## Remind the user to download pretrained models if needed - check_model_paths(encoder_path=args.enc_models_dir, synthesizer_path=args.syn_models_dir, - vocoder_path=args.voc_models_dir) - - # Launch the toolbox - Toolbox(**vars(args)) diff --git a/spaces/Kevin676/Voice-Cloning/app.py b/spaces/Kevin676/Voice-Cloning/app.py deleted file mode 100644 index 95fe157698c0d8565f46ce52efa9ed945eaf3d8e..0000000000000000000000000000000000000000 --- a/spaces/Kevin676/Voice-Cloning/app.py +++ /dev/null @@ -1,192 +0,0 @@ -#from turtle import title -import gradio as gr - -import git -import os -os.system('git clone https://github.com/Edresson/Coqui-TTS -b multilingual-torchaudio-SE TTS') -os.system('pip install -q -e TTS/') -os.system('pip install -q torchaudio==0.9.0') - -os.system('pip install voicefixer --upgrade') -from voicefixer import VoiceFixer -voicefixer = VoiceFixer() - - -import sys -TTS_PATH = "TTS/" - -# add libraries into environment -sys.path.append(TTS_PATH) # set this if TTS is not installed globally - -import os -import string -import time -import argparse -import json - -import numpy as np -import IPython -from IPython.display import Audio - -import torch -import torchaudio -from speechbrain.pretrained import SpectralMaskEnhancement - -enhance_model = SpectralMaskEnhancement.from_hparams( -source="speechbrain/metricgan-plus-voicebank", -savedir="pretrained_models/metricgan-plus-voicebank", -#run_opts={"device":"cuda"}, -) - -from TTS.tts.utils.synthesis import synthesis -from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols -try: - from TTS.utils.audio import AudioProcessor -except: - from TTS.utils.audio import AudioProcessor - - -from TTS.tts.models import setup_model -from TTS.config import load_config -from TTS.tts.models.vits import * - -OUT_PATH = 'out/' - -# create output path -os.makedirs(OUT_PATH, exist_ok=True) - -# model vars -MODEL_PATH = '/home/user/app/best_model_latest.pth.tar' -CONFIG_PATH = '/home/user/app/config.json' -TTS_LANGUAGES = "/home/user/app/language_ids.json" -TTS_SPEAKERS = "/home/user/app/speakers.json" -USE_CUDA = torch.cuda.is_available() - -# load the config -C = load_config(CONFIG_PATH) - - -# load the audio processor -ap = AudioProcessor(**C.audio) - -speaker_embedding = None - -C.model_args['d_vector_file'] = TTS_SPEAKERS -C.model_args['use_speaker_encoder_as_loss'] = False - -model = setup_model(C) -model.language_manager.set_language_ids_from_file(TTS_LANGUAGES) -# print(model.language_manager.num_languages, model.embedded_language_dim) -# print(model.emb_l) -cp = torch.load(MODEL_PATH, map_location=torch.device('cpu')) -# remove speaker encoder -model_weights = cp['model'].copy() -for key in list(model_weights.keys()): - if "speaker_encoder" in key: - del model_weights[key] - -model.load_state_dict(model_weights) - - -model.eval() - -if USE_CUDA: - model = model.cuda() - -# synthesize voice -use_griffin_lim = False - -os.system('pip install -q pydub ffmpeg-normalize') - -CONFIG_SE_PATH = "config_se.json" -CHECKPOINT_SE_PATH = "SE_checkpoint.pth.tar" - -from TTS.tts.utils.speakers import SpeakerManager -from pydub import AudioSegment -import librosa - -SE_speaker_manager = SpeakerManager(encoder_model_path=CHECKPOINT_SE_PATH, encoder_config_path=CONFIG_SE_PATH, use_cuda=USE_CUDA) - -def compute_spec(ref_file): - y, sr = librosa.load(ref_file, sr=ap.sample_rate) - spec = ap.spectrogram(y) - spec = torch.FloatTensor(spec).unsqueeze(0) - return spec - - - -def greet(Text,Voicetoclone,VoiceMicrophone): - text= "%s" % (Text) - if Voicetoclone is not None: - reference_files= "%s" % (Voicetoclone) - print("path url") - print(Voicetoclone) - sample= str(Voicetoclone) - else: - reference_files= "%s" % (VoiceMicrophone) - print("path url") - print(VoiceMicrophone) - sample= str(VoiceMicrophone) - size= len(reference_files)*sys.getsizeof(reference_files) - size2= size / 1000000 - if (size2 > 0.012) or len(text)>2000: - message="File is greater than 30mb or Text inserted is longer than 2000 characters. Please re-try with smaller sizes." - print(message) - raise SystemExit("File is greater than 30mb. Please re-try or Text inserted is longer than 2000 characters. Please re-try with smaller sizes.") - else: - os.system('ffmpeg-normalize $sample -nt rms -t=-27 -o $sample -ar 16000 -f') - reference_emb = SE_speaker_manager.compute_d_vector_from_clip(reference_files) - model.length_scale = 1 # scaler for the duration predictor. The larger it is, the slower the speech. - model.inference_noise_scale = 0.3 # defines the noise variance applied to the random z vector at inference. - model.inference_noise_scale_dp = 0.3 # defines the noise variance applied to the duration predictor z vector at inference. - text = text - model.language_manager.language_id_mapping - language_id = 0 - - print(" > text: {}".format(text)) - wav, alignment, _, _ = synthesis( - model, - text, - C, - "cuda" in str(next(model.parameters()).device), - ap, - speaker_id=None, - d_vector=reference_emb, - style_wav=None, - language_id=language_id, - enable_eos_bos_chars=C.enable_eos_bos_chars, - use_griffin_lim=True, - do_trim_silence=False, - ).values() - print("Generated Audio") - IPython.display.display(Audio(wav, rate=ap.sample_rate)) - #file_name = text.replace(" ", "_") - #file_name = file_name.translate(str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav' - file_name="Audio.wav" - out_path = os.path.join(OUT_PATH, file_name) - print(" > Saving output to {}".format(out_path)) - ap.save_wav(wav, out_path) - - voicefixer.restore(input=out_path, # input wav file path - output="audio1.wav", # output wav file path -# cuda=True, # whether to use gpu acceleration' - cuda = False, - mode = 0) # You can try out mode 0, 1, or 2 to find out the best result - - noisy = enhance_model.load_audio( - "audio1.wav" - ).unsqueeze(0) - - enhanced = enhance_model.enhance_batch(noisy, lengths=torch.tensor([1.])) - torchaudio.save("enhanced.wav", enhanced.cpu(), 16000) - - return "enhanced.wav" - -gr.Interface( - fn=greet, - inputs=[gr.inputs.Textbox(label='请输入您想要合成的文字，请自觉合法合规使用！'),gr.Audio(type="filepath", source="upload",label='请上传您喜欢的声音(wav/mp3文件, max. 30mb)'),gr.Audio(source="microphone", type="filepath", label = '请用麦克风上传您喜欢的声音，与文件上传二选一即可')], - outputs="audio", - title="🥳💬💕 - Voice Cloning/声音合成测试版（目前只支持英文文本合成，中文版正在开发中，敬请期待）", - description = "注意❗：请不要生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习使用。用户生成内容与程序开发者无关，请自觉合法合规使用，违反者一切后果自负。", - article = "🤖 - 让有人文关怀的AI造福每一个人！AI向善，文明璀璨！TalktoAI - Enable the future！", -).launch() diff --git a/spaces/KingBlaze1227/PC-PICKERS/style.css b/spaces/KingBlaze1227/PC-PICKERS/style.css deleted file mode 100644 index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000 --- a/spaces/KingBlaze1227/PC-PICKERS/style.css +++ /dev/null @@ -1,28 +0,0 @@ -body { - padding: 2rem; - font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif; -} - -h1 { - font-size: 16px; - margin-top: 0; -} - -p { - color: rgb(107, 114, 128); - font-size: 15px; - margin-bottom: 10px; - margin-top: 5px; -} - -.card { - max-width: 620px; - margin: 0 auto; - padding: 16px; - border: 1px solid lightgray; - border-radius: 16px; -} - -.card p:last-child { - margin-bottom: 0; -} diff --git a/spaces/LZRi/LZR-Bert-VITS2/text/tone_sandhi.py b/spaces/LZRi/LZR-Bert-VITS2/text/tone_sandhi.py deleted file mode 100644 index 0f45b7a72c5d858bcaab19ac85cfa686bf9a74da..0000000000000000000000000000000000000000 --- a/spaces/LZRi/LZR-Bert-VITS2/text/tone_sandhi.py +++ /dev/null @@ -1,351 +0,0 @@ -# Copyright (c) 2021 PaddlePaddle 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 typing import List -from typing import Tuple - -import jieba -from pypinyin import lazy_pinyin -from pypinyin import Style - - -class ToneSandhi(): - def __init__(self): - self.must_neural_tone_words = { - '麻烦', '麻利', '鸳鸯', '高粱', '骨头', '骆驼', '马虎', '首饰', '馒头', '馄饨', '风筝', - '难为', '队伍', '阔气', '闺女', '门道', '锄头', '铺盖', '铃铛', '铁匠', '钥匙', '里脊', - '里头', '部分', '那么', '道士', '造化', '迷糊', '连累', '这么', '这个', '运气', '过去', - '软和', '转悠', '踏实', '跳蚤', '跟头', '趔趄', '财主', '豆腐', '讲究', '记性', '记号', - '认识', '规矩', '见识', '裁缝', '补丁', '衣裳', '衣服', '衙门', '街坊', '行李', '行当', - '蛤蟆', '蘑菇', '薄荷', '葫芦', '葡萄', '萝卜', '荸荠', '苗条', '苗头', '苍蝇', '芝麻', - '舒服', '舒坦', '舌头', '自在', '膏药', '脾气', '脑袋', '脊梁', '能耐', '胳膊', '胭脂', - '胡萝', '胡琴', '胡同', '聪明', '耽误', '耽搁', '耷拉', '耳朵', '老爷', '老实', '老婆', - '老头', '老太', '翻腾', '罗嗦', '罐头', '编辑', '结实', '红火', '累赘', '糨糊', '糊涂', - '精神', '粮食', '簸箕', '篱笆', '算计', '算盘', '答应', '笤帚', '笑语', '笑话', '窟窿', - '窝囊', '窗户', '稳当', '稀罕', '称呼', '秧歌', '秀气', '秀才', '福气', '祖宗', '砚台', - '码头', '石榴', '石头', '石匠', '知识', '眼睛', '眯缝', '眨巴', '眉毛', '相声', '盘算', - '白净', '痢疾', '痛快', '疟疾', '疙瘩', '疏忽', '畜生', '生意', '甘蔗', '琵琶', '琢磨', - '琉璃', '玻璃', '玫瑰', '玄乎', '狐狸', '状元', '特务', '牲口', '牙碜', '牌楼', '爽快', - '爱人', '热闹', '烧饼', '烟筒', '烂糊', '点心', '炊帚', '灯笼', '火候', '漂亮', '滑溜', - '溜达', '温和', '清楚', '消息', '浪头', '活泼', '比方', '正经', '欺负', '模糊', '槟榔', - '棺材', '棒槌', '棉花', '核桃', '栅栏', '柴火', '架势', '枕头', '枇杷', '机灵', '本事', - '木头', '木匠', '朋友', '月饼', '月亮', '暖和', '明白', '时候', '新鲜', '故事', '收拾', - '收成', '提防', '挖苦', '挑剔', '指甲', '指头', '拾掇', '拳头', '拨弄', '招牌', '招呼', - '抬举', '护士', '折腾', '扫帚', '打量', '打算', '打点', '打扮', '打听', '打发', '扎实', - '扁担', '戒指', '懒得', '意识', '意思', '情形', '悟性', '怪物', '思量', '怎么', '念头', - '念叨', '快活', '忙活', '志气', '心思', '得罪', '张罗', '弟兄', '开通', '应酬', '庄稼', - '干事', '帮手', '帐篷', '希罕', '师父', '师傅', '巴结', '巴掌', '差事', '工夫', '岁数', - '屁股', '尾巴', '少爷', '小气', '小伙', '将就', '对头', '对付', '寡妇', '家伙', '客气', - '实在', '官司', '学问', '学生', '字号', '嫁妆', '媳妇', '媒人', '婆家', '娘家', '委屈', - '姑娘', '姐夫', '妯娌', '妥当', '妖精', '奴才', '女婿', '头发', '太阳', '大爷', '大方', - '大意', '大夫', '多少', '多么', '外甥', '壮实', '地道', '地方', '在乎', '困难', '嘴巴', - '嘱咐', '嘟囔', '嘀咕', '喜欢', '喇嘛', '喇叭', '商量', '唾沫', '哑巴', '哈欠', '哆嗦', - '咳嗽', '和尚', '告诉', '告示', '含糊', '吓唬', '后头', '名字', '名堂', '合同', '吆喝', - '叫唤', '口袋', '厚道', '厉害', '千斤', '包袱', '包涵', '匀称', '勤快', '动静', '动弹', - '功夫', '力气', '前头', '刺猬', '刺激', '别扭', '利落', '利索', '利害', '分析', '出息', - '凑合', '凉快', '冷战', '冤枉', '冒失', '养活', '关系', '先生', '兄弟', '便宜', '使唤', - '佩服', '作坊', '体面', '位置', '似的', '伙计', '休息', '什么', '人家', '亲戚', '亲家', - '交情', '云彩', '事情', '买卖', '主意', '丫头', '丧气', '两口', '东西', '东家', '世故', - '不由', '不在', '下水', '下巴', '上头', '上司', '丈夫', '丈人', '一辈', '那个', '菩萨', - '父亲', '母亲', '咕噜', '邋遢', '费用', '冤家', '甜头', '介绍', '荒唐', '大人', '泥鳅', - '幸福', '熟悉', '计划', '扑腾', '蜡烛', '姥爷', '照顾', '喉咙', '吉他', '弄堂', '蚂蚱', - '凤凰', '拖沓', '寒碜', '糟蹋', '倒腾', '报复', '逻辑', '盘缠', '喽啰', '牢骚', '咖喱', - '扫把', '惦记' - } - self.must_not_neural_tone_words = { - "男子", "女子", "分子", "原子", "量子", "莲子", "石子", "瓜子", "电子", "人人", "虎虎" - } - self.punc = "：，；。？！“”‘’':,;.?!" - - # the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041 - # e.g. - # word: "家里" - # pos: "s" - # finals: ['ia1', 'i3'] - def _neural_sandhi(self, word: str, pos: str, - finals: List[str]) -> List[str]: - - # reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺 - for j, item in enumerate(word): - if j - 1 >= 0 and item == word[j - 1] and pos[0] in { - "n", "v", "a" - } and word not in self.must_not_neural_tone_words: - finals[j] = finals[j][:-1] + "5" - ge_idx = word.find("个") - if len(word) >= 1 and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶": - finals[-1] = finals[-1][:-1] + "5" - elif len(word) >= 1 and word[-1] in "的地得": - finals[-1] = finals[-1][:-1] + "5" - # e.g. 走了, 看着, 去过 - # elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}: - # finals[-1] = finals[-1][:-1] + "5" - elif len(word) > 1 and word[-1] in "们子" and pos in { - "r", "n" - } and word not in self.must_not_neural_tone_words: - finals[-1] = finals[-1][:-1] + "5" - # e.g. 桌上, 地下, 家里 - elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}: - finals[-1] = finals[-1][:-1] + "5" - # e.g. 上来, 下去 - elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开": - finals[-1] = finals[-1][:-1] + "5" - # 个做量词 - elif (ge_idx >= 1 and - (word[ge_idx - 1].isnumeric() or - word[ge_idx - 1] in "几有两半多各整每做是")) or word == '个': - finals[ge_idx] = finals[ge_idx][:-1] + "5" - else: - if word in self.must_neural_tone_words or word[ - -2:] in self.must_neural_tone_words: - finals[-1] = finals[-1][:-1] + "5" - - word_list = self._split_word(word) - finals_list = [finals[:len(word_list[0])], finals[len(word_list[0]):]] - for i, word in enumerate(word_list): - # conventional neural in Chinese - if word in self.must_neural_tone_words or word[ - -2:] in self.must_neural_tone_words: - finals_list[i][-1] = finals_list[i][-1][:-1] + "5" - finals = sum(finals_list, []) - return finals - - def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]: - # e.g. 看不懂 - if len(word) == 3 and word[1] == "不": - finals[1] = finals[1][:-1] + "5" - else: - for i, char in enumerate(word): - # "不" before tone4 should be bu2, e.g. 不怕 - if char == "不" and i + 1 < len(word) and finals[i + - 1][-1] == "4": - finals[i] = finals[i][:-1] + "2" - return finals - - def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]: - # "一" in number sequences, e.g. 一零零, 二一零 - if word.find("一") != -1 and all( - [item.isnumeric() for item in word if item != "一"]): - return finals - # "一" between reduplication words shold be yi5, e.g. 看一看 - elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]: - finals[1] = finals[1][:-1] + "5" - # when "一" is ordinal word, it should be yi1 - elif word.startswith("第一"): - finals[1] = finals[1][:-1] + "1" - else: - for i, char in enumerate(word): - if char == "一" and i + 1 < len(word): - # "一" before tone4 should be yi2, e.g. 一段 - if finals[i + 1][-1] == "4": - finals[i] = finals[i][:-1] + "2" - # "一" before non-tone4 should be yi4, e.g. 一天 - else: - # "一" 后面如果是标点，还读一声 - if word[i + 1] not in self.punc: - finals[i] = finals[i][:-1] + "4" - return finals - - def _split_word(self, word: str) -> List[str]: - word_list = jieba.cut_for_search(word) - word_list = sorted(word_list, key=lambda i: len(i), reverse=False) - first_subword = word_list[0] - first_begin_idx = word.find(first_subword) - if first_begin_idx == 0: - second_subword = word[len(first_subword):] - new_word_list = [first_subword, second_subword] - else: - second_subword = word[:-len(first_subword)] - new_word_list = [second_subword, first_subword] - return new_word_list - - def _three_sandhi(self, word: str, finals: List[str]) -> List[str]: - if len(word) == 2 and self._all_tone_three(finals): - finals[0] = finals[0][:-1] + "2" - elif len(word) == 3: - word_list = self._split_word(word) - if self._all_tone_three(finals): - # disyllabic + monosyllabic, e.g. 蒙古/包 - if len(word_list[0]) == 2: - finals[0] = finals[0][:-1] + "2" - finals[1] = finals[1][:-1] + "2" - # monosyllabic + disyllabic, e.g. 纸/老虎 - elif len(word_list[0]) == 1: - finals[1] = finals[1][:-1] + "2" - else: - finals_list = [ - finals[:len(word_list[0])], finals[len(word_list[0]):] - ] - if len(finals_list) == 2: - for i, sub in enumerate(finals_list): - # e.g. 所有/人 - if self._all_tone_three(sub) and len(sub) == 2: - finals_list[i][0] = finals_list[i][0][:-1] + "2" - # e.g. 好/喜欢 - elif i == 1 and not self._all_tone_three(sub) and finals_list[i][0][-1] == "3" and \ - finals_list[0][-1][-1] == "3": - - finals_list[0][-1] = finals_list[0][-1][:-1] + "2" - finals = sum(finals_list, []) - # split idiom into two words who's length is 2 - elif len(word) == 4: - finals_list = [finals[:2], finals[2:]] - finals = [] - for sub in finals_list: - if self._all_tone_three(sub): - sub[0] = sub[0][:-1] + "2" - finals += sub - - return finals - - def _all_tone_three(self, finals: List[str]) -> bool: - return all(x[-1] == "3" for x in finals) - - # merge "不" and the word behind it - # if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error - def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - new_seg = [] - last_word = "" - for word, pos in seg: - if last_word == "不": - word = last_word + word - if word != "不": - new_seg.append((word, pos)) - last_word = word[:] - if last_word == "不": - new_seg.append((last_word, 'd')) - last_word = "" - return new_seg - - # function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听" - # function 2: merge single "一" and the word behind it - # if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error - # e.g. - # input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')] - # output seg: [['听一听', 'v']] - def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - new_seg = [] - # function 1 - for i, (word, pos) in enumerate(seg): - if i - 1 >= 0 and word == "一" and i + 1 < len(seg) and seg[i - 1][ - 0] == seg[i + 1][0] and seg[i - 1][1] == "v": - new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0] - else: - if i - 2 >= 0 and seg[i - 1][0] == "一" and seg[i - 2][ - 0] == word and pos == "v": - continue - else: - new_seg.append([word, pos]) - seg = new_seg - new_seg = [] - # function 2 - for i, (word, pos) in enumerate(seg): - if new_seg and new_seg[-1][0] == "一": - new_seg[-1][0] = new_seg[-1][0] + word - else: - new_seg.append([word, pos]) - return new_seg - - # the first and the second words are all_tone_three - def _merge_continuous_three_tones( - self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - new_seg = [] - sub_finals_list = [ - lazy_pinyin( - word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) - for (word, pos) in seg - ] - assert len(sub_finals_list) == len(seg) - merge_last = [False] * len(seg) - for i, (word, pos) in enumerate(seg): - if i - 1 >= 0 and self._all_tone_three( - sub_finals_list[i - 1]) and self._all_tone_three( - sub_finals_list[i]) and not merge_last[i - 1]: - # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi - if not self._is_reduplication(seg[i - 1][0]) and len( - seg[i - 1][0]) + len(seg[i][0]) <= 3: - new_seg[-1][0] = new_seg[-1][0] + seg[i][0] - merge_last[i] = True - else: - new_seg.append([word, pos]) - else: - new_seg.append([word, pos]) - - return new_seg - - def _is_reduplication(self, word: str) -> bool: - return len(word) == 2 and word[0] == word[1] - - # the last char of first word and the first char of second word is tone_three - def _merge_continuous_three_tones_2( - self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - new_seg = [] - sub_finals_list = [ - lazy_pinyin( - word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) - for (word, pos) in seg - ] - assert len(sub_finals_list) == len(seg) - merge_last = [False] * len(seg) - for i, (word, pos) in enumerate(seg): - if i - 1 >= 0 and sub_finals_list[i - 1][-1][-1] == "3" and sub_finals_list[i][0][-1] == "3" and not \ - merge_last[i - 1]: - # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi - if not self._is_reduplication(seg[i - 1][0]) and len( - seg[i - 1][0]) + len(seg[i][0]) <= 3: - new_seg[-1][0] = new_seg[-1][0] + seg[i][0] - merge_last[i] = True - else: - new_seg.append([word, pos]) - else: - new_seg.append([word, pos]) - return new_seg - - def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - new_seg = [] - for i, (word, pos) in enumerate(seg): - if i - 1 >= 0 and word == "儿" and seg[i-1][0] != "#": - new_seg[-1][0] = new_seg[-1][0] + seg[i][0] - else: - new_seg.append([word, pos]) - return new_seg - - def _merge_reduplication( - self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - new_seg = [] - for i, (word, pos) in enumerate(seg): - if new_seg and word == new_seg[-1][0]: - new_seg[-1][0] = new_seg[-1][0] + seg[i][0] - else: - new_seg.append([word, pos]) - return new_seg - - def pre_merge_for_modify( - self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - seg = self._merge_bu(seg) - try: - seg = self._merge_yi(seg) - except: - print("_merge_yi failed") - seg = self._merge_reduplication(seg) - seg = self._merge_continuous_three_tones(seg) - seg = self._merge_continuous_three_tones_2(seg) - seg = self._merge_er(seg) - return seg - - def modified_tone(self, word: str, pos: str, - finals: List[str]) -> List[str]: - finals = self._bu_sandhi(word, finals) - finals = self._yi_sandhi(word, finals) - finals = self._neural_sandhi(word, pos, finals) - finals = self._three_sandhi(word, finals) - return finals diff --git a/spaces/LanguageBind/LanguageBind/languagebind/depth/configuration_depth.py b/spaces/LanguageBind/LanguageBind/languagebind/depth/configuration_depth.py deleted file mode 100644 index 0d3901b2cf96635384c1e7d1e99845a66cd6c786..0000000000000000000000000000000000000000 --- a/spaces/LanguageBind/LanguageBind/languagebind/depth/configuration_depth.py +++ /dev/null @@ -1,425 +0,0 @@ -import copy -import os -from typing import Union - -from transformers import PretrainedConfig -from transformers.utils import logging - -logger = logging.get_logger(__name__) - - - - - - - -class CLIPTextConfig(PretrainedConfig): - r""" - This is the configuration class to store the configuration of a [`CLIPTextModel`]. It is used to instantiate a CLIP - text encoder according to the specified arguments, defining the model architecture. Instantiating a configuration - with the defaults will yield a similar configuration to that of the text encoder of the CLIP - [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) architecture. - - Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the - documentation from [`PretrainedConfig`] for more information. - - Args: - vocab_size (`int`, *optional*, defaults to 49408): - Vocabulary size of the CLIP text model. Defines the number of different tokens that can be represented by - the `inputs_ids` passed when calling [`CLIPModel`]. - hidden_size (`int`, *optional*, defaults to 512): - Dimensionality of the encoder layers and the pooler layer. - intermediate_size (`int`, *optional*, defaults to 2048): - Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. - num_hidden_layers (`int`, *optional*, defaults to 12): - Number of hidden layers in the Transformer encoder. - num_attention_heads (`int`, *optional*, defaults to 8): - Number of attention heads for each attention layer in the Transformer encoder. - max_position_embeddings (`int`, *optional*, defaults to 77): - The maximum sequence length that this model might ever be used with. Typically set this to something large - just in case (e.g., 512 or 1024 or 2048). - hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`): - The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, - `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported. - layer_norm_eps (`float`, *optional*, defaults to 1e-5): - The epsilon used by the layer normalization layers. - attention_dropout (`float`, *optional*, defaults to 0.0): - The dropout ratio for the attention probabilities. - initializer_range (`float`, *optional*, defaults to 0.02): - The standard deviation of the truncated_normal_initializer for initializing all weight matrices. - initializer_factor (`float`, *optional*, defaults to 1): - A factor for initializing all weight matrices (should be kept to 1, used internally for initialization - testing). - - Example: - - ```python - >>> from transformers import CLIPTextConfig, CLIPTextModel - - >>> # Initializing a CLIPTextConfig with openai/clip-vit-base-patch32 style configuration - >>> configuration = CLIPTextConfig() - - >>> # Initializing a CLIPTextModel (with random weights) from the openai/clip-vit-base-patch32 style configuration - >>> model = CLIPTextModel(configuration) - - >>> # Accessing the model configuration - >>> configuration = model.config - ```""" - model_type = "clip_text_model" - - def __init__( - self, - vocab_size=49408, - hidden_size=512, - intermediate_size=2048, - projection_dim=512, - num_hidden_layers=12, - num_attention_heads=8, - max_position_embeddings=77, - hidden_act="quick_gelu", - layer_norm_eps=1e-5, - attention_dropout=0.0, - initializer_range=0.02, - initializer_factor=1.0, - # This differs from `CLIPTokenizer`'s default and from openai/clip - # See https://github.com/huggingface/transformers/pull/24773#issuecomment-1632287538 - pad_token_id=1, - bos_token_id=49406, - eos_token_id=49407, - **kwargs, - ): - super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) - - self.vocab_size = vocab_size - self.hidden_size = hidden_size - self.intermediate_size = intermediate_size - self.projection_dim = projection_dim - self.num_hidden_layers = num_hidden_layers - self.num_attention_heads = num_attention_heads - self.max_position_embeddings = max_position_embeddings - self.layer_norm_eps = layer_norm_eps - self.hidden_act = hidden_act - self.initializer_range = initializer_range - self.initializer_factor = initializer_factor - self.attention_dropout = attention_dropout - self.add_time_attn = False ###################################### - - @classmethod - def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": - cls._set_token_in_kwargs(kwargs) - - config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) - - # get the text config dict if we are loading from CLIPConfig - if config_dict.get("model_type") == "clip": - config_dict = config_dict["text_config"] - - if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: - logger.warning( - f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " - f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." - ) - - return cls.from_dict(config_dict, **kwargs) - - - - -class CLIPVisionConfig(PretrainedConfig): - r""" - This is the configuration class to store the configuration of a [`CLIPVisionModel`]. It is used to instantiate a - CLIP vision encoder according to the specified arguments, defining the model architecture. Instantiating a - configuration with the defaults will yield a similar configuration to that of the vision encoder of the CLIP - [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) architecture. - - Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the - documentation from [`PretrainedConfig`] for more information. - - Args: - hidden_size (`int`, *optional*, defaults to 768): - Dimensionality of the encoder layers and the pooler layer. - intermediate_size (`int`, *optional*, defaults to 3072): - Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. - num_hidden_layers (`int`, *optional*, defaults to 12): - Number of hidden layers in the Transformer encoder. - num_attention_heads (`int`, *optional*, defaults to 12): - Number of attention heads for each attention layer in the Transformer encoder. - image_size (`int`, *optional*, defaults to 224): - The size (resolution) of each image. - patch_size (`int`, *optional*, defaults to 32): - The size (resolution) of each patch. - hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`): - The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, - `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported. - layer_norm_eps (`float`, *optional*, defaults to 1e-5): - The epsilon used by the layer normalization layers. - attention_dropout (`float`, *optional*, defaults to 0.0): - The dropout ratio for the attention probabilities. - initializer_range (`float`, *optional*, defaults to 0.02): - The standard deviation of the truncated_normal_initializer for initializing all weight matrices. - initializer_factor (`float`, *optional*, defaults to 1): - A factor for initializing all weight matrices (should be kept to 1, used internally for initialization - testing). - - Example: - - ```python - >>> from transformers import CLIPVisionConfig, CLIPVisionModel - - >>> # Initializing a CLIPVisionConfig with openai/clip-vit-base-patch32 style configuration - >>> configuration = CLIPVisionConfig() - - >>> # Initializing a CLIPVisionModel (with random weights) from the openai/clip-vit-base-patch32 style configuration - >>> model = CLIPVisionModel(configuration) - - >>> # Accessing the model configuration - >>> configuration = model.config - ```""" - - model_type = "clip_vision_model" - - def __init__( - self, - hidden_size=768, - intermediate_size=3072, - projection_dim=512, - num_hidden_layers=12, - num_attention_heads=12, - num_channels=3, - image_size=224, - patch_size=32, - hidden_act="quick_gelu", - layer_norm_eps=1e-5, - attention_dropout=0.0, - initializer_range=0.02, - initializer_factor=1.0, - - add_time_attn=False, ################################ - num_frames=1, ################################ - force_patch_dropout=0.0, ################################ - lora_r=2, ################################ - lora_alpha=16, ################################ - lora_dropout=0.0, ################################ - num_mel_bins=0.0, ################################ - target_length=0.0, ################################ - max_depth=10, - video_decode_backend='decord', ######################### - **kwargs, - ): - super().__init__(**kwargs) - - self.hidden_size = hidden_size - self.intermediate_size = intermediate_size - self.projection_dim = projection_dim - self.num_hidden_layers = num_hidden_layers - self.num_attention_heads = num_attention_heads - self.num_channels = num_channels - self.patch_size = patch_size - self.image_size = image_size - self.initializer_range = initializer_range - self.initializer_factor = initializer_factor - self.attention_dropout = attention_dropout - self.layer_norm_eps = layer_norm_eps - self.hidden_act = hidden_act - - self.add_time_attn = add_time_attn ################ - self.num_frames = num_frames ################ - self.force_patch_dropout = force_patch_dropout ################ - self.lora_r = lora_r ################ - self.lora_alpha = lora_alpha ################ - self.lora_dropout = lora_dropout ################ - self.num_mel_bins = num_mel_bins ################ - self.target_length = target_length ################ - self.max_depth = max_depth ################ - self.video_decode_backend = video_decode_backend ################ - - @classmethod - def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": - cls._set_token_in_kwargs(kwargs) - - config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) - - # get the vision config dict if we are loading from CLIPConfig - if config_dict.get("model_type") == "clip": - config_dict = config_dict["vision_config"] - - if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: - logger.warning( - f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " - f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." - ) - - return cls.from_dict(config_dict, **kwargs) - - -class LanguageBindDepthConfig(PretrainedConfig): - r""" - [`CLIPConfig`] is the configuration class to store the configuration of a [`CLIPModel`]. It is used to instantiate - a CLIP model according to the specified arguments, defining the text model and vision model configs. Instantiating - a configuration with the defaults will yield a similar configuration to that of the CLIP - [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) architecture. - - Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the - documentation from [`PretrainedConfig`] for more information. - - Args: - text_config (`dict`, *optional*): - Dictionary of configuration options used to initialize [`CLIPTextConfig`]. - vision_config (`dict`, *optional*): - Dictionary of configuration options used to initialize [`CLIPVisionConfig`]. - projection_dim (`int`, *optional*, defaults to 512): - Dimentionality of text and vision projection layers. - logit_scale_init_value (`float`, *optional*, defaults to 2.6592): - The inital value of the *logit_scale* paramter. Default is used as per the original CLIP implementation. - kwargs (*optional*): - Dictionary of keyword arguments. - - Example: - - ```python - >>> from transformers import CLIPConfig, CLIPModel - - >>> # Initializing a CLIPConfig with openai/clip-vit-base-patch32 style configuration - >>> configuration = CLIPConfig() - - >>> # Initializing a CLIPModel (with random weights) from the openai/clip-vit-base-patch32 style configuration - >>> model = CLIPModel(configuration) - - >>> # Accessing the model configuration - >>> configuration = model.config - - >>> # We can also initialize a CLIPConfig from a CLIPTextConfig and a CLIPVisionConfig - >>> from transformers import CLIPTextConfig, CLIPVisionConfig - - >>> # Initializing a CLIPText and CLIPVision configuration - >>> config_text = CLIPTextConfig() - >>> config_vision = CLIPVisionConfig() - - >>> config = CLIPConfig.from_text_vision_configs(config_text, config_vision) - ```""" - - model_type = "LanguageBindDepth" - is_composition = True - - def __init__( - self, text_config=None, vision_config=None, projection_dim=512, logit_scale_init_value=2.6592, **kwargs - ): - # If `_config_dict` exist, we use them for the backward compatibility. - # We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot - # of confusion!). - text_config_dict = kwargs.pop("text_config_dict", None) - vision_config_dict = kwargs.pop("vision_config_dict", None) - - super().__init__(**kwargs) - - # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in - # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most - # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`. - if text_config_dict is not None: - if text_config is None: - text_config = {} - - # This is the complete result when using `text_config_dict`. - _text_config_dict = CLIPTextConfig(**text_config_dict).to_dict() - - # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different. - for key, value in _text_config_dict.items(): - if key in text_config and value != text_config[key] and key not in ["transformers_version"]: - # If specified in `text_config_dict` - if key in text_config_dict: - message = ( - f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. " - f'The value `text_config_dict["{key}"]` will be used instead.' - ) - # If inferred from default argument values (just to be super careful) - else: - message = ( - f"`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The " - f'value `text_config["{key}"]` will be overriden.' - ) - logger.warning(message) - - # Update all values in `text_config` with the ones in `_text_config_dict`. - text_config.update(_text_config_dict) - - if vision_config_dict is not None: - if vision_config is None: - vision_config = {} - - # This is the complete result when using `vision_config_dict`. - _vision_config_dict = CLIPVisionConfig(**vision_config_dict).to_dict() - # convert keys to string instead of integer - if "id2label" in _vision_config_dict: - _vision_config_dict["id2label"] = { - str(key): value for key, value in _vision_config_dict["id2label"].items() - } - - # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different. - for key, value in _vision_config_dict.items(): - if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]: - # If specified in `vision_config_dict` - if key in vision_config_dict: - message = ( - f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different " - f'values. The value `vision_config_dict["{key}"]` will be used instead.' - ) - # If inferred from default argument values (just to be super careful) - else: - message = ( - f"`vision_config_dict` is provided which will be used to initialize `CLIPVisionConfig`. " - f'The value `vision_config["{key}"]` will be overriden.' - ) - logger.warning(message) - - # Update all values in `vision_config` with the ones in `_vision_config_dict`. - vision_config.update(_vision_config_dict) - - if text_config is None: - text_config = {} - logger.info("`text_config` is `None`. Initializing the `CLIPTextConfig` with default values.") - - if vision_config is None: - vision_config = {} - logger.info("`vision_config` is `None`. initializing the `CLIPVisionConfig` with default values.") - - self.text_config = CLIPTextConfig(**text_config) - self.vision_config = CLIPVisionConfig(**vision_config) - - self.projection_dim = projection_dim - self.logit_scale_init_value = logit_scale_init_value - self.initializer_factor = 1.0 - - @classmethod - def from_text_vision_configs(cls, text_config: CLIPTextConfig, vision_config: CLIPVisionConfig, **kwargs): - r""" - Instantiate a [`CLIPConfig`] (or a derived class) from clip text model configuration and clip vision model - configuration. - - Returns: - [`CLIPConfig`]: An instance of a configuration object - """ - - return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs) - - def to_dict(self): - """ - Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. - - Returns: - `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, - """ - output = copy.deepcopy(self.__dict__) - output["text_config"] = self.text_config.to_dict() - output["vision_config"] = self.vision_config.to_dict() - output["model_type"] = self.__class__.model_type - return output - - - - - - - - - - diff --git a/spaces/LanguageBind/LanguageBind/training/file_utils.py b/spaces/LanguageBind/LanguageBind/training/file_utils.py deleted file mode 100644 index 395cf7df0acc164c6851f17834d793f5852d4605..0000000000000000000000000000000000000000 --- a/spaces/LanguageBind/LanguageBind/training/file_utils.py +++ /dev/null @@ -1,83 +0,0 @@ -import logging -import os -import multiprocessing -import subprocess -import time -import fsspec -import torch -from tqdm import tqdm - -def remote_sync_s3(local_dir, remote_dir): - # skip epoch_latest which can change during sync. - result = subprocess.run(["aws", "s3", "sync", local_dir, remote_dir, '--exclude', '*epoch_latest.pt'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) - if result.returncode != 0: - logging.error(f"Error: Failed to sync with S3 bucket {result.stderr.decode('utf-8')}") - return False - - logging.info(f"Successfully synced with S3 bucket") - return True - -def remote_sync_fsspec(local_dir, remote_dir): - # FIXME currently this is slow and not recommended. Look into speeding up. - a = fsspec.get_mapper(local_dir) - b = fsspec.get_mapper(remote_dir) - - for k in a: - # skip epoch_latest which can change during sync. - if 'epoch_latest.pt' in k: - continue - - logging.info(f'Attempting to sync {k}') - if k in b and len(a[k]) == len(b[k]): - logging.debug(f'Skipping remote sync for {k}.') - continue - - try: - logging.info(f'Successful sync for {k}.') - b[k] = a[k] - except Exception as e: - logging.info(f'Error during remote sync for {k}: {e}') - return False - - return True - -def remote_sync(local_dir, remote_dir, protocol): - logging.info('Starting remote sync.') - if protocol == 's3': - return remote_sync_s3(local_dir, remote_dir) - elif protocol == 'fsspec': - return remote_sync_fsspec(local_dir, remote_dir) - else: - logging.error('Remote protocol not known') - return False - -def keep_running_remote_sync(sync_every, local_dir, remote_dir, protocol): - while True: - time.sleep(sync_every) - remote_sync(local_dir, remote_dir, protocol) - -def start_sync_process(sync_every, local_dir, remote_dir, protocol): - p = multiprocessing.Process(target=keep_running_remote_sync, args=(sync_every, local_dir, remote_dir, protocol)) - return p - -# Note: we are not currently using this save function. -def pt_save(pt_obj, file_path): - of = fsspec.open(file_path, "wb") - with of as f: - torch.save(pt_obj, file_path) - -def pt_load(file_path, map_location=None): - if file_path.startswith('s3'): - logging.info('Loading remote checkpoint, which may take a bit.') - of = fsspec.open(file_path, "rb") - with of as f: - out = torch.load(f, map_location=map_location) - return out - -def check_exists(file_path): - try: - with fsspec.open(file_path): - pass - except FileNotFoundError: - return False - return True diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/modules/uvr5/modules.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/modules/uvr5/modules.py deleted file mode 100644 index a2d4404a145bc915e6d96ee27ac0fcc408b5e90c..0000000000000000000000000000000000000000 --- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/modules/uvr5/modules.py +++ /dev/null @@ -1,107 +0,0 @@ -import os -import traceback -import logging - -logger = logging.getLogger(__name__) - -import ffmpeg -import torch - -from assets.configs.config import Config -from lib.infer.modules.uvr5.mdxnet import MDXNetDereverb -from lib.infer.modules.uvr5.preprocess import AudioPre, AudioPreDeEcho - -config = Config() - - -def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0): - infos = [] - try: - inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - save_root_vocal = ( - save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - ) - save_root_ins = ( - save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - ) - if model_name == "onnx_dereverb_By_FoxJoy": - pre_fun = MDXNetDereverb(15, config.device) - else: - func = AudioPre if "DeEcho" not in model_name else AudioPreDeEcho - pre_fun = func( - agg=int(agg), - model_path=os.path.join( - os.getenv("weight_uvr5_root"), model_name + ".pth" - ), - device=config.device, - is_half=config.is_half, - ) - if inp_root != "": - paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)] - else: - paths = [path.name for path in paths] - for path in paths: - inp_path = os.path.join(inp_root, path) - need_reformat = 1 - done = 0 - try: - info = ffmpeg.probe(inp_path, cmd="ffprobe") - if ( - info["streams"][0]["channels"] == 2 - and info["streams"][0]["sample_rate"] == "44100" - ): - need_reformat = 0 - pre_fun._path_audio_( - inp_path, save_root_ins, save_root_vocal, format0 - ) - done = 1 - except: - need_reformat = 1 - traceback.print_exc() - if need_reformat == 1: - tmp_path = "%s/%s.reformatted.wav" % ( - os.path.join(os.environ["temp"]), - os.path.basename(inp_path), - ) - os.system( - "ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y" - % (inp_path, tmp_path) - ) - inp_path = tmp_path - try: - if done == 0: - pre_fun.path_audio( - inp_path, save_root_ins, save_root_vocal, format0 - ) - infos.append("%s->Success" % (os.path.basename(inp_path))) - yield "\n".join(infos) - except: - try: - if done == 0: - pre_fun._path_audio_( - inp_path, save_root_ins, save_root_vocal, format0 - ) - infos.append("%s->Success" % (os.path.basename(inp_path))) - yield "\n".join(infos) - except: - infos.append( - "%s->%s" % (os.path.basename(inp_path), traceback.format_exc()) - ) - yield "\n".join(infos) - except: - infos.append(traceback.format_exc()) - yield "\n".join(infos) - finally: - try: - if model_name == "onnx_dereverb_By_FoxJoy": - del pre_fun.pred.model - del pre_fun.pred.model_ - else: - del pre_fun.model - del pre_fun - except: - traceback.print_exc() - if torch.cuda.is_available(): - torch.cuda.empty_cache() - logger.info("Executed torch.cuda.empty_cache()") - yield "\n".join(infos) diff --git a/spaces/Liviox24/LoanEligibilityPrediction/app.py b/spaces/Liviox24/LoanEligibilityPrediction/app.py deleted file mode 100644 index 794fe8552db250c6098199f6e338c03c194c1748..0000000000000000000000000000000000000000 --- a/spaces/Liviox24/LoanEligibilityPrediction/app.py +++ /dev/null @@ -1,440 +0,0 @@ -# -*- coding: utf-8 -*- -"""LoanEligibilityPrediction.ipynb - -Automatically generated by Colaboratory. - -Original file is located at - https://colab.research.google.com/drive/15wGr9tHgIq7Ua4af83Z0UqfAsH8dyOEZ - -# IMPORT LIBRERIE -""" - -# Commented out IPython magic to ensure Python compatibility. -import numpy as np -import pandas as pd -import seaborn as sns -import gradio as gr -import matplotlib.pyplot as plt -# %matplotlib inline - -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import MinMaxScaler -from sklearn.preprocessing import StandardScaler - -"""# COLLEZIONE DATI""" - -url = "https://raw.githubusercontent.com/livio-24/LoanEligibilityPrediction/main/dataset.csv" - -#caricamento dataset in un pandas dataframe -dataset = pd.read_csv(url) - -"""# EXPLORATORY DATA ANALYSIS""" - -#prime 5 righe -dataset.head() - -#numero righe e colonne -dataset.shape - -dataset.describe() -#misure statistiche - -#info sulle colonne -#5 variabili numeriche e 8 variabili categoriche -dataset.info() - -#Distribuzione variabile target -dataset['Loan_Status'].value_counts() - -# numero di valori mancanti in ogni colonna -# verranno gestiti successivamente nella fase di data cleaning -dataset.isnull().sum() - -#eliminiamo colonna Loan_ID perché inutile -dataset.drop(columns='Loan_ID', axis = 1, inplace=True) - -dataset.head() - -"""**DATA VISUALIZATION - ANALISI UNIVARIATA** - -VARIABILI CATEGORICHE -""" - -#visualizzazione valori variabili catagoriche in percentuale -dataset['Gender'].value_counts(normalize=True).plot.bar(title='Gender') -plt.show() -dataset['Married'].value_counts(normalize=True).plot.bar(title='Married') -plt.show() -dataset['Self_Employed'].value_counts(normalize=True).plot.bar(title='Self_Employed') -plt.show() -dataset['Credit_History'].value_counts(normalize=True).plot.bar(title='Credit_History') -plt.show() - -"""Risultati: -- 80% dei candidati nel dataset è maschio -- Circa il 65% dei candidati nel dataset è sposato/a -- Circa il 15% lavora in proprio -- Circa l'85% ha ripagato i propri debiti - -VARIABILI ORDINALI -""" - -#visualizzazione valori variabili ordinali in percentuale -dataset['Dependents'].value_counts(normalize=True).plot.bar(title='Dependents') -plt.show() -dataset['Education'].value_counts(normalize=True).plot.bar(title='Education') -plt.show() -dataset['Property_Area'].value_counts(normalize=True).plot.bar(title='Property_Area') -plt.show() - -"""Risultati: -- La maggior parte dei candidati non ha familiari dipendenti -- Circa l'80% dei candidati ha una laurea -- La maggior parte dei candidati vive in un'area semiurbana - -VARIABILI NUMERICHE -""" - -#visualizzazione distribuzione variabile 'ApplicantIncome' -sns.distplot(dataset['ApplicantIncome']) -plt.show() -#boxplot per individuazione outliers -dataset.boxplot(['ApplicantIncome']) -plt.show() - -#visualizzazione distribuzione variabile 'CoapplicantIncome' -sns.distplot(dataset['CoapplicantIncome']) -plt.show() -#boxplot per individuazione outliers -dataset.boxplot(['CoapplicantIncome']) -plt.show() - -#visualizzazione distribuzione variabile 'LoanAmount' -sns.distplot(dataset['LoanAmount']) -plt.show() -dataset.boxplot(['LoanAmount']) -plt.show() - -#dataset['LoanAmount'].hist(bins=20) - -#visualizzazione distribuzione variabile 'Loan_Amount_Term' -sns.distplot(dataset['Loan_Amount_Term']) -plt.show() -dataset.boxplot(['Loan_Amount_Term']) -plt.show() - -"""La maggior parte delle features numeriche ha degli outliers - -**Matrice di correlazione** -""" - -correlation_matrix = dataset.corr() - -# heat map per visualizzare matrice di correlazione -sns.heatmap(correlation_matrix, cbar=True, fmt='.1f', annot=True, cmap='coolwarm') -#plt.savefig('Correlation Heat map', bbox_inches='tight') - -"""Non ci sono molte variabili correlate tra di loro, le uniche due sono ApplicantIncome - LoanAmount""" - -#conversione variabili categoriche in numeriche -dataset.replace({'Gender':{'Male':0, 'Female':1}, 'Married' :{'No':0, 'Yes':1}, 'Education':{'Not Graduate':0, 'Graduate':1}, 'Self_Employed':{'No':0, 'Yes':1}, 'Property_Area':{'Rural':0, 'Urban':1, 'Semiurban':2}, 'Loan_Status':{'N':0, 'Y':1}}, inplace = True) - - -# replacing the value of 3+ to 4 -dataset['Dependents'].replace(to_replace='3+', value=4, inplace=True) - -"""# DATA CLEANING - -**CONTROLLO VALORI MANCANTI** -""" - -dataset.isnull().sum() - -#Sostituiamo i valori mancanti con la moda per le variabili categoriche -dataset['Gender'].fillna(dataset['Gender'].mode()[0], inplace=True) -dataset['Married'].fillna(dataset['Married'].mode()[0], inplace=True) -dataset['Dependents'].fillna(dataset['Dependents'].mode()[0], inplace=True) -dataset['Self_Employed'].fillna(dataset['Self_Employed'].mode()[0], inplace=True) -dataset['Credit_History'].fillna(dataset['Credit_History'].mode()[0], inplace=True) - -#Utilizziamo la mediana poiché la variabile ha degli outliers, quindi non è un buon approccio utilizzare la media -dataset['LoanAmount'].fillna(dataset['LoanAmount'].median(), inplace=True) -#dataset['LoanAmount'].fillna(dataset['LoanAmount'].mean(), inplace=True) - -dataset['Loan_Amount_Term'].value_counts() - -#Nella variabile Loan_Amount_Term possiamo notare che 360 è il valore che si ripete di più, quindi utilizziamo la moda -dataset['Loan_Amount_Term'].fillna(dataset['Loan_Amount_Term'].mode()[0], inplace=True) - -dataset.isnull().sum() - -#Per trasformare Dtype di Dependents in int -dataset['Dependents'] = dataset['Dependents'].astype(str).astype(int) -dataset.info() - -"""**GESTIONE OUTLIERS**""" - -fig, axs = plt.subplots(2, 2, figsize=(10, 8)) - -#Distribuzioni prima di applicare log -sns.histplot(data=dataset, x="ApplicantIncome", kde=True, ax=axs[0, 0], color='green') -sns.histplot(data=dataset, x="CoapplicantIncome", kde=True, ax=axs[0, 1], color='skyblue') -sns.histplot(data=dataset, x="LoanAmount", kde=True, ax=axs[1, 0], color='orange') - -# Log Transformation per normalizzare la distribuzione - -dataset.ApplicantIncome = np.log(dataset.ApplicantIncome) -dataset.CoapplicantIncome = np.log(dataset.CoapplicantIncome + 1) -dataset.LoanAmount = np.log(dataset.LoanAmount) - -fig, axs = plt.subplots(2, 2, figsize=(10, 8)) - -#Distribuzioni dopo aver applicato log -sns.histplot(data=dataset, x="ApplicantIncome", kde=True, ax=axs[0, 0], color='green') -sns.histplot(data=dataset, x="CoapplicantIncome", kde=True, ax=axs[0, 1], color='skyblue') -sns.histplot(data=dataset, x="LoanAmount", kde=True, ax=axs[1, 0], color='orange') - -"""Possiamo notare che la distribuzione è migliorata dopo aver applicato il logaritmo - -# SPLIT DATASET -""" - -#definizione variabili dipendenti e indipendenti - -x = dataset.drop('Loan_Status', axis = 1) -y = dataset['Loan_Status'] - -#split dataset - -X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42, stratify = y) - -print("X_train dataset: ", X_train.shape) -print("y_train dataset: ", y_train.shape) -print("X_test dataset: ", X_test.shape) -print("y_test dataset: ", y_test.shape) - -y_test.value_counts() - -#Distribuzione della variabile dipendente -plt.figure(figsize=(5,5)) -pd.value_counts(dataset['Loan_Status']).plot.bar() -plt.xlabel('Loan_Status') -plt.ylabel('Frequency') -dataset['Loan_Status'].value_counts() -plt.savefig('target_distr', bbox_inches='tight') - -"""# DATA SCALING""" - -#Normalizzazione -scaler = MinMaxScaler(feature_range=(0, 1)) -X_train = scaler.fit_transform(X_train) -X_test = scaler.fit_transform(X_test) - -#z-score -#scaler = StandardScaler() -#X_train=scaler.fit_transform(X_train) -#X_test=scaler.transform(X_test) - -df = pd.DataFrame(X_train, columns = x.columns) - -df - -"""# FEATURE SELECTION""" - -#feature selection supervisionata - -from sklearn.feature_selection import SelectKBest -from sklearn.feature_selection import chi2, f_classif -from numpy import set_printoptions - -fs = SelectKBest(score_func=chi2,k=5) -fs.fit_transform(X_train, y_train) - -X_new_train = fs.transform(X_train) -X_new_test = fs.transform(X_test) -print(X_new_train.shape) - -x.columns[fs.get_support(indices=True)] -print("features selezionate: ", x.columns[fs.get_support(indices=True)].tolist()) - -"""# COSTRUZIONE MODELLI""" - -models = [] -precision = [] -accuracy = [] -recall = [] -f1 = [] - -"""**LOGISTIC REGRESSION**""" - -from sklearn.linear_model import LogisticRegression -from sklearn.metrics import classification_report, confusion_matrix, plot_confusion_matrix, accuracy_score ,recall_score, precision_score, f1_score - -logisticRegr = LogisticRegression() -logisticRegr.fit(X_new_train, y_train) - -y_train_pred = logisticRegr.predict(X_new_train) -y_test_pred = logisticRegr.predict(X_new_test) - -fig, ax = plt.subplots(figsize=(8, 8)) -plot_confusion_matrix(logisticRegr, X_new_test, y_test, ax=ax) -plt.show() -#print(confusion_matrix(y_test, y_test_pred)) - -#Risultati ottenuti -print(classification_report(y_test, y_test_pred)) -print("Accuracy on training data:",accuracy_score(y_train, y_train_pred)) -print("Accuracy on test data:",accuracy_score(y_test, y_test_pred)) - -models.append('Logistic Regression') -accuracy.append(accuracy_score(y_test, y_test_pred)) -recall.append(recall_score(y_test, y_test_pred)) -precision.append(precision_score(y_test, y_test_pred)) -f1.append(f1_score(y_test, y_test_pred)) - -"""**DECISION TREE**""" - -from sklearn.tree import DecisionTreeClassifier - -tree_model = DecisionTreeClassifier( random_state=42) -tree_model.fit(X_new_train, y_train) - -y_train_pred = tree_model.predict(X_new_train) -y_test_pred = tree_model.predict(X_new_test) - -fig, ax = plt.subplots(figsize=(8, 8)) -plot_confusion_matrix(logisticRegr, X_new_test, y_test, ax=ax) -plt.show() - -print(classification_report(y_test, y_test_pred)) -print("Accuracy on training data:",accuracy_score(y_train, y_train_pred)) -print("Accuracy on test data:",accuracy_score(y_test, y_test_pred)) - -models.append('Decision Tree') -accuracy.append(accuracy_score(y_test, y_test_pred)) -recall.append(recall_score(y_test, y_test_pred)) -precision.append(precision_score(y_test, y_test_pred)) -f1.append(f1_score(y_test, y_test_pred)) - -"""**NAIVE BAYES**""" - -from sklearn.naive_bayes import GaussianNB - -NB = GaussianNB() -NB.fit(X_new_train, y_train) - -y_train_pred = NB.predict(X_new_train) -y_test_pred = NB.predict(X_new_test) - -fig, ax = plt.subplots(figsize=(8, 8)) -plot_confusion_matrix(NB, X_new_test, y_test, ax=ax) -plt.show() - -print(classification_report(y_test, y_test_pred)) -print("Accuracy on training data:",accuracy_score(y_train, y_train_pred)) -print("Accuracy on test data:",accuracy_score(y_test, y_test_pred)) - -models.append('Naive Bayes') -accuracy.append(accuracy_score(y_test, y_test_pred)) -recall.append(recall_score(y_test, y_test_pred)) -precision.append(precision_score(y_test, y_test_pred)) -f1.append(f1_score(y_test, y_test_pred)) - -"""**RANDOM FOREST**""" - -from sklearn.ensemble import RandomForestClassifier - -RandomForest = RandomForestClassifier() -RandomForest.fit(X_new_train, y_train) - -y_train_pred = RandomForest.predict(X_new_train) -y_test_pred = RandomForest.predict(X_new_test) - -fig, ax = plt.subplots(figsize=(8, 8)) -plot_confusion_matrix(RandomForest, X_new_test, y_test, ax=ax) -plt.show() - -print(classification_report(y_test, y_test_pred)) -print("Accuracy on training data:",accuracy_score(y_train, y_train_pred)) -print("Accuracy on test data:",accuracy_score(y_test, y_test_pred)) - -models.append('Random Forest') -accuracy.append(accuracy_score(y_test, y_test_pred)) -recall.append(recall_score(y_test, y_test_pred)) -precision.append(precision_score(y_test, y_test_pred)) -f1.append(f1_score(y_test, y_test_pred)) - -"""**XGBOOST**""" - -from xgboost import XGBClassifier - -XGB = XGBClassifier() -XGB.fit(X_new_train, y_train) - -y_train_pred = XGB.predict(X_new_train) -y_test_pred = XGB.predict(X_new_test) - -fig, ax = plt.subplots(figsize=(8, 8)) -plot_confusion_matrix(XGB, X_new_test, y_test, ax=ax) -plt.show() - -print(classification_report(y_test, y_test_pred)) -print("Accuracy on training data:",accuracy_score(y_train, y_train_pred)) -print("Accuracy on test data:",accuracy_score(y_test, y_test_pred)) - -models.append('XGBoost') -accuracy.append(accuracy_score(y_test, y_test_pred)) -recall.append(recall_score(y_test, y_test_pred)) -precision.append(precision_score(y_test, y_test_pred)) -f1.append(f1_score(y_test, y_test_pred)) - -"""**CONFRONTO METRICHE**""" - -compare = pd.DataFrame({'Model': models, - 'Accuracy': accuracy, - 'Precision': precision, - 'Recall': recall, - 'f1_score': f1}) -compare.sort_values(by='Accuracy', ascending=False) -#print(compare.to_latex()) - -def loan(Gender, Married, Dependents, Education, Self_Employed, ApplicantIncome, CoapplicantIncome, LoanAmount, Loan_Amount_Term, Credit_History, Property_Area): -#turning the arguments into a numpy array - Marr = 0 if Married == 'No' else 1 - Educ = 0 if Education == 'Not Graduate' else 1 - CredHis = 0 if Credit_History == '0: bad credit history' else 1 - Dep = 4 if Dependents == '3+' else Dependents - Gen = 0 if Gender == 'Male' else 1 - Self_Empl = 0 if Self_Employed == 'No' else 1 - if Property_Area == 'Rural': PA = 0 - elif Property_Area == 'Urban': PA = 1 - else: PA = 2 - - - - instance = np.array([Marr, Educ, CoapplicantIncome, CredHis, PA, Gen, Self_Empl, Dependents, ApplicantIncome, LoanAmount, Loan_Amount_Term]) - - - #reshaping into 2D array - instance_resh = instance.reshape(1,-1) - new_instance_resh = scaler.transform(instance_resh) - new_instance_resh = np.delete(new_instance_resh, [5,6,7,8,9,10], axis=1) - prediction = logisticRegr.predict(new_instance_resh) - - return ("Loan approved" if prediction[0] == 1 else "Loan not approved") - -app = gr.Interface(fn=loan, - inputs=[gr.Radio(['Male', 'Female']), - gr.Radio(['Yes', 'No']), - gr.Radio(['0', '1', '2', '3+']), - gr.Radio(['Graduate', 'Not Graduate']), - gr.Radio(['Yes', 'No']), - "number", - "number", - "number", - "number", - gr.Radio(['0: bad credit history', '1: good credit history']), - gr.Radio(['Urban', 'Semiurban', 'Rural'])], - outputs="text", - title = "Loan Eligibility Prediction") -app.launch(debug=True) \ No newline at end of file diff --git a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/recog_models/abinet.py b/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/recog_models/abinet.py deleted file mode 100644 index 19c6b66731f0b205741037ece8d6b49f91d0110b..0000000000000000000000000000000000000000 --- a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/recog_models/abinet.py +++ /dev/null @@ -1,70 +0,0 @@ -# num_chars depends on the configuration of label_convertor. The actual -# dictionary size is 36 + 1 ( ). -# TODO: Automatically update num_chars based on the configuration of -# label_convertor -num_chars = 37 -max_seq_len = 26 - -label_convertor = dict( - type='ABIConvertor', - dict_type='DICT36', - with_unknown=False, - with_padding=False, - lower=True, -) - -model = dict( - type='ABINet', - backbone=dict(type='ResNetABI'), - encoder=dict( - type='ABIVisionModel', - encoder=dict( - type='TransformerEncoder', - n_layers=3, - n_head=8, - d_model=512, - d_inner=2048, - dropout=0.1, - max_len=8 * 32, - ), - decoder=dict( - type='ABIVisionDecoder', - in_channels=512, - num_channels=64, - attn_height=8, - attn_width=32, - attn_mode='nearest', - use_result='feature', - num_chars=num_chars, - max_seq_len=max_seq_len, - init_cfg=dict(type='Xavier', layer='Conv2d')), - ), - decoder=dict( - type='ABILanguageDecoder', - d_model=512, - n_head=8, - d_inner=2048, - n_layers=4, - dropout=0.1, - detach_tokens=True, - use_self_attn=False, - pad_idx=num_chars - 1, - num_chars=num_chars, - max_seq_len=max_seq_len, - init_cfg=None), - fuser=dict( - type='ABIFuser', - d_model=512, - num_chars=num_chars, - init_cfg=None, - max_seq_len=max_seq_len, - ), - loss=dict( - type='ABILoss', - enc_weight=1.0, - dec_weight=1.0, - fusion_weight=1.0, - num_classes=num_chars), - label_convertor=label_convertor, - max_seq_len=max_seq_len, - iter_size=3) diff --git a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/text/sanskrit.py b/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/text/sanskrit.py deleted file mode 100644 index 0223aaac384a2f850f5bc20651fc18eb964607d0..0000000000000000000000000000000000000000 --- a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/text/sanskrit.py +++ /dev/null @@ -1,62 +0,0 @@ -import re -from indic_transliteration import sanscript - - -# List of (iast, ipa) pairs: -_iast_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('a', 'ə'), - ('ā', 'aː'), - ('ī', 'iː'), - ('ū', 'uː'), - ('ṛ', 'ɹ`'), - ('ṝ', 'ɹ`ː'), - ('ḷ', 'l`'), - ('ḹ', 'l`ː'), - ('e', 'eː'), - ('o', 'oː'), - ('k', 'k⁼'), - ('k⁼h', 'kʰ'), - ('g', 'g⁼'), - ('g⁼h', 'gʰ'), - ('ṅ', 'ŋ'), - ('c', 'ʧ⁼'), - ('ʧ⁼h', 'ʧʰ'), - ('j', 'ʥ⁼'), - ('ʥ⁼h', 'ʥʰ'), - ('ñ', 'n^'), - ('ṭ', 't`⁼'), - ('t`⁼h', 't`ʰ'), - ('ḍ', 'd`⁼'), - ('d`⁼h', 'd`ʰ'), - ('ṇ', 'n`'), - ('t', 't⁼'), - ('t⁼h', 'tʰ'), - ('d', 'd⁼'), - ('d⁼h', 'dʰ'), - ('p', 'p⁼'), - ('p⁼h', 'pʰ'), - ('b', 'b⁼'), - ('b⁼h', 'bʰ'), - ('y', 'j'), - ('ś', 'ʃ'), - ('ṣ', 's`'), - ('r', 'ɾ'), - ('l̤', 'l`'), - ('h', 'ɦ'), - ("'", ''), - ('~', '^'), - ('ṃ', '^') -]] - - -def devanagari_to_ipa(text): - text = text.replace('ॐ', 'ओम्') - text = re.sub(r'\s*।\s*$', '.', text) - text = re.sub(r'\s*।\s*', ', ', text) - text = re.sub(r'\s*॥', '.', text) - text = sanscript.transliterate(text, sanscript.DEVANAGARI, sanscript.IAST) - for regex, replacement in _iast_to_ipa: - text = re.sub(regex, replacement, text) - text = re.sub('(.)[`ː]*ḥ', lambda x: x.group(0) - [:-1]+'h'+x.group(1)+'*', text) - return text diff --git a/spaces/Marne/MockingBird/mockingbirdforuse/vocoder/hifigan/__init__.py b/spaces/Marne/MockingBird/mockingbirdforuse/vocoder/hifigan/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Marshalls/testmtd/analysis/pymo/mocapplayer/libs/threejs/dat.gui.min.js b/spaces/Marshalls/testmtd/analysis/pymo/mocapplayer/libs/threejs/dat.gui.min.js deleted file mode 100644 index 8f5808005e760f05eabfd454f73a0c1d8f5c3e6e..0000000000000000000000000000000000000000 --- a/spaces/Marshalls/testmtd/analysis/pymo/mocapplayer/libs/threejs/dat.gui.min.js +++ /dev/null @@ -1,14 +0,0 @@ -/** - * dat-gui JavaScript Controller Library - * http://code.google.com/p/dat-gui - * - * Copyright 2011 Data Arts Team, Google Creative Lab - * - * 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 - * - * 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This makes it easier to work incrementally, butlocalStorageis fragile, and your friends may not see the same values you do.This application, developed by James L. Weaver, - # demonstrates a conversational agent implemented with OpenAI GPT-3.5 and LangChain. - # When necessary, it leverages tools for complex math, searching the internet, and accessing news and weather. - # Uses talking heads from Ex-Human. - # For faster inference without waiting in queue, you may duplicate the space. - #
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surprise_level_state, sadness_level_state, disgust_level_state, anger_level_state, - lang_level_state, translate_to_state, literary_style_state, - qa_chain_state, docsearch_state, use_embeddings_state, - force_translate_state], - outputs=[chatbot, history_state, video_html, my_file, audio_html, tmp_aud_file, message]) - - openai_api_key_textbox.change(set_openai_api_key, - inputs=[openai_api_key_textbox, use_gpt4_state], - outputs=[chain_state, express_chain_state, llm_state, embeddings_state, - qa_chain_state, memory_state, use_gpt4_state]) - openai_api_key_textbox.submit(set_openai_api_key, - inputs=[openai_api_key_textbox, use_gpt4_state], - outputs=[chain_state, express_chain_state, llm_state, embeddings_state, - qa_chain_state, memory_state, use_gpt4_state]) - -block.launch(debug=True) \ No newline at end of file diff --git a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/modules/rope.py b/spaces/SuYuanS/AudioCraft_Plus/audiocraft/modules/rope.py deleted file mode 100644 index 503e6748df2bb72b3c864c20b37cba5498ffdd21..0000000000000000000000000000000000000000 --- a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/modules/rope.py +++ /dev/null @@ -1,121 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import typing as tp - -from torch import nn -import torch - - -class XPos(nn.Module): - """Length-extrapolatable positional embedding (xPos) from [Sun et al 2022](https://arxiv.org/abs/2212.10554v1). - This applies an exponential decay to the RoPE rotation matrix. - - Args: - dim (int): Embedding dimension. - smoothing (float): Smoothing factor applied to the decay rates. - base_scale (int): Base decay rate, given in terms of scaling time. - device (torch.device, optional): Device on which to initialize the module. - dtype (torch.dtype): dtype to use to generate the embedding. - """ - def __init__(self, dim: int, smoothing: float = 0.4, base_scale: int = 512, - device=None, dtype: torch.dtype = torch.float32): - super().__init__() - assert dim % 2 == 0 - assert dtype in [torch.float64, torch.float32] - self.dtype = dtype - self.base_scale = base_scale - - half_dim = dim // 2 - adim = torch.arange(half_dim, device=device, dtype=dtype) - decay_rates = (adim / half_dim + smoothing) / (1.0 + smoothing) - self.register_buffer("decay_rates", decay_rates) - self.decay: tp.Optional[torch.Tensor] = None - - def get_decay(self, start: int, end: int): - """Create complex decay tensor, cache values for fast computation.""" - if self.decay is None or end > self.decay.shape[0]: - assert isinstance(self.decay_rates, torch.Tensor) # Satisfy type checker. - idx = torch.arange(end, device=self.decay_rates.device, dtype=self.dtype) - power = idx / self.base_scale - scale = self.decay_rates ** power.unsqueeze(-1) - self.decay = torch.polar(scale, torch.zeros_like(scale)) - return self.decay[start:end] # [T, C/2] - - -class RotaryEmbedding(nn.Module): - """Rotary positional embedding (RoPE) from [Su et al 2022](https://arxiv.org/abs/2104.09864). - - Args: - dim (int): Embedding dimension (twice the number of frequencies). - max_period (float): Maximum period of the rotation frequencies. - xpos (bool): Use xPos, applies an exponential decay to rotation matrix. - scale (float): Scale of positional embedding, set to 0 to deactivate. - device (torch.device, optional): Device on which to initialize the module. - dtype (torch.dtype): dtype to use to generate the embedding. - """ - def __init__(self, dim: int, max_period: float = 10000.0, xpos: bool = False, - scale: float = 1.0, device=None, dtype: torch.dtype = torch.float32): - super().__init__() - assert dim % 2 == 0 - self.scale = scale - assert dtype in [torch.float64, torch.float32] - self.dtype = dtype - - adim = torch.arange(0, dim, 2, device=device, dtype=dtype)[: (dim // 2)] - frequencies = 1.0 / (max_period ** (adim / dim)) - self.register_buffer("frequencies", frequencies) - self.rotation: tp.Optional[torch.Tensor] = None - - self.xpos = XPos(dim, device=device, dtype=dtype) if xpos else None - - def get_rotation(self, start: int, end: int): - """Create complex rotation tensor, cache values for fast computation.""" - if self.rotation is None or end > self.rotation.shape[0]: - assert isinstance(self.frequencies, torch.Tensor) # Satisfy type checker. - idx = torch.arange(end, device=self.frequencies.device, dtype=self.dtype) - angles = torch.outer(idx, self.frequencies) - self.rotation = torch.polar(torch.ones_like(angles), angles) - return self.rotation[start:end] - - def rotate(self, x: torch.Tensor, start: int = 0, invert_decay: bool = False): - """Apply rope rotation to query or key tensor.""" - T = x.shape[1] - rotation = self.get_rotation(start, start + T).unsqueeze(0).unsqueeze(2) - - if self.xpos: - decay = self.xpos.get_decay(start, start + T).unsqueeze(0).unsqueeze(2) - else: - decay = 1.0 - - if invert_decay: - decay = decay ** -1 - - x_complex = torch.view_as_complex(x.to(self.dtype).reshape(*x.shape[:-1], -1, 2)) - scaled_rotation = (rotation * decay) * self.scale + (1.0 - self.scale) - x_out = torch.view_as_real(x_complex * scaled_rotation).flatten(-2) - - return x_out.type_as(x) - - def rotate_qk(self, query: torch.Tensor, key: torch.Tensor, start: int = 0): - """ Apply rope rotation to both query and key tensors. - Supports streaming mode, in which query and key are not expected to have the same shape. - In streaming mode, key will be of length [P + C] with P the cached past timesteps, but - query will be [C] (typically C == 1). - - Args: - query (torch.Tensor): Query to rotate. - key (torch.Tensor): Key to rotate. - start (int): Start index of the sequence for time offset. - """ - query_timesteps = query.shape[1] - key_timesteps = key.shape[1] - streaming_offset = key_timesteps - query_timesteps - - query_out = self.rotate(query, start + streaming_offset) - key_out = self.rotate(key, start, invert_decay=True) - - return query_out, key_out diff --git a/spaces/SuYuanS/AudioCraft_Plus/tests/losses/__init__.py b/spaces/SuYuanS/AudioCraft_Plus/tests/losses/__init__.py deleted file mode 100644 index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000 --- a/spaces/SuYuanS/AudioCraft_Plus/tests/losses/__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/SuYuanS/AudioCraft_Plus/tests/modules/test_rope.py b/spaces/SuYuanS/AudioCraft_Plus/tests/modules/test_rope.py deleted file mode 100644 index 067c6f067acbf27fb0fef5c2b812c22474c4fcd0..0000000000000000000000000000000000000000 --- a/spaces/SuYuanS/AudioCraft_Plus/tests/modules/test_rope.py +++ /dev/null @@ -1,168 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import torch - -from audiocraft.modules.rope import RotaryEmbedding -from audiocraft.modules.transformer import StreamingTransformer, set_efficient_attention_backend - - -def test_rope(): - set_efficient_attention_backend('xformers') - B, T, H, C = 8, 75, 16, 128 - - rope = RotaryEmbedding(dim=C) - xq = torch.rand((B, T, H, C)) - xk = torch.rand((B, T, H, C)) - xq_out, xk_out = rope.rotate_qk(xq, xk, start=7) - - assert list(xq_out.shape) == [B, T, H, C] - assert list(xk_out.shape) == [B, T, H, C] - - -def test_rope_io_dtypes(): - set_efficient_attention_backend('xformers') - B, T, H, C = 8, 75, 16, 128 - - rope_32 = RotaryEmbedding(dim=C, dtype=torch.float32) - rope_64 = RotaryEmbedding(dim=C, dtype=torch.float64) - - # Test bfloat16 inputs w/ both 32 and 64 precision rope. - xq_16 = torch.rand((B, T, H, C)).to(torch.bfloat16) - xk_16 = torch.rand((B, T, H, C)).to(torch.bfloat16) - xq_out, xk_out = rope_32.rotate_qk(xq_16, xk_16) - assert xq_out.dtype == torch.bfloat16 - xq_out, xk_out = rope_64.rotate_qk(xq_16, xk_16) - assert xq_out.dtype == torch.bfloat16 - - # Test float32 inputs w/ both 32 and 64 precision rope. - xq_32 = torch.rand((B, T, H, C)).to(torch.float32) - xk_32 = torch.rand((B, T, H, C)).to(torch.float32) - xq_out, xk_out = rope_32.rotate_qk(xq_32, xk_32) - assert xq_out.dtype == torch.float32 - xq_out, xk_out = rope_64.rotate_qk(xq_32, xk_32) - assert xq_out.dtype == torch.float32 - - -def test_transformer_with_rope(): - set_efficient_attention_backend('xformers') - torch.manual_seed(1234) - for pos in ['rope', 'sin_rope']: - tr = StreamingTransformer( - 16, 4, 2, custom=True, dropout=0., layer_scale=0.1, - positional_embedding=pos) - tr.eval() - steps = 12 - x = torch.randn(3, steps, 16) - - out = tr(x) - assert list(out.shape) == list(x.shape) - - -@torch.no_grad() -def test_rope_streaming(): - set_efficient_attention_backend('xformers') - torch.manual_seed(1234) - tr = StreamingTransformer( - 16, 4, 2, causal=True, dropout=0., - custom=True, positional_embedding='rope') - tr.eval() - steps = 12 - x = torch.randn(3, steps, 16) - - ref = tr(x) - - with tr.streaming(): - outs = [] - frame_sizes = [1] * steps - - for frame_size in frame_sizes: - frame = x[:, :frame_size] - x = x[:, frame_size:] - outs.append(tr(frame)) - - out = torch.cat(outs, dim=1) - assert list(out.shape) == [3, steps, 16] - delta = torch.norm(out - ref) / torch.norm(out) - assert delta < 1e-6, delta - - -@torch.no_grad() -def test_rope_streaming_past_context(): - set_efficient_attention_backend('xformers') - torch.manual_seed(1234) - - for context in [None, 10]: - tr = StreamingTransformer( - 16, 4, 1 if context else 2, - causal=True, past_context=context, custom=True, - dropout=0., positional_embedding='rope') - tr.eval() - - steps = 20 - x = torch.randn(3, steps, 16) - ref = tr(x) - - with tr.streaming(): - outs = [] - frame_sizes = [1] * steps - - for frame_size in frame_sizes: - frame = x[:, :frame_size] - x = x[:, frame_size:] - outs.append(tr(frame)) - - out = torch.cat(outs, dim=1) - assert list(out.shape) == [3, steps, 16] - delta = torch.norm(out - ref) / torch.norm(out) - assert delta < 1e-6, delta - - -def test_rope_memory_efficient(): - set_efficient_attention_backend('xformers') - torch.manual_seed(1234) - tr = StreamingTransformer( - 16, 4, 2, custom=True, dropout=0., layer_scale=0.1, - positional_embedding='rope') - tr_mem_efficient = StreamingTransformer( - 16, 4, 2, dropout=0., memory_efficient=True, layer_scale=0.1, - positional_embedding='rope') - tr_mem_efficient.load_state_dict(tr.state_dict()) - tr.eval() - steps = 12 - x = torch.randn(3, steps, 16) - - with torch.no_grad(): - y = tr(x) - y2 = tr_mem_efficient(x) - # Check at float precision b/c this is the rope default. - assert torch.allclose(y, y2, atol=1e-7), (y - y2).norm() - - -def test_rope_with_xpos(): - set_efficient_attention_backend('xformers') - B, T, H, C = 8, 75, 16, 128 - - rope = RotaryEmbedding(dim=C, xpos=True) - xq = torch.rand((B, T, H, C)) - xk = torch.rand((B, T, H, C)) - xq_out, xk_out = rope.rotate_qk(xq, xk, start=7) - - assert list(xq_out.shape) == [B, T, H, C] - assert list(xk_out.shape) == [B, T, H, C] - - -def test_positional_scale(): - set_efficient_attention_backend('xformers') - B, T, H, C = 8, 75, 16, 128 - - rope = RotaryEmbedding(dim=C, xpos=True, scale=0.0) - xq = torch.rand((B, T, H, C)) - xk = torch.rand((B, T, H, C)) - xq_out, xk_out = rope.rotate_qk(xq, xk, start=7) - - assert torch.allclose(xq, xq_out) - assert torch.allclose(xk, xk_out) diff --git a/spaces/Sumit7864/Image-Enhancer/docs/FAQ.md b/spaces/Sumit7864/Image-Enhancer/docs/FAQ.md deleted file mode 100644 index 843f4dd847487066a1c7c105c7292e2de0bd5f1a..0000000000000000000000000000000000000000 --- a/spaces/Sumit7864/Image-Enhancer/docs/FAQ.md +++ /dev/null @@ -1,10 +0,0 @@ -# FAQ - -1. **Q: How to select models?**-# Powered by LangChain 🦜️🔗 -#
-A: Please refer to [docs/model_zoo.md](docs/model_zoo.md) - -1. **Q: Can `face_enhance` be used for anime images/animation videos?**
-A: No, it can only be used for real faces. It is recommended not to use this option for anime images/animation videos to save GPU memory. - -1. **Q: Error "slow_conv2d_cpu" not implemented for 'Half'**
-A: In order to save GPU memory consumption and speed up inference, Real-ESRGAN uses half precision (fp16) during inference by default. However, some operators for half inference are not implemented in CPU mode. You need to add **`--fp32` option** for the commands. For example, `python inference_realesrgan.py -n RealESRGAN_x4plus.pth -i inputs --fp32`. diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/test_pylabtools.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/test_pylabtools.py deleted file mode 100644 index dd1a0ff58b5396466f5328ad91ba769c67291c8c..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/test_pylabtools.py +++ /dev/null @@ -1,270 +0,0 @@ -"""Tests for pylab tools module. -""" - -# Copyright (c) IPython Development Team. -# Distributed under the terms of the Modified BSD License. - - -from binascii import a2b_base64 -from io import BytesIO - -import pytest - -matplotlib = pytest.importorskip("matplotlib") -matplotlib.use('Agg') -from matplotlib.figure import Figure - -from matplotlib import pyplot as plt -from matplotlib_inline import backend_inline -import numpy as np - -from IPython.core.getipython import get_ipython -from IPython.core.interactiveshell import InteractiveShell -from IPython.core.display import _PNG, _JPEG -from .. import pylabtools as pt - -from IPython.testing import decorators as dec - - -def test_figure_to_svg(): - # simple empty-figure test - fig = plt.figure() - assert pt.print_figure(fig, "svg") is None - - plt.close('all') - - # simple check for at least svg-looking output - fig = plt.figure() - ax = fig.add_subplot(1,1,1) - ax.plot([1,2,3]) - plt.draw() - svg = pt.print_figure(fig, "svg")[:100].lower() - assert "doctype svg" in svg - - -def _check_pil_jpeg_bytes(): - """Skip if PIL can't write JPEGs to BytesIO objects""" - # PIL's JPEG plugin can't write to BytesIO objects - # Pillow fixes this - from PIL import Image - buf = BytesIO() - img = Image.new("RGB", (4,4)) - try: - img.save(buf, 'jpeg') - except Exception as e: - ename = e.__class__.__name__ - raise pytest.skip("PIL can't write JPEG to BytesIO: %s: %s" % (ename, e)) from e - -@dec.skip_without("PIL.Image") -def test_figure_to_jpeg(): - _check_pil_jpeg_bytes() - # simple check for at least jpeg-looking output - fig = plt.figure() - ax = fig.add_subplot(1,1,1) - ax.plot([1,2,3]) - plt.draw() - jpeg = pt.print_figure(fig, 'jpeg', pil_kwargs={'optimize': 50})[:100].lower() - assert jpeg.startswith(_JPEG) - -def test_retina_figure(): - # simple empty-figure test - fig = plt.figure() - assert pt.retina_figure(fig) == None - plt.close('all') - - fig = plt.figure() - ax = fig.add_subplot(1,1,1) - ax.plot([1,2,3]) - plt.draw() - png, md = pt.retina_figure(fig) - assert png.startswith(_PNG) - assert "width" in md - assert "height" in md - - -_fmt_mime_map = { - 'png': 'image/png', - 'jpeg': 'image/jpeg', - 'pdf': 'application/pdf', - 'retina': 'image/png', - 'svg': 'image/svg+xml', -} - -def test_select_figure_formats_str(): - ip = get_ipython() - for fmt, active_mime in _fmt_mime_map.items(): - pt.select_figure_formats(ip, fmt) - for mime, f in ip.display_formatter.formatters.items(): - if mime == active_mime: - assert Figure in f - else: - assert Figure not in f - -def test_select_figure_formats_kwargs(): - ip = get_ipython() - kwargs = dict(bbox_inches="tight") - pt.select_figure_formats(ip, "png", **kwargs) - formatter = ip.display_formatter.formatters["image/png"] - f = formatter.lookup_by_type(Figure) - cell = f.keywords - expected = kwargs - expected["base64"] = True - expected["fmt"] = "png" - assert cell == expected - - # check that the formatter doesn't raise - fig = plt.figure() - ax = fig.add_subplot(1,1,1) - ax.plot([1,2,3]) - plt.draw() - formatter.enabled = True - png = formatter(fig) - assert isinstance(png, str) - png_bytes = a2b_base64(png) - assert png_bytes.startswith(_PNG) - -def test_select_figure_formats_set(): - ip = get_ipython() - for fmts in [ - {'png', 'svg'}, - ['png'], - ('jpeg', 'pdf', 'retina'), - {'svg'}, - ]: - active_mimes = {_fmt_mime_map[fmt] for fmt in fmts} - pt.select_figure_formats(ip, fmts) - for mime, f in ip.display_formatter.formatters.items(): - if mime in active_mimes: - assert Figure in f - else: - assert Figure not in f - -def test_select_figure_formats_bad(): - ip = get_ipython() - with pytest.raises(ValueError): - pt.select_figure_formats(ip, 'foo') - with pytest.raises(ValueError): - pt.select_figure_formats(ip, {'png', 'foo'}) - with pytest.raises(ValueError): - pt.select_figure_formats(ip, ['retina', 'pdf', 'bar', 'bad']) - -def test_import_pylab(): - ns = {} - pt.import_pylab(ns, import_all=False) - assert "plt" in ns - assert ns["np"] == np - - -class TestPylabSwitch(object): - class Shell(InteractiveShell): - def init_history(self): - """Sets up the command history, and starts regular autosaves.""" - self.config.HistoryManager.hist_file = ":memory:" - super().init_history() - - def enable_gui(self, gui): - pass - - def setup(self): - import matplotlib - def act_mpl(backend): - matplotlib.rcParams['backend'] = backend - - # Save rcParams since they get modified - self._saved_rcParams = matplotlib.rcParams - self._saved_rcParamsOrig = matplotlib.rcParamsOrig - matplotlib.rcParams = dict(backend='Qt4Agg') - matplotlib.rcParamsOrig = dict(backend='Qt4Agg') - - # Mock out functions - self._save_am = pt.activate_matplotlib - pt.activate_matplotlib = act_mpl - self._save_ip = pt.import_pylab - pt.import_pylab = lambda *a,**kw:None - self._save_cis = backend_inline.configure_inline_support - backend_inline.configure_inline_support = lambda *a, **kw: None - - def teardown(self): - pt.activate_matplotlib = self._save_am - pt.import_pylab = self._save_ip - backend_inline.configure_inline_support = self._save_cis - import matplotlib - matplotlib.rcParams = self._saved_rcParams - matplotlib.rcParamsOrig = self._saved_rcParamsOrig - - def test_qt(self): - s = self.Shell() - gui, backend = s.enable_matplotlib(None) - assert gui == "qt" - assert s.pylab_gui_select == "qt" - - gui, backend = s.enable_matplotlib("inline") - assert gui == "inline" - assert s.pylab_gui_select == "qt" - - gui, backend = s.enable_matplotlib("qt") - assert gui == "qt" - assert s.pylab_gui_select == "qt" - - gui, backend = s.enable_matplotlib("inline") - assert gui == "inline" - assert s.pylab_gui_select == "qt" - - gui, backend = s.enable_matplotlib() - assert gui == "qt" - assert s.pylab_gui_select == "qt" - - def test_inline(self): - s = self.Shell() - gui, backend = s.enable_matplotlib("inline") - assert gui == "inline" - assert s.pylab_gui_select == None - - gui, backend = s.enable_matplotlib("inline") - assert gui == "inline" - assert s.pylab_gui_select == None - - gui, backend = s.enable_matplotlib("qt") - assert gui == "qt" - assert s.pylab_gui_select == "qt" - - def test_inline_twice(self): - "Using '%matplotlib inline' twice should not reset formatters" - - ip = self.Shell() - gui, backend = ip.enable_matplotlib("inline") - assert gui == "inline" - - fmts = {'png'} - active_mimes = {_fmt_mime_map[fmt] for fmt in fmts} - pt.select_figure_formats(ip, fmts) - - gui, backend = ip.enable_matplotlib("inline") - assert gui == "inline" - - for mime, f in ip.display_formatter.formatters.items(): - if mime in active_mimes: - assert Figure in f - else: - assert Figure not in f - - def test_qt_gtk(self): - s = self.Shell() - gui, backend = s.enable_matplotlib("qt") - assert gui == "qt" - assert s.pylab_gui_select == "qt" - - gui, backend = s.enable_matplotlib("gtk") - assert gui == "qt" - assert s.pylab_gui_select == "qt" - - -def test_no_gui_backends(): - for k in ['agg', 'svg', 'pdf', 'ps']: - assert k not in pt.backend2gui - - -def test_figure_no_canvas(): - fig = Figure() - fig.canvas = None - pt.print_figure(fig) diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/documents/video.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/documents/video.py deleted file mode 100644 index fad4a0e843a401ff081552269181620708979679..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/documents/video.py +++ /dev/null @@ -1,121 +0,0 @@ -from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar, Union - -import numpy as np - -from docarray.base_doc import BaseDoc -from docarray.documents import AudioDoc -from docarray.typing import AnyEmbedding, AnyTensor, VideoBytes -from docarray.typing.tensor.abstract_tensor import AbstractTensor -from docarray.typing.tensor.video.video_tensor import VideoTensor -from docarray.typing.url.video_url import VideoUrl -from docarray.utils._internal.misc import import_library - -if TYPE_CHECKING: - import tensorflow as tf # type: ignore - import torch -else: - tf = import_library('tensorflow', raise_error=False) - torch = import_library('torch', raise_error=False) - - -T = TypeVar('T', bound='VideoDoc') - - -class VideoDoc(BaseDoc): - """ - Document for handling video. - - The Video Document can contain: - - - a [`VideoUrl`][docarray.typing.url.VideoUrl] (`VideoDoc.url`) - - an [`AudioDoc`][docarray.documents.AudioDoc] (`VideoDoc.audio`) - - a [`VideoTensor`](../../../api_references/typing/tensor/video) (`VideoDoc.tensor`) - - an [`AnyTensor`](../../../api_references/typing/tensor/tensor) representing the indices of the video's key frames (`VideoDoc.key_frame_indices`) - - an [`AnyEmbedding`](../../../api_references/typing/tensor/embedding) (`VideoDoc.embedding`) - - a [`VideoBytes`][docarray.typing.bytes.VideoBytes] object (`VideoDoc.bytes_`) - - You can use this Document directly: - - ```python - from docarray.documents import VideoDoc - - # use it directly - vid = VideoDoc( - url='https://github.com/docarray/docarray/blob/main/tests/toydata/mov_bbb.mp4?raw=true' - ) - vid.tensor, vid.audio.tensor, vid.key_frame_indices = vid.url.load() - # model = MyEmbeddingModel() - # vid.embedding = model(vid.tensor) - ``` - - You can extend this Document: - - ```python - from typing import Optional - - from docarray.documents import TextDoc, VideoDoc - - - # extend it - class MyVideo(VideoDoc): - name: Optional[TextDoc] - - - video = MyVideo( - url='https://github.com/docarray/docarray/blob/main/tests/toydata/mov_bbb.mp4?raw=true' - ) - video.name = TextDoc(text='my first video') - video.tensor = video.url.load().video - # model = MyEmbeddingModel() - # video.embedding = model(video.tensor) - ``` - - You can use this Document for composition: - - ```python - from docarray import BaseDoc - from docarray.documents import TextDoc, VideoDoc - - - # compose it - class MultiModalDoc(BaseDoc): - video: VideoDoc - text: TextDoc - - - mmdoc = MultiModalDoc( - video=VideoDoc( - url='https://github.com/docarray/docarray/blob/main/tests/toydata/mov_bbb.mp4?raw=true' - ), - text=TextDoc(text='hello world, how are you doing?'), - ) - mmdoc.video.tensor = mmdoc.video.url.load().video - - # or - mmdoc.video.bytes_ = mmdoc.video.url.load_bytes() - mmdoc.video.tensor = mmdoc.video.bytes_.load().video - ``` - """ - - url: Optional[VideoUrl] - audio: Optional[AudioDoc] = AudioDoc() - tensor: Optional[VideoTensor] - key_frame_indices: Optional[AnyTensor] - embedding: Optional[AnyEmbedding] - bytes_: Optional[VideoBytes] - - @classmethod - def validate( - cls: Type[T], - value: Union[str, AbstractTensor, Any], - ) -> T: - if isinstance(value, str): - value = cls(url=value) - elif isinstance(value, (AbstractTensor, np.ndarray)) or ( - torch is not None - and isinstance(value, torch.Tensor) - or (tf is not None and isinstance(value, tf.Tensor)) - ): - value = cls(tensor=value) - - return super().validate(value) diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/utils/_internal/_typing.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/utils/_internal/_typing.py deleted file mode 100644 index a008d562144becb33f7b0fe53a7df3ec192f308d..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/utils/_internal/_typing.py +++ /dev/null @@ -1,50 +0,0 @@ -from typing import Any, Optional - -from typing_extensions import get_origin -from typing_inspect import get_args, is_typevar, is_union_type - -from docarray.typing.tensor.abstract_tensor import AbstractTensor - - -def is_type_tensor(type_: Any) -> bool: - """Return True if type is a type Tensor or an Optional Tensor type.""" - return isinstance(type_, type) and issubclass(type_, AbstractTensor) - - -def is_tensor_union(type_: Any) -> bool: - """Return True if type is a Union of type Tensors.""" - is_union = is_union_type(type_) - if is_union is None: - return False - else: - return is_union and all( - (is_type_tensor(t) or issubclass(t, type(None))) for t in get_args(type_) - ) - - -def change_cls_name(cls: type, new_name: str, scope: Optional[dict] = None) -> None: - """Change the name of a class. - - :param cls: the class to change the name of - :param new_name: the new name - :param scope: the scope in which the class is defined - """ - if scope: - scope[new_name] = cls - cls.__qualname__ = cls.__qualname__[: -len(cls.__name__)] + new_name - cls.__name__ = new_name - - -def safe_issubclass(x: type, a_tuple: type) -> bool: - """ - This is a modified version of the built-in 'issubclass' function to support non-class input. - Traditional 'issubclass' calls can result in a crash if the input is non-class type (e.g. list/tuple). - - :param x: A class 'x' - :param a_tuple: A class, or a tuple of classes. - :return: A boolean value - 'True' if 'x' is a subclass of 'A_tuple', 'False' otherwise. - Note that if the origin of 'x' is a list or tuple, the function immediately returns 'False'. - """ - if (get_origin(x) in (list, tuple, dict, set)) or is_typevar(x): - return False - return issubclass(x, a_tuple) diff --git a/spaces/TabPFN/TabPFNEvaluation/TabPFN/priors/__init__.py b/spaces/TabPFN/TabPFNEvaluation/TabPFN/priors/__init__.py deleted file mode 100644 index 3407398b08379f975aa59cb35e731b82d2a50360..0000000000000000000000000000000000000000 --- a/spaces/TabPFN/TabPFNEvaluation/TabPFN/priors/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from . import fast_gp, mlp, flexible_categorical, differentiable_prior, prior_bag - - - diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/commands/debug.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/commands/debug.py deleted file mode 100644 index 2a3e7d298f393ed8532e4f11913635efc94cb329..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/commands/debug.py +++ /dev/null @@ -1,199 +0,0 @@ -import importlib.resources -import locale -import logging -import os -import sys -from optparse import Values -from types import ModuleType -from typing import Any, Dict, List, Optional - -import pip._vendor -from pip._vendor.certifi import where -from pip._vendor.packaging.version import parse as parse_version - -from pip._internal.cli import cmdoptions -from pip._internal.cli.base_command import Command -from pip._internal.cli.cmdoptions import make_target_python -from pip._internal.cli.status_codes import SUCCESS -from pip._internal.configuration import Configuration -from pip._internal.metadata import get_environment -from pip._internal.utils.logging import indent_log -from pip._internal.utils.misc import get_pip_version - -logger = logging.getLogger(__name__) - - -def show_value(name: str, value: Any) -> None: - logger.info("%s: %s", name, value) - - -def show_sys_implementation() -> None: - logger.info("sys.implementation:") - implementation_name = sys.implementation.name - with indent_log(): - show_value("name", implementation_name) - - -def create_vendor_txt_map() -> Dict[str, str]: - with importlib.resources.open_text("pip._vendor", "vendor.txt") as f: - # Purge non version specifying lines. - # Also, remove any space prefix or suffixes (including comments). - lines = [ - line.strip().split(" ", 1)[0] for line in f.readlines() if "==" in line - ] - - # Transform into "module" -> version dict. - return dict(line.split("==", 1) for line in lines) - - -def get_module_from_module_name(module_name: str) -> ModuleType: - # Module name can be uppercase in vendor.txt for some reason... - module_name = module_name.lower().replace("-", "_") - # PATCH: setuptools is actually only pkg_resources. - if module_name == "setuptools": - module_name = "pkg_resources" - - __import__(f"pip._vendor.{module_name}", globals(), locals(), level=0) - return getattr(pip._vendor, module_name) - - -def get_vendor_version_from_module(module_name: str) -> Optional[str]: - module = get_module_from_module_name(module_name) - version = getattr(module, "__version__", None) - - if not version: - # Try to find version in debundled module info. - assert module.__file__ is not None - env = get_environment([os.path.dirname(module.__file__)]) - dist = env.get_distribution(module_name) - if dist: - version = str(dist.version) - - return version - - -def show_actual_vendor_versions(vendor_txt_versions: Dict[str, str]) -> None: - """Log the actual version and print extra info if there is - a conflict or if the actual version could not be imported. - """ - for module_name, expected_version in vendor_txt_versions.items(): - extra_message = "" - actual_version = get_vendor_version_from_module(module_name) - if not actual_version: - extra_message = ( - " (Unable to locate actual module version, using" - " vendor.txt specified version)" - ) - actual_version = expected_version - elif parse_version(actual_version) != parse_version(expected_version): - extra_message = ( - " (CONFLICT: vendor.txt suggests version should" - " be {})".format(expected_version) - ) - logger.info("%s==%s%s", module_name, actual_version, extra_message) - - -def show_vendor_versions() -> None: - logger.info("vendored library versions:") - - vendor_txt_versions = create_vendor_txt_map() - with indent_log(): - show_actual_vendor_versions(vendor_txt_versions) - - -def show_tags(options: Values) -> None: - tag_limit = 10 - - target_python = make_target_python(options) - tags = target_python.get_tags() - - # Display the target options that were explicitly provided. - formatted_target = target_python.format_given() - suffix = "" - if formatted_target: - suffix = f" (target: {formatted_target})" - - msg = "Compatible tags: {}{}".format(len(tags), suffix) - logger.info(msg) - - if options.verbose < 1 and len(tags) > tag_limit: - tags_limited = True - tags = tags[:tag_limit] - else: - tags_limited = False - - with indent_log(): - for tag in tags: - logger.info(str(tag)) - - if tags_limited: - msg = ( - "...\n[First {tag_limit} tags shown. Pass --verbose to show all.]" - ).format(tag_limit=tag_limit) - logger.info(msg) - - -def ca_bundle_info(config: Configuration) -> str: - levels = set() - for key, _ in config.items(): - levels.add(key.split(".")[0]) - - if not levels: - return "Not specified" - - levels_that_override_global = ["install", "wheel", "download"] - global_overriding_level = [ - level for level in levels if level in levels_that_override_global - ] - if not global_overriding_level: - return "global" - - if "global" in levels: - levels.remove("global") - return ", ".join(levels) - - -class DebugCommand(Command): - """ - Display debug information. - """ - - usage = """ - %prog""" - ignore_require_venv = True - - def add_options(self) -> None: - cmdoptions.add_target_python_options(self.cmd_opts) - self.parser.insert_option_group(0, self.cmd_opts) - self.parser.config.load() - - def run(self, options: Values, args: List[str]) -> int: - logger.warning( - "This command is only meant for debugging. " - "Do not use this with automation for parsing and getting these " - "details, since the output and options of this command may " - "change without notice." - ) - show_value("pip version", get_pip_version()) - show_value("sys.version", sys.version) - show_value("sys.executable", sys.executable) - show_value("sys.getdefaultencoding", sys.getdefaultencoding()) - show_value("sys.getfilesystemencoding", sys.getfilesystemencoding()) - show_value( - "locale.getpreferredencoding", - locale.getpreferredencoding(), - ) - show_value("sys.platform", sys.platform) - show_sys_implementation() - - show_value("'cert' config value", ca_bundle_info(self.parser.config)) - show_value("REQUESTS_CA_BUNDLE", os.environ.get("REQUESTS_CA_BUNDLE")) - show_value("CURL_CA_BUNDLE", os.environ.get("CURL_CA_BUNDLE")) - show_value("pip._vendor.certifi.where()", where()) - show_value("pip._vendor.DEBUNDLED", pip._vendor.DEBUNDLED) - - show_vendor_versions() - - show_tags(options) - - return SUCCESS diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/_loop.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/_loop.py deleted file mode 100644 index 01c6cafbe53f1fcb12f7b382b2b35e2fd2c69933..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/_loop.py +++ /dev/null @@ -1,43 +0,0 @@ -from typing import Iterable, Tuple, TypeVar - -T = TypeVar("T") - - -def loop_first(values: Iterable[T]) -> Iterable[Tuple[bool, T]]: - """Iterate and generate a tuple with a flag for first value.""" - iter_values = iter(values) - try: - value = next(iter_values) - except StopIteration: - return - yield True, value - for value in iter_values: - yield False, value - - -def loop_last(values: Iterable[T]) -> Iterable[Tuple[bool, T]]: - """Iterate and generate a tuple with a flag for last value.""" - iter_values = iter(values) - try: - previous_value = next(iter_values) - except StopIteration: - return - for value in iter_values: - yield False, previous_value - previous_value = value - yield True, previous_value - - -def loop_first_last(values: Iterable[T]) -> Iterable[Tuple[bool, bool, T]]: - """Iterate and generate a tuple with a flag for first and last value.""" - iter_values = iter(values) - try: - previous_value = next(iter_values) - except StopIteration: - return - first = True - for value in iter_values: - yield first, False, previous_value - first = False - previous_value = value - yield first, True, previous_value diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_appengine_environ.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_appengine_environ.py deleted file mode 100644 index 8765b907d70c4a530bc90dc88f24b3df73473b01..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_appengine_environ.py +++ /dev/null @@ -1,36 +0,0 @@ -""" -This module provides means to detect the App Engine environment. -""" - -import os - - -def is_appengine(): - return is_local_appengine() or is_prod_appengine() - - -def is_appengine_sandbox(): - """Reports if the app is running in the first generation sandbox. - - The second generation runtimes are technically still in a sandbox, but it - is much less restrictive, so generally you shouldn't need to check for it. - see https://cloud.google.com/appengine/docs/standard/runtimes - """ - return is_appengine() and os.environ["APPENGINE_RUNTIME"] == "python27" - - -def is_local_appengine(): - return "APPENGINE_RUNTIME" in os.environ and os.environ.get( - "SERVER_SOFTWARE", "" - ).startswith("Development/") - - -def is_prod_appengine(): - return "APPENGINE_RUNTIME" in os.environ and os.environ.get( - "SERVER_SOFTWARE", "" - ).startswith("Google App Engine/") - - -def is_prod_appengine_mvms(): - """Deprecated.""" - return False diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/exceptions.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/exceptions.py deleted file mode 100644 index cba6f3f560f71b3b15ab6aaf21dde4f1bba1bd00..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/exceptions.py +++ /dev/null @@ -1,323 +0,0 @@ -from __future__ import absolute_import - -from .packages.six.moves.http_client import IncompleteRead as httplib_IncompleteRead - -# Base Exceptions - - -class HTTPError(Exception): - """Base exception used by this module.""" - - pass - - -class HTTPWarning(Warning): - """Base warning used by this module.""" - - pass - - -class PoolError(HTTPError): - """Base exception for errors caused within a pool.""" - - def __init__(self, pool, message): - self.pool = pool - HTTPError.__init__(self, "%s: %s" % (pool, message)) - - def __reduce__(self): - # For pickling purposes. - return self.__class__, (None, None) - - -class RequestError(PoolError): - """Base exception for PoolErrors that have associated URLs.""" - - def __init__(self, pool, url, message): - self.url = url - PoolError.__init__(self, pool, message) - - def __reduce__(self): - # For pickling purposes. - return self.__class__, (None, self.url, None) - - -class SSLError(HTTPError): - """Raised when SSL certificate fails in an HTTPS connection.""" - - pass - - -class ProxyError(HTTPError): - """Raised when the connection to a proxy fails.""" - - def __init__(self, message, error, *args): - super(ProxyError, self).__init__(message, error, *args) - self.original_error = error - - -class DecodeError(HTTPError): - """Raised when automatic decoding based on Content-Type fails.""" - - pass - - -class ProtocolError(HTTPError): - """Raised when something unexpected happens mid-request/response.""" - - pass - - -#: Renamed to ProtocolError but aliased for backwards compatibility. -ConnectionError = ProtocolError - - -# Leaf Exceptions - - -class MaxRetryError(RequestError): - """Raised when the maximum number of retries is exceeded. - - :param pool: The connection pool - :type pool: :class:`~urllib3.connectionpool.HTTPConnectionPool` - :param string url: The requested Url - :param exceptions.Exception reason: The underlying error - - """ - - def __init__(self, pool, url, reason=None): - self.reason = reason - - message = "Max retries exceeded with url: %s (Caused by %r)" % (url, reason) - - RequestError.__init__(self, pool, url, message) - - -class HostChangedError(RequestError): - """Raised when an existing pool gets a request for a foreign host.""" - - def __init__(self, pool, url, retries=3): - message = "Tried to open a foreign host with url: %s" % url - RequestError.__init__(self, pool, url, message) - self.retries = retries - - -class TimeoutStateError(HTTPError): - """Raised when passing an invalid state to a timeout""" - - pass - - -class TimeoutError(HTTPError): - """Raised when a socket timeout error occurs. - - Catching this error will catch both :exc:`ReadTimeoutErrors - ` and :exc:`ConnectTimeoutErrors `. - """ - - pass - - -class ReadTimeoutError(TimeoutError, RequestError): - """Raised when a socket timeout occurs while receiving data from a server""" - - pass - - -# This timeout error does not have a URL attached and needs to inherit from the -# base HTTPError -class ConnectTimeoutError(TimeoutError): - """Raised when a socket timeout occurs while connecting to a server""" - - pass - - -class NewConnectionError(ConnectTimeoutError, PoolError): - """Raised when we fail to establish a new connection. Usually ECONNREFUSED.""" - - pass - - -class EmptyPoolError(PoolError): - """Raised when a pool runs out of connections and no more are allowed.""" - - pass - - -class ClosedPoolError(PoolError): - """Raised when a request enters a pool after the pool has been closed.""" - - pass - - -class LocationValueError(ValueError, HTTPError): - """Raised when there is something wrong with a given URL input.""" - - pass - - -class LocationParseError(LocationValueError): - """Raised when get_host or similar fails to parse the URL input.""" - - def __init__(self, location): - message = "Failed to parse: %s" % location - HTTPError.__init__(self, message) - - self.location = location - - -class URLSchemeUnknown(LocationValueError): - """Raised when a URL input has an unsupported scheme.""" - - def __init__(self, scheme): - message = "Not supported URL scheme %s" % scheme - super(URLSchemeUnknown, self).__init__(message) - - self.scheme = scheme - - -class ResponseError(HTTPError): - """Used as a container for an error reason supplied in a MaxRetryError.""" - - GENERIC_ERROR = "too many error responses" - SPECIFIC_ERROR = "too many {status_code} error responses" - - -class SecurityWarning(HTTPWarning): - """Warned when performing security reducing actions""" - - pass - - -class SubjectAltNameWarning(SecurityWarning): - """Warned when connecting to a host with a certificate missing a SAN.""" - - pass - - -class InsecureRequestWarning(SecurityWarning): - """Warned when making an unverified HTTPS request.""" - - pass - - -class SystemTimeWarning(SecurityWarning): - """Warned when system time is suspected to be wrong""" - - pass - - -class InsecurePlatformWarning(SecurityWarning): - """Warned when certain TLS/SSL configuration is not available on a platform.""" - - pass - - -class SNIMissingWarning(HTTPWarning): - """Warned when making a HTTPS request without SNI available.""" - - pass - - -class DependencyWarning(HTTPWarning): - """ - Warned when an attempt is made to import a module with missing optional - dependencies. - """ - - pass - - -class ResponseNotChunked(ProtocolError, ValueError): - """Response needs to be chunked in order to read it as chunks.""" - - pass - - -class BodyNotHttplibCompatible(HTTPError): - """ - Body should be :class:`http.client.HTTPResponse` like - (have an fp attribute which returns raw chunks) for read_chunked(). - """ - - pass - - -class IncompleteRead(HTTPError, httplib_IncompleteRead): - """ - Response length doesn't match expected Content-Length - - Subclass of :class:`http.client.IncompleteRead` to allow int value - for ``partial`` to avoid creating large objects on streamed reads. - """ - - def __init__(self, partial, expected): - super(IncompleteRead, self).__init__(partial, expected) - - def __repr__(self): - return "IncompleteRead(%i bytes read, %i more expected)" % ( - self.partial, - self.expected, - ) - - -class InvalidChunkLength(HTTPError, httplib_IncompleteRead): - """Invalid chunk length in a chunked response.""" - - def __init__(self, response, length): - super(InvalidChunkLength, self).__init__( - response.tell(), response.length_remaining - ) - self.response = response - self.length = length - - def __repr__(self): - return "InvalidChunkLength(got length %r, %i bytes read)" % ( - self.length, - self.partial, - ) - - -class InvalidHeader(HTTPError): - """The header provided was somehow invalid.""" - - pass - - -class ProxySchemeUnknown(AssertionError, URLSchemeUnknown): - """ProxyManager does not support the supplied scheme""" - - # TODO(t-8ch): Stop inheriting from AssertionError in v2.0. - - def __init__(self, scheme): - # 'localhost' is here because our URL parser parses - # localhost:8080 -> scheme=localhost, remove if we fix this. - if scheme == "localhost": - scheme = None - if scheme is None: - message = "Proxy URL had no scheme, should start with http:// or https://" - else: - message = ( - "Proxy URL had unsupported scheme %s, should use http:// or https://" - % scheme - ) - super(ProxySchemeUnknown, self).__init__(message) - - -class ProxySchemeUnsupported(ValueError): - """Fetching HTTPS resources through HTTPS proxies is unsupported""" - - pass - - -class HeaderParsingError(HTTPError): - """Raised by assert_header_parsing, but we convert it to a log.warning statement.""" - - def __init__(self, defects, unparsed_data): - message = "%s, unparsed data: %r" % (defects or "Unknown", unparsed_data) - super(HeaderParsingError, self).__init__(message) - - -class UnrewindableBodyError(HTTPError): - """urllib3 encountered an error when trying to rewind a body""" - - pass diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/platformdirs/version.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/platformdirs/version.py deleted file mode 100644 index 9f6eb98e8f0ef41d6fab05af6e9c24c5ef8a04b8..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/platformdirs/version.py +++ /dev/null @@ -1,4 +0,0 @@ -# file generated by setuptools_scm -# don't change, don't track in version control -__version__ = version = '2.6.2' -__version_tuple__ = version_tuple = (2, 6, 2) diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/msvccompiler.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/msvccompiler.py deleted file mode 100644 index c3823e257ef1de3a79d7f297f38f3bf0bf06f24b..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/msvccompiler.py +++ /dev/null @@ -1,692 +0,0 @@ -"""distutils.msvccompiler - -Contains MSVCCompiler, an implementation of the abstract CCompiler class -for the Microsoft Visual Studio. -""" - -# Written by Perry Stoll -# hacked by Robin Becker and Thomas Heller to do a better job of -# finding DevStudio (through the registry) - -import sys -import os -import warnings -from .errors import ( - DistutilsExecError, - DistutilsPlatformError, - CompileError, - LibError, - LinkError, -) -from .ccompiler import CCompiler, gen_lib_options -from ._log import log - -_can_read_reg = False -try: - import winreg - - _can_read_reg = True - hkey_mod = winreg - - RegOpenKeyEx = winreg.OpenKeyEx - RegEnumKey = winreg.EnumKey - RegEnumValue = winreg.EnumValue - RegError = winreg.error - -except ImportError: - try: - import win32api - import win32con - - _can_read_reg = True - hkey_mod = win32con - - RegOpenKeyEx = win32api.RegOpenKeyEx - RegEnumKey = win32api.RegEnumKey - RegEnumValue = win32api.RegEnumValue - RegError = win32api.error - except ImportError: - log.info( - "Warning: Can't read registry to find the " - "necessary compiler setting\n" - "Make sure that Python modules winreg, " - "win32api or win32con are installed." - ) - pass - -if _can_read_reg: - HKEYS = ( - hkey_mod.HKEY_USERS, - hkey_mod.HKEY_CURRENT_USER, - hkey_mod.HKEY_LOCAL_MACHINE, - hkey_mod.HKEY_CLASSES_ROOT, - ) - - -warnings.warn( - "msvccompiler is deprecated and slated to be removed " - "in the future. Please discontinue use or file an issue " - "with pypa/distutils describing your use case.", - DeprecationWarning, -) - - -def read_keys(base, key): - """Return list of registry keys.""" - try: - handle = RegOpenKeyEx(base, key) - except RegError: - return None - L = [] - i = 0 - while True: - try: - k = RegEnumKey(handle, i) - except RegError: - break - L.append(k) - i += 1 - return L - - -def read_values(base, key): - """Return dict of registry keys and values. - - All names are converted to lowercase. - """ - try: - handle = RegOpenKeyEx(base, key) - except RegError: - return None - d = {} - i = 0 - while True: - try: - name, value, type = RegEnumValue(handle, i) - except RegError: - break - name = name.lower() - d[convert_mbcs(name)] = convert_mbcs(value) - i += 1 - return d - - -def convert_mbcs(s): - dec = getattr(s, "decode", None) - if dec is not None: - try: - s = dec("mbcs") - except UnicodeError: - pass - return s - - -class MacroExpander: - def __init__(self, version): - self.macros = {} - self.load_macros(version) - - def set_macro(self, macro, path, key): - for base in HKEYS: - d = read_values(base, path) - if d: - self.macros["$(%s)" % macro] = d[key] - break - - def load_macros(self, version): - vsbase = r"Software\Microsoft\VisualStudio\%0.1f" % version - self.set_macro("VCInstallDir", vsbase + r"\Setup\VC", "productdir") - self.set_macro("VSInstallDir", vsbase + r"\Setup\VS", "productdir") - net = r"Software\Microsoft\.NETFramework" - self.set_macro("FrameworkDir", net, "installroot") - try: - if version > 7.0: - self.set_macro("FrameworkSDKDir", net, "sdkinstallrootv1.1") - else: - self.set_macro("FrameworkSDKDir", net, "sdkinstallroot") - except KeyError: - raise DistutilsPlatformError( - """Python was built with Visual Studio 2003; -extensions must be built with a compiler than can generate compatible binaries. -Visual Studio 2003 was not found on this system. If you have Cygwin installed, -you can try compiling with MingW32, by passing "-c mingw32" to setup.py.""" - ) - - p = r"Software\Microsoft\NET Framework Setup\Product" - for base in HKEYS: - try: - h = RegOpenKeyEx(base, p) - except RegError: - continue - key = RegEnumKey(h, 0) - d = read_values(base, r"{}\{}".format(p, key)) - self.macros["$(FrameworkVersion)"] = d["version"] - - def sub(self, s): - for k, v in self.macros.items(): - s = s.replace(k, v) - return s - - -def get_build_version(): - """Return the version of MSVC that was used to build Python. - - For Python 2.3 and up, the version number is included in - sys.version. For earlier versions, assume the compiler is MSVC 6. - """ - prefix = "MSC v." - i = sys.version.find(prefix) - if i == -1: - return 6 - i = i + len(prefix) - s, rest = sys.version[i:].split(" ", 1) - majorVersion = int(s[:-2]) - 6 - if majorVersion >= 13: - # v13 was skipped and should be v14 - majorVersion += 1 - minorVersion = int(s[2:3]) / 10.0 - # I don't think paths are affected by minor version in version 6 - if majorVersion == 6: - minorVersion = 0 - if majorVersion >= 6: - return majorVersion + minorVersion - # else we don't know what version of the compiler this is - return None - - -def get_build_architecture(): - """Return the processor architecture. - - Possible results are "Intel" or "AMD64". - """ - - prefix = " bit (" - i = sys.version.find(prefix) - if i == -1: - return "Intel" - j = sys.version.find(")", i) - return sys.version[i + len(prefix) : j] - - -def normalize_and_reduce_paths(paths): - """Return a list of normalized paths with duplicates removed. - - The current order of paths is maintained. - """ - # Paths are normalized so things like: /a and /a/ aren't both preserved. - reduced_paths = [] - for p in paths: - np = os.path.normpath(p) - # XXX(nnorwitz): O(n**2), if reduced_paths gets long perhaps use a set. - if np not in reduced_paths: - reduced_paths.append(np) - return reduced_paths - - -class MSVCCompiler(CCompiler): - """Concrete class that implements an interface to Microsoft Visual C++, - as defined by the CCompiler abstract class.""" - - compiler_type = 'msvc' - - # Just set this so CCompiler's constructor doesn't barf. We currently - # don't use the 'set_executables()' bureaucracy provided by CCompiler, - # as it really isn't necessary for this sort of single-compiler class. - # Would be nice to have a consistent interface with UnixCCompiler, - # though, so it's worth thinking about. - executables = {} - - # Private class data (need to distinguish C from C++ source for compiler) - _c_extensions = ['.c'] - _cpp_extensions = ['.cc', '.cpp', '.cxx'] - _rc_extensions = ['.rc'] - _mc_extensions = ['.mc'] - - # Needed for the filename generation methods provided by the - # base class, CCompiler. - src_extensions = _c_extensions + _cpp_extensions + _rc_extensions + _mc_extensions - res_extension = '.res' - obj_extension = '.obj' - static_lib_extension = '.lib' - shared_lib_extension = '.dll' - static_lib_format = shared_lib_format = '%s%s' - exe_extension = '.exe' - - def __init__(self, verbose=0, dry_run=0, force=0): - super().__init__(verbose, dry_run, force) - self.__version = get_build_version() - self.__arch = get_build_architecture() - if self.__arch == "Intel": - # x86 - if self.__version >= 7: - self.__root = r"Software\Microsoft\VisualStudio" - self.__macros = MacroExpander(self.__version) - else: - self.__root = r"Software\Microsoft\Devstudio" - self.__product = "Visual Studio version %s" % self.__version - else: - # Win64. Assume this was built with the platform SDK - self.__product = "Microsoft SDK compiler %s" % (self.__version + 6) - - self.initialized = False - - def initialize(self): - self.__paths = [] - if ( - "DISTUTILS_USE_SDK" in os.environ - and "MSSdk" in os.environ - and self.find_exe("cl.exe") - ): - # Assume that the SDK set up everything alright; don't try to be - # smarter - self.cc = "cl.exe" - self.linker = "link.exe" - self.lib = "lib.exe" - self.rc = "rc.exe" - self.mc = "mc.exe" - else: - self.__paths = self.get_msvc_paths("path") - - if len(self.__paths) == 0: - raise DistutilsPlatformError( - "Python was built with %s, " - "and extensions need to be built with the same " - "version of the compiler, but it isn't installed." % self.__product - ) - - self.cc = self.find_exe("cl.exe") - self.linker = self.find_exe("link.exe") - self.lib = self.find_exe("lib.exe") - self.rc = self.find_exe("rc.exe") # resource compiler - self.mc = self.find_exe("mc.exe") # message compiler - self.set_path_env_var('lib') - self.set_path_env_var('include') - - # extend the MSVC path with the current path - try: - for p in os.environ['path'].split(';'): - self.__paths.append(p) - except KeyError: - pass - self.__paths = normalize_and_reduce_paths(self.__paths) - os.environ['path'] = ";".join(self.__paths) - - self.preprocess_options = None - if self.__arch == "Intel": - self.compile_options = ['/nologo', '/O2', '/MD', '/W3', '/GX', '/DNDEBUG'] - self.compile_options_debug = [ - '/nologo', - '/Od', - '/MDd', - '/W3', - '/GX', - '/Z7', - '/D_DEBUG', - ] - else: - # Win64 - self.compile_options = ['/nologo', '/O2', '/MD', '/W3', '/GS-', '/DNDEBUG'] - self.compile_options_debug = [ - '/nologo', - '/Od', - '/MDd', - '/W3', - '/GS-', - '/Z7', - '/D_DEBUG', - ] - - self.ldflags_shared = ['/DLL', '/nologo', '/INCREMENTAL:NO'] - if self.__version >= 7: - self.ldflags_shared_debug = ['/DLL', '/nologo', '/INCREMENTAL:no', '/DEBUG'] - else: - self.ldflags_shared_debug = [ - '/DLL', - '/nologo', - '/INCREMENTAL:no', - '/pdb:None', - '/DEBUG', - ] - self.ldflags_static = ['/nologo'] - - self.initialized = True - - # -- Worker methods ------------------------------------------------ - - def object_filenames(self, source_filenames, strip_dir=0, output_dir=''): - # Copied from ccompiler.py, extended to return .res as 'object'-file - # for .rc input file - if output_dir is None: - output_dir = '' - obj_names = [] - for src_name in source_filenames: - (base, ext) = os.path.splitext(src_name) - base = os.path.splitdrive(base)[1] # Chop off the drive - base = base[os.path.isabs(base) :] # If abs, chop off leading / - if ext not in self.src_extensions: - # Better to raise an exception instead of silently continuing - # and later complain about sources and targets having - # different lengths - raise CompileError("Don't know how to compile %s" % src_name) - if strip_dir: - base = os.path.basename(base) - if ext in self._rc_extensions: - obj_names.append(os.path.join(output_dir, base + self.res_extension)) - elif ext in self._mc_extensions: - obj_names.append(os.path.join(output_dir, base + self.res_extension)) - else: - obj_names.append(os.path.join(output_dir, base + self.obj_extension)) - return obj_names - - def compile( # noqa: C901 - self, - sources, - output_dir=None, - macros=None, - include_dirs=None, - debug=0, - extra_preargs=None, - extra_postargs=None, - depends=None, - ): - if not self.initialized: - self.initialize() - compile_info = self._setup_compile( - output_dir, macros, include_dirs, sources, depends, extra_postargs - ) - macros, objects, extra_postargs, pp_opts, build = compile_info - - compile_opts = extra_preargs or [] - compile_opts.append('/c') - if debug: - compile_opts.extend(self.compile_options_debug) - else: - compile_opts.extend(self.compile_options) - - for obj in objects: - try: - src, ext = build[obj] - except KeyError: - continue - if debug: - # pass the full pathname to MSVC in debug mode, - # this allows the debugger to find the source file - # without asking the user to browse for it - src = os.path.abspath(src) - - if ext in self._c_extensions: - input_opt = "/Tc" + src - elif ext in self._cpp_extensions: - input_opt = "/Tp" + src - elif ext in self._rc_extensions: - # compile .RC to .RES file - input_opt = src - output_opt = "/fo" + obj - try: - self.spawn([self.rc] + pp_opts + [output_opt] + [input_opt]) - except DistutilsExecError as msg: - raise CompileError(msg) - continue - elif ext in self._mc_extensions: - # Compile .MC to .RC file to .RES file. - # * '-h dir' specifies the directory for the - # generated include file - # * '-r dir' specifies the target directory of the - # generated RC file and the binary message resource - # it includes - # - # For now (since there are no options to change this), - # we use the source-directory for the include file and - # the build directory for the RC file and message - # resources. This works at least for win32all. - h_dir = os.path.dirname(src) - rc_dir = os.path.dirname(obj) - try: - # first compile .MC to .RC and .H file - self.spawn([self.mc] + ['-h', h_dir, '-r', rc_dir] + [src]) - base, _ = os.path.splitext(os.path.basename(src)) - rc_file = os.path.join(rc_dir, base + '.rc') - # then compile .RC to .RES file - self.spawn([self.rc] + ["/fo" + obj] + [rc_file]) - - except DistutilsExecError as msg: - raise CompileError(msg) - continue - else: - # how to handle this file? - raise CompileError( - "Don't know how to compile {} to {}".format(src, obj) - ) - - output_opt = "/Fo" + obj - try: - self.spawn( - [self.cc] - + compile_opts - + pp_opts - + [input_opt, output_opt] - + extra_postargs - ) - except DistutilsExecError as msg: - raise CompileError(msg) - - return objects - - def create_static_lib( - self, objects, output_libname, output_dir=None, debug=0, target_lang=None - ): - if not self.initialized: - self.initialize() - (objects, output_dir) = self._fix_object_args(objects, output_dir) - output_filename = self.library_filename(output_libname, output_dir=output_dir) - - if self._need_link(objects, output_filename): - lib_args = objects + ['/OUT:' + output_filename] - if debug: - pass # XXX what goes here? - try: - self.spawn([self.lib] + lib_args) - except DistutilsExecError as msg: - raise LibError(msg) - else: - log.debug("skipping %s (up-to-date)", output_filename) - - def link( # noqa: C901 - self, - target_desc, - objects, - output_filename, - output_dir=None, - libraries=None, - library_dirs=None, - runtime_library_dirs=None, - export_symbols=None, - debug=0, - extra_preargs=None, - extra_postargs=None, - build_temp=None, - target_lang=None, - ): - if not self.initialized: - self.initialize() - (objects, output_dir) = self._fix_object_args(objects, output_dir) - fixed_args = self._fix_lib_args(libraries, library_dirs, runtime_library_dirs) - (libraries, library_dirs, runtime_library_dirs) = fixed_args - - if runtime_library_dirs: - self.warn( - "I don't know what to do with 'runtime_library_dirs': " - + str(runtime_library_dirs) - ) - - lib_opts = gen_lib_options(self, library_dirs, runtime_library_dirs, libraries) - if output_dir is not None: - output_filename = os.path.join(output_dir, output_filename) - - if self._need_link(objects, output_filename): - if target_desc == CCompiler.EXECUTABLE: - if debug: - ldflags = self.ldflags_shared_debug[1:] - else: - ldflags = self.ldflags_shared[1:] - else: - if debug: - ldflags = self.ldflags_shared_debug - else: - ldflags = self.ldflags_shared - - export_opts = [] - for sym in export_symbols or []: - export_opts.append("/EXPORT:" + sym) - - ld_args = ( - ldflags + lib_opts + export_opts + objects + ['/OUT:' + output_filename] - ) - - # The MSVC linker generates .lib and .exp files, which cannot be - # suppressed by any linker switches. The .lib files may even be - # needed! Make sure they are generated in the temporary build - # directory. Since they have different names for debug and release - # builds, they can go into the same directory. - if export_symbols is not None: - (dll_name, dll_ext) = os.path.splitext( - os.path.basename(output_filename) - ) - implib_file = os.path.join( - os.path.dirname(objects[0]), self.library_filename(dll_name) - ) - ld_args.append('/IMPLIB:' + implib_file) - - if extra_preargs: - ld_args[:0] = extra_preargs - if extra_postargs: - ld_args.extend(extra_postargs) - - self.mkpath(os.path.dirname(output_filename)) - try: - self.spawn([self.linker] + ld_args) - except DistutilsExecError as msg: - raise LinkError(msg) - - else: - log.debug("skipping %s (up-to-date)", output_filename) - - # -- Miscellaneous methods ----------------------------------------- - # These are all used by the 'gen_lib_options() function, in - # ccompiler.py. - - def library_dir_option(self, dir): - return "/LIBPATH:" + dir - - def runtime_library_dir_option(self, dir): - raise DistutilsPlatformError( - "don't know how to set runtime library search path for MSVC++" - ) - - def library_option(self, lib): - return self.library_filename(lib) - - def find_library_file(self, dirs, lib, debug=0): - # Prefer a debugging library if found (and requested), but deal - # with it if we don't have one. - if debug: - try_names = [lib + "_d", lib] - else: - try_names = [lib] - for dir in dirs: - for name in try_names: - libfile = os.path.join(dir, self.library_filename(name)) - if os.path.exists(libfile): - return libfile - else: - # Oops, didn't find it in *any* of 'dirs' - return None - - # Helper methods for using the MSVC registry settings - - def find_exe(self, exe): - """Return path to an MSVC executable program. - - Tries to find the program in several places: first, one of the - MSVC program search paths from the registry; next, the directories - in the PATH environment variable. If any of those work, return an - absolute path that is known to exist. If none of them work, just - return the original program name, 'exe'. - """ - for p in self.__paths: - fn = os.path.join(os.path.abspath(p), exe) - if os.path.isfile(fn): - return fn - - # didn't find it; try existing path - for p in os.environ['Path'].split(';'): - fn = os.path.join(os.path.abspath(p), exe) - if os.path.isfile(fn): - return fn - - return exe - - def get_msvc_paths(self, path, platform='x86'): - """Get a list of devstudio directories (include, lib or path). - - Return a list of strings. The list will be empty if unable to - access the registry or appropriate registry keys not found. - """ - if not _can_read_reg: - return [] - - path = path + " dirs" - if self.__version >= 7: - key = r"{}\{:0.1f}\VC\VC_OBJECTS_PLATFORM_INFO\Win32\Directories".format( - self.__root, - self.__version, - ) - else: - key = ( - r"%s\6.0\Build System\Components\Platforms" - r"\Win32 (%s)\Directories" % (self.__root, platform) - ) - - for base in HKEYS: - d = read_values(base, key) - if d: - if self.__version >= 7: - return self.__macros.sub(d[path]).split(";") - else: - return d[path].split(";") - # MSVC 6 seems to create the registry entries we need only when - # the GUI is run. - if self.__version == 6: - for base in HKEYS: - if read_values(base, r"%s\6.0" % self.__root) is not None: - self.warn( - "It seems you have Visual Studio 6 installed, " - "but the expected registry settings are not present.\n" - "You must at least run the Visual Studio GUI once " - "so that these entries are created." - ) - break - return [] - - def set_path_env_var(self, name): - """Set environment variable 'name' to an MSVC path type value. - - This is equivalent to a SET command prior to execution of spawned - commands. - """ - - if name == "lib": - p = self.get_msvc_paths("library") - else: - p = self.get_msvc_paths(name) - if p: - os.environ[name] = ';'.join(p) - - -if get_build_version() >= 8.0: - log.debug("Importing new compiler from distutils.msvc9compiler") - OldMSVCCompiler = MSVCCompiler - from distutils.msvc9compiler import MSVCCompiler - - # get_build_architecture not really relevant now we support cross-compile - from distutils.msvc9compiler import MacroExpander # noqa: F811 diff --git a/spaces/Tej3/ECG_Classification/models/CNN.py b/spaces/Tej3/ECG_Classification/models/CNN.py deleted file mode 100644 index 6a4a8f22eabf0b3dc1d2255974d5384bea427204..0000000000000000000000000000000000000000 --- a/spaces/Tej3/ECG_Classification/models/CNN.py +++ /dev/null @@ -1,213 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from torchinfo import summary - -# Not in use yet -class Conv1d_layer(nn.Module): - def __init__(self, in_channel, out_channel, kernel_size) -> None: - super().__init__() - self.conv = nn.Conv1d(in_channels=in_channel, out_channels=out_channel, kernel_size=kernel_size) - self.batch_norm = torch.nn.BatchNorm1d(out_channel) - self.dropout = nn.Dropout1d(p=0.5) - - def forward(self, x): - x= self.conv(x) - x = self.batch_norm(x) - x = self.dropout(x) - return x - -class CNN(nn.Module): - def __init__(self, ecg_channels=12): - super(CNN, self).__init__() - self.name = "CNN" - self.conv1 = nn.Conv1d(ecg_channels, 16, 7) - self.pool1 = nn.MaxPool1d(2, 2) - self.conv2 = nn.Conv1d(16, 32, 5) - self.pool2 = nn.MaxPool1d(2, 2) - self.conv3 = nn.Conv1d(32, 48, 3) - self.pool3 = nn.MaxPool1d(2, 2) - self.fc0 = nn.Linear(5856, 512) - self.fc1 = nn.Linear(512, 128) - self.fc2 = nn.Linear(128, 5) - self.activation = nn.ReLU() - def forward(self, x, notes=None): - x = self.pool1(self.activation(self.conv1(x))) - x = self.pool2(self.activation(self.conv2(x))) - x = self.pool3(self.activation(self.conv3(x))) - x = x.view(x.size(0),-1) - x = self.activation(self.fc0(x)) - x = self.activation(self.fc1(x)) - x = self.fc2(x) - x = x.squeeze(1) - return x - - -class MMCNN_SUM(nn.Module): - def __init__(self, ecg_channels=12): - super(MMCNN_SUM, self).__init__() - # ECG processing Layers - self.name = "MMCNN_SUM" - self.conv1 = Conv1d_layer(ecg_channels, 16, 7) - self.pool1 = nn.MaxPool1d(2, 2) - self.conv2 = Conv1d_layer(16, 32, 5) - self.pool2 = nn.MaxPool1d(2, 2) - self.conv3 = Conv1d_layer(32, 48, 3) - self.pool3 = nn.MaxPool1d(2, 2) - self.fc0 = nn.Linear(5856, 512) - self.fc1 = nn.Linear(512, 128) - self.fc2 = nn.Linear(128, 5) - - # Clinical Notes Processing Layers - self.fc_emb = nn.Linear(768, 128) - self.norm = nn.LayerNorm(128) - - self.activation = nn.ReLU() - - def forward(self, x, notes): - # ECG Processing - x = self.pool1(self.activation(self.conv1(x))) - x = self.pool2(self.activation(self.conv2(x))) - x = self.pool3(self.activation(self.conv3(x))) - x = x.view(x.size(0),-1) - x = self.activation(self.fc0(x)) - x = self.activation(self.fc1(x)) - - # Notes Processing - notes = notes.view(notes.size(0),-1) - notes = self.activation(self.fc_emb(notes)) - - x = self.fc2(self.norm(x + notes)) - x = x.squeeze(1) - return x - -class MMCNN_CAT(nn.Module): - def __init__(self, ecg_channels=12): - super(MMCNN_CAT, self).__init__() - # ECG processing Layers - self.name = "MMCNN_CAT" - self.conv1 = nn.Conv1d(ecg_channels, 16, 7) - self.pool1 = nn.MaxPool1d(2, 2) - self.conv2 = nn.Conv1d(16, 32, 5) - self.pool2 = nn.MaxPool1d(2, 2) - self.conv3 = nn.Conv1d(32, 48, 3) - self.pool3 = nn.MaxPool1d(2, 2) - self.fc0 = nn.Linear(5856, 512) - self.fc1 = nn.Linear(512, 128) - self.fc2 = nn.Linear(256, 5) - - # Clinical Notes Processing Layers - self.fc_emb = nn.Linear(768, 128) - self.norm = nn.LayerNorm(128) - - self.activation = nn.ReLU() - - def forward(self, x, notes): - # ECG Processing - x = self.pool1(self.activation(self.conv1(x))) - x = self.pool2(self.activation(self.conv2(x))) - x = self.pool3(self.activation(self.conv3(x))) - x = x.view(x.size(0),-1) - x = self.activation(self.fc0(x)) - x = self.activation(self.fc1(x)) - - # Notes Processing - notes = notes.view(notes.size(0),-1) - notes = self.activation(self.fc_emb(notes)) - - x = self.fc2(torch.cat((x,notes),dim=1)) - x = x.squeeze(1) - return x -class MMCNN_ATT(nn.Module): - def __init__(self, ecg_channels=12): - super(MMCNN_ATT, self).__init__() - # ECG processing Layers - self.name = "MMCNN_ATT" - self.conv1 = nn.Conv1d(ecg_channels, 16, 7) - self.pool1 = nn.MaxPool1d(2, 2) - self.conv2 = nn.Conv1d(16, 32, 5) - self.pool2 = nn.MaxPool1d(2, 2) - self.conv3 = nn.Conv1d(32, 48, 3) - self.pool3 = nn.MaxPool1d(2, 2) - self.fc0 = nn.Linear(5856, 512) - self.fc1 = nn.Linear(512, 128) - self.fc2 = nn.Linear(128, 5) - - # Clinical Notes Processing Layers - self.fc_emb = nn.Linear(768, 128) - self.norm1 = nn.LayerNorm(128) - self.norm2 = nn.LayerNorm(128) - - self.attention = nn.MultiheadAttention(128, 8, batch_first=True) - self.activation = nn.ReLU() - - def forward(self, x, notes): - # ECG Processing - x = self.pool1(self.activation(self.conv1(x))) - x = self.pool2(self.activation(self.conv2(x))) - x = self.pool3(self.activation(self.conv3(x))) - x = x.view(x.size(0),-1) - x = self.activation(self.fc0(x)) - x = self.activation(self.fc1(x)) - x = self.norm1(x) - - # Notes Processing - notes = notes.view(notes.size(0),-1) - notes = self.activation(self.fc_emb(notes)) - notes = self.norm2(notes) - notes=notes.unsqueeze(1) - x=x.unsqueeze(1) - x,_= self.attention(notes, x, x) - x = self.fc2(x) - x = x.squeeze(1) - return x - -class MMCNN_SUM_ATT(nn.Module): - def __init__(self, ecg_channels=12): - super(MMCNN_SUM_ATT, self).__init__() - # ECG processing Layers - self.name = "MMCNN_SUM_ATT" - self.conv1 = nn.Conv1d(ecg_channels, 16, 7) - self.pool1 = nn.MaxPool1d(2, 2) - self.conv2 = nn.Conv1d(16, 32, 5) - self.pool2 = nn.MaxPool1d(2, 2) - self.conv3 = nn.Conv1d(32, 48, 3) - self.pool3 = nn.MaxPool1d(2, 2) - self.fc0 = nn.Linear(5856, 512) - self.fc1 = nn.Linear(512, 128) - self.fc2 = nn.Linear(128, 5) - - # Clinical Notes Processing Layers - self.fc_emb = nn.Linear(768, 128) - self.norm = nn.LayerNorm(128) - - self.attention = nn.MultiheadAttention(128, 8, batch_first=True) - self.activation = nn.ReLU() - - def forward(self, x, notes): - # ECG Processing - x = self.pool1(self.activation(self.conv1(x))) - x = self.pool2(self.activation(self.conv2(x))) - x = self.pool3(self.activation(self.conv3(x))) - x = x.view(x.size(0),-1) - x = self.activation(self.fc0(x)) - x = self.activation(self.fc1(x)) - - # Notes Processing - notes = notes.view(notes.size(0),-1) - notes = self.activation(self.fc_emb(notes)) - x = self.norm(x + notes) - - x=x.unsqueeze(1) - # print(x.shape) - x,_= self.attention(x, x, x) - - x = self.fc2(x) - x = x.squeeze(1) - return x - -if __name__ == "__main__": - model = CNN() - # model = Conv1d_layer(12, 16, 7) - summary(model, input_size = (1, 12, 1000)) - \ No newline at end of file diff --git a/spaces/TencentARC/Caption-Anything/caption_anything/captioner/blip2.py b/spaces/TencentARC/Caption-Anything/caption_anything/captioner/blip2.py deleted file mode 100644 index b3b27b5e71ea6c6bae7ef0df09cf3e1f035fb869..0000000000000000000000000000000000000000 --- a/spaces/TencentARC/Caption-Anything/caption_anything/captioner/blip2.py +++ /dev/null @@ -1,71 +0,0 @@ -import torch -from PIL import Image -import numpy as np -from typing import Union -from transformers import AutoProcessor, Blip2ForConditionalGeneration - -from caption_anything.utils.utils import is_platform_win, load_image -from .base_captioner import BaseCaptioner -import time - -class BLIP2Captioner(BaseCaptioner): - def __init__(self, device, dialogue: bool = False, enable_filter: bool = False): - super().__init__(device, enable_filter) - self.device = device - self.dialogue = dialogue - self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 - self.processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b") - if is_platform_win(): - self.model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", device_map="sequential", torch_dtype=self.torch_dtype) - else: - self.model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", device_map='sequential', load_in_8bit=True) - - @torch.no_grad() - def inference(self, - image: Union[np.ndarray, Image.Image, str], - filter=False, - args={}): - args['return_ppl'] = args.get('return_ppl', False) - args['text_prompt'] = args.get('text_prompt', 'Question: what does the image show? Answer:') - args['reference_caption'] = args.get('reference_caption', []) - - image = load_image(image, return_type="pil") - result = {} - if not self.dialogue: - inputs = self.processor(image, text = args['text_prompt'], return_tensors="pt").to(self.device, self.torch_dtype) - out = self.model.generate(**inputs, return_dict_in_generate=True, output_scores=True, max_new_tokens=50) - caption = self.processor.decode(out.sequences[0], skip_special_tokens=True).strip() - if self.enable_filter and filter: - print('reference caption: {}, caption: {}'.format(args['reference_caption'], caption)) - clip_score = self.filter_caption(image, caption, args['reference_caption']) - result['clip_score'] = clip_score - if args['return_ppl']: - ppl_score = torch.stack(out.scores, dim=1).softmax(dim=2).log().max(dim=2)[0].sum(dim=1)[0] - result['ppl_score'] = ppl_score.item() - print(f"\nProcessed ImageCaptioning by BLIP2Captioner, Output Text: {caption}") - result['caption'] = caption - return result - else: - context = [] - template = "Question: {} Answer: {}." - while(True): - input_texts = input() - if input_texts == 'end': - break - prompt = " ".join([template.format(context[i][0], context[i][1]) for i in range(len(context))]) + " Question: " + input_texts + " Answer:" - inputs = self.processor(image, text = prompt, return_tensors="pt").to(self.device, self.torch_dtype) - out = self.model.generate(**inputs, max_new_tokens=50) - captions = self.processor.decode(out[0], skip_special_tokens=True).strip() - context.append((input_texts, captions)) - result['caption'] = captions - return result - -if __name__ == '__main__': - - dialogue = False - model = BLIP2Captioner(device='cuda:4', dialogue = dialogue, cache_dir = '/nvme-ssd/fjj/Caption-Anything/model_cache') - image_path = 'test_images/img2.jpg' - seg_mask = np.zeros((224,224)) - seg_mask[50:200, 50:200] = 1 - print(f'process image {image_path}') - print(model.inference_seg(image_path, seg_mask)) diff --git a/spaces/Tetel/secondbing/claude.py b/spaces/Tetel/secondbing/claude.py deleted file mode 100644 index 92b22bad3736820c002b2c86e89d7e77b6bdc21d..0000000000000000000000000000000000000000 --- a/spaces/Tetel/secondbing/claude.py +++ /dev/null @@ -1,62 +0,0 @@ -import asyncio -import json -import os - -from slack_sdk.web.async_client import AsyncWebClient - -if os.path.exists("claude.json"): - with open("claude.json") as f: - try: - claude_config = json.load(f) - except json.JSONDecodeError: - claude_config = {} -else: - claude_config = {} - - -class Chatbot: - def __init__( - self, - slack_user_token=claude_config.get("slackUserToken"), - slack_channel_id=claude_config.get("slackChannelId"), - claude_member_id=claude_config.get("claudeMemberId"), - proxy=None, - ): - self.client = AsyncWebClient(token=slack_user_token, proxy=proxy) - self.slack_channel_id = slack_channel_id - self.claude_member_id = claude_member_id - - async def ask_stream(self, message): - if len(message) < 3000: # Slack truncates message at ~3000 characters - response = await self.client.chat_postMessage(channel=self.slack_channel_id, text=message) - thread_ts = response["ts"] - else: - response = await self.client.chat_postMessage(channel=self.slack_channel_id, text=message[:3000]) - thread_ts = response["ts"] - await self.client.chat_postMessage( - channel=self.slack_channel_id, - text=message[3000:], - thread_ts=thread_ts, - ) - - await self.client.chat_postMessage( - channel=self.slack_channel_id, - text=f'<@{self.claude_member_id}> [assistant](#message)', - thread_ts=thread_ts, - ) - - while True: - await asyncio.sleep(1) - replies_response = await self.client.conversations_replies(channel=self.slack_channel_id, ts=thread_ts) - all_replies = replies_response["messages"] - for reply in all_replies: - if reply["user"] == self.claude_member_id: - break - else: - continue - - if reply["text"].endswith("_Typing…_"): - yield reply["text"][:-11] - else: - yield reply["text"] - break diff --git a/spaces/Tinki/text_generator/README.md b/spaces/Tinki/text_generator/README.md deleted file mode 100644 index 685ffbcd7e8c521b70cd5dfc36e99acc4c988c1b..0000000000000000000000000000000000000000 --- a/spaces/Tinki/text_generator/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Text Generator -emoji: 🌖 -colorFrom: red -colorTo: yellow -sdk: gradio -sdk_version: 3.11.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Uday007/startup-profit-predictor/app.py b/spaces/Uday007/startup-profit-predictor/app.py deleted file mode 100644 index ffc1c9a2b66b55a7b542ddf544850385f32f62f9..0000000000000000000000000000000000000000 --- a/spaces/Uday007/startup-profit-predictor/app.py +++ /dev/null @@ -1,41 +0,0 @@ -import gradio as gr -import pandas as pd -import numpy as np -from joblib import load - -def predict_profit( - RandDSpend,Administration,MarketingSpend,State -): - model=load("startup.jb") - - # Create dict array from parameters - data={ - "RandDSpend":[RandDSpend], - "Administration":[Administration], - "MarketingSpend":[MarketingSpend], - "State":[State] - } - - xin=pd.DataFrame(data) - Profit=model.predict(xin) - return Profit[0] - -ui=gr.Interface( - fn=predict_profit, - inputs=[ - gr.inputs.Textbox(placeholder="R&D Amount",numeric=True,label="R&D SPEND"), - gr.inputs.Textbox(placeholder="Administration Amount",numeric=True,label="ADMINISTRATION"), - gr.inputs.Textbox(placeholder="Marketing Amount",numeric=True,label="MARKETING SPEND"), - gr.Dropdown(["New York","California","Florida"],label="STATE"), - ], - - title="STARTUP PROFIT PREDICTOR", - outputs="text", - examples=[[165349.2,136897.8,471784.1,"New York"], - [67532.53,105751.03,304768.73,"Florida"], - [64664.71,139553.16,137962.62,"California"]] - -) - -if __name__=="__main__": - ui.launch() diff --git a/spaces/VickyKira/NASAGPT/client/js/chat.js b/spaces/VickyKira/NASAGPT/client/js/chat.js deleted file mode 100644 index 8a4449e0fd94c629867d62f53f5467f8e8292ca7..0000000000000000000000000000000000000000 --- a/spaces/VickyKira/NASAGPT/client/js/chat.js +++ /dev/null @@ -1,508 +0,0 @@ -const query = (obj) => - Object.keys(obj) - .map((k) => encodeURIComponent(k) + "=" + encodeURIComponent(obj[k])) - .join("&"); -const url_prefix = document.querySelector("body").getAttribute("data-urlprefix"); -const markdown = window.markdownit(); -const message_box = document.getElementById(`messages`); -const message_input = document.getElementById(`message-input`); -const box_conversations = document.querySelector(`.top`); -const spinner = box_conversations.querySelector(".spinner"); -const stop_generating = document.querySelector(`.stop-generating`); -const send_button = document.querySelector(`#send-button`); -const user_image = ` 
`;
-const gpt_image = `
`;
-let prompt_lock = false;
-
-hljs.addPlugin(new CopyButtonPlugin());
-
-message_input.addEventListener("blur", () => {
- window.scrollTo(0, 0);
-});
-
-message_input.addEventListener("focus", () => {
- document.documentElement.scrollTop = document.documentElement.scrollHeight;
-});
-
-const delete_conversations = async () => {
- localStorage.clear();
- await new_conversation();
-};
-
-const handle_ask = async () => {
- message_input.style.height = `80px`;
- window.scrollTo(0, 0);
- let message = message_input.value;
-
- if (message.length > 0) {
- message_input.value = ``;
- message_input.dispatchEvent(new Event("input"));
- await ask_gpt(message);
- }
-};
-
-const remove_cancel_button = async () => {
- stop_generating.classList.add(`stop-generating-hiding`);
-
- setTimeout(() => {
- stop_generating.classList.remove(`stop-generating-hiding`);
- stop_generating.classList.add(`stop-generating-hidden`);
- }, 300);
-};
-
-const ask_gpt = async (message) => {
- try {
- message_input.value = ``;
- message_input.innerHTML = ``;
- message_input.innerText = ``;
-
- add_conversation(window.conversation_id, message.substr(0, 16));
- window.scrollTo(0, 0);
- window.controller = new AbortController();
-
- jailbreak = document.getElementById("jailbreak");
- model = document.getElementById("model");
- prompt_lock = true;
- window.text = ``;
- window.token = message_id();
-
- stop_generating.classList.remove(`stop-generating-hidden`);
-
- add_user_message_box(message);
-
- message_box.scrollTop = message_box.scrollHeight;
- window.scrollTo(0, 0);
- await new Promise((r) => setTimeout(r, 500));
- window.scrollTo(0, 0);
-
- message_box.innerHTML += `
-
- `;
-
- message_box.scrollTop = message_box.scrollHeight;
- window.scrollTo(0, 0);
- await new Promise((r) => setTimeout(r, 1000));
- window.scrollTo(0, 0);
-
- const response = await fetch(`${url_prefix}/backend-api/v2/conversation`, {
- method: `POST`,
- signal: window.controller.signal,
- headers: {
- "content-type": `application/json`,
- accept: `text/event-stream`,
- },
- body: JSON.stringify({
- conversation_id: window.conversation_id,
- action: `_ask`,
- model: model.options[model.selectedIndex].value,
- jailbreak: jailbreak.options[jailbreak.selectedIndex].value,
- meta: {
- id: window.token,
- content: {
- conversation: await get_conversation(window.conversation_id),
- internet_access: document.getElementById("switch").checked,
- content_type: "text",
- parts: [
- {
- content: message,
- role: "user",
- },
- ],
- },
- },
- }),
- });
-
- const reader = response.body.getReader();
-
- while (true) {
- const { value, done } = await reader.read();
- if (done) break;
-
- chunk = decodeUnicode(new TextDecoder().decode(value));
-
- if (
- chunk.includes(`